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Scientia Horticulturae 216 (2017) 29–37

Contents lists available at ScienceDirect

Scientia Horticulturae

journa l h om epage: www.elsev ier .com/ locate /sc ihor t i

dentification of preharvest factors determining postharvest ripeningehaviors in ‘Hass’ avocado under long term storage

ebastián A. Riveraa, Raúl Ferreyrab, Paula Robledoa, Gabriel Sellesb, Mary Lu Arpaiac,orge Saavedrad, Bruno G. Defilippia,∗

Unidad de Postcosecha, Instituto de Investigaciones Agropecuarias (INIA-La Platina), Casilla 439/3, Santiago, ChileInstituto de Investigaciones Agropecuarias (INIA-La Platina), Casilla 439/3, Santiago, ChileDepartment of Botany and Plant Sciences, University of California, Riverside, CA92521, United StatesDATACHEM Agrofood: Grupo de Quimiometría Aplicada en Agroalimentos, Escuela de Ingeniería de Alimentos, Pontificia Universidad Católica dealparaíso, Waddington 716 Playa Ancha, Valparaíso 2360100, Chile

r t i c l e i n f o

rticle history:eceived 17 August 2016ccepted 23 December 2016vailable online 5 January 2017

eywords:ruit variabilityry mater contentruit qualityultivariate analysis

a b s t r a c t

A major challenge for the global avocado industry is to provide a homogenous product in terms of fruit-ripening behavior, especially considering the significant variability in quality that can be found withina box or pallet of the fruit. The broad range of conditions under which trees are grown, particularlywith regard to climate, soil and agronomical management, can influence this ripening variability. Theaims of this study were (i) to determine the variability in fruit ripening among ‘Hass’ avocado grownunder different conditions in Chile and (ii) to understand the postharvest fruit-ripening behavior of‘Hass’ avocado due to the combined effect of several preharvest variables. Preharvest variables wereevaluated at 42 experimental sites in Chile during three consecutive seasons. In addition, avocados withover 21% dry matter were collected at each site during each season and stored for 35 d at 5 ◦C undernormal atmospheric conditions before being ripened at 20 ◦C. Indicators of ripening behavior, such as thesoftening rate (SOFTRATE), change in peel color (COLO35) and days at 20 ◦C necessary to reach the ready-to-eat stage (RTE35), were evaluated. As expected, high fruit variability in terms of ripening behavior wasobserved among the experimental sites and seasons. Multivariate analysis showed that the seasonal meanminimum air temperature, seasonal degree-days, trunk diameter and fruit firmness at harvest had a pro-portional relationship with postharvest SOFTRATE and COLOR35 during storage and a significant inverserelationship with RTE35. Conversely, the leaf area index, number of plants per hectare, and irrigationmanagement at the bloom stage had a proportional relationship with RTE35 and an inverse relation-

ship with SOFTRATE and COLOR35. Moreover, all of the three postharvest ripening behavior indicatorswere significantly (p < 0.05) estimated by predictive models considering preharvest variables. Therefore,attempting to predict postharvest behavior by considering only a single preharvest variable could be amisleading simplification of reality because several factors, including climate/environmental, agronomicmanagement and physiological variables, influence the ripening behavior of ‘Hass’ avocado fruit.

© 2016 Elsevier B.V. All rights reserved.

. Introduction

Chile is an important worldwide producer of avocado, exportingearly 60% of production, mainly to Europe and the U.S.A. Avo-ado cv ‘Hass’ is the most important cultivar in Chile comprising

ore than 90% of the fresh avocado exports and reaching 133,415 t

n the 2013–2014 export season (www.asoex.com). However, thencreased production both in Chile (39,303 ha) and in other avo-

∗ Corresponding author at: Santa Rosa 11610, La Pintana, Santiago, Chile.E-mail address: bdefilip@inia.cl (B.G. Defilippi).

ttp://dx.doi.org/10.1016/j.scienta.2016.12.024304-4238/© 2016 Elsevier B.V. All rights reserved.

cado exporting countries such as Mexico and Peru may result in aprice reduction due to simultaneous high market volumes. There-fore, an important issue to be considered is arrival at market of ahigh-quality product even after several days of shipping and stor-age (longer than 30–40 d). Fruit softening during cold storage isconsidered to be an indicator of postharvest ripening behavior inavocado fruit (Magwaza and Tesfay, 2015), and external appearance(e.g., peel color) is an important indicator of ‘Hass’ avocado ripeness

and acceptability at distribution centers and retail stores (Cox et al.,2004; Magwaza and Tesfay, 2015). Therefore, considering that avo-cado consumers can easily discriminate a soft, ready-to-eat fruitfrom an unripe fruit, a major challenge for the Chilean industry is

