Nondestructive moisture content determination of three different market type in-shell peanuts using...

ORIGINAL PAPER

Nondestructive moisture content determination of three differentmarket type in-shell peanuts using near infrared reflectancespectroscopy

Chari V. Kandala • Jaya Sundaram

Received: 2 May 2013 / Accepted: 17 February 2014 / Published online: 8 March 2014

� Springer Science+Business Media New York (Outside the USA) 2014

Abstract A near infrared reflectance (NIR) method is

presented here by which the average moisture content

(MC) of about 100 g of in-shell peanuts could be deter-

mined rapidly and nondestructively. MCs of three market

type peanuts, Runners, Valencia and Virginia were deter-

mined by this method while the peanuts were in their shells

(in-shell peanuts). The MC range of the peanuts tested was

between 6 and 26 %. NIR reflectance measurements were

made at 1 nm intervals in the wavelength range of

1,000–1,800 nm and the spectral data was modeled using

partial least squares regression analysis. Eight different

models were developed by utilizing different data prepro-

cessing methods such as, Norris-Gap first derivative with a

gap size of 3, peak normalization with 1,680 nm (which is

the no absorbance wavelength for water), and transforma-

tion from reflectance to absorption. Applying model fitness

measures, a suitable model was selected out of these.

Predicted values of the samples tested were compared with

the values determined by the standard air-oven method.

The predicted values agreed well with the air-oven values

with an R2 value better than 0.93 for all three types of in-

shell peanuts. This method being rapid, nondestructive, and

non contact, may be suitable for measuring and monitoring

MCs of different types of peanuts, while they are in their

shells itself, in the peanut industry.

Keywords In-shell peanuts � Near infrared spectroscopy �Moisture content � Runners � Valencia � Virginia � Multiple

linear regression

Introduction

Peanuts are an important crop in the southeastern US, and

moisture content (MC) of peanuts is an important factor to

be measured and controlled in their marketing, drying,

processing and storage. Freshly dug peanuts may have a

MC1 as high as 40 % but when they are left on the vines to

dry, their MC would be reduced to around 20 %. However,

they have to be artificially dried in drying trailers, equipped

with hot air-blowers, to reduce their MC values to less than

11 % to meet the grading standards [1]. At a commercial

facility there could be as many as 200 loads of peanuts at

various drying stages. The average MC of samples from

each load has to be periodically measured to determine

whether or not the desired MC level has been achieved. It

is desirable that the MC measurement be done rapidly and

nondestructively on in-shell peanuts during the drying

process to avoid over-drying of peanuts because, over-

drying not only increases the cost of drying but also affects

the quality of the peanuts adversely [2]. Most of the

commercial instruments, presently available to determine

the MC of peanuts, need shelling and cleaning of the

peanut samples, and in some cases some sort of sample

preparation such as grinding and packing. This is cum-

bersome, time consuming, and destructive. It would be

useful if the MC of the peanuts could be measured on the

in-shell peanuts itself rapidly and nondestructively, par-

ticularly at the peanut buying points, where MC of the

peanuts is an important factor in fixing the sale price.

Development of an accurate, rapid, and nondestructive

method to determine MC of in-shell peanuts could save

considerable time and labor during the drying process, andC. V. Kandala (&) � J. Sundaram

National Peanut Research Laboratory, ARS, USDA, Dawson,

GA 39842, USA

e-mail: [email protected] 1 Moisture content is expressed in % wet basis throughout this paper.

123

Food Measure (2014) 8:132–141

DOI 10.1007/s11694-014-9173-8

prevent the loss of large quantities of edible peanuts used

for MC measurements.

When infrared radiation is incident on a sample, light is

absorbed selectively according to a certain frequency of

vibration of the molecules in the sample. All organic

molecules have absorption bands in the near infrared

reflectance (NIR) region. Since the hydrogen atom has light

mass, overtones and combination bands of hydrogen

bearing functional groups like C–H, O–H, N–H, COOH

etc. dominate in the NIR region. These show broad and

overlapping bands that are not much suitable for structural

studies but have advantages in the quantitative analysis of

major components [3]. NIR instruments have very high

signal to noise ratio. The spectral information is repeated as

overtones and combination bands. The intensity of these

bands is less towards the shorter wavelengths. However,

the absorptivity of NIR bands is much lower than the mid-

infrared bands, enabling the NIR radiation to penetrate

deeper into a sample, and providing a much better oppor-

tunity to analyze the constituents of the sample. This is

particularly true with food materials which have very low

absorption behavior.

Visible and near-infrared (NIR) spectroscopy were used

earlier in the classification of bulk cereals such as wheat,

and the NIR spectrum was found to give better classifica-

tion than the visible [4]. NIR transmittance spectroscopy

was also attempted for nondestructive determination of oil

content in peanuts [5]. NIR reflectance spectroscopy was

used earlier for MC determination in processed cheese [6],

and for oil content measurement in peanuts [7]. There are

some commercial instruments such as Zeltex ZX8002

(Zeltex Inc., Hagerstown, MD 21740, USA) that use the

NIR transmission method to determine the moisture, oil

and protein in agricultural products. These instruments are

useful for measuring MCs of whole grain samples such as

wheat, barley, corn, soy, and rice, but are not useful for

whole peanut kernels or in-shell peanuts because of their

larger size. Attempts were made earlier to determine the

MC of in-shell peanuts, using a custom made NIR spec-

trometer [8]. In that work two varieties of peanuts both

grown in the same geographic area were used. The

objective of this study is to investigate if this rapid and

nondestructive method of NIR reflectance spectroscopy,

using a commercial spectrometer, would be suitable for

determining the MC of three commonly used varieties of

in-shell peanuts, grown in different geographic locations in

United States. The specific objectives are: (1) to collect

NIR reflectance measurements of in-shell peanuts without

any sample preparation requirements, (2) to develop PLSR

models to predict the MC of three varieties of in-shell

peanut samples, and (3) to validate the developed model

with in-shell peanuts not used in the calibration for the

different varieties.

Materials and methods

Peanut samples

Peanut samples of the Runners and Virginia market types

grown in Georgia, and Valencia peanuts grown in New

Mexico, harvested during the year 2008, cleaned, dried and

stored in cold storage at the National Peanut Research

Laboratory were used for these measurements. The initial

average MC of stored Runner type peanuts was 9 %, and

the MC of both Virginia and Valencia peanuts was about

6 %, as determined by the standard air-oven method [9].

The Runner type peanuts were divided into 17 sublots.

Leaving one sublot at the original moisture level of 9 %,

appropriate quantities of water were added to the other

sublots to raise their moisture levels to obtain 17 different

moisture groups ranging from 9 to 27 %. Valencia and

Virginia peanuts were divided into 12 and 15 sub-lots

respectively, and each sublot was placed in a separate air

tight plastic container. Appropriate quantities of water were

added to each container, to raise the moisture levels in

steps of 2 % increments. This resulted in 12 and 15

moisture levels for Valencia and Virginia type peanuts

respectively, in the moisture range of 6–26 %. The peanut

lots were kept in cold storage for a week in air-tight con-

tainers to equilibrate, and were rotated often in their con-

tainers to facilitate equilibration. After a week, the

containers were removed from cold storage and allowed to

reach room temperature before taking the NIR measure-

ments. The final MC of each sub-lot after the equilibration

was determined, using the air-oven method described

above, for three 100 g samples from each sub-lot. The

average of the three replicates was taken as the MC of each

sub-lot (reference value) and each sub-lot was labeled

accordingly. The peanut pod samples, after conditioning,

were separated into calibration and validation groups.

NIR reflectance measurement

The instrument set up used for collecting NIR reflectance

data of peanut samples, is shown in Fig. 1. It consisted of

an NIR spectrometer (Model: Quality Spec Pro, Analytical

Spectral Devices, Boulder, CO, USA), a halogen lamp, a

fiber optic probe, and a turntable accessory [10]. The

spectrometer had a wavelength range of 1,000–1,800 nm.

The halogen lamp mounted on top of the turntable acces-

sory illuminates a circular area of 50 mm diameter on the

2 Mention of company or trade names is for the purpose of

description only and does not imply endorsement by the US

Department of Agriculture.

Nondestructive moisture content determination 133

123

turntable, where peanut samples were placed. The turn

table rotates at a speed of 25 rpm enabling uniform dis-

tribution of light over the sample. The reflected light from

the peanuts is directed into the spectrometer by a fiber optic

cable with a PVC sheath.

In-shell peanuts from each sublot of the three market

types were evenly spread (usually in two layers) in a

glass Petri dish, 85 mm in diameter and 15 mm in depth.

Reflectance spectra at 1 nm intervals were collected in

the wave length range between 1,000 and 1,800 nm. An

average of 16 measurements and an integration time of

10 ms were used throughout the measurements. A 50 %

gray Spectralon plate (Labsphere, NorthSutton, NH,

USA) was used to acquire a reference scan which inci-

dentally doubled the integration time and improved the

signal–noise ratio. However, this procedure yielded

reflectance values greater than 1 occasionally, which

gave raise to negative values during absorbance trans-

formation. To avoid the occurrence of negative values,

the reflectance spectra were scaled down with a factor of

two to keep the reflectance values always less than one.

The reference scan was updated every 10 min during the

measurement process. These procedures were repeated

on all the samples in each moisture level, 30 times, to

get replicated spectra. These measurements were done on

both calibration and validation sets of the three market

type peanuts,

Data analysis

NIR spectral data were analyzed using multivariate data

analysis software (Unscrambler Version 9.7, CAMO ASA,

USA). Reflection values of the in-shell peanut samples,

measured from 1,000 to 1,800 nm at 0.5 nm intervals, were

considered as independent variables, and the MC of the

peanuts formed the dependent variables for the analysis.

The reflection spectra data was converted into the absorp-

tion mode using the Unscrambler software for further

analysis. Both absorption and reflection spectra data were

used, independently, for developing models for MC

determination. Using data from the calibration data, partial

least square (PLS) regression analysis was conducted to

develop an empirical equation to estimate the MC of the

peanuts. Before doing the PLS analysis, certain mathe-

matical preprocessing procedures [11] such as, Norris gap

first derivative, and peak normalization at 1,680 nm, were

performed separately, on both absorption and reflection

raw spectral data for obtaining better spectral resolution.

Similarly, PLS analysis was performed on both raw and

preprocessed spectral data, to obtain different calibration

models. Prior to developing PLS calibration models,

spectra were mean-centered, and a 10 fold cross-validation

procedure was followed to determine optimum number of

PLS factors. Best calibration model was selected based on

standard error of calibration (SEC), and coefficient of

multiple determinations (R2). All calibration models were

used to predict the MC of validation set samples. Goodness

of fit was evaluated based on the standard error of pre-

diction (SEP) obtained by comparing the reference (oven

measured) MC values with the predicted moisture con-

centration. Bias values, which correspond to the average

difference between the standard reference and predicted

values, were also considered in model selection. Final

model selection was made giving weightage to both SEC

and SEP values and the number of factors used in the

regression.

Results and discussion

Figures 2, 3 and 4 show the average (of 30 samples at each

MC level) reflectance moisture spectra of Runner, Valencia

and Virginia type peanuts respectively for different MC

levels. Peaks represent high reflection of NIR electro-

magnetic energy. The height and shape of the spectra are

dependent on scattering of light. The scatter is influenced

by sample size and shape, and the reflectance nature of the

sample surface. In NIR reflectance spectroscopy, a beam of

radiation is impinged on the sample, penetrates to a certain

extent into the sample, gets diffused, and then reflected

back to the detector. It could be seen in these figures, that

the averaged spectra of different moisture levels have

similar shapes but, the amplitudes are different. The dif-

ferences in amplitude of spectra for various moisture levels

were amplified by incorporating different data prepro-

cessing steps such as Norris Gap first derivative, peak

normalization with 1,680 nm (which is the no absorbance

wavelength for water), and absorbance transformation, to

further resolve overlapping peaks and increase the contrast

between the MC levels. Figures 5, 6 and 7 show the pre-

processed NIR reflectance spectra of different market types

Fig. 1 Near-infrared reflectance measurement setup. 1 Source, 2

spectrometer, 3 computer and monitor, 4 in-shell peanuts in Petri dish

mounted on the turn table

134 C. V. Kandala, J. Sundaram

123

at various moisture levels. Peak normalization (Figs. 5b,

6b, 7b) at 1,680 nm shows the clear peak difference for

water bands at 1,150 and 1,450 nm for three MC levels for

the three market types. Figures 5a, 6a and 7a show the

Norris gap first derivative spectra of in-shell peanuts at

three MC levels for the three market types. The first

derivative spectra of all the varieties have a trough corre-

sponding to each moisture concentration peak in the ori-

ginal spectra. The first derivative curve at different

moisture concentration shows slightly different peak posi-

tions for each level. For example, for the Valencia peanuts

(Fig. 6a) at the MC level of 21.69 %, a clear peak is seen at

1,390 nm. The same peak occurs at 1,405 nm for 10.07 %

MC level and at 1,410 nm for the 6.18 % MC level. In

lower moisture levels, spectra shift was about 20 nm.

Similar kind of shift occurred for the other two peanut

types too.

Since the derivative curves show modified wavelength

spectrum with more details by enhancing the high fre-

quencies, the shifts in peaks are more clearly seen. Water

absorption bands are influenced by effects of solutes like

ions and organic monomers in water and hydrogen bonds

as well, in which the water bonds are strongly associated.

The shift of absorption peaks to lower or higher wave-

lengths are related to the solute’s hydration potential and

the strength of the hydrogen bonds. In the peanut sample

there might be more strong hydrogen bonds present, which

could influence the NIR reflection. In pure water, hydrogen

molecules that are attached with O–H groups are consid-

ered as free hydrogen, i.e., they do not attach with any

other foreign molecules. This kind of free hydrogen O–H

groups give more intensive absorption peaks at lower

wavelengths such as for pure water at 975 nm [12, 13]. But

the O–H groups with hydrogen molecules that are bonded

with carbon, nitrogen or some other molecules, as in food

materials, produce water absorption peaks at higher

wavelengths. Therefore, as the moisture concentration in

the peanuts decreases there might be an increased number

of O–H groups, with bonded hydrogen molecules as

mentioned above, which shifted the water absorption peak

towards higher wavelengths for low moisture samples.

Tables 1, 2, 3, 4, 5 and 6 show the fitness measures of

calibration and validation groups of the three types of in-

shell peanuts, obtained using both absorbance and reflec-

tance spectra. Runner type in-shell peanuts, with a cali-

bration set of 270 samples (9 MC levels, 30 samples in

each level), gave R2 values of 0.92 or better for any of the

eight models used (Table 1). Valencia type, with a cali-

bration set of 210 Samples (7 MC levels), gave R2 values of

0.96 or better (Table 3), and Virginia type in-shell peanuts,

with a calibration set of 240 (8 MC levels), gave R2 values

of 0.74 or better (Table 5) for each of the preprocessed

spectral data as well as for raw data. All the calibration

model equations were used to predict the moisture content

of the 240 Runner, 150 Valencia and 210 Virginia type

samples in the validation sample sets. The fitness measures

0.35

0.4

0.45

0.5

0.55

0.6

0.65

0.7

1000 1100 1200 1300 1400 1500 1600 1700 1800

Ref

lect

ance

val

ue

Wavelength, nm

Runner type In-shell peanuts

MC 9.03%

MC15.04%

Fig. 2 NIR reflectance spectra of Runner type in-shell peanuts at

different moisture levels

0.25

0.3

0.35

0.4

0.45

0.5

0.55

0.6

1000 1100 1200 1300 1400 1500 1600 1700 1800

Ref

lect

ance

val

ue

Wavelength, nm

Valencia type In-shell peanuts

MC 6.18%

MC 10.07%

MC 21.69%

Fig. 3 NIR reflectance spectra of Valencia type in-shell peanuts at


0.4

0.45

0.5

0.55

0.6

0.65

0.7

1000 1100 1200 1300 1400 1500 1600 1700 1800

Ref

lect

ance

val

ues

Wavelength, nm

Virginia type Inshell peanuts MC 6.9%MC 10.8%MC 19.4%MC 22.3%

Fig. 4 NIR reflectance spectra of Virginia type in-shell peanuts at



123

of the predictions were shown in Tables 2, 4 and 6 for

Runner, Valencia and Virginia types respectively.

Of the different PLSR calibration models shown in

Table 1 for the Runner type in-shell peanuts, models 2, 4, 6

and 8 performed well with an R2 value of 0.98 or better.

Looking at their SEC3 and RMSEC values models 6 and 8

exhibit considerably low values compared with other

models. From the two, model 8, in virtue of having a low

bias value compared with model 6 was considered as the

best among the calibration models. However, all the

models were used to predict the MC of the validation sets

to further assess their suitability. Fitness measures of the

validation set runner in-shell peanuts were shown in

Table 2. Among these, models 2, 6, 7 and 8 showed an R2

of 0.91 or better. Of these four, model 8 exhibited the best

R2 value (0.92), and low SEP4 (1.44), RMSEP (1.49) and

bias (-0.63) values. Thus considering both calibration and

prediction fitness measures model 8 has qualified as the

best model. Model 8 was selected as the best model for MC

prediction of unknown samples of runner type in-shell

peanuts.

Table 3 shows the different PLSR models for calibration

set of Valencia type in-shell peanuts. All eight models

showed an R2 value of 0.96 or better. The RMSEC and

SEC values for models 3, 4, 7 and 8, and the bias value of

model 5 are considerably large. Therefore models 3, 4, 5, 7

Fig. 5 Preprocessed NIR

reflectance spectra of Runner

type in-shell peanuts at different

moisture levels a Norris Gap

first derivative, b Peak

normalization at 1,680 nm

3 SEC = 1n�p�1

Pn

i¼1

e2i

� �12

where n is the number of observations, p is

the number of variables in the regression equation with which the

calibration is performed, and ei is the difference between the observed

and reference value for the ith observation.

4 SEP = 1n�1

Pn

i¼1

ðei � �eÞ2� �1

2

where n is the number of observations,

ei is the difference in the moisture content predicted and that

determined by the reference method for the ith sample, and �e is the

mean of ei for all of the samples.


123

and 8 were not considered as good models. Models 1, 2,

and 6, all have an R2 of 0.99. Comparing the SEC,

RMSEC, and bias values model 2 with an SEC of 0.47 and

RMSEC of 0.43 but with a lowest bias value of 0.2 9 10-6

was selected as the best model. It may be noted that both

models 1 and 6 had slightly better SEC and RMSEC values

than model 2, but exhibited much larger bias values. Since,

all the models gave good R2 values they were all used to

predict the MCs of the validation set of samples. Fitness

measures for the validation set of Valencia type in-shell

peanuts are shown in Table 4. It can be seen that, all the

models, except model 3, gave an R2 value of 0.8 or better.

Though model 5 gave the highest R2 (0.89), and the lowest

SEP (1.16), RMSEP (1.56), and bias (?1.17) values, the

number of factors used for prediction were 5, which is very

high among all the models. On the other hand, model 2

with an R2 value of 0.86, and low values of RMSEP (1.77)

and SEP (0.97) used only 3 factors to predict the MC.

Using less number of factors makes a model more suitable

for prediction. Therefore model 2 was selected as the best

model of the MC prediction of unknown samples from

Valencia type in-shell peanuts. Model 2 was already

selected as the best model according to the calibration

fitness measures, and the prediction fitness measures con-

firmed this.

Table 5 shows the different PLSR models for calibration

set of Virginia type in-shell peanuts. Best R2 value of 0.99

was obtained for model 3, and it also has the lowest SEC of

0.79 and the lowest RMSEC of 0.74. It used a moderate

number of three factors in the regression and has the sec-

ond best bias value of -0.238 9 10-6 of all the models.

Only, model 2 has a better bias value than model 3 but it


reflectance spectra of Valencia


moisture levels a Norris gap




123

did not have good R2, SEC and RMSEC values to be

considered as a worthy model for MC prediction. All other

models exhibited SEC values of more than 2, and R2 values

less than 0.81. However, all models were used with the

validation sets of the Virginia type in-shell peanuts, and

fitness measures of the validation sets were shown in

Table 6. It can be seen that the highest R2 of 0.88 was

obtained for model 3, and it also has lowest SEP (1.35) and

the lowest RMSEP (1.59). All other models gave much

higher SEP values. Thus, model 3 was selected as the best


reflectance spectra of Virginia


moisture levels, a Norris gap



Table 1 Fitness measures of

Runner type in-shell peanuts:

calibration

No. Models No. of factors R2 RMSEC SEC Bias (10-6)

1 Reflectance 3 0.94 1.52 1.61 ?0.42

2 Reflectance ? derivative 3 0.98 0.89 0.95 -1.59

3 Reflectance ? normalization 3 0.94 1.45 1.53 ?2.76

4 Reflectance ? normalization ? derivative 3 0.98 0.85 0.90 -2.65

5 Absorbance 3 0.95 1.35 1.43 -0.85

6 Absorbance ? derivative 4 0.99 0.33 0.35 ?1.70

7 Absorbance ? normalization 3 0.92 1.69 1.79 -3.07

8 Absorbance ? normalization ? derivative 4 0.99 0.36 0.38 ?0.32


123

model for MC prediction for Virginia type in-shell peanuts.

Calibration fitness measures already showed that model 3

is the best among the models tested.

Graphical comparison between the MC values

obtained by the standard air-oven method and the NIR

method using the best model for each variety of peanuts

is given in Figs. 8, 9, 10, 11, 12 and 13. The results,

both calibration and validation for Runner type peanuts

are shown in Figs. 8 and 9. Similar results for Valencia

type in-shell peanuts are shown in Figs. 10 and 11, and


Runner type in-shell peanuts:

validation

No. Models No. of factors R2 RMSEP SEP Bias

1 Reflectance 3 0.84 2.05 2.12 -0.52


3 Reflectance ? normalize 3 0.88 1.77 1.61 -0.94

4 Reflectance ? normalize ? derivative 3 0.88 1.76 1.50 -1.06

5 Absorbance 3 0.74 2.63 2.71 -0.69

6 Absorbance ? derivative 4 0.91 1.59 1.49 -0.78

7 Absorbance ? normalize 3 0.91 1.55 1.44 -0.76

8 Absorbance ? normalize ? derivative 4 0.92 1.49 1.44 -0.63


Valencia type in-shell peanuts:

calibration

No. Models No. of factors R2 RMSEC SEC Bias 9 10-6

1 Reflectance 4 0.99 0.27 0.29 -0.48

2 Reflectance ? derivative 3 0.99 0.43 0.47 ?0.20

3 Reflectance ? normalization 3 0.98 0.76 0.82 ?0.48

4 Reflectance ? normalization ? derivative 3 0.99 0.58 0.62 ?0.48

5 Absorbance 4 0.99 0.38 0.41 ?1.84

6 Absorbance ? normalization 3 0.99 0.30 0.33 ?0.89

7 Absorbance ? derivative 3 0.99 0.56 0.61 ?0.75

8 Absorbance ? normalization ? derivative 2 0.96 1.04 1.12 ?0.48


Valencia type in-shell peanuts:

validation

No. Models No. of factors R2 RMSEP SEP Bias

1 Reflectance 4 0.82 2.02 1.69 1.35

2 Reflectance ? derivative 3 0.86 1.77 0.97 1.55

3 Reflectance ? normalization 3 0.79 2.18 1.43 1.76

4 Reflectance ? normalization ? derivative 3 0.86 1.82 0.94 1.61

5 Absorbance 5 0.89 1.56 1.16 1.17

6 Absorbance ? normalization 3 0.83 1.95 1.69 1.23

7 Absorbance ? derivative 3 0.84 1.94 1.07 1.69

8 Absorbance ? normalization ? derivative 2 0.81 2.11 1.46 1.66


Virginia type in-shell peanuts:

calibration

No. Models Factors R2 RMSEC SEC Bias (10-6)

1 Reflectance 1 0.98 0.85 0.91 1.907

2 Reflectance ? derivative 1 0.79 2.87 3.07 0.000

3 Reflectance ? normalization 3 0.99 0.74 0.79 -0.238

4 Reflectance ? normalization ? derivative 1 0.81 2.74 2.93 0.835

5 Absorbance 1 0.90 1.99 2.13 0.835


7 Absorbance ? normalization 1 0.74 3.18 3.40 0.238

8 Absorbance ? normalization ? derivative 1 0.79 2.91 3.11 0.596


123

results for Virginia type in-shell peanuts are shown in

Figs. 12 and 13. Shown in the plots are the predicted

MC values, averaged over 30 samples from each

moisture levels of calibration or validation sets along

the Y-axis, and the average of oven determined values

(reference values) of 3 replicates from that moisture

level, along the X-axis. Good correlations between the

predicted and reference MC values were obtained on

both calibration and validation set samples for all three

types of peanuts.

y = 1.0021x - 0.0438R² = 0.9999

8

13

18

23

28

33

8 13 18 23 28 33

Pre

dic

ted

mo

istu

re c

on

ten

t, %

w.b

Reference moisture content, % w.b

Runner type in-shell peanuts_Calibration set_Absorption+Normalization+derivative

Fig. 8 Comparison of predicted and oven moisture values for

calibration set average spectra of Runner type in-shell peanuts

y = 0.9847x + 0.3658R² = 0.8627

9

11

13

15

17

19

21

23

25

27

29

9 11 13 15 17 19 21 23 25 27 29

Pre

dic

ted

mo

istu

re c

on

ten

t, %

w.b

Reference Moisture content, % w.b

Runner type in-shell peanuts_Validation set_Absorption+Normalization+Derivative


validation set average spectra of Runner type in-shell peanuts

y = 0.9928x + 0.1074R² = 0.9928

0

5

10

15

20

25

0 5 10 15 20 25Pre

dic

ted

mo

istu

re c

on

ten

t, %

w.b


Valencia type in-shell peanuts _Calibration set_Reflectance + Derivative


calibration set average spectra of Valencia type in-shell peanuts

y = 1.1354x - 0.4471R² = 0.989

0

5

10

15

20

25

0 5 10 15 20 25Pre

dic

ted

mo

istt

ure

co

nte

nt,

% w

.b


Valencia type in-shell peanuts_Validation set_Reflectance + Derivative


validation set average spectra of Valencia type in-shell peanuts


Virginia type in-shell peanuts:

validation

No. Models Factors R2 RMSEP SEP Bias

1 Reflectance 4 0.87 1.67 1.79 0.221


3 Reflectance ? normalization 3 0.88 1.59 1.35 0.977

4 Reflectance ? normalization ? derivative 1 0.77 2.17 2.34 -0.055

5 Absorbance 1 0.61 2.84 3.05 -0.300


7 Absorbance ? normalization 1 0.58 2.96 3.15 -0.500

8 Absorbance ? normalization ? derivative 1 0.66 2.66 2.86 -0.252


123

The slightly different predicted values at some moisture

levels as seen in Fig. 13 could be due to the faster dehy-

dration that occurred at these levels due to the heat radiated

from the halogen light source during the duration of the

measurement. Using a heat absorbing filter between the

lamp and the sample may improve the predictions at all

MC levels.

Conclusion

Using NIR reflectance method which is non-contact, non-

destructive and rapid, the average MC of in-shell peanuts

in a sample of 100–150 g could be measured within

acceptable accuracies as compared with their standard air-

oven values. The method is applicable to different market

types grown in different places in the United States. Pres-

ently, there are no known commercial instruments that

would measure the MC of in-shell peanuts using the NIR

method. Ability to determine the average MC of peanut

samples of 100 g or more would be useful in the peanut

industry at different phases. This work could be useful in

the development of a low-cost commercial instrument

using the NIR principles.

References

1. USDA, AMS Farmers Stock Peanuts Inspection Instructions.

Updated 2000 (USDA, Washington, 2000)

2. C.L. Butts, Incremental cost of over-drying farmers’ stock pea-

nuts. Appl. Eng. Agric. 11(5), 671–675 (1995)

3. M. Iwamoto, S. Kawano, Advantages and disadvantages of NIR

applications for the food industry, in Making Light Work:

Advances in Near Infrared Spectroscopy, ed. by I. Murray, I.A.

Cowe (Wiley, Weinheim, 1992), pp. 367–375

4. L.A. Mohan, C. Karunakaran, D.S. Jayas, N.D.G. White, Clas-

sification of bulk cereals using visible and NIR reflectance

characteristics. Can. Biosyst. Eng. 47(7), 7–14 (2005)

5. J.B. Mishra, R.S. Mathur, D.M. Bhatt, Near-infrared transmit-

tance spectroscopy: a potential tool for non-destructive determi-

nation of oil content in groundnuts. J. Sci. Food Agric. 80,

237–240 (2000)

6. C.G. Blazquez, D.C. O’Donnell, D. O’Callaghan, V. Howard,

Prediction of moisture, fat and inorganic salts in processed cheese

by near infrared reflectance spectroscopy and multivariate data

analysis. J. Near Infrared Spectrosc. 12(3), 149 (2004)

7. G. Fox, A. Cruickshank, Near infrared reflectance as a rapid and

inexpensive surrogate measurement of composition and oil con-

tent of peanuts (Arachis hypogaea L.). J. Near Infrared Spectrosc.

13(5), 287 (2005)

8. J. Sundaram, C.V.K. Kandala, K.N. Govindarajan, J. Subbiah,

Sensing of moisture content of in-shell peanuts by NIR reflec-

tance spectroscopy. J. Sens. Technol. 2(1), 1–7 (2012). doi:10.

4236/jst.2012.21001

9. ASAE Standards, S410.1: Moisture Measurements: Peanuts

(ASAE, St. Joseph, 2000)

10. K.N. Govindarajan, C.V.K. Kandala, J. Subbiah, NIR reflectance

spectroscopy for nondestructive moisture content determination

in peanut kernels. Trans. ASABE. 52(5), 1661–1665 (2009)

11. H. Martens, T. Naes, Multivariate calibration by data compres-

sion, in Near-infrared Technology in the Agricultural and Food

Industries, 2nd edn., ed. by P. Williams, K. Norris (American

Association of Cereal Chemists, St. Paul, 2001), pp. 59–99

12. R.P. Marvin, M. Singh, Calibration of a near infrared transmis-

sion grain analyzer for extractable starch in maize. Biosyst. Eng.

89(1), 79–83 (2004)

13. A. Inoue, K. Kojima, Y. Taniguchi, K. Suzuki, Near-infrared

spectra of water and aqueous electrolyte solutions at high pres-

sures. J. Solut. Chem. 13(11), 811–823 (1984)

y = 0.9863x + 0.2411R² = 0.9862

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Virginia type in-shell peanuts_Calibration set_Reflectance + Normalization


calibration set average spectra of Virginia type in-shell peanuts

y = 0.9832x + 1.2294R² = 0.9277

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Virginia type in-shell peanuts_Validation set_Reflectance + Normalization


validation set average spectra of Virginia type in-shell peanuts


123

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