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Thermal and refining processes, not fermentation, tend to reduce lipotropiccapacity of plant-based foods†

Anthony Fardet,*ab Jean-Francois Martinab and Jean-Michel Chardignyab

Received 15th March 2011, Accepted 23rd July 2011

DOI: 10.1039/c1fo10041f

Plant-based foods (PBF) are relevant and diversified sources of lipotropes, which are compounds

preventing excess hepatic fat deposits. In a first study, we defined the lipotropic capacity (LC, %) of raw

PBF as the means of 8 lipotrope densities (LD, mg/100 kcal), each expressed relative to that of

a reference food ranking the highest considering its mean 8 LD ranks (LCraw asparagus ¼ 100%)

(A. Fardet, J.-F. Martin and J. M. Chardigny, J. Food Comp. Anal., 2011, DOI: 10.1016/j.

jfca.2011.1003.1013). We showed that vegetables appeared as the best source of lipotropes on a 100

kcal-basis compared to legumes, cereals, fruits and nuts. The main objective of this second study was to

quantify the effect of processing on LD and LC of raw PBF based on lipotrope contents collected in

a USDA (United State Department of Agriculture) database and the literature, i.e. betaine, choline,

myo-inositol, methionine, magnesium, niacin, pantothenic acid and folate contents. Choline and

betaine densities were not significantly affected by processing while methionine and lipotropic

micronutrient densities were significantly decreased, especially for magnesium, pantothenate and

folates. Myo-inositol density decreases were insignificant due to lower product number resulting from

limited literature data. Lipotropic micronutrient densities were more affected by processing than other

densities. Fermentations increased betaine (median change of +32%) and choline (+34%) densities.

Canning and boiling vegetables increased choline densities (+26%). Globally, processing significantly

reduced LC by �20%, fermentations being less drastic (median change of �5%) than refining (�33%)

and thermal treatments (�16%). More specifically, canning increased LC of beetroot (536 vs 390%) and

common bean (40 vs 36%) as fermentation towards LC grape (14 vs 7% for wine). Results were then

mainly discussed based on percentages of lipotrope content changes on a dry-weight basis. Results of

this study also showed that the LC is quite a relevant index to estimate effect of processing on lipotropic

potential of PBF.

Introduction

Increased consumption of whole-grain cereals, legumes, fruits

and vegetables may be protective against the development of age-

related and/or chronic diseases that are, for the most significant

in terms of public health, cardiovascular diseases, diabetes,

cancers and obesity, the most conclusive results being observed

in humans consuming whole-grain cereals.2 Among mechanisms

involved, the most studied have been the antioxidant, anticarci-

nogenic and hypolipidemic effects of phyto-micronutrients, and

the role of fibre-type compounds on digestive physiology and

carbohydrate and lipid metabolisms. The ability of phytochem-

icals of numerous plant-based foods (PBF) to limit excess hepatic

aINRA, UMR 1019, UNH, CRNH Auvergne, F-63000 Clermont-Ferrand,FrancebClermont Universit�e, Universit�e d’Auvergne, Unit�e de Nutrition Humaine,BP 10448, F-63000 Clermont-Ferrand, France

† Electronic supplementary information (ESI) available. See DOI:10.1039/c1fo10041f

This journal is ª The Royal Society of Chemistry 2011

fat deposits has been largely less studied and emphasized, espe-

cially in humans. Yet, hepatic steatosis or fatty liver may be

diagnosed in situations of alcoholism, overweight, obesity,

hyperlipidemia, non-insulinodependent type 2 diabetes and

malnutrition3–6 and is the first step that may lead to more severe

pathologies like steatohepatitis, fibrosis and cirrhosis.7 More-

over, patients with hepatic steatosis present an increased risk of

developing cardiovascular diseases,8 those with non-alcoholic

steatohepatitis-related cirrhosis an increased risk of developing

liver cancer9 and type 1 diabetic subjects with non-alcoholic fatty

liver disease (NAFLD) have a higher prevalence of chronic

kidney diseases and retinopathy.10 Like obesity, fatty liver may

be therefore the onset for the development of a cascade of

numerous other chronic diseases. Accordingly, it has been shown

to be an early predictor of other metabolic disorders.11

The prevalence of NAFLD seems to largely depend on the

diagnostic method used: Bloomgarden reported that ‘‘the prev-

alence of NAFLD varies from 3 to 20% of the population based

on elevated transaminase and from 16 to 19% based on

Food Funct., 2011, 2, 483–504 | 483

ultrasound screening’’ adding that ‘‘autopsy series suggest that

11–36% of the population has NAFLD’’.7 More striking, 95% of

obese and 50–70% of type 2 diabetic people are affected by

NAFLD.7 In year 2000, it was estimated that hepatic steatosis

would affect around 30 millions American obese adults and from

10 to 24% of the general population in some countries.4 These

percentages are relatively high. It is therefore quite surprising

that the potential positive effect of plant-based food bioactive

compounds for preventing excess hepatic fat deposits in humans

has been rather neglected till today.

Such compounds have been very early called lipotropes.12

Lipotropes act on lipid metabolism by hastening removal and/or

preventing excessive lipid deposits,2 mainly triglycerides.13 First

and main recognized PBF lipotropes are betaine, choline, myo-

inositol, methionine and carnitine.2 They are generally completed

by magnesium and some B vitamins that are niacin, pantothenic

acid and folates that may indirectly sustain the physiological

action of main lipotropes.2 However, since then, many other

phytochemicals have been reported to decrease hepatic triglyc-

eride or total lipid content in various animal models of fatty

livers and, although they have been, to our knowledge, never

named lipotropes as such, may be considered as contributing to

the overall lipotropic effect of PBF.2 They are organosulfur

compounds (e.g. s-allyl-cysteine), unsaturated FA (probably

mainly n-3 PUFAs such as a-linolenic and/or n-9 MUFA such as

oleic acid), phosphatidylinositol, acetic acid, melatonin, deoxy-

nojirimycin, phytic acid, soluble and insoluble fiber (e.g. lignins,

pectin and guar gum), oligofructose (e.g. fructans like inulin),

resistant starch (RS), phenolic acids, flavonoids (e.g. epi-

gallocatechin gallate), lignans (e.g. sesamin), stilbenes, curcumin,

coumarin, caffeine, g-oryzanol and saponins, as well as plant

protein extracts or isolates (e.g. from soybean or lupin), as

having a lipotropic effect.2

What is the most striking and appears as quite paradoxical in

a first view is the scarcity of human intervention studies that aim

at unravelling lipotropic effect of PBF compounds or products2

whereas steatosis (non-alcoholic type) is highly prevalent among

Western populations.4 Thus, up today, putting apart some quite

old clinical reports made on isolated individuals with hepatic

dysfunctions or troubles (e.g. as result of alcoholism),2 and that

were improved in some cases via administration of choline

chloride,14 relevant interventional studies have been lead with

either commercial lipotropic complex15,16 and tablets17 or with

isolated compounds like n-3 polyunsaturated fatty acids,18–24

betaine,25–27 carnitine,28 tea pigment capsules29 and silymarin.30

All showed reduced degree of liver stetaosis and/or improvement

in liver functions following lipotrope administration. Other

studies have been mainly lead in animal models of hepatic

steatosis.2

Observational studies are also quite rare. The few results

obtained showed that high-coffee consumption in patients with

advanced hepatitis C-related liver disease was associated with

less severe steatosis (p for trend ¼ 0.047),31 that non-alcoholic

fatty liver disease (NAFLD) patients appear to consume exces-

sive sugar-sweetened soft drinks (>0.5 L per day)32 and more

fructose than controls,33 that high alcohol consumption was

positively and significantly correlated with diffuse liver steatosis

in non-cirrhotic patients34 and that moderate alcohol consump-

tion was associated with lower steatohepatitis prevalence in

484 | Food Funct., 2011, 2, 483–504

NAFLD patients.35 Reported average total caloric, fat, saturated

fat and simple carbohydrate intake over 10 days was also

significantly and positively associated with liver fat scores in

obese and sedentary men.36 Finally, obese subjects on a high-fat

diet during 3 weeks significantly increased their intra-hepatic

lipid content by 17%.37 It is therefore not surprising that nutri-

tional strategies like calorie restriction and/or weight loss have

lead to significant improvement of liver steatosis.38,39

In a previous study, we showed that raw PBF, compared to

animal-based foods (ABF), are more diversified sources of

lipotropes but that both may be complementary: while ABFs

seem to be richer sources of choline, methionine and niacin on

a 100 kcal-basis, PBFs are richer sources of betaine,myo-inositol,

magnesium, pantothenic acid and folates. We also unravelled

that vegetables, compared to cereals, legumes, fruits, nuts and

seeds, have the highest lipotrope densities (LD, in mg lipotropes/

100 kcal), especially for B vitamins.1 High vegetables LD was

mainly due to the combination of both relatively high content in

lipotropes and low caloric content, generally below 70 kcal/100 g

of food. Cereals, legumes, nuts and seeds, although having higher

lipotrope contents than vegetables, have lower LD due to their

high caloric content.1

In this previous study, the objective was also to simply define

and calculate the lipotropic potential of PBF by considering

equally each lipotrope in a single index.1 The only sum of LDwas

not possible since depreciating the importance of lipotropes with

very low density as folates (within range 0–0.747 mg/100 kcal)

compared to choline (within range 1–369 mg/100 kcal). So, we

have defined the lipotropic capacity (LC) based on 8 LD that are

betaine, choline, methionine, myo-inositol, magnesium, niacin,

pantothenic acid and folate densities. The 8 compounds were

chosen based on scientific evidence and available data for lipo-

trope contents LC.1,2 On a mathematical basis, LC is the average

LD of these 8 LD expressed as a percentage of the LD of

a reference PBF. The reference food was raw asparagus and its

LC was set to 100%. It was chosen within our database

(Supplementary Table 1†) as the raw PBF ranking the highest for

the mean rank of the 8 LD (Supplementary Table 3).1 Based on

this new nutritional index, we have then shown that most raw

vegetables, i.e. raw spinach (LC of 672% relative to asparagus),

beetroot (390%), lettuce (92%), broccoli (90%), algae (84%),

celery (76%), cucumber (74%), tomato (70%), sweet pepper

(66%), cabbage (65%) and radish (63%), had a high LC. Citrus

fruits have also a relatively high LC within the range 41–51%,

notably due to their high free myo-inositol content. Other fruits

reach more heterogeneous values with 107% for blackberry and

7% for grape when taking extremes. Concerning other food

groups, whole-grain quinoa and amaranth (pseudo-cereals) have

LC of respectively 155 and 42%, common bean and soybean seed

of respectively 36 and 33%, and sesame seed, peanut and almond

of respectively 26, 20 and 14%. Interestingly, PBF of different

botanical origins may have similar LD profiles, e.g. blackberry vs

sweet pepper, common bean vs sesame seed, peanut vs peach,

almond vs banana and onion vs plum as we showed through

hierarchical classification analysis.1 In the end, we proposed that

LC may be a new valuable index – and complementary to other

nutritional indices – to evaluate potential health benefits of PBF,

notably for guiding food choices in case of mild steatosis or high

potential risk.1

This journal is ª The Royal Society of Chemistry 2011

However, although some PBF may be eaten raw like tomato,

lettuce or sweet bell pepper, most of them are processed, either

minimally like boiled vegetables and dried fruits or highly as

white bread and ketchup. An important issue is therefore the

evolution of PBF LD and LC upon processing. Scientific litera-

ture dealing with the effect of processing upon phytonutrient

contents of PBF is rather abundant. It is not our objective to

describe it herein. Briefly, one may say that the level to which the

PBF phytonutrient content is affected varies greatly according to

the processing conditions used in terms of pressure, degree of

refining, temperature and/or water content, but also to the

conditions of storage as it had been shown with micronutrient

contents of fresh, frozen and canned fruits and vegetables.40,41

Concerning the 8 selected lipotrope, betaine content was shown

to importantly decrease in spinach, pasta, frozen peas and sil-

verbeet upon boiling.42 However, to our knowledge, there is no

study dealing with choline and free myo-inositol content evolu-

tion upon processing. Data for methionine, B vitamins and

magnesium are largely more numerous. An interesting way to

reach a first estimation of the effect of processing upon PBF LD

and LC may be based on data collected from various databases

or articles for lipotrope contents of raw and processed PBF.

Based on the same 8 lipotropes as in our first study, our main

objective was therefore to estimate effect of various technological

processes upon PBF LD and LC, either considering them as

a whole (i.e. raw vs processed PBF) or by considering each

process separately (e.g. fermentation, boiling, refining, etc.).

Otherwise, among other lipotropic compounds, literature data

for total phenolic compound (TPC) and myo-inositol phosphate

(IP) contents are the most available for a large number of PBF.1

Effect of processing on TPC and IP densities was therefore also

considered.1 Raw and processed vegetable, fruit, cereal, legume,

nut and seed, and beverage products were extracted from

a database of 132 PBF (Supplementary Table 1†) whose imple-

mentation was previously described in details.1 Our database

allowed reaching relevant representativeness of PBF botanical

diversity and of processes applied. Among processed PBF, only

edible products were considered.

Materials and methods

Selection and classification of raw and processed food products

Extraction of raw and processed PBF was realised from a list of

132 products mainly selected from USDA Choline & Betaine

database.43 Selection has been previously described in detail.1

The effect of processing was analysed according to two

perspectives: (1) the overall effect of processing (overall un-

specific processing, OP) by comparing mean and median LD/LC

and mean ranking of all raw vs edible processed PBF; and (2) the

effect of specific processes (SP) by comparing mean and median

LD/LC of 41 pairs of raw vs processed product (e.g. raw common

bean vs boiled/canned common bean; grape vs grape juice, raisin

and wine or barley malted flour vs beer).

To study effect of OP on PBF LD, the 121 PBF were clustered

into 2 groups: group 1 corresponded to raw products (edible or

not) and included 6 cereals, 2 legumes, 19 vegetables, 10 nuts and

seeds and 19 fruits (n ¼ 56 raw PBF); and group 2 corresponded

to edible processed products and included 11 cereals, 3 legumes,

This journal is ª The Royal Society of Chemistry 2011

24 vegetables, 5 nuts and seeds, 7 fruits and 15 beverages (n ¼ 65

edible processed PBF) (Supplementary Table 1†). The 11 non-

selected products corresponded to non-edible processed prod-

ucts: they were intermediary products of cereal milling (i.e.wheat

bran, wheat germ, white wheat flour, raw white rice, maize and

oat brans), malted barley flour, roasted buckwheat groats,

whole-grain masa, dry pasta and defatted soybean flour. Dry

instant coffee and lemonade concentrate have been considered as

edible products since they are diluted with water only for

consumption. Although having been submitted to milling,

whole-grain cereal flours (i.e. from maize, oat, rye and wheat)

have been considered as raw products assuming that these

products, as cereal flours of very low-grade extraction, were

minimally-processed cereals that remain designed as whole

grains (dark rye flour being the same as whole-grain rye flour).

The effect of processing was also considered by food group.

Thus, from the 121 products were constituted 5 food groups that

were cereals (C group), legumes (L group), vegetables (V group),

nuts and seeds (N group) and fruits (F group).1 Beverages, being

all processed products from various botanical origins, were dis-

patched within the 5 other groups as follows: beer within the

processed cereals, soybean milk within the processed legumes,

canned condensed tomato soup within the processed vegetables,

coconut milk within the processed nuts, and fruit juices, wines,

carbonated orange soda and lemonade concentrate within the

processed fruits. Brewed tea and coffeewere not incorporated into

a food group due to their very low caloric content (respectively 1.2

and 0.7 kcal/100 g of food) that yielded very high LD and that

would have led to importantly overestimate mean LD of pro-

cessed food groups; dry instant coffee and carbonated cola were

not included aswell within a particular processed foodgroup since

being botanically too distinct and distant from the 5 food groups.

Effect of processing was finally considered by process type (i.e.

SP). Products were clustered within 3 process groups that are

thermal, fermentative and refining processes. Thermal processes

included toasting, baking, drying, canning and boiling.

Fermentation included baking, acidic fermentation and alcoholic

beverage production. Refining processes were all technological

treatments that lead to ingredient losses, mainly fiber fraction,

and they included cereal milling, tomato and potato processing,

soybean flour defatting, and transformation of fruits into juices

or soda and of peanut into butter. Fiber losses for all these

products were checked from USDA databases.44 Thus, cooked

roasted buckwheat groats, cooked white rice, cooked pasta,

boiled common beans, boiled asparagus, boiled broccoli, boiled

cabbage, boiled carrot, boiled spinach, baked potato with skin,

canned common beans, canned beetroot, canned pear, canned

peach, dried grapes (raisins), dried plums (prunes), toasted wheat

germ and toasted French/Vienna bread together with the corre-

sponding raw/unprocessed products were included within the

Thermal group; beer, wine, whole and white wheat breads,

cucumber pickles and sauerkraut together with the correspond-

ing raw products were included in the Fermentation group; and

white wheat flour, cooked white rice, defatted soybean flour,

oven-heated French fries, potato chips, canned tomato sauce,

canned tomato paste, ketchup, canned condensed tomato soup,

peanut butter, fruit juices (apple, grape and orange juices) and

carbonated orange soda together with the corresponding raw/

unprocessed products were included within the Refining group.

Food Funct., 2011, 2, 483–504 | 485

In addition, some products that have been selected for

measuring effect of OP were not selected for studying the effect of

SP since they have no counterpart as processed or raw product,

e.g. raw celery; and products initially removed as non-edible

processed products, i.e. barley malted flour, roasted buckwheat

groats, white rice, white wheat flour, wheat germ, dry pasta and

defatted soybean flour, were now considered since having a raw

or an edible processed paired product, i.e. respectively beers,

cooked roasted buckwheat groats, cooked white rice, boiled

pasta, toasted wheat germ, white wheat bread and raw soybean

seed. This corresponded to 28 raw and 40 processed products

with white wheat flour (C16) being both raw (vs white wheat

bread: effect of baking) and processed (vs whole-grain wheat

flour: effect of milling) product, whole-grain wheat flour (C15)

being compared to both whole wheat bread (effect of baking) and

white flour (effect of milling), and cooked white rice (C12) being

compared to both raw white rice (effect of cooking) and cooked

brown rice (effect of polishing). Besides, several processed

products were derived from the same raw products that are

common bean, soybean, cabbage, potato, tomato, grape and

orange. We finally reached 41 pairs of raw vs processed PBF.

Calculation of the average rank and of the lipotropic density and

capacity

The LD was the [lipotrope content (mg/100 g of food) � 100/

caloric content (kcal/100 g of food)] ratio and was expressed in

mg lipotrope/100 kcal.

Processed PBF were first ranked according to each lipotrope,

TPC, IP and sums of lipotrope densities. Mean ranks were then

calculated for each processed PBF by averaging the rank

obtained for each LD, as follows:

Mean rankPBF ¼ (P

rankPBF)/number of lipotrope (n ¼ 7 or 8)

with ‘‘rankPBF’’ being rank number for each processed PBF

product from LD1 to LD8. Each product was finally classified

according to its mean calculated rank from the lowest to the

highest. Classification for raw products has been given in

a previous study in the supplementary material of ref. 1. Mean

ranks were also calculated for each raw and processed food

groups, as follows:

Mean rankfood group ¼ (P

rankfood group)/number of lipotrope

(n ¼ 7 or 8)

with ‘‘rankfood group’’ being calculating by averaging ranks of all

products constitutive of the raw or processed group for each LD.

Plant-based food LC was calculated as previously described1

with raw asparagus as the reference food product (LC ¼ 100%):

LCfood (%) ¼ (P

[(LDfood/LDraw asparagus) � 100])/n

with [(LDfood/LDraw asparagus) � 100] calculated for each lipo-

trope and n being the number of lipotrope considered, either 8 or

7 (depending on available literature data for potentially available

myo-inositol (PAI) content; see Fardet et al.1). Betaine, choline,

PAI, methionine, magnesium, niacin, pantothenic acid and folate

densities for raw asparagus were respectively 16.6, 95.1, 118.2,

130.7, 83.2, 5.811, 1.628 and 0.309 mg/100 kcal (calculated from

486 | Food Funct., 2011, 2, 483–504

Supplementary Table 1†). Briefly, the LC allows both estimation

of lipotropic potential of food products in a simple way (by

considering equally the respective weight of the 8 LD) and

comparison of them for this property.1

Statistical analyses

Due to non-Gaussian distribution of densities for each of the 8 or

sum of lipotropes, bilateral non-parametric Mann-Whitney’s test

was applied to measure the effect of OP upon LD and LC for all

productswithout food group distinction and for the 5 food groups

(LDonly). Since brewed coffee and tea LDwere very high – due to

their very low caloric content of�1 kcal/100mL – the effect of OP

was estimated without these products. The effect of SP onLDwas

also measured, firstly without process type distinction and

secondly by considering Thermal, Refining and Fermentation

groups. Among Thermal treatments, distinction was also made

between Thermal treatments with (boiling and canning) and

without (toasting, baking and drying) water. Effect of SP was

measured with the Wilcoxon matched-pairs signed-ranks test. A

level of p < 0.05 was considered significant for both tests.

Lipotrope density distributions for raw and processed prod-

ucts were otherwise characterized by means � standard devia-

tion, mean rank � standard deviation, range (min- and

max-values), median (50% quartile) and 25%- and 75%-quartiles.

Similarities and differences for LD profiles (n ¼ 8) of raw vs

processed PBF was analysed and visualized through principal

component analysis (PCA) and hierarchical classification (HC)

using Euclidian distance and Ward aggregation. All statistical

analyses have been realised on a PC computer with the XLSTAT

software (Addinsoft Editors, Paris, France).

Results

Ranking of processed PBFs according to their LD and sum of

LDs were described in details in supplementary material 1 (see

Expanded results, i.e. comments to Supplementary Tables 2–6).†

Effect of processing without food group distinction

a. Effect of overall un-specific processing. Effect of OP was

first evaluated globally by comparing mean LD of raw products

(edible or not) with that of edible processed products (Table 1).

Except betaine density, lower mean LD were always accompa-

nied by higher mean rank, the most important mean rank

differences being reached for magnesium (+16.1 rank, p < 0.05),

pantothenic acid (+19.1, p < 0.05), folates (+17.4, p < 0.05), total

B vitamin (+10.6, p ¼ 0.094), BeChIMe (betaine + choline +

myo-inositol + methionine, +11.4, p < 0.05) and total 8 lipotrope

(+13.0, p < 0.05) densities (Table 1, left-hand part). Considering

mean rank change for the 8 LD, mean ranking increased by 9.6%,

indicating that processed PBF were less well ranked than raw

PBF towards LD. The effect was more marked for lipotropic

micronutrients (magnesium and B vitamins, mean increased

ranking ¼ +12.8%) than for main lipotropes (mean increased

ranking ¼ +6.5%).

Then, raw and processed products were compared for LD

profile similarities and differences through PCA and HC. The

loading plot showed that raw products as a whole (additional

variable) tended to be characterized by higher betaine, choline,

This journal is ª The Royal Society of Chemistry 2011

Table

1Effectofprocessinguponlipotropedensities

ofplant-basedfoods

Lipotropes

Effectofoverallprocessing:unspecifica

Effectofspecificprocesses

b

Means�

SD

Meanrankc

�SD

Range

[min.–max.]

Range(foodcodes)d

Quartiles

eMeans�

SD

Meanrankc

�SD

Range

[min.–max.]

Range

(foodcodes)d

Quartiles

e

Betaine

Raw

44.3

�166.9

60.8

�35.0

[0–887.1]

[V34–V56]

0.2–0.6–2.5

43.0

�147.4

40.0

�23.8

[0.1–775.5]

[L28–V69]

0.3–0.5–3.5

Processed

23.0

�91.6

59.2

�34.2

[0–674.2]

[B120–V42]

0.2–0.5–3.6

29.6

�111.8

42.5

�23.8

[0–674.2]

[V55–V42]

0.2–0.5–4.5

Choline

Raw

27.4

�24.5

56.2

�29.0

[4.1–95.1]

[N89–V36]

9.8–18.1–34.2

27.7

�23.3

38.1

�20.4

[2.9–95.1]

[C16–V36]

9.9–19.9–43.1

Processed

31.4

�42.1

63.4

�38.7

[1.0–232.3]

[B125–V52]

5.7–16.6–38.9

28.8

�34.5

44.4

�26.8

[1.2–140.4]

[B128–V44]

4.4–18.9–39.5

Methionine

Raw

61.1

�52.1

55.8

�32.6

[2.1–206.8]

[F92–V39]

22.0–47.4–82.1

60.3

�40.6

37.9

�21.4

[2.1–179.9]

[F92–V43]

41.8–49.5–72.7

Processed

51.8

�49.0

63.7

�35.9

[0–224.3]

[B128–V70]

10.8–39.7–70.5

55.8

�56.4

44.6

�25.8

j[0–224.4]

[B119/128–L32]

19.1–43.3–66.4

Magnesium

Raw

63.4

�70.3

51.5

�31.4

[9.7–465.2]

[F100–V39]

25.5–49.1–77.6

51.2

�45.5

36.7

�22.9

[6.4–267.3]

[C16–V69]

24.3–41.7–70.7

Processed

45.3

�50.1

67.6

�35.6

j[0–311.7]

[B121–V70]

14.6–33.5–53.3

42.5

�52.2

45.8

�24.2

j[2.0–311.7]

[B128–V70]

12.2–26.0–53.1

Niacin

Raw

1.79�

2.68

55.9

�34.0

[0.04–19.00]

[N78–V59]

0.55–0.98–2.18

1.54�

1.31

37.0

�23.8

[0.19–5.81]

[F92–V36]

0.40–1.48–2.04

Processed

1.36�

1.62

63.6

�34.8

[0–10.81]

[B121/128–B123]

0.33–1.07–1.74

1.14�

1.10

45.5

�23.4

j[0–5.44]

[B128–V37]

0.34–0.84–1.64

Pantothenic

acid

Raw

0.71�

0.92

49.9

�32.7

[0.03–5.93]

[N78–V59]

0.24–0.41–0.97

0.48�

0.51

36.7

�21.6

[0.07–2.61]

[C16–V54]

0.19–0.37–0.57

Processed

0.40�

0.51

69.0

�33.7

j[0–3.03]

[B121/128–V52]

0.09–0.23–0.46

0.38�

0.42

45.8

�25.4

j[0–2.16]

[B128–V44]

0.09–0.22–0.44

Folates

Raw

0.096�

0.132

50.7

�30.7

[0.003–0.572]

[F100–V57]

0.013–0.040–0.097

0.086�

0.108

36.0

�22.4

[0.002–0.476]

[C11–V69]

0.011–0.425–0.096

Processed

0.063�

0.124

68.1

�35.6

j[0–0.747]

[B118/121/123/128/

131–V37]

0.004–0.014–0.067

0.070�

0.145

46.4

�24.3

j[0–0.747]

[B118/128/

131–V37]

0.005–0.015–0.070

TotalBvitamins

Raw

2.600�

3.531

54.4

�33.7

[0.074–25.048]

[N78–V59]

0.961–1.656–3.138

2.115�

1.665

36.3

�24.0

[0.331–7.748]

[F92–V36]

0.683–1.825–2.658

Processed

1.827�

1.869

65.0

�34.7

k[0–10.845]

[B121/128–B123]

0.443–1.422–2.419

1.586�

1.447

46.2

�22.9

j[0–7.312]

[B128–V37]

0.487–1.291–2.090

IPf

Raw

42.4

�39.2

33.5

�19.6

[0–179.3]

[V57/F103–V59]

10.6–36.8–60.6

47.0

�40.2

15.6

�10.2

[1.3–105.5]

[V73–L28]

12.8–39.0–81.3

Processed

34.7

�35.9

37.8

�20.8

[0–112.7]

[B128–V68]

8.2–15.4–58.2

47.3

�52.0

16.9

�10.4

[1.7–192.5]

[V76–L32]

10.9–21.5–75.3

PAIg

Raw

105.8

�134.6

26.2

�15.3

[0–668.4]

[V53/N

77/F94-F95]

14.4–52.5–139.9

144.9

�137.0

9.5

�6.0

[21.9–316.5]

[F100–F107]

22.6–85.3–304.0

Processed

62.8

�103.0

34.5

�16.8

l[0–329.3]

[C20–22/N

80/B121–B127]

1.5–12.3–87.7

65.9

�94.5

13.0

�6.8

[2.0–329.3]

[B128–B127]

12.3–27.3–88.3

BeC

hIM

eg

Raw

232.4

�196.7

25.6

�15.3

[20.3–965.1]

[F94–V69]

93.2–160.7–318.8

248.4

�179.8

9.3

�5.9

[42.2–561.4]

[F100–V41]

68.6–295.0–373.8

Processed

147.9

�188.7

37.0

�17.7

j[3.2–752.5]

[B121–V42]

31.6–80.0–171.1

171.8

�220.7

13.3

�6.6

[3.4–752.5]

[B128–V42]

45.2–94.9–206.8

Total8lipotropes

g

Raw

294.5

�233.7

25.0

�14.9

[52.2–1235.4]

[F100–V69]

125.6–225.7–384.1

290.4

�203.0

9.0

�6.0

[52.2–629.2]

[F100–V41]

79.1–381.0–426.4

Processed

174.5

�210.5

38.0

�17.5

j[3.2–817.2]

[B121–V42]

47.5–95.9–227.2

198.5

�236.8

13.5

�6.3

[5.4–817.2]

[B128–V42]

62.8–102.7–262.5

This journal is ª The Royal Society of Chemistry 2011 Food Funct., 2011, 2, 483–504 | 487

Table

1(C

ontd.)

Lipotropes

Effectofoverallprocessing:unspecifica

Effectofspecificprocesses

b

Means�

SD

Meanrankc

�SD

Range

[min.–max.]

Range(foodcodes)d

Quartiles

eMeans�

SD

Meanrankc

�SD

Range

[min.–max.]

Range

(foodcodes)d

Quartiles

e

TPCh

Raw

473.6

�526.3

43.9

�26.8

[8.2–2514.5]

[N87–F95]

117.3–290.5–566.6

359.4

�260.2

22.5

�15.0

[34.8–967.0]

[C15–F113]

140.6–290.5–553.9

Processed

403.0

�833.8

55.6

�28.8

j[0–5113.7]

[B128–B123]

63.1–145.3–459.7

219.9

�228.3

32.1

�15.4

j[0–797.4]

[B128–V37]

64.8–122.7–397.8

Total8+TPCi

Raw

799.1

�644.0

22.8

�14.2

[142.5–3316.4]

[N77–F95]

354.9–723.9–997.1

686.9

�321.0

7.8

�4.6

[277.1–1135.6]

[F100–V41]

277.1–758.1–1301.7

Processed

367.1

�339.6

36.7

�14.0

j[5.4–1301.7]

[B128–V42]

164.3–232.3–547.3

367.2

�356.7

14.7

�6.4

j[5.4–1301.7]

[B128–V42]

176.4–233.5–452.3

aAllrawandprocessed

plant-basedfoodswereconsidered:however,dueto

theirverylowcaloriccontent,brewed

coffee

andteawereexcluded

from

processed

productsto

avoid

anoverestimationof

density

values;resultsare

expressed

inmg/100kcal.

bOnlypairsofrawvs

processed

plant-basedfoodsare

considered,e.g.rawcommonbeanvs

boiled,rawcommonbeanvs

canned

commonbean,raw

whiterice

vscooked

whiterice,etc.;resultsare

expressed

inmg/100kcal.cMeanrankcorrespondsto

themeansofalltheranksforeither

raworprocessed

PBFrelativeto

thetotality

ofPBF(e.g.n

¼119

fortheeffect

ofunspecificoverallprocessingandn¼

82productsfortheeffect

ofspecificprocesses

forbetaine,choline,methionine,magnesium,niacin,pantothenicacid,folate

andtotalBvitamin

densities);themeanrankhastherefore

tobealwaysconsidered

inrelationwiththesum

ofallraw

andprocessed

PBFforthelipotropedensity

considered.dRaw

andprocessed

plant-basedfoods

descriptionbyfoodcodeare

tobefoundin

Supplementary

Table

1,ESI.†

eValues

forthe25,50(m

edian)and75%

quartiles

correspondto

thelipotropedensity

intervalsin

which25,50and75%

oftheraw

orprocessed

plant-basedfoodsare

included.fIP:myo-inositolmoitiesderived

from

myo-inositolphosphates(from

IP6to

IP1):foreffect

oftheoverallprocessing,raw

¼45products

andprocessed

¼24products;foreffect

ofspecificprocesses,raw

¼16productsandprocessed

¼16products.

gPAI:Potentiallyavailable

myo-inositolfraction;BeC

hIM

e:sum

ofbetaine,

choline,

myo-inositol(PAI)

andmethionine;

total8lipotropes:betaine,

choline,

myo-inositol(PAI),methionine,

magnesium,niacin,pantothenic

acidandfolates;

foreffect

oftheoverallprocessingupon

PAI,

BeC

hIM

eandtotal8lipotropedensities,raw

¼38productsandprocessed

¼20products;

foreffect

ofspecificprocesses

uponPAI,

BeC

hIM

eandtotal8lipotropedensities,raw

¼11

productsandprocessed

¼11products.

hTPC:Totalphenoliccompoundsasdetermined

bytheFolinCiocalteu’scolorimetricmethod;foreffect

oftheoverallprocessing,raw

¼55productsand

processed

¼42products;foreffect

ofspecificprocesses,raw

¼27productsandprocessed

¼27products.

iForeffect

oftheoverallprocessing,raw

¼37productsandprocessed

¼16products;

foreffect

ofspecificprocesses,raw

¼11productsandprocessed

¼11products.

jTheeffect

ofprocessingwassignificantatthelevel

ofp<0.05:fortestingeffect

ofoverallunspecificprocessing,

non-parametricbilateralMann-W

hitney’stest

wasused(left-handpart

ofTable

1);fortestingeffect

ofspecificprocesses,Wilcoxonmatched-pairssigned-rankstest

wasused,i.e.

n¼

41pairsof

raw

vsprocessed

productsexceptforIP

(n¼

16pairs),PAI(n

¼11),BeC

hIM

e(n

¼11),total8lipotropes

(n¼

11),TPC

(n¼

27)andtotal8+TPC

(n¼

11)(right-handpart

ofTable

1).

kp¼

0.094.lp¼

0.057.

488 | Food Funct., 2011, 2, 483–504 This journal is ª The Royal Society of Chemistry 2011

magnesium and B vitamin densities (active variables) while the

effect appeared less marked between raw and processed products

for PAI and methionine densities (Fig. 1A). Analysis of PCA

score plot based on loading plot allowed deducing that products

on the right-hand part of the plot had higher densities in choline,

betaine, magnesium and B vitamins while those on left-hand part

had lower densities for these lipotropes. In addition, products in

the upper right part of the plot had higher PAI density while

those at the bottom right had higher methionine density

(Fig. 1B). Thus, highly refined and/or processed products,

energy-dense foods and fruits (except blackberry) exhibited an

LD profile less well than most vegetables, legumes and brewed

tea. Among processed products, boiled and canned vegetables

and legume, and brewed tea exhibited better LD profile than

processed cereals and beverages, especially carbonated sodas.

Raw and canned beetroot, raw and boiled cabbage, and raw and

canned common bean had relatively closed LD profiles. Black-

berry had an exceptionally high PAI density and algae a high

methionine density. Otherwise, citrus fruits and their corre-

sponding juices had closed LD profiles in the upper-left part of

the plot.

To summarize, processed products tended to be clustered into

the following groups: (1) brewed tea and boiled green beans; (2)

canned beetroot and common bean and boiled cabbage; (3)

whole wheat bread; (4) citrus fruit juices; (5) highly refined and/

or energy-dense products (Fig. 1B); while raw products tended to

be clustered as follows: (1) vegetables with high LD; (2) vegeta-

bles with intermediate LD; (3) blackberry; (4) cereals, legumes,

nuts and seeds; (5) citrus fruits; and (6) other fruits, onion,

avocado and carrot (Fig. 1B).

Hierarchical classification supplied supplementary informa-

tion about LD profiles. Products were early clustered within 4

classes (Fig. 2) with their corresponding mean LD profiles (Table

2). Except C4 composed of only brewed tea, other classes (C1–

C3) contained both raw and processed products. Class C2 was

characterized by the highest density in betaine and included

whole-grain quinoa, raw and canned beetroot and spinach, and

in total 8 lipotrope and B vitamin densities as well. Class C1 had

intermediate mean LD and was composed of raw and boiled

vegetables and legume products, of blackberry, of whole-grain

amaranth and sesame seed. Brewed tea from C4 had a LD profile

very distinct from those of other classes. Finally, C3 was

characterized by the lowest densities in total 8 lipotrope and B

vitamins and was composed of refined, highly processed and

energy-dense vegetables, fruits and their derived products,

almond, peanut, processed and refined cereal products, whole-

grain oat, wines and sodas (Table 2). The latest products were

clustered, the more they had similar LD profile (Fig. 2). Thus,

some unexpected products, irrespective of being processed or

not, had relatively closed LD profiles as e.g. avocado vs chips or

raw cabbage vs boiled green beans (Table 3 and Fig. 2). Between

early and late clustering, intermediate clusters allowed classifi-

cation of products within more specific food groups than the 4

pre-defined classes, such as e.g. (1) citrus fruits and juices, and

watermelon; (2) tomato soup, ketchup, avocado, potato chips

and pineapple; (3) raisins, sodas, blueberry, dried flaked coconut

meat, grape, pear, grape juice and banana; (4) plum, peach,

kiwifruit and onion; (5) peanut, processed cereals and whole-

grain oat; (6) apple, apple juice and wines; (7) raw radish, celery,

This journal is ª The Royal Society of Chemistry 2011

algae/seaweed and boiled cabbage; (8) whole-grain amaranth and

sesame seed; or (9) raw soybean and common bean.

Multivariate analyses therefore revealed that LD profiles of

processed products were not so importantly distinct from the

corresponding raw products. Otherwise, except grain products

(cereals, legumes and oleaginous seeds) that tended to exhibit

homogeneity of LD profiles, other clusters did not fully match up

with the 5 previously defined food groups (C, L, V, N and F

groups) and may assemble PBF of different botanical origins.

b. Effect of specific processes.All 41 pairs of raw vs processed

PBF were first considered as a whole. Specific processes (SP)

decreased all LD and sums of LD as reflected by increased mean

ranks: +20% for sum of 8 lipotropes (NS), +18% for BeChIMe

(NS), +16% for PAI (NS), +13% for folates (p < 0.05), +12% for

total B vitamins (p < 0.05), +11% for pantothenic acid (p < 0.05),

+11% for magnesium (p < 0.05), +10% for niacin (p < 0.05), +8%

for methionine (p < 0.05), +8% for choline (NS) and +3% for

betaine (NS) (Table 1; right-hand part). Considering mean rank

changes for the 8 LD, mean ranking increased from +10.0%,

indicating that processed PBF were less indeed ranked than raw

PBF towards LD. As for OP, the effect was less marked for main

lipotropes (means of +8.8%) than for lipotropic micronutrients

(means of +11.3%).

Lipotrope density variations following processing resulted

from both changes in caloric and/or lipotrope contents. Thus, for

a more thorough interpretation of LD variations, lipotrope loss

and gain percentages have been also evaluated on dry weight

basis (dwb) for each process and each compound. An important

point to underline is that data compared in this study arose of

different sources for both raw and processed PBF, i.e. general

database and/or specific database and/or original scientific

papers. When data for both raw and processed PBF were

collected within USDA database, the product defined as rawmay

not be exactly from the same source/lot as the raw product used

for processing, e.g. raw common bean vs boiled and canned

common bean. Conversely, in original scientific papers, raw and

boiled products generally arise of same lots. In this latter case,

the evolution of the lipotrope content upon processing was

therefore more precise. In our study, there is undoubtedly an

initial variability between raw products that were used for the

products defined as ‘‘raw’’ and ‘‘processed’’. Thus, it would be

hazardous to draw conclusions from only a 10% difference in LD

and/or lipotrope content following processing. Therefore, we

chose to consider only lipotrope content variations above +10%

or below �10%, the threshold that we estimated to be at least

that of lipotrope density/content variability for raw PBF. A

difference between �10 and +10% was therefore not considered

as a meaningful processing effect. Results by process type were as

follows (Table 4; see also Expanded Results and Supplementary

Tables 7–9 in the ESI† for a detailed description of the influence

of processing on lipotrope densities and contents product by

product – TPC and IP contents being included):

Thermal treatments. Considering the 13 pairs of boiled/canned

raw vs processed products as a whole, thermal treatments with

water tended to decrease betaine, PAI and micronutrient

contents (median changes # �14%), to increase choline content

while having no marked effect on methionine content. Sums of

Food Funct., 2011, 2, 483–504 | 489

Fig. 1 A–B: Principal component analysis loading (A) and score (B) plots derived from the ‘‘59 (food items, 38 raw and 21 processed) � 8 (lipotrope

densities)’’ matrix (PC1 � PC2 plan represents 72% of total variance). On the loading plot are shown both active (8 lipotrope densities, �) and

supplementary (raw and processed products, C) variables. Green and blue colours on the scores plot respectively correspond to raw and processed

plant-based foods. Food codes can be found in Supplementary Table 1 in the ESI.†

490 | Food Funct., 2011, 2, 483–504 This journal is ª The Royal Society of Chemistry 2011

Fig. 2 Plot of the hierarchical classification derived from the ‘‘59 (food items, 38 raw and 21 processed)� 8 (lipotrope density)’’ matrix. The highest the

level of dissimilarity, the most plant-based foods have different lipotrope density profile. Classes C1, C2, C3, C4 and C5 cluster raw (green) and

processed (blue) plant-based foods based on the level of similarity for their lipotrope density profile.

betaine-choline-methionine, of B vitamin and of 7 lipotrope (PAI

excluded due to lower number of raw vs processed pairs) contents

were all decreased, effect being more marked for sum of B

vitamin contents (median change ¼ �23%). Considering the 8

lipotropes, boiling/canning decreased content by ��20%

(median of the 8 medians was �22%, and �19% when excluding

PAI content).

Considering the 5 pairs of toasted/baked/dried raw vs pro-

cessed products as a whole, thermal treatments without water

tended to decrease betaine, methionine, PAI, pantothenic acid

and folates content (median changes # �12%) while having no

marked effect on choline, magnesium and niacin contents.

Considering the 8 lipotropes, toasting/baking/drying as a whole

decreased contents, PAI excluded (Median2 change ¼ �12%) or

not (Median1 change ¼ �19%).

Considering now the 18 pairs of products as a whole, thermal

treatments � water tended to decrease betaine, PAI, micro-

nutrient and sums of lipotrope contents (median changes #

�10%) while having no marked effect on choline and methionine

contents. Densities (mg/100 kcal) tended to change in a similar

way to the contents on a dwb. Considering the 8 lipotropes,

thermal treatments decreased content by ��22% (Median1

change ¼ �23% and Median2 change ¼ �21%).

Refining treatments. Considering the 14 pairs of products as

a whole, refining tended to decrease choline, methionine, PAI,

This journal is ª The Royal Society of Chemistry 2011

micronutrient, sum of B vitamin and sum of 7 lipotrope contents

(median changes#�10%) while increasing betaine content (median

change ¼ +24%) and having no marked effect on sum of betaine-

choline-methioninecontent (medianchange¼+3%).Exceptbetaine,

densities changed in a similar way to contents. Considering the 8

lipotropes, refining processes decreased content by ��30%

(Median1 change ¼ �31% andMedian2 change¼ �30%).

Fermentations. Considering the 6 pairs of products as a whole,

fermentation tended to decrease methionine, magnesium and

pantothenic acid contents (median changes # �12), to increased

betaine, choline, PAI, sum of betaine-choline-methionine and

sum of 7 lipotrope contents (median changes $ +24%) while

having no marked effect on niacin, folate and sum of B vitamin

contents. Except niacin and folates, densities changed in a similar

way to contents. Considering the 8 lipotropes, fermentation had

no effect on content (Median1 change ¼ +3% and Median2

change ¼ �1%).

All processes. Specific processes tended to decrease betaine,

methionine, PAI and micronutrient contents (median changes #

�13%) while having no marked effect on choline content. Sums

of lipotrope contents tended to be slightly decreased: �5% for

betaine-choline-methionine, �16% for B vitamins and �10% for

the 7 lipotropes. Except betaine, densities globally changed in

a way similar to contents. Significativity was reached for all LDs

Food Funct., 2011, 2, 483–504 | 491

Table 2 Mean lipotrope densities of the 4 classes of products as determined by hierarchical classificationa

Food product codesb Betaine Choline PAIc Methionine Magnesium Niacin Vit. B5d FolatesP

B vitaminse Totalf

C1g Raw: V34-V36-V43-V46-V49-V53-V54-V57-V67-V73N89F95L28-L31C1 2.8 52.5 140.0 95.5 86.7 2.13 0.98 0.183 3.29 380.92

Processed: V40-V47L30

C2 Raw: V41-V69C10 522.8 36.4 15.5 64.2 113.8 1.11 0.44 0.229 7.78 754.53

Processed: V42

C3 Raw: V38-V50-V60C8N77-N84F92-F94-F96-F100-F102-F103-F104-F107-F108-F110-F112-F113-F116-F117

Processed: V65-V76 2.4 11.1 74.8 22.7 21.4 0.76 0.31 0.024 1.10 133.55C12-C20-C21-C22N80F101B118-B121-B124-B126-B127-B128-B131-B132

C4 Processed: B130 143.4 31.1 210.3 0 252.3 0 0.93 0.421 1.35 638.44

a Results are expressed in mg/100 kcal; n¼ 38 raw and 21 processed plant-based food products. b C, L, V, N, F and B respectively correspond to Cereal,Legume, Vegetable, Nut and seed, Fruit and Beverage groups (food description according to its code can be found in Supplementary Table 1, ESI†).c PAI is potentially availablemyo-inositol fraction. d Vit. B5 is pantothenic acid. e B vitamins are the sum of niacin, pantothenic acid and folate densities.f Total is the sum of the 8 lipotrope densities. g C is the class as defined by hierarchical classification.

except those of betaine and choline, and of PAI as well but the

number of paired products was lower. Considering the 8 lipo-

tropes, SP decreased content and densities by 18–19%.

Effect of overall un-specific processing by food group

Processing tended to increase mean rank of PBF groups, indi-

cating that processed PBF groups were generally less well ranked

than raw PBF groups towards LD (Table 5). Thus, putting aside

L group due to the very low number of products, increased

percentage of mean rank was: for magnesium, C¼N (+35%; p <

0.05) > F (+29%; p < 0.05) > V (+15; NS); for total B vitamin, F

(+36%; p < 0.05) > V (+20%; p < 0.05) > N (+13%; p < 0.05) > C

(+8%; NS); for PAI, C (+31%; p ¼ 0.057) > N (+25%; n ¼ 1

processed PBF) > V (+13%; NS) > F (+10%; NS); for BeChIMe:

C (+50%; p ¼ 0.057) ¼ N (+50%; n ¼ 1 processed PBF) > F

(+14%; NS) > V (+13%; NS); and for sum of 8 LD: C (+50%; p¼0.057) ¼ N (+50%; n ¼ 1 processed PBF) > F (+20; NS) > V

(+15%; NS).

The influence of OP upon TPC (Table 5) and IP (Supple-

mentary Table 10) densities by food group is described in

Expanded Results (see ESI†).

Effect of processing on lipotrope capacity

a. Effect of overall un-specific processing. Due to limited

availability of PAI data, LC for the 8 lipotropes could have been

492 | Food Funct., 2011, 2, 483–504

calculated for only 38 raw and 21 processed products (Table 6).

Raw and processed products had respectively mean LC of 72 and

55%, medians of 38 and 18%, and mean ranks of 25.6 and 37.0.

The increased mean rank of +19% for processed PBF was

significant. Raw spinach, raw and canned beetroot, whole-grain

quinoa, brewed tea and blackberry were the only PBF with LC

above that of raw asparagus, the reference food (LC ¼ 100%).

Among raw PBF, except blackberry and citrus, nuts, seeds and

fruits had generally low LC below 35%. Among processed PBF,

refined products had low LC below 30%.

Canned beetroot and canned common bean had higher LC

than corresponding raw products. Concerning grape, fermenta-

tion into wine increased by 2-fold the LC (from 7 to 14%) while

transformation into juice had no effect and drying decreased LC

by almost 2-fold (LC raisins ¼ 4%): however, grape and all its

derived products had low LC below 15. Transformation of

grape, apple and orange into juices had no effect on LC. Boiling

cabbage decreased LC from 65 to 38%, i.e. almost by 2-fold.

Transformation of raw tomato into ketchup and canned

condensed tomato soup was drastic towards LC that was

reduced from 70 to respectively 20 and 13%. Compared to raw

orange (LC ¼ 51%), carbonated orange soda had a LC of 1%.

Finally, whole wheat bread had a LC �2-fold higher than white

wheat bread.

Compared to multivariate analyses, LC ranking globally

matched the 4 classes given by HC, i.e. PBF products clustered

within classes C2, C1 and C3 had respectively high,

This journal is ª The Royal Society of Chemistry 2011

Table

3Lipotropedensity

profilesof9pairsofplant-basedfoodproductsasassociatedbyhierarchicalclassificationa

Class

C1b

Class

C1

Class

C1

Class

C1

Class

C2

Class

C3

Class

C3

Class

C3

Class

C3

Raw

cabbage

V46c

Boiled

green

beans

V40

Raw

tomato

V73

Blackberry

F95

Lettuce

V57

Sweet

pepper

V49

Whole-

grain

Amaranth;

C1

Sesame

seed;

N89

Whole-

grain

quinoa

C10

Raw

beetroot

V41

Avocado

V38

Chips,

V65

Dried

flaked

coconut

meat,N80

Blueberry,

F96

Whole

wheat

bread,

C20

Raw

peanut,

N84

Kiwifruit,

F103

Raw

onion,

F96

Betaine

3.2

0.4

0.4

1.2

1.0

0.5

19.8

0.1

174.7

466.9

0.85

0.04

0.3

0.4

26.3

0.1

0.9

0.2

Choline

64.6

66.5

43.1

33.8

76.8

28.3

20.4

4.1

20.4

19.4

9.2

2.2

4.0

11.3

11.6

9.2

14.3

16.7

PAId

154.4

322.2

304.0

668.4

161.6

273.3

25.2

8.8

9.2

23.3

5.1

6.5

026.1

04.4

255.4

93.5

Methionine

72.7

85.6

45.0

16.4

138.1

58.2

65.7

139.0

72.7

51.6

24.1

20.2

12.8

22.7

66.4

55.0

44.1

26.0

Magnesium

87.8

114.1

70.7

78.5

95.3

55.7

77.3

54.5

58.2

66.1

18.9

12.8

10.1

11.3

36.8

29.1

31.3

27.4

Niacin

1.51

1.82

3.82

2.54

2.31

3.69

0.37

0.92

0.81

0.96

1.24

0.77

0.06

0.79

1.64

2.09

0.63

0.31

Pantothenat

0.70

0.23

0.57

1.08

1.09

1.05

0.30

0.05

0.29

0.45

0.95

0.80

0.15

0.23

0.24

0.31

0.34

0.32

eFolates

0.210

0.109

0.096

0.098

0.572

0.076

0.014

0.018

0.014

0.313

0.058

0.014

0.002

0.011

0.021

0.042

0.046

0.061

aResultsare

expressed

inmglipotrope/100kcal.

bClasses

are

those

defined

byhierarchicalclassification(see

Table

2).

cFoodcodeasdefined

inSupplementary

Table

1,ESI.†

dPAIispotentially

available

myo-inositol.

This journal is ª The Royal Society of Chemistry 2011

intermediate and low LC (Tables 2 and 6). In addition, median

difference of �20% between LC of raw and processed PBF

(Table 6) was closed to median of �19% obtained when

considering the 8 medians of each LD change for all treatments

(n ¼ 8 lipotropes and n ¼ 41 pairs of raw vs processed prod-

ucts, Table 4).

b. Effect of specific processes. The 41 processes taking as

a whole significantly decreased LC by �19% (p ¼ 0.0004, Table

7) which was closed to median obtained for LD change for all

treatments, i.e. �18% (Median2 also based on 7 LD, Table 4).

Considering process type, similarly to median LD changes

(Median2, Table 4), refining was the most drastic (p ¼ 0.011,

�33%, Table 7 vs �33%, Table 4), followed by thermal treat-

ments (p ¼ 0.012, �16%, Table 7 vs �19%, Table 4) and

fermentations (NS, �5%, Table 7 vs + 9%, Table 4).

When looking at each process separately, except white wheat

bread (+115%), defatted soybean flour (+83%), canned beet-

root (+38%) and sauerkraut (+30%), all other process either

reduced LC (<�10%) or had no marked effect (�10% # % LC

change # +10%). Of all food groups, L group (raw common

bean and soybean) appeared as the less affected by processing,

especially for soybean and its derived products, with �11% #

% LC change # +83% (median ¼ �4%), then C group (�79%

# % LC change # +115%; median ¼ �21%), V group (�76%

# % LC change # +38%; median ¼ �27%) and F group

(�97% # % LC change # +2%; median ¼ �29%) (result not

shown).

c. Lipotrope capacity of cereal brans and wheat germ. The LC

of cereal brans was based on 7 LD (PAI density excluded) while

that of wheat germ was based on both 7 and 8 LD (Table 6).

Wheat bran LC was more than 10-fold higher than maize bran

LC that was 2-fold higher than oat bran LC. Based on 7 LD,

wheat germ had at least a 2-fold lower LC than wheat bran, i.e.

348 vs 748%. Adding PAI density reduced wheat germ LC down

to 308%, i.e. by �11%.

Discussion

The LD of processed PBFs alone, i.e. not compared to raw PBFs,

has been discussed in the Expanded Discussion (ESI†). Here,

only effect of processing was discussed.

Effect of processing on lipotrope densities and contents

Due to the limited number of both products and data collected

for PAI content, some conclusions have to be considered

cautiously and should be rather regarded as tendencies. In

addition, due to non-Gaussian distributions of LD and lipo-

trope content of processed and raw products, the effect of

processing cannot be evaluated based on means, but should be

considered first via the medians, min- and max-values and

mean ranking.

a. Synthesis of main results. Multivariate analyses showed

that raw and corresponding paired processed products had no

distinct LD profiles (e.g. fruits vs juices, canned or boiled vs

raw vegetables and legumes) and that the main differences had

Food Funct., 2011, 2, 483–504 | 493

Table 4 Plant-based food lipotrope density and content changes following thermal, refining and fermentative treatmentsa

Thermal treatments

With and without water (boiling, canning,toasting, baking and drying)(n ¼ 18 initial pairs)

With water (boiling and canning)(n ¼ 13 initial pairs)

Without water (toasting, baking anddrying) (n ¼ 5 initial pairs)

Density change(%, mg/100 kcal)

Content change(%, dry weight basis)

Density change(%, mg/100 kcal)

Content change(%, dry weight basis)

Density change(%, mg/100 kcal)

Content change(%, dry weight basis)

Betaine �24m [�89/+45]b �31 [�91/+53] �25n [�89/+45] �36 [�91/+53] �1 [�86/0] �26 [�84/�1]Choline +6 [�93/+81] +6 [�94/+84] +26 [�93/+81] +17 [�94/+84] �5 [�63/+10] �5 [�64/+11]Methionine �8 [�62/+136] �1 [�63/+115] �4 [�32/+136] +3 [�29/+115] �12 [�62/+20] �12 [�63/+19]Bet-Chol-Metc �13 [�55/+33] �10 [�53/+30] �13 [�55/+33] �6 [�53/+30] �34 [�42/+3] �33 [�51/+3]Magnesium �9 [�73/+41] �11 [�70/+43] �18n [�73/+41] �19 [�70/+43] +4n [ + 2/+28] +5 [ + 2/+26]Niacin �19m [�62/+13] �25 [�59/+28] �27m [�62/+13] �31 [�59/+28] �9 [�22/+9] �10 [�23/+10]Pantothenate �32m [�83/+29] �28 [�95/+38] �29n [�83/+29] �25 [�95/+38] �40 [�57/+2] �42 [�58/+3]Folates �29 [�93/+81] �21 [�88/+163] �10 [�93/+150] �14 [�88/+163] �33 [�86/+16] �28 [�85/+18]P

B vitaminsd �25m [�70/+35] �24 [�81/+44] �20m [�70/+35] �23 [�81/+44] �33 [�49/+9] �25 [�48/+10]P7 lipotropese �11 [�55/+7] �11 [�57/+11] �6 [�55/+7] �13 [�57/+11] �20 [�35/�10] �10 [�41/�7]

PAIf �54 [�82/+314] �52 [�81/+305] �33 [�82/+314] �28 [�81/+305] �75g �75g

Median1h �22 �23 �22 �22 �11 �19Median2i �19 �21 �18 �19 �9 �12TPCj �43m [�88/+60] �46 [�86/+74] �47n [�88/+60] �52 [�86/+74] �38 [�43/�33] �40 [�46/�33]IPk �5 [�25/+78] �7 [�21/+89] �5 [�25/+78] �7 [�21/+89] —l —l

Refining treatments (n ¼ 14 pairs) Fermentative treatments (n ¼ 6 pairs) All treatments (n ¼ 41 pairs)

Density change(%, mg/100 kcal)

Content change(%, dry weight basis)

Density change(%, mg/100 kcal)

Content change(%, dry weight basis)

Density change(%, mg/100 kcal)

Content change(%, dry weight basis)

Betaine 0 [�98/+1650] +24 [�98/+1790] +32 [�100/+2320] +35 [�100/+2516] 0 [�100/+2320] �18 [�100/+2516]Choline �33n [�94/+134] �34 [�92/+58] +34 [�62/+114] +32 [�76/+215] +2 [�94/+134] +3 [�94/+215]Methionine �33m [�100/+87] �30 [�100/+27] �22 [�100/+17] �50 [�100/+5] �11m [�100/+136] �17 [�100/+115]Bet-Chol-Metc �9m [�80/+578] +3 [�86/+577] +24 [�68/+805] +36 [�80/+871] �6m [�80/+805] �5 [�86/+871]Magnesium �46m [�92/+50] �33 [�90/+75] +9 [�46/+44] �12 [�64/+130] �18m [�92/+50] �19 [�90/+130]Niacin �31m [�100/+130] �20 [�100/+71] �21m [�90/�3] +6 [�94/+30] �19m [�100/+130] �13 [�100/+71]Pantothenate �38 [�100/+274] �15 [�100/+205] �27 [�86/+27] �17 [�91/+14] �31m [�100/+447] �25 [�100/+397]Folates �59m [�100/+17] �54 [�100/+90] +18 [�95/+55] �1 [�97/+122] �43m [�100/+150] �33 [�100/+163]P

B vitaminsd �33m [�100/+140] �24 [�100/+62] �1 [�90/+12] +9 [�94/+40] �25m [�100/+142] �16 [�100/+122]P7 lipotropese �25m [�84/+204] �10 [�93/+207] +15 [�74/+356] +24 [�84/+384] �9m [�84/+356] �10 [�93/+384]

PAIf �43 [�99/+16] �32 [�99/+23] +573g +573g �33 [�99/+314] �28 [�99/+573]Median1h �35 �31 +13 +3 �19 �19Median2i �33 �30 +9 �1 �18 �18TPCj �44 [�100/+97] �50 [�100/+98] +81 [�44/+169] +83 [�41/+297] �43m [�100/+169] �41 [�100/+297]IPk +14 [�100/+137] +32 [�100/+217] �30 [�36/�24] �27 [�35/�18] �7 [�100/+137] �7 [�100/+217]

a For all thermal treatments, numbers of paired products are 4, 4, 4, 11, 4 and 5 for respectively PAI, BeChIMe,P

lipotropes, TPC, Total and IP; Forthermal treatment with water, numbers of paired products are 3, 3, 3, 9, 3 and 5 for respectively PAI, BeChIMe,

Plipotropes, TPC, Total and IP; For

thermal treatment without water, numbers of paired products are 1, 1, 1, 2, 1 and 0 for respectively PAI, BeChIMe,P

lipotropes, TPC, Total and IP;For refining treatments, numbers of paired products are 6, 6, 6, 10, 6 and 7 for respectively PAI, BeChIMe,

Plipotropes, TPC, Total and IP; for

fermentative treatments, numbers of paired products are 1, 1, 1, 3, 1 and 2 for respectively PAI, BeChIMe,P

lipotropes, TPC, Total and IP; For alltreatments, numbers of paired products are 11, 11, 11, 27, 11 and 17 for respectively PAI, BeChIMe,

Plipotropes, TPC, Total and IP; All

treatments also include whole-grain masa, tofu and soybean milk not initially classified with thermal, refining and fermentative groups. b Numbersare median followed by [min/max] values. c Bet-Chol-Met is the sum of betaine, choline and methionine; myo-inositol was excluded due to lowernumber of data. d PB vitamins is the sum of niacin, pantothenate and folates. e P7 lipotropes is the sum of betaine, choline, methionine,magnesium and B vitamins; myo-inositol was excluded due to lower number of data. f PAI: potentially available myo-Inositol. g Only one pair ofraw vs processed product could have been considered. h Median1 corresponds to the median of the 8 medians obtained for each lipotrope densitiesor contents. i Median2 corresponds to the median of the 7 medians obtained for betaine, choline, methionine, magnesium and B vitamin densities orcontents (due to lower number of paired raw vs processed products for which PAI content could have been found, i.e. n ¼ 11, compared to otherlipotrope contents, i.e. n ¼ 41). j TPC: total phenolic compounds. k IP: myo-inositol phosphate. l No data available. m The effect of processing onLD was significant (p < 0.05, Wilcoxon matched-pairs signed-ranks test; see also Table 1); the significativity of process effect was tested only ondensities not on contents. n p ¼ 0.063 for the effect of thermal treatments without water on magnesium density; p ¼ 0.080, p ¼ 0.068, p ¼ 0.068 andp ¼ 0.074 for the effect of thermal treatments with water on respectively betaine, magnesium, pantothenic acid and TPC density; p ¼ 0.068 for theeffect of refining on choline density.

494 | Food Funct., 2011, 2, 483–504 This journal is ª The Royal Society of Chemistry 2011

Table

5Effectofoverallprocessinguponlipotropedensities

byfoodgroupa

Cereals

Legumes

Vegetables

Nuts

andseeds

Fruits

Rawb

Processed

cRaw

Processed

Raw

Processed

Raw

Processed

Raw

Processed

Magnesium

Means

60�

22

31�

25

64�

647�

2108�

104

73�

65

44�

21

23�

12

29�

20

14�

9Meanrank

5.3

�3.3

11.6

�5.0

l1.5

�0.7

4.5

�1.3

18.8

�12.4

25.3

�12.7

6.4

�4.2

12.0

�6.0

l12.9

�8.5

22.6

�8.5

l

Range

[37–94]

[9–91]

[60–69]

[45–50]

[19–465]

[13–312]

[17–85]

[10–45]

[10–79]

[2–29]

Product

code

[C5–C14]

[C21–C19]

[L28–L31]

[L30–B129]

[V38–V39]

[V65–V70]

[N86–N81]

[N78–N79]

[F100–F95]

[B128–F99]

Quartiles

d46–54–73

11–25–43

62–64–66

46–47–48

56–88–107

34–54–75

28–42–55

17–22–25

18–24–31

7–14–19

PBvitaminse

Means

1.37�

0.74

1.32�

0.84

0.90�

0.30

0.99�

0.456

5.04�

5.19

2.88�

1.52

0.89�

0.77

0.70�

0.85

1.63�

0.98

0.59�

0.41

Meanrank

8.5

�4.8

10.0

�5.7

3.5

�2.1

3.5

�2.1

17.6

�12.6

26.2

�12.0

l7.7

�5.0

9.8

�4.4

11.9

�8.5

23.9

�6.5

l

Range

[0.67–2.37]

[0.35–2.93]

[0.68–1.11]

[0.62–1.66]

[0.69–25.05]

[0.42–7.31]

[0.07–2.44]

[0.21–2.44]

[0.33–3.72]

[0–1.37]

Product

code

[C8–C15]

[C26–C27]

[L31–L28]

[L33–B129]

[V60–V59]

[V55–V37]

[N78–N84]

[N78–N85]

[F92–F95]

[B128–F93]

Quartiles

0.80–1.15–1.94

0.48–1.43–1.91

—f

0.78–0.84–1.06

2.72–3.70–5.45

1.80–2.42–3.62

0.34–0.71–0.96

0.36–0.39–0.43

1.10–1.32–2.26

0.29–0.47–0.92

ng

612

24

19

25

10

619

14

PAIh Means

12.3

�11.7

1.0

�1.9

18.4

�8.7

101.8

99.7

�94.0

68.9

�124.5

4.4

�4.4

0162.4

�174.3

105.3

�120.0

Meanrank

2.3

�1.5

4.5

�1.0

m2.5

�0.7

110.2

�5.4

13.0

�8.1

2.0

�1.0

310.8

�6.0

13.0

�7.5

Range

[2.4–25.2]

[0–3.9]

[12.3–24.6]

—[0–304.0]

6.5–322.2

[0–8.8]

—[0–668.4]

2.0–329.3

Product

code

[C8–C1]

[C20/C22–C12]

[L31–L28]

L30

[V53–V73]

[V65–V40]

[N77–N89]

N80

[F94–F95]

[B128–B127]

Quartiles

5.8–9.2–17.2

0–0–1.0

——

19.5–93.5–136.4

10.6–21.5–31.6

2.2–4.4–6.6

—44.8–113.8–254.9

15.4–87.1–143.9

BeC

hIM

ei

Means

172�

92

80�

21

161�

2260

321�

226

256�

294

88�

57

17

195�

183

117�

129

Meanrank

2.0

�1.0

5.5

�1.3

m2.5

�0.7

110.2

�5.4

13.0

�8.1

2.0

�1.0

410.5

�6.0

13.6

�7.5

Range

[108–277]

[56–104]

[159–162]

—[39–171]

[29–753]

[42–152]

—[20–720]

[3–356]

Product

code

[C8–C10]

[C12–C20]

[L28–L31]

L30

[V38–V69]

[V65–V42]

[N77–N89]

N80

[F94–F95]

[B128–B127]

Quartiles

120–131–204

68–80–92

160–161–161

—171–306–372

60–111–395

55–69–110

—69–145–299

24–95–159

P8lipotropes

j

Means

234.9

�91.3

97.6

�33.1

225.7

�7.7

305.8

418.8

�274.2

306.6

�321.6

132.8

�64.8

27.4

223.8

�196.1

131.9

�136.5

Meanrank

2.0

�1.0

5.5

�1.3

m2.5

�0.7

110.1

�5.3

13.2

�8.1

2.0

�1.0

410.1

�5.8

14.4

�7.3

Range

[159–336]

[67–143]

[220–231]

—[60–1235]

[43–817]

[91–208]

—[53–802]

[5–381]

Product

code

[C8–C10]

[C12–C20]

[L28–L31]

L30

[V38–V69]

[V65–V42]

[N77–N89]

N80

[F100–F95]

[B128–B127]

Quartiles

184–209–273

78–90–110

——

274–421–472

81–156–498

96–100–154

—89–163–326

38–103–180

n3

42

115

63

115

7

TPCk

Means

46�

26

69�

43

225�

143

67�

5556�

411

538�

495

126�

100

42�

39

717�

677

206�

166

Meanrank

6.5

�2.9

5.4

�4.0

1.5

�0.7

4.0

�1.0

17.5

�9.9

18.6

�10.9

5.9

�3.8

9.5

�3.1

11.8

�7.4

23.8

�7.6

l

Range

[23–94]

[20–123]

[124–326]

[63–73]

[3–1701]

[19–2058]

[8–281]

[5–89]

[160–2515]

[112–553]

Product

code

[C8–C10]

[C13–B119]

[L31–L28]

[B129–L30]

[V62–V49]

[B131–V35]

[N87–N90]

[N83–N85]

[F106–F95]

[B128–F114]

Quartiles

29–39–50

30–82–88

—64–66–69

276–506–623

220–480–626

52–101–215

12–37–67

258–437–898

112–148–274

n6

52

319

16

94

19

12

This journal is ª The Royal Society of Chemistry 2011 Food Funct., 2011, 2, 483–504 | 495

Table

5(C

ontd.)

Cereals

Legumes

Vegetables

Nuts

andseeds

Fruits

Rawb

Processed

cRaw

Processed

Raw

Processed

Raw

Processed

Raw

Processed

P8lipotropes

j+TPCk

Means

291.7

�126.7

180.0

�72.3

450.7

�135.5

378.4

963.4

�568.7

623.2

�461.3

156.1

�19.2

—868.4

�757.5

285.1

�227.6

Meanrank

2.3

�1.5

4.0

�1.4

2.0

�1.4

29.6

�5.6

13.2

�6.6

1.5

�0.7

—8.8

�5.4

17.3

�4.6

l

Range

[183–431]

[129–231]

[355–547]

—[159–2136]

[112–1302]

[143–170]

—[229–3316]

[5–663]

Product

code

[C8–C10]

[C22–C20]

[L31–L28]

L30

[V38–V69]

[B131–V42]

[N77–N84]

—[F94–F95]

[B128–B126]

Quartiles

222–262–346

——

—679–950–1109

304–611–788

——

421–733–949

176–215–380

n3

22

115

52

015

7

aResultsare

expressed

inmg/100kcal;beverages,beingallprocessed

PBF,havebeendispatched

amongthe5solid/sem

i-solidprocessed

foodgroups.

bBoth

edibleandnon-ediblerawproductsare

considered.cOnly

edible

processed

productsare

considered.dValues

forthe25,50(m

edian)and75%

quartiles

correspondto

thelipotropedensity

intervalsin

which25,50and75%

oftheraw

or

processed

plant-basedfoodsare

included.eP

Bvitaminsis

thesum

ofniacin,pantothenic

acidandfolate

densities.fNovaluesince

number

ofproductsis

<3.gNumber

ofproductsconsidered

forcalculatingstatisticaldescriptors.hPAIis

thepotentiallyavailable

myo-insoitolfraction.iSum

ofbetaine,

choline,

potentiallyavailable

myo-inositol(PAI)

andmethioninedensities.jP

8lipotropes

isthesum

ofbetaine,

choline,

myo-inositol(PAI),methionine,

magnesium,niacin,pantothenic

acidandfolate

densities.kTPC

istotalphenoliccompounds.

lSignificanteffect

of

processingatp<0.05(non-parametricMann-W

hitney’stest).

mp¼

0.057(non-parametricMann-W

hitney’stest).

496 | Food Funct., 2011, 2, 483–504

to be found in the botanical origin of PBF, degree of refining

and/or energy content. Indeed, with some exceptions and

putting aside refined and/or energy-dense PBF, fruits, grain

products (cereals, legumes, nuts and seed) and vegetables ten-

ded to form separate clusters. However, the large cluster for

vegetables emphasized very heterogenous LD profiles. In

addition to spatial clusters given by PCA, HC has supplied new

information by associating PBF for similar LD profiles at

different level of similarity: thus, although not directly grasped

on the PCA plot, whole-grain quinoa and beetroot had closed

LD profiles and all associated products were not always from

the same botanical origin (e.g. kiwifruit vs raw onion or

avocado vs chips).

Considering all the 8 LD, both OP (n¼ 56 raw vs 63 processed

products) and SP (n ¼ 41 pairs of raw vs processed products)

increased processed PBF mean ranking by �+10%. Median

changes for all lipotrope densities and contents following SP

were respectively �19 and �19%, and �18 and �18% when

excluding PAI density and content.

Considering each lipotrope separately, mean choline and

betaine densities were not significantly affected by both OP and

SP while methionine and lipotropic micronutrient densities

were decreased, especially magnesium, pantothenic acid and

folate densities (p < 0.05 for both OP and SP). Considering

PAI density, decreases were not significant, but this has to be

attributed to lower number of products. Otherwise, lipotropic

micronutrient densities (i.e. magnesium and B vitamins) tended

to be more affected by processing (mean ranking increase of

�+12.1%) than main lipotrope densities (i.e. BeChIMe, mean

ranking increase of �+7.7%). Considering products by food

groups, F group appeared as the most affected by OP, espe-

cially towards magnesium and total B vitamin densities (p <

0.05). Except sauerkraut and wine, the way LD changed upon

processing was generally similar to the way lipotrope contents

(dwb) changed.

Of all treatments, fermentation appeared less drastic towards

LD (median change of +13%) than refining (�35%) and

thermal treatments (�22%), and it tended to increase betaine

(+32%) and choline (+34%) densities. Canning (except fruits)

and boiling vegetables also tended to increase choline densities

(median change of +26%). Refining is undoubtedly the most

drastic treatment for LD and lipotrope contents (median

changes of respectively �35 and �31%). Among B vitamins,

folate density appeared as the most affected (median change of

�43%) compared to niacin (�19%) and pantothenic acid

(�31%).

In the following parts, discussion and interpretation of results

was mainly based on content changes (dwb). It is also important

to mention that of all 8 lipotropes, only methionine may not be

water soluble, notably when included within structured protein

networks. All others are recognized as hydrosoluble compounds

likely to be released into water. Besides, the rarity of literature

data about the effects of processing upon the main lipotrope

contents of PBF was limiting for explaining all calculated

changes. The effect of processing on magnesium, B vitamin, TPC

and IP densities has been discussed in the Expanded Discussion

(see ESI†). Since their content may increase upon processing, the

lipotropic potential of acetate and resistant starch (RS) has been

also considered and discussed in Expanded discussion.

This journal is ª The Royal Society of Chemistry 2011

Table 6 Lipotropic capacity of raw and processed plant-based foods based on 8 lipotrope densities

LCa (%) LC (%)

Raw asparagus (reference) 100 Raw asparagus (reference) 100

Raw (edible or not): n ¼ 38b Processed (edible): n ¼ 21V69c: Spinach (C2)d 672 V42: Canned beetroot (C2) 536V41: Beetroot (C2) 390 B130: Brewed tea (C4) 196C10: Quinoa, whole-grain (C2) 155 V40: Boiled green beans (C1) 79F95: Blackberry (C1) 107 B127: Orange juice (C3) 49V36: Asparagus (C1) 100 L30: Canned common bean (C1) 40V57: Lettuce (C1) 92 C20: Whole wheat bread (C3) 39V43: Broccoli (C1) 90 V47: Boiled cabbage (C1) 38V34: Algae (C1) 84 B126: Lime juice (C3) 34V53: Celery (C1) 76 C22: French/Vienna bread (C3) 27V54: Cucumber, peeled (C1) 74 B131: Tomato soup (C3) 20V73: Tomato (C1) 70 C21: White wheat bread (C3) 18V49: Sweet bell pepper (C1) 66 B132: Wine (C3) 14V46: Cabbage (C1) 65 V76: Ketchup (C3) 13V67: Radish (C1) 63 V65: Chips (C3) 13F107: Orange (C3) 51 B118: Apple juice (C3) 13F102: Grapefruit (C3) 46 C12: Cooked white rice (C3) 10F103: Kiwifruit (C3) 44 B124: Grape juice (C3) 8C1: Amaranth, whole-grain (C1) 42 N80: Dried flaked coconut meat (C3) 5F104: Mandarin orange (C3) 41 F101: Raisins (C3) 4L28: Common bean (C1) 36 B128: Carbonated orange juice (C3) 1F108: Peach (C3) 33 B121: Carbonated cola (C3) 1V50: Carrot (C3) 33L31: Soybean seed (C1) 33 Means processed � SD 55 � 118F116: Strawberry (C3) 28 Median 18F117: Watermelon (C3) 28 Mean rank 37.0 � 17.9f

C8: Oat flour, whole-grain (C3) 28N89: Sesame seed (C1) 26V60: Onion (C3) 24F113: Plum (C3) 23N84: Peanut (C3) 20 Cereal brans:V38: Avocado (C3) 20 C17: Wheat bran 748F112: Pineapple (C3) 20 C6: Maize bran 72N77: Almond (C5) 14 C9: Oat bran 37F92: Apple with skin (C3) 14F96: Blueberry (C3) 12 C18: Wheat germ 2 (n ¼ 7 LDe) 348F94: Banana (C3) 12 C18: Wheat germ 1 (n ¼ 8 LD) 308F110: Pear (C3) 11F100: Grapes (C3) 7

Means raw � SD 72 � 119Median 38Mean rank 25.6 � 15.2

a LC is lipotropic capacity based on 8 lipotrope densities except for LC of cereal brans (from maize, oat and wheat) that are based on 7 lipotropedensities (PAI density being excluded) and for LC of wheat germ that is based on either 7 or 8 lipotrope densities (LD). b Due to absence of PAIdata for 62 raw and processed PBF products among the initial 121, LC could have been calculated for only 38 raw and 21 processed PBF products.c Food codes with corresponding food description can be found in Supplementary Table 1, ESI.† d C1–C4 correspond to the 4 classes as defined byhierarchical classification. e LD is lipotrope density (mg/100 kcal). f The effect of processing was significant (p ¼ 0.015, non-parametric bilateralMann-Whitney’s test).

b. Effect of processing on main lipotrope densities and contents

Betaine. Overall un-specific processing (OP) did not signifi-

cantly modify betaine density. Despite a median content change

of �18%, SP did not also significantly affect betaine density

(median ¼ 0). However, this apparent status quo masked

heterogeneous effects on betaine densities and contents depend-

ing on process considered.

Literature data about effect of processing on PBF betaine

content are rather scarce. The only study reported severe losses

upon boiling for peas (43%), spinach (70%), silverbeet (73%)

and pasta (76–84%), but it is not indicated if losses were

expressed on dry or fresh weight-basis.42 Based on our data, we

estimated 23 and 78% losses in respectively boiled pasta and

This journal is ª The Royal Society of Chemistry 2011

spinach. More generally, boiling led to betaine losses ranging

from �18 (common bean) to �91% (broccoli) (median change

of �70%) confirming results of de Zwart et al.42 and confirming

relevant betaine release into boiling water. Such release was

supported by increased betaine content in soybean milk

(+493%) and fruit juices (from +43 to +177%) and decreased

content in masa or nixtamalized whole-grain cornmeal (�84%)

compared to raw soybean seed, fruits and whole-grain maize

flour, respectively. Nixtamalisation is a traditional process

involving soaking, cooking of maize grain within alkaline

solution, usually limewater, and dehulling. Masa is nixta-

malized maize dough and betaine loss was very likely to result

from release into alkaline solution.

Food Funct., 2011, 2, 483–504 | 497

Table 7 Lipotropic capacity of the 41 pairs of raw vs processed plant-based foods based on 7 lipotrope densities

Raw LC1 (%)a Processed LC2 (%) Change (%)b

Barley malt flour 38 Beer (F)c 28 �28Roasted buckwheat groats 27 Cooked roasted buckwheat groats (T + W) 26 �4Whole-grain maize cornmeal 20 Whole-grain masa 17 �12White rice 16 Cooked white rice (T + W) 11 �28Wheat germ 348 Toasted wheat germ (T-W) 234 �33Cooked brown rice 21 Cooked white rice (R) 11 �47Whole-grain wheat flour 45 White wheat flour (R) 10 �79Whole-grain wheat flour 45 Whole wheat bread (F) 45 0White wheat flour 10 White wheat bread (F) 21 +115French/Vienna bread 30 Toasted French/Vienna bread (T-W) 30 �3Dry pasta 44 Boiled pasta (T + W) 35 �21Raw common bean 38 Boiled bean (T + W) 36 �4Raw common bean 38 Canned bean (T + W) 33 �11Raw soybean 36 Defatted soybean flour (R) 67 +83Raw soybean 36 Tofu 33 �9Raw soybean 36 Soybean milk 38 +3Raw asparagus 100 Boiled asparagus (T + W) 107 +7Raw beetroot 442 Canned beetroot (T + W) 609 +38Raw broccoli 90 Boiled broccoli (T + W) 89 �1Raw cabbage 55 Boiled cabbage (T + W) 40 �27Raw cabbage 55 Sauerkraut (F) 71 +30Raw carrot 34 Boiled carrot (T + W) 31 �7Peeled cucumber 70 Pickles (F) 24 �66Raw potato 24 Baked potato (T-W) 23 �4Raw potato 24 Oven-heated French fries (R) 15 �39Raw potato 24 Chips (R) 14 �40Raw spinach 765 Boiled spinach (T + W) 280 �63Raw tomato 43 Canned tomato sauce (R) 36 �15Raw tomato 43 Canned tomato paste (R) 31 �27Raw tomato 43 Ketchup (R) 10 �76Raw tomato 43 Tomato soup (R) 21 �50Raw peanut 22 Peanut butter (R) 19 �13Apple 5 Apple juice (R) 4 �26Grape 6 Raisin (T-W) 4 �29Grape 6 Grape juice (R) 6 +2Grape 6 Wine (F) 5 �10Orange 20 Orange juice (R) 16 �19Orange 20 Carbonated orange juice (R) 1 �97Peach 20 Canned peach (T + W) 7 �64Pear 7 Canned pear (T + W) 3 �56Plum 13 Prunes (T-W) 9 �31

Means �SD 68 � 35 Means �SD 52 � 47 �19 � 39Mean rank �SD 37 � 23 Mean rank �SD 46 � 24 +11f

Median [min/max] 36 [5/765] Median [min/max] 24 [1/609] �19 [�97/+115]

Process type LC1 (%) Process type LC2 (%) Change (%)

Thermal treatments (n ¼ 18 pairs)d 36 [6/765]e Thermal treatments (n ¼ 18 pairs) 32 [3/609]e �16f [�64/+38]With water (n ¼ 13 pairs) 38 [7/765] With water (n ¼ 13 pairs) 35 [3/609] �11g [�64/+38]Without water (n ¼ 5 pairs) 24 [6/348] Without water (n ¼ 5 pairs) 23 [4/234] �29g [�33/�3]

Refining processes (n ¼ 14 pairs) 24 [5/45] Refining processes (n ¼ 14 pairs) 14 [1/67] �33f [�97/+83]Fermentations (n ¼ 6 pairs) 42 [6/70] Fermentations (n ¼ 6 pairs) 26 [5/71] �5 [�66/+115]

a LC is the Lipotrope capacity as defined in the Materials and methods section. b Change was calculated as [(LC1 � LC2) � 100/LC1].c F, T +W, T-W

and R correspond respectively to Fermentations, Thermal treatments +water, Thermal treatments �Water (dry) and Refining processes. d Correspondto the number of paired of raw vs processed products considered to calculate medians, min- and max-values. e Values are median [min/max]. f The effectof processing was significant for all process (n ¼ 41 pairs, p ¼ 0.0004, Wilcoxon matched-pairs signed-ranks test), Thermal treatments (n ¼ 18 pairs, p ¼0.012) and Refining processes (n ¼ 14 pairs, p ¼ 0.011), but not for Fermentations (p > 0.05). g p ¼ 0.080 for Thermal treatments with water and p ¼0.063 for Thermal treatments without water.

Contrary to boiling, canning enhanced betaine content from

+3% (peach) to +53% (beetroot). This effect was also supported

by increased betaine content in canned refined tomato products

that are tomato sauce (+478%), paste (+20%) and condensed

soup (+1790%). Since betaine can not be synthesized upon

498 | Food Funct., 2011, 2, 483–504

canning, one plausible explanation might be that it exists within

these food matrices as a fraction of trapped betaine that would

have initially escaped analysis and that was freed by canning

under high pressure and temperatures above 100 �C in presence

of water.

This journal is ª The Royal Society of Chemistry 2011

Betaine losses upon thermal treatments without water (i.e.

toasting, baking and drying) appeared quite paradoxical

compared to previous results and difficult to explain. Indeed, our

results suggested that betaine would be sensitive to and degraded

by increased temperatures. However, this was not supported by

increased betaine contents following canning that involves very

high temperatures.

Cereal refining was a drastic process towards betaine content

since, following bran and germ removing, wheat flour lost 98% of

betaine. Recently, average betaine contents of 22.9 and 103.0 mg/

100 g have been reported for respectively white wheat and whole-

grain wheat flours,45 i.e. a decrease of 78% in agreement with our

results, again clearly showing that betaine is concentrated in bran

fraction,46 more particularly in the aleurone layer.47 Similarly to

wheat, when comparing cooked brown and cooked white rice

(i.e. polished rice), 49% loss were estimated on dwb. These results

also agreed with those of Bruce et al. that measured betaine

contents of 0.9 and 0.5 mg/100 g for respectively brown and white

rice.45 Explanation for the betaine content increase of defatted

soybean flour compared to raw soybean seed (+107%) remains

uncertain, but defatting may have concentrated betaine within

flour.

Except acidic fermentation of cucumber into pickles and

cabbage into sauerkraut, all other fermentative processes

increased betaine content from +21% for whole wheat bread to

+2516% for white wheat bread. First, this may suggest various

contributions of the fermentative microbial flora to betaine

content in beer, wine and breads. For example, based on

baker’s yeast betaine content of 3.6 mg/100 g43 and on common

white bread recipe (10 g salt, 10 g yeast and 500 g white wheat

flour), we roughly estimated that yeast might increased initial

betaine content of white wheat flour by �+44% (this involves

that bacteria do not consume betaine). Secondly, besides

microflora, concerning alcoholic beverages, the betaine content

of wine may also partly originates from the beet sugar added to

increase alcohol content of cheap wines;48 and that of beer

probably originates from barley malted flour that contains 66

mg betaine/100 g.43

Choline. As for betaine, both OP and SP did not significantly

modify choline densities; and medians for both density and

content changes were near 0 for the 41 pairs of raw vs processed

products. However, considering each process type, some

tendencies were revealed regarding content changes.

While baking potato and toasting cereals had no effect,

drying importantly decreased choline content to an extent

greater than for betaine. We have no explanation for this.

However, contrary to betaine, boiling vegetables, cereals –

except white rice – and common beans increased choline

contents (median change for boiling was +17%, result not

shown). Effect of canning resembled that observed with betaine

concerning beetroot and common bean with increased contents

but not for peach and pear (decrease of ��70%). Such results

do not agree with hydrosolubility of choline; but they may be

partly explained by heterogeneity of compounds from which

choline moieties are derived for calculating total choline

content, i.e. free choline, glycerophosphocholine, phosphocho-

line, phosphatidylcholine and sphingomyelin, which is not the

case for betaine.43

This journal is ª The Royal Society of Chemistry 2011

For refining, despite tendency to a decreased choline content

(median ¼ �34%), there was no clear homogeneity of changes

except for cereal products. Thus, white wheat flour had its

choline content reduced by�67%when compared to whole-grain

wheat flour: indeed, similarly to betaine, choline is concentrated

within bran and germ fractions of wheat.46,47 This was confirmed

by results of Bruce et al. for whole-grain vs white wheat flour

(means of 10.6 vs 5.5 mg/100 g) and bread (means of 16.1 vs 11.9

mg/100 g) choline contents.45High choline content difference was

also calculated when comparing cooked brown vs cooked white

rice (�81%). Difference between raw brown (3 mg/100 g) and

white (2.3 mg/100 g) rice was less marked in the study of Bruce

et al.45 However, cooking might have accentuated differences.

Decreased choline contents in canned tomato sauce and

condensed soup, ketchup, oven-heated French fries, potato

chips, and apple and grape juices suggested that choline would be

more bound to fibre and/or skin fraction than betaine, which

could explain why boiling vegetables, common beans and some

cereal products have concentrated choline within the food

matrix.

Choline content changes following fermentation were similar

to those of betaine: decreased content following acidic fermen-

tation for pickles and sauerkraut and increased content for wine,

beer and breads. In natural lactic acid fermented cornmeal,

choline content was first shown to decrease by 38% after 1 day of

fermentation, then to return to its normal level after 4 days.49

These observations tended to support the hypothesis that acidic

fermentation may degrade choline. For breads, based on choline

content of baker’s yeast given by USDA (32 mg/100 g)43 and on

common bread recipe, we roughly estimated that yeast might

increased initial choline content of white and whole-grain wheat

flours by respectively �+61 and �+20%. Values of +95 and

+25% were calculated for respectively white and whole wheat

breads.

Methionine. While OP had no significant effect on methionine

density, SP significantly reduced methionine density (median

change of �11%).

Concerning thermal treatments, except spinach and wheat

germ, they led to either no change or to decreased content. As for

choline, drying surprisingly led to content reductions whereas

toasting and baking had no marked effects: we found no relevant

explanation for this. For thermal treatments in the presence of

water, the increased methionine content in spinach following

boiling also remains unexplained compared to other boiled

vegetables. However, results tended to reveal that canning

degrades methionine in beetroot, peach and pear but not in

common bean. This effect was supported by reductions also

observed for canned tomato products. Maillard browning reac-

tion, i.e. reduction of a sugar with methionine, is the most cited

explanation for methionine degradation,50 but methionine may

be also simply degraded by thermal treatments or oxidized by

hydroperoxides to its sulphoxides.50 More generally, the use of

food model systems showed that free methionine losses during

elevated temperature processing were influenced by protein,

sugar and water activity, suggesting a food matrix-dependent

effect.51 Interactions among food components may therefore

explain why methionine in cereal products and common bean

appeared more resistant to thermal treatments than in vegetables

Food Funct., 2011, 2, 483–504 | 499

and fruits, maybe since included within a more structured and

insoluble protein network than in fruits and vegetables.

Concerning refining, median for content change was of �30%.

Except defatted soybean flour and apple juice, there was some-

what tendency to content reduction that probably resulted from

thermal treatments accompanying refining process in oven-

heated French fries, potato chips, tomato-derived products and

fruit juice packaging.

Concerning fermentations, median for content change was of

�50%. Except breads (no changes), alcoholic (�94% for wine

and �100% for beer) and acidic (�63% for pickles and �36% for

sauerkraut) fermentations importantly degraded methionine.

Even by taking maximum value found in literature for wine

methionine content, i.e. �0.54 mg/100 mL, calculated from

Barrado et al.52 for Spanish Rueda-type wine (n ¼ 10 values),

methionine reduction remains high ��91%. While microflora

may be responsible for methionine degradation into beer and

wine, acidic fermentation may have partly oxidized methionine.

Otherwise, increased free methionine contents were measured in

the water soluble fraction of the batter of black gram and rice

blend (idli) after 20 h fermentation.53 Similarly to idli water

soluble fraction, release of free methionine may have also

occurred in sauerkraut and pickle juices upon fermentation

leading to reduced content within the food matrix. However,

sorghum flour fermented for producing bread (Sudanese kisra)

led to methionine enrichment;54 and in Cheonggukjang, a tradi-

tional Korean fermented soy food, methionine content first

decreased up to 20 h fermentation by ��80% then increased

after 50 h fermentation by �+70% compared to the 0 hour-

time.55 In this latter case, methionine would be produced by

Bacillus subtilis following nutrient degradation.55

Based on our estimations for methionine content changes, we

hypothesized that the effect of processing onmethionine contents

tended to differ according to food group more than with process

type even if tendencies were observed for canning and refining.

Accordingly, we calculated medians of +5, +8, �27 and �63%

for respectively C, L, V and F groups (results not shown). Cereal

and legume methionine contents appeared therefore to be not

markedly affected by processing contrary to fruits and vegetables

with one exception for each food group that were beer (�100%,

C group), soybean milk (�44%, L group), boiled spinach

(+115%, V group) and apple juice (+20%, F group).

Myo-inositol. Myo-inositol belongs to the cyclitol family and

is one of 9 isomers of inositol, the only form up today with

a demonstrated lipotropic effect.56 It is present in PBF as free

form or conjugated with phosphate (e.g. phytate) or glycosyl (e.g.

galactinol and di-glycosylated myo-inositol) groups. Since it is

potentially readily available within the digestive tract, only myo-

inositol moieties derived from soluble free myo-inositol and

glycosylatedmyo-inositol were considered in what we have called

the PAI fraction. This fraction may also include myo-inositol

moieties derived from phosphatidylinositol (i.e. lipid-bound

myo-inositol) when PAI content was deduced from total and IP

contents. For other cases, since data about PBF lipid-bound

myo-inositol content are almost non-existent, PAI content was

probably under-estimated.

Due to the way PAI contents were estimated (i.e. by sub-

tracting IP to total myo-inositol content in most cases)1 and due

500 | Food Funct., 2011, 2, 483–504

to the low number of products for statistical analyses, effect of

processing on PAI density was more difficult to grasp than for

other lipotropes and results must be therefore considered

cautiously. The low number of product indeed explain why,

despite the tendency to reduced PAI densities following pro-

cessing, effect was not significant for SP (median¼�33%, n¼ 11

pairs of products). However, effect of OP was closed to signi-

ficativity (p ¼ 0.057, 30 raw vs 20 processed products). Results

have to be therefore interpreted product by product.

Concerning thermal treatments without water, as for choline

(�55%) and methionine (�46%), PAI loss of �75% for grape

upon drying remains difficult to explain. One hypothesis may be

linked to the way grapes were dried, i.e. in an oven and not with

natural sun temperature. This would mean a high sensitivity of

myo-inositol to increased temperature: other calculated changes

would tend not to contradict this hypothesis. Otherwise, total

myo-inositol content value used for calculating PAI content may

have been under-estimated due to the fact that raisins used in

Clements and Darnell database57 may have higher water content

than that given for raisins in USDA database, i.e. 15.4%;44

however, by considering, for example, a 25% water content for

raisins of Clements and Darnell database instead of 15.4%,

content changes forwarded from �75% to only �67%. Another

plausible explanation may be found in PAI content variability

according to grape variety, e.g. according to color: thus, from

literature data, we estimated that white wines might have higher

meanmyo-inositol content (means of 95 mg/100 mL, n¼ 8 wines)

than red wines (means of 73 mg/100 mL, n ¼ 4 wines) and we

found a high range of �10–248 mg myo-inositol/100 mL

(calculated from references in Supplementary Material 21).

Heterogeneity of PAI content changes following canning of

beetroot (�28%) and common bean (+305%), and boiling of

cabbage (�81%) suggested both degradation of IP fraction into

freemyo-inositol, then leaching of it into boiling water. In canned

bean, IP degradation would be sufficiently important to over-

come PAI losses. This explanation was partly supported by the

data we have collected for IP6 (phytate)-IP1 content of raw and

canned common beans (see Supplementary Table 10): indeed,

IP6, IP5, IP4 and IP3 percentages forwarded from respectively

80.4, 10.7, 1.8, 0.2 to 71.6, 20.0, 5.7 and 1.4% following canning

unravelling IP6 degradation. Canning was already reported to

reduce phytate content in beans.58 In beetroot and cabbage,

initial IP content is largely lower (respectively �8 and �45 mg/

100 g on dwb) than in common bean (�325 mg/100 g) and initial

PAI contents (respectively �65 and �398 mg/100 g) were higher

than IP content, contrary to common bean whose PAI content is

76 mg/100 g (calculated from Supplementary Table 1). Our

hypothesis was therefore that in beetroot and cabbage, PAI

losses in water have exceeded IP degradation.

Concerning refining treatments, transformation of orange into

carbonated soda and of tomato into ketchup and canned

condensed tomato soup importantly reduced PAI content

(<�85%), PAI being possibly removed with the fiber fraction.

Finally, all fruit juices have PAI content �23% higher than

corresponding raw fruits: however, this result was artefactual

and remains to be confirmed since raw fruit PAI content have

been deduced from juice PAI content59 assuming that producing

fruit juices mainly removes fibre fraction and considering that

PAI fraction was released into juice.1

This journal is ª The Royal Society of Chemistry 2011

Concerning fermentations, only one change could have been

estimated for wines (+573%). Higher PAI content of wine has

been already reported.60 However, such a high increase may be

not representative of reality: indeed, for grape, PAI content was

derived from only one Spanish grape variety purchased at local

market,59 while for wines, numerous data have been collected to

calculate the aggregated value of 73 mg/100 mL derived from

both red and white wines and from several brands, i.e. French,

Spanish and German.1 The initial range was 10.2–248.3 mg/

100 mL which will respectively give �7% and +2714% PAI

change on dwb. One may also not excluded that myo-inositol

would be produced upon grape fermentation via, for example,

IP6 degradation.

Effect of processing on plant-based food lipotropic capacity

Considering each lipotrope separately for each process is not an

easy way to study the effect of processing on overall PBF lipo-

tropic potential. In a previous study, we have shown that the only

sum of lipotrope densities is not nutritionally relevant, and that

the LC (as defined in the Materials and methods section), that

takes into account at equal weight each LD whatever its absolute

value, be it 0.01 or 10 mg/100 kcal, may be an interesting and

simple nutritional index for expressing overall PBF lipotropic

potential,1 and consequently evaluating effect of processing.

Processed PBF mean LC was 24% significantly lower than raw

PBF mean LC following OP with 19% higher mean ranking and

around 2-fold higher median. Based on the 41 pairs of raw vs

processed products and on all LD except that of PAI, LCmedian

and mean change was of �19% (p ¼ 0.0004). Lipotrope capacity

change following fermentations (�5%, effect not significant) was

less marked (median change of �5%) than following refining

(�33%, p ¼ 0.011), then thermal treatments (�16%, p ¼ 0.013),

either with (�11%, p ¼ 0.080) or without water (�29%, p ¼0.063). Only white wheat flour, soybean flour, beetroot and

cabbage had their LC markedly increased following respectively

baking (+115%), defatting (+83%), canning (+38%) and

fermentation (+30%). Finally, among the 5 food groups, legumes

(raw common bean and soybean) appeared to be the least

affected by processing (median ¼ �4%).

Interestingly, canned beetroot and canned common bean had

a higher LC than raw products, emphasizing that canning should

not always be considered as a negative process towards nutri-

tional value of PBF. A similar conclusion has been previously

reached with canned fruits and vegetables for their vitamins C

and B and phenolic compound contents.40 On the contrary,

boiling appeared more drastic towards lipotropic potential of

vegetables as regards with the almost 2-fold lower LC of boiled

cabbage compared to raw cabbage (38 vs 75%).

Refining undoubtedly reduced lipotropic potential of PBF: for

example, processing tomato decreased its LC from 70 (raw

tomato) to 20 (tomato soup) and 13 (ketchup). Highly refined

PBF that are orange soda, chips and dried flaked coconut meat

exhibited very low LC (#13%). Otherwise, whole wheat bread

(LC ¼ 39%) had a higher LC than white breads, either common

(LC ¼ 18%) or French/Vienna (LC ¼ 27%), which should incite

to avoid refining wheat flour too importantly. Conversely,

transformation of fruits into juice appeared to have no negative

effect on the overall lipotropic potential.

This journal is ª The Royal Society of Chemistry 2011

An important issue was the fermentation of wheat flours into

breads. Although we have no data for the PAI content of whole-

grain and white wheat flours, we may reasonably suppose that it

was not very high. In addition, those of breads were closed to 0.

Thus, excluding PAI density, LC of whole-grain and white wheat

flours were respectively of 45 and 10%, and those of corre-

sponding breads were respectively 45 and 21%. These results

emphasized both the necessity of using less refined wheat flours,

and that despite refining white wheat flour baking increases its

LC by �2-fold. It was probably due to baker’s yeast microflora

(i.e. Saccharomyces cerevisiae) during the fermentative process

that may increase phytonutrient content as has been shown with

fermentation of wholemeal rye flour61 and tempeh (made from

fermented cooked soya beans),62 or more specifically with ribo-

flavin content in white bread vs white flour.63 Both our results

and those of literature therefore suggest that fermentations may

enhance lipotropic potential of initial raw PBF. One exception

was the important decreased of LC when comparing raw peeled

cucumber with pickles (from 70 to 24% based on 7 LD). The low

lipotropic potential of pickles might be simply due to lower

degree of maturity relative to cucumbers used for pickling, i.e. an

initial lower content of lipotropes than in raw peeled mature

cucumbers.

Our results showed that canned beetroot, brewed tea, boiled

green beans, orange juice, canned common bean, whole wheat

bread and boiled cabbage are relevant processed PBF for their

LC. In addition, considering coffee PAI density �0 (sugar

content given by USDA database is 0), coffee LC would be of

�470% (results not shown), more than 2-fold that of tea.

However, due to their very low caloric content of around 1 kcal/

100 mL, their high lipotropic potential should be especially

considered for regular or high tea and/or coffee drinkers.

Accordingly, baseline high-coffee consumption ($3 cups/day)

was recently shown to be associated with less severe steatosis on

biopsy in patients with advanced hepatitis C-related liver disease

(19.1% patients with stetaosis grade 0 for high-coffee drinkers vs

12.2% for non-coffee drinkers, p for trend¼ 0.047).31Conversely,

the low LC of some fruit-derived beverages like wine (LC ¼ 14),

beers (LC ¼ 28%), sodas (LC ¼ 1) and non citrus fruit juices

(LC ¼ 13 for apple and 8 for grape) indirectly support results

from the few observational studies showing: (1) that excess

sugar-sweetened soft drinks (notably Coca Cola and fruit juices

containing caramel) consumption (>500 cm3/day) was largely

more prevalent in patients diagnosed with fatty liver (80%) than

in healthy controls (17%);32 and also (2) that high alcohol

consumption was directly correlated with diffuse steatosis in

non-cirrhotic patients.34 Conversely, moderate alcohol

consumption in NAFLD patients was associated with ‘‘lower

odds of diagnostic features for nonalcoholic steatohepatitis’’

compared to non-alcohol drinkers.35 However, steatosis devel-

opment based on regular high alcohol or high refined sugar

consumption is based on different impaired physiological

mechanisms.2Yet, beers and wines are probably among alcoholic

beverages that have the highest contents in phytochemicals

suggesting very low LC for other alcohols that generally contain

more energy and less phytonutrients.

These few observational studies might give a first rough indi-

cation about LC thresholds above which processed PBF may

have a significant lipotropic effect in humans and below which

Food Funct., 2011, 2, 483–504 | 501

processed PBFmay be steatogen when regularly and importantly

consumed. Thus, energy-dense beverages (soft drinks and alco-

holic beverages) with LC below 30% should not be consumed in

excess; while beverages with LC above 100% like coffee would be

rather protective in regular and/or high drinkers. The example of

alcohol would also tend to show that not only the LC value is

important but also amount of product consumed, what might be

called in fine the lipotropic load.

Conclusions and perspectives

Processing globally reduced the lipotropic potential of plant-

based foods by �20%

Very globally, without process distinction, processing would

decrease lipotropic potential of PBF by ��20%. From a process

perspective, our results first clearly showed that refined products

have a low lipotropic potential, as regards with either LD or LC.

Secondly, processing tended to degrade or release micronutrient

lipotropes (magnesium and B vitamins) more than other 4 main

lipotropes, especially compared to betaine, choline and methio-

nine. And among B vitamin, folate was more frequently nega-

tively affected than pantothenic acid, then niacin. Among food

groups, legume LC was the least affected by processing. Practi-

cally, high degrees of PBF refining should be therefore avoided.

Fermentation may increase lipotropic potential of plant-based

foods

Otherwise, results also showed that fermentative processes may

be favourable to LD or at least have no effect on it as shown with

breads, sauerkraut and wine. Indeed, fermentation was previ-

ously shown to increase density in some phytonutrients, resulting

in a potential enhanced bioactivity of cereals.61,64,65 More

generally, the literature tends to show that fermentative processes

are above all able to release bound compounds into their free

form – as, for example, with TPC,66 niacin67 and methionine68 –

which can be of interest to increase their bioavailability and, in

the end, their lipotropic effect since several PBF lipotropes are

present in both bound and free forms. Therefore, fermentative

processes appear not sufficiently valorized and applied to PBF in

Western countries.

Canning may increase content in main lipotropes

By considering LC changes, canned beetroot and common bean

had higher LC than raw products. Canning is therefore not so

drastic a process as it appears at first view: it increased betaine

content in common bean, beetroot and pear, choline content in

common bean and beetroot, and PAI content in common bean;

and in products with initial high phytate content, it may degrade

myo-inositol phosphates in free myo-inositol. Our results would

therefore tend to emphasize that canning is quite favourable to

main LD and would reinforce the conclusion drawn by Rickman

et al. that canned foods should not be always regarded ‘‘as less

nutritious than fresh or frozen products’’.40 However, further

studies are needed to confirm or not the potential nutritional

benefit of PBF canning.

502 | Food Funct., 2011, 2, 483–504

Other processes

Some specific processed PBF were emphasized as having their

lipotropic potential only little affected following processing.

Thus, defatted soybean flour that ranked high for total LD

among PBF,1 had 7 LD increased compared to raw soybean,

making this product nutritionally relevant for its lipotropic

potential: indeed, excluding PAI density, its LC was almost 2-

fold that of raw soybean. To go further, considering or not PAI

density, transformation of soybean into tofu and soybean milk,

of grape into juice, of raw peanut into butter, nixtamalization of

whole-grain cornmeal into masa, cooking roasted buckwheat

groats, toasting French/Vienna bread, boiling common bean,

boiling asparagus, broccoli and carrot, and baking potato with

skin almost did not affect LC (�10% # % LC change # +10%).

Special attention has also to be paid to the brewed beverages of

tea and coffee for their high LC of respectively 196 and 469%

when based on 8 LD, notably as regards with the recent study of

Freedman et al. showing less severe steatosis in high-coffee – but

not high-tea – drinkers.31 However, as discussed by the authors,

the question remains which specific coffee compounds are

involved in this beneficial effect. Since both brewed coffee and tea

are both rich in TPC, especially on a dwb with respectively 14.9

and 35.0 g/100 g (Supplementary Table 1), but also in caffeine

(>6 g/100 g dwb, USDA database), the lipotropic effect may be

therefore ascribed to other compounds.

Finally, besides these particular processes, one may also

optimize processing to release free myo-inositol from phytate in

cereal products like barley.69 Notably, extrusion process is

known to have a significant effect upon phytate dephosphory-

lation.70 Besides, germination is also a natural process able to

increase phytonutrient density as shown with TPC content of 12

germinated edible seed species71 and with free amino acids, a-

tocopherol, g-oryzanol, thiamine, niacin and pyridoxine in

germinated rough rice.72

Perspectives

Finally, we must bear in mind that processed PBF may also

contain significant amounts – when summed – of other phy-

tonutrients with a potential lipotropic effect such as organo-

sulfur compounds, n-3 polyunsaturated fatty acids, acetic acid,

carnitine, lignans, flavonoids, stilbenes, curcumin, saponins,

fibre (lignins included), oligosaccharides and/or resistant

starch.2 As one goes along with completion of databases for

these compounds, LC definition will have to include them and

perhaps a new reference food other than raw asparagus would

have to be re-defined. While waiting for this, results of this

study showed that this simple index was quite useful to estimate

effect of processing on lipotropic potential of PBF. More

particularly, it can be used as a simple tool to rapidly charac-

terize the lipotropic potential of foods as, for example, those

produced by agro-food industry, and our results provide some

early and interesting information to optimize through processes

the PBF LC. Indeed, this will be helpful to produce lipotrope-

dense PBF, notably for subjects with mild steatosis (5–33% of

the liver) or prone to develop steatosis (in preventive nutrition),

as this is done for low-glycaemic index foods recommend for

type 2 diabetic subjects.

This journal is ª The Royal Society of Chemistry 2011

It however remains that human intervention studies are

strongly needed to correlate LC values of processed PBF with

lipotropic effects in humans, and also to define the LC threshold

above which significant physiological effect may be reached to

prevent steatosis development in its initial stage, notably among

the obese, diabetic and/or alcoholic population. In the end, based

on each food LC, one may calculate the diet LC, which appears

still more relevant since chronic disease development is related to

the way of eating, not solely to consumption of isolated foods.

Abbreviations

ABF

This journal is ª T

Animal-based foods

BeChIMe

Sum of betaine, choline, myo-inositol and

methionine

dwb

Dry-weight basis

HC

Hierarchical classification

IP

Myo-inositol phosphate

IP6

Phytate or myo-inositol hexakisphosphate

LC

Lipotrope capacity

LD

Lipotrope density

OP

Overall un-specific processing

PBF

Plant-based foods

PCA

Principal component analysis

PAI

Potentially available myo-inositol (included

myo-inositol moieties derived from soluble free

myo-inositol and glycosylated myo-inositol)

RS

Resistant starch

SP

Specific processes

TPC

Total phenolic compounds

USDA

United State Department of Agriculture

Acknowledgements

Sabine Rossi and Francoise Barr�e are gratefully acknowledged

for their precious assistance in collecting references (INRA-

SDAR, F-63122 Saint Gen�es Champanelle, France).

References

1 A. Fardet, J.-F. Martin and J. M. Chardigny, J. Food Comp. Anal.,2011, DOI: 10.1016/j.jfca.2011.1003.1013.

2 A. Fardet and J. M. Chardigny,Crit. Rev. Food Sci. Nutr., 2011, DOI:10.1080/10408398.10402010.10549596.

3 J. F. Silverman, W. J. Pories and J. F. Caro, Pathology Annual, 1989,24, 275–302.

4 P. Angulo, N. Engl. J. Med., 2002, 346, 1221–1231.5 C. S. Lieber, Clin. Chim. Acta, 1997, 257, 59–84.6 J. F. Doherty, E. J. Adam, G. E. Griffin andM. H. Golden,Arch. Dis.Child., 1992, 67, 1348–1352.

7 Z. T. Bloomgarden, Diabetes Care, 2005, 28, 1518–1523.8 G. Targher and G. Arcaro, Atherosclerosis, 2007, 191, 235–240.9 M. S. Ascha, I. A. Hanouneh, R. Lopez, T. A. R. Tamimi,A. F. Feldstein and N. N. Zein, Hepatology, 2010, 51, 1972–1978.

10 G. Targher, L. Bertolini, M. Chonchol, S. Rodella, G. Zoppini,G. Lippi, L. Zenari and E. Bonora,Diabetologia, 2010, 53, 1341–1348.

11 H. J. Kim, H. J. Kim, K. E. Lee, D. J. Kim, S. K. Kim, C. W. Ahn,S.-K. Lim, K. R. Kim, H. C. Lee, K. B. Huh and B. S. Cha, Arch.Intern. Med., 2004, 164, 2169–2175.

12 C. H. Best, Nature, 1935, 135, 821–822.13 L. A. Adams, P. Angulo and K. D. Lindor,Can.Med. Assoc. J., 2005,

172, 899–905.

he Royal Society of Chemistry 2011

14 A. H. Russakoff and H. Blumberg, Ann. Intern. Med., 1944, 21, 848–862.

15 J. A. Colson and C. Gallay, Toulouse M�ed., 1964, 65, 207–229.16 H. Warembourg and M. Bertrand, Lille M�ed., 1964, 37, 285–289.17 G. Nadeau, Y. Rouleau and J. Delage, Laval M�ed., 1954, 19, 52–58.18 L. Spadaro, O. Magliocco, D. Spampinato, S. Piro, C. Oliveri,

C. Alagona, G. Papa, A. M. Rabuazzo and F. Purrello, Dig. LiverDis., 2008, 40, 194–199.

19 F. Zhu, S. Liu, X. Chen, Z. Huang and D. Zhang, World J.Gastroenterol., 2008, 14, 6395–6400.

20 A. J. Cussons, G. F. Watts, T. A. Mori and B. G. A. Stuckey, J. Clin.Endocrinol. Metab., 2009, 94, 3842–3848.

21 M. Capanni, F. Calella, M. R. Biagini, S. Genise, L. Raimondi,G. Bedogni, G. Svegliati-Baroni, F. Sofi, S. Milani, R. Abbate,C. Surrenti and A. Casini, Aliment. Pharmacol. Ther., 2006, 23,1143–1151.

22 F. Sofi, I. Giangrandi, F. Cesari, I. Corsani, R. Abbate, G. F. Gensiniand A. Casini, Int. J. Food Sci. Nutr., 2010, 61, 792–802.

23 N. Tanaka, K. Sano, A. Horiuchi, E. Tanaka, K. Kiyosawa andT. Aoyama, J. Clin. Gastroenterol., 2008, 42, 413–418.

24 A. Hatzitolios, C. Savopoulos, G. Lazaraki, I. Sidiropoulos,P. Haritanti, A. Lefkopoulos, G. Karagiannopoulou, V. Tzioufaand K. Dimitrios, Indian J. Gastroenterol., 2004, 23, 131–134.

25 M. F. Abdelmalek, P. Angulo, R. A. Jorgensen, P. B. Sylvestre andK. D. Lindor, Am. J. Gastroenterol., 2001, 96, 2711–2717.

26 S. Mukherjee, T. Bernard, D. Schafer, A. Barak, M. Sorrell andD. Tuma, J. Hepatol., 2005, 42, 610.

27 H. Shapiro, M. Tehilla, J. Attal-Singer, R. Bruck, R. Luzzatti andP. Singer, Clin. Nutr., 2011, 30, 6–19.

28 M. Malaguarnera, M. P. Gargante, C. Russo, T. Antic, M. Vacante,M. Malaguarnera, T. Avitabile, G. Li Volti and F. Galvano, Am. J.Gastroenterol., 2010, 105, 1338–1345.

29 D. Mu, J. X. Yu and D. Li, J. Norman Bethune Univ. Med. Sci., 1997,23, 528–530.

30 L. Abenavoli, G. Aviello, R. Capasso, N. Milic and F. Capasso,Hepatitis Monthly, 2011, 11, 173–177.

31 N. D. Freedman, J. E. Everhart, K. L. Lindsay, M. G. Ghany,T. M. Curto, M. L. Shiffman, W. M. Lee, A. S. Lok, A. M. DiBisceglie, H. L. Bonkovsky, J. C. Hoefs, J. L. Dienstag,C. Morishima, C. C. Abnet, R. Sinha and H.-C. T. Grp,Hepatology, 2009, 50, 1360–1369.

32 A. Abid, O. Taha, W. Nseir, R. Farah, M. Grosovski and N. Assy, J.Hepatol., 2009, 51, 918–924.

33 S. Thuy, R. Ladurner, V. Volynets, S. Wagner, S. Strahl,A. Konigsrainer, K. P. Maier, S. C. Bischoff and I. Bergheim, J.Nutr., 2008, 138, 1452–1455.

34 L. A. Kondili, G. Taliani, G. Cerga, M. E. Tosti, A. Babameto andB. Resuli, Eur. J. Gastroenterol. Hepatol., 2005, 17, 155–159.

35 W. Dunn, E. M. Brunt, A. J. Sanyal, A. J. McCullough, A. Unalp,J. Tonascia and J. B. Schwimmer, J. Hepatol., 2009, 50, 89.

36 J. L. Kuk, L. E. Davidson, R. Hudson, K. Kilpatrick, K. Bacskai andR. Ross, Appl. Physiol., Nutr., Metab., 2008, 33, 239–245.

37 N. A. van Herpen, V. B. Schrauwen-Hinderling, G. Schaart,R. P. Mensink and P. Schrauwen, J. Clin. Endocrinol. Metab., 2011,96, E691–E695.

38 J. D. Browning, J. A. Baker, T. Rogers, J. Davis, S. Satapati andS. C. Burgess, Am. J. Clin. Nutr., 2011.

39 M. Lazo, S. F. Solga, A. Horska, S. Bonekamp, A. M. Diehl,F. L. Brancati, L. E. Wagenknecht, F. X. Pi-Sunyer, S. E. Kahnand J. M. Clark, Diabetes Care, 2010, 33, 2156–2163.

40 J. C. Rickman, D. M. Barrett and C. M. Bruhn, J. Sci. Food Agric.,2007, 87, 930–944.

41 J. C. Rickman, C. M. Bruhn and D. M. Barrett, J. Sci. Food Agric.,2007, 87, 1185–1196.

42 F. J. de Zwart, S. Slow, R. J. Payne, M. Lever, P. M. George,J. A. Gerrard and S. T. Chambers, Food Chem., 2003, 83, 197–204.

43 K. Y. Patterson, S. A. Bhagwat, J. R. Williams, J. C. Howe, J. M.Holden, in collaboration with S. H. Zeisel, K. A. Dacosta and M.-H. Mar, USDA Database for the Choline Content of CommonFoods, U.S. Department of Agriculture, Agricultural ResearchService, Nutrient Data Laboratory, 2008, http://www.nal.usda.gov/fnic/foodcomp/Data/Choline/Choln02.pdf.

44 U.S. Department of Agriculture, Agricultural Research Service,Nutrient Data Laboratory, USDA National Nutrient Database for

Food Funct., 2011, 2, 483–504 | 503

Standard Reference, 2005, http://www.nal.usda.gov/fnic/foodcomp/Data/SR18/reports/sr18page.htm.

45 S. J. Bruce, P. A. Guy, S. Rezzi and A. B. Ross, J. Agric. Food Chem.,2010, 58, 2055–2061.

46 R. Likes, R. L. Madl, S. H. Zeisel and S. A. S. Craig, J. Cereal Sci.,2007, 46, 93–95.

47 S. F. Graham, J. H. Hollis, M. Migaud and R. A. Browne, J. Agric.Food Chem., 2009, 57, 1948–1951.

48 M.-H. Mar and S. H. Zeisel, Med. Hypotheses, 1999, 53, 383–385.49 F. A. Murdock and M. L. Fields, J. Food Sci., 1984, 49, 373–375.50 J. Mauron, J. Nutr. Sci. Vitaminol., 1990, 36, S57–69.51 J. C. Wolf, D. R. Thompson, J. J. Warthesen and G. A. Reineccius, J.

Food Sci., 1981, 46, 1074–1078.52 E. Barrado, J. A. Rodriguez and Y. Castrillejo, Talanta, 2009, 78,

672–675.53 V. W. Padhye and D. K. Salunkhe, J. Food Biochem., 1978, 2, 327–

347.54 A. H. E. Tinay, A. M. A. Gadir and M. E. Hidai, J. Sci. Food Agric.,

1979, 30, 859–863.55 M. K. Park, I. H. Cho, S. Lee, H.-K. Choi, D.-Y. Kwon and

Y.-S. Kim, Food Chem., 2010, 122, 1313–1319.56 Y. Okazaki, T. Setoguchi and T. Katayama, Biosci., Biotechnol.,

Biochem., 2006, 70, 2766–2770.57 R. Clements Jr and B. Darnell, Am. J. Clin. Nutr., 1980, 33, 1954–

1967.58 M. M. Tabekhia and B. S. Luh, J. Food Sci., 1980, 45, 406–408.

504 | Food Funct., 2011, 2, 483–504

59 M. L. Sanz, M. Villamiel and I. Martinez-Castro, Food Chem., 2004,87, 325–328.

60 A. Ournac, Ann. Nutr. Alim., 1970, 24.61 K. Katina, K.-H. Liukkonen, A. Kaukovirta-Norja, H. Adlercreutz,

S.-M. Heinonen, A.-M. Lampi, J.-M. Pihlava and K. Poutanen, J.Cereal Sci., 2007, 46, 348–355.

62 R. Djurtoft and J. P. Nielsen, J. Plant Foods, 1984, 5, 135–141.63 F. Batifoulier, M.-A. Verny, E. Chanliaud, C. R�em�esy and

C. Demign�e, Eur. J. Agron., 2006, 25, 163–169.64 K. Katina, A. Laitila, R. Juvonen, K.-H. Liukkonen, S. Kariluoto,

V. Piironen, R. Landberg, P. �Aman and K. Poutanen, FoodMicrobiol., 2007, 24, 175–186.

65 J. K. Chavan and S. S. Kadam, Crit. Rev. Food Sci. Nutr., 1989, 28,349–400.

66 T. M. Dordevic, S. S. Siler-Marinkovic and S. I. Dimitrijevic-Brankovic, Food Chem., 2010, 119, 957–963.

67 N. Dhankher and B. M. Chauhan, Int. J. Food Sci. Technol., 1987, 22,173–176.

68 C. G. Rizzello, L. Nionelli, R. Coda, M. De Angelis andM. Gobbetti,Food Chem., 2010, 119, 1079–1089.

69 E.-L. Bergman, K. Fredlund, P. Reinikainen and A.-S. Sandberg, J.Cereal Sci., 1999, 29, 261–272.

70 A. Proskova, Czech J. Food Sci., 1998, 16, 215–220.71 B. A. Cevallos-Casals and L. Cisneros-Zevallos, Food Chem., 2010,

119, 1485–1490.72 A. Moongngarm and N. Saetung, Food Chem., 2010, 122, 782–788.

This journal is ª The Royal Society of Chemistry 2011