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o provide a homogenous product in terms of quality and ripen-ng attributes. However, high variability in terms of fruit origin,uality and ripeness can be found within a pallet of the fruit, aituation that is critical because it complicates prediction of theostharvest shelf life and the time necessary for ripening. Indeed,rediction of ripening behavior at harvest is considered to be aajor challenge because both exogenous and endogenous factors

ffect fruit ripening (Blakey et al., 2009). For example, a propor-ion of the observed variability in fruit quality and ripening is dueo the broad range of conditions in which the trees are grown,specially with respect to environmental factors such as the tem-erature during the growing season (Arpaia, 1994; Sams, 1999:oolf and Ferguson, 2000) and level of sun exposure (Woolf et al.,

999; Woolf et al., 2000). In addition, fruit-ripening behavior andostharvest quality can be associated with different cultural prac-ices, including irrigation management (Bower, 1988), and alsoith the composition of macronutrients and micronutrients, such

s calcium (Witney et al., 1990; Thorp et al., 1997), nitrogen (Arpaiat al., 1996) and zinc (Vorster and Bezuidenhout, 1988), withinhe fruit. Furthermore, some metabolites of photosynthesis, suchs the seven-carbon sugars mannoheptulose and perseitol, haveeen linked to the ripening process (Liu et al., 2002; Blakey et al.,012). It is generally recognized that the dry matter content cane used as a maturity indicator of fruit at harvest (OECD, 2004).owever, we suggest that utilization of the dry matter concentra-

ion for predicting fruit-ripening behavior must be accompaniedy other preharvest variables. Moreover, the combined effect ofeveral preharvest variables on postharvest fruit-ripening behavioras not yet been reported. Therefore, this study was conducted (i)o determine the variability in fruit ripening among different ‘Hass’vocado growing conditions in Chile and (ii) to understand theostharvest ripening behaviors of avocados stored under regularir due to the combined effect of several preharvest variables (i.e.,limate/environment, planting design, plant nutrition, irrigationanagement and plant physiology and production parameters).

. Material and methods

.1. Experimental sites and fruit material

During three consecutive seasons (2008/2009; 2009/2010; and010/2011), ‘Hass’ avocados were harvested from 42 experimen-al sites of 1000 m2 located in central Chile; the trees were grownnder different climate, planting design and agricultural manage-ent conditions. Of these sites, twenty are located in the Aconcaguaalley, six in the Maipo Valley and sixteen in La Ligua Valley. Green

ruits of homogenous size that were free of external defects werearvested from each experimental site based on the dry matterDM) content, considering fruit with a DM ≥ 20.0%. After harvest,he fruit was transported to the Postharvest Unit on the same dayor storage at 4.8 ± 0.4 ◦C for 35 d under regular air (0.04% CO2 and1.0% O2) and 91 ± 5% RH. After storage, the fruit was exposed to aemperature of 20 ◦C (19 ± 0.5 ◦C) and an RH of 35 ± 4% (shelf life)ntil ripening.

.2. Determination of preharvest and postharvest variables

In total, 33 preharvest variables and 3 postharvest variablesere assessed during the three consecutive seasons (2008/2009;

009/2010; and 2010/2011) at the 42 experimental sites.

.3. Climate/environment

The degree days (DD13), mean seasonal maximum air temper-ture (TMAX), mean seasonal minimum air temperature (TMIN),ean seasonal relative humidity (RH), and mean seasonal solar

ulturae 216 (2017) 29–37

radiation per day (SOLARAD) were calculated using data obtainedfrom experimental meteorological stations (Decagon Devices Inc.,WA) located at each of the 42 experimental sites. The meteoro-logical stations were equipped with a five-channel data logger(model Em50, Decagon Devices Inc.) that recorded weather condi-tions every 60 min. Degree days were calculated using a minimumthreshold temperature of 13 ◦C.

2.4. Plant nutrition

The fruit nitrogen (FN), potassium (FK), calcium (FCa), magne-sium (FMg), and boron (FB) contents and the fruit nitrogen/calcium(FN/Ca), calcium/boron (FCa/B), calcium/potassium (FCa/K), andpotassium/magnesium (FK/Mg) ratios were estimated from 3 sub-samples of 5 fruits at harvest maturity per experimental site andseason. In addition, the leaf phosphorus (LP) and zinc (LZn) con-tents were estimated using 3 sub-samples of 60–80 fully matureand expanded leaves for each experimental site and season. Springleaves that were 5–6 months old were obtained from summer-growth branches that had stopped developing. Fruit and leafsampling was performed randomly from 6 trees per experimentalsite. The nutrient contents of the fruits and leaves were analyzedaccording to the methodologies proposed by Sadzawka et al. (2007).The nitrogen content was analyzed using the standard Kjeldahlmethod (Vapodest 505 Gerhardt, Germany). Calcium, magnesiumand zinc were estimated by atomic absorption spectrophotome-try (Analist 200, Perkin Elmer, CA), and potassium was estimatedby atomic emission spectrophotometry (Analist 200, Perkin Elmer,CA). Boron and phosphorus were estimated by colorimetric analysis(Lambda 3B, Perkin Elmer, CA).

2.5. Site/planting characteristics

The altitude over sea level (ALT), planting slope (SLOPE), macro-porous content in the first soil horizon (MACROPOROUS), plantsper hectare (PLANTHECT) and plant age (PLANTAGE) were deter-mined at each of the 42 experimental sites. The planting slope wascalculated using a global positioning system (Nuvi 205, Garmin,KS), and the macroporous content was calculated following themethodology proposed by Ball and Smith (1991).

2.6. Plant physiology and production parameters

The trunk diameter (TRUNKDIAM), leaf area index (LAI), num-ber of fruits per tree (FRNUM) and individual fruit weight(FRUITWEIGHT) were determined using six healthy, productivetrees per experimental site. The trunk diameter was measured forthe rootstock using a digital caliper (Mitutoyo, Japan) at 10 cmbelow the graft during the fruit-set stage. The LAI was estimatedduring the fruit-set stage on the basis of the photosynthetic activeradiation (PAR), determined using a Sunfleck PAR Ceptometer(Decagon Devices Inc.), intercepted by the plant foliage at midday.The fruit weight was measured for 20 fruits at harvest maturity foreach of the six trees per experimental site.

2.7. Irrigation management

The percentage of applied water in relation to crop evapo-transpiration (ETc) at the bloom (BLH2O), fruit-set (FSH2O), andfruit-development (FDH2O) stages were calculated based on theapplied water determined using a volumetric meter installed withthe irrigation equipment at each experimental site. In addition,

the amount of precipitation obtained from the experimental mete-orological stations was considered when estimating the appliedwater. ETc was calculated using LAI at different fruit stages,and the reference evapotranspiration (ET0) was calculated from

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he Penman-Monteith equation using data obtained from thexperimental meteorological stations. Finally, the mean irrigationercentage (MEANH20) was determined considering the monthly

rrigation management for each season.

.8. Fruit quality at harvest

From each experimental site, 3 sub-samples of 20 fruits weresed for quality determination at harvest, considering fruit firm-ess at harvest (HFIRM), dry matter content (DM) and fruit ageFRUITAGE). The flesh firmness was measured using a penetrome-er (Effegi, Milan, Italy) equipped with a 4 mm plunger tip. The fruitge was determined considering the number of days from fruit setntil harvest. The dry matter content was estimated by oven dry-

ng, as follows: half of each vertically sliced fruit was peeled, theeed coat was removed, and the flesh was chopped and weighed;ach sample was dried for 24 h at 103 ◦C until a constant weightas reached.

.9. Postharvest ripening

Postharvest ripening indicators were determined in 3 sub-amples of 20 fruits harvested from six healthy trees perxperimental site. The ripening parameters were measured dur-ng cold storage at 5 ◦C and during exposure at 20 ◦C until each fruitipened. The fruit-softening rate (SOFTRATE) and change in peelolor (COLOR35) were measured during 35 d of storage at 5 ◦C. Theoftening rate was calculated using the flesh firmness at harvestnd during storage, as follows:

ofteningrate = (flesh firmness at harvest − storage flesh firmness)number of days of storage

The peel color was assessed visually using a hedonic scale withcores from 1 to 5: 1 = green over 100% of the peel surface; 2 = 20%olored black/purple on green; 3 = 60% colored black/purple onreen; 4 = purple over 100% of the peel surface; 5 = black over 100%f the peel surface. The percentage of fruit with peel color changerom green to black was calculated considering the percentage ofruits with a score between 3 and 5 at the end of the storage period.he number of days at 20 ◦C necessary to reach the ripe stage after5 d of cold storage (RTE35) was measured for 3 sub-samples of 20ruits for each of the 42 experimental sites after 35 d at 5 ◦C and the0 ◦C incubation period until a fruit firmness between 4.0 and 8.0 Nas reached. Additionally, external and internal fruit physiologicalisorders and other damage were assessed at the ripe stage usingedonic scales from 1 to 5: 1 = no occurrence; 2 = slight damage;

= moderate damage; 4 = moderately severe damage; 5 = severeamage.

.10. Data management and statistical analysis

To determine the variability in preharvest parameters and fruituality attributes, the data collected over the three sampling yearsere analyzed using an exploratory descriptive analysis, and the

oefficient of variation (CV) was calculated as follows:

V = Standard Deviation

Meanx100

Multivariate analysis was performed to study the relationshipetween the 32 preharvest and 3 postharvest ripening vari-bles. Because of the importance of multivariate measurements inhemistry, Principal Component Analysis (PCA) is likely the most

idespread multivariate chemometric technique, and it is certainly

he most widespread technique used in multivariate exploratoryata analyses (Brereton, 2009). PCA is an unsupervised exploratoryool that processes a matrix array, such as variables per case that can

ulturae 216 (2017) 29–37 31

display the main variations between samples and sample groupsand the relationships between samples and variables in orthogonalplanes that represent the direction of greatest variance in the data.PCA is a set of linear combinations of p-random variables (x1, x2,.., xp) that allow information to be condensed in two ways: first, byidentifying relationships between different observations that com-prise the scores matrix; and second, by determining relationshipsbetween different variables of the data set, known as the loadingsmatrix (Saavedra and Cordova, 2011). The axes derived from theanalysis represent the maximum variance directions, with the firstprincipal component (PC1) located along the direction of maxi-mum variance of the data set, the second component (PC2) disposedalong the direction of the second greatest variance, and so on. AllPCs are simultaneously orthogonal to each other, and there is no co-variance among them. In addition, the line projected by each PC isthe best fit to all points simultaneously through least-squares opti-mization (Eriksson et al., 2006; Saavedra et al., 2013). In this study,PCA was performed considering the 35 variables obtained from the42 experimental sites during the three consecutive seasons. Thedata was previously standardized for the PCA analysis.

Partial Least Squares Regression (PLS), a latent structure pro-jection method using partial least squares, i.e., a multivariateanalysis, was performed to model the effect of preharvest param-eters on postharvest fruit-ripening indicators. This multivariatelinear regression method predicts one set of data from another andtreats both separately (Ballabio et al., 2006), and the goal of PLS isto search for directions that maximize the covariance between thematrix of predictor variables (Z) and the matrix of response vari-ables (Y). PLS relies on analysis of two variable sets that representthe predictor and response variable set; a linear parametric rela-tionship exists between these variable sets (Kruger and Xie, 2012).The multivariate analyses were based on the nonlinear iterativepartial least squares (NIPALS) algorithm (Wold et al., 2001). All datawere centered and scaled prior to the analysis and validated bya full cross-validation routine, minimizing the predicted residualsum of squares function (PRESS) to avoid overfitting of the models(Cen et al., 2007). Additionally, based on this analysis, a correlationmatrix was performed considering the relationships between eachof the 42 preharvest and postharvest variables, and the Spearmancorrelation coefficient for each relationship was calculated. Thepostharvest ripening attributes (SOFTRATE, COLOR35 and RTE35)were used as dependent variables, and the 33 preharvest variableswere used as predictor variables. Finally, a Ridge Regression Anal-ysis (RRA) was performed. RRA is an alternative to ordinary leastsquares and is one of several biased regression estimators proposedin the literature. It is an alternative to deleting the regressor due tothe presence of collinearity or multicollinearity (Ryan, 2009).

The statistical analyses were performed with the software Info-Stat (version 2015, Universidad Nacional de Cordoba, Argentina)and SIMCA-P+ 12 (Umetrics AB, Sweden).

3. Results and discussion

3.1. Variability of preharvest parameters and postharvestripening behavior

The descriptive analysis showed wide variability in the prehar-vest parameters and postharvest ripening behavior among the 42planting sites and the three sampling seasons (Table 1). The plant-ing slope (CV = 83.6%), leaf area index (CV = 82.7%), leaf zinc content(CV = 75.9%), fruit calcium/potassium ratio (CV = 73.6%), fruit cal-

cium/boron ratio (CV = 65.7%), altitude over sea level (CV = 62.8%),fruit boron content (CV = 55.8%) and irrigation management at thebloom stage (CV = 53.3%) showed the highest coefficients of vari-ation among the preharvest variables. Therefore, the observed

32 S.A. Rivera et al. / Scientia Horticulturae 216 (2017) 29–37

Table 1Abbreviation, units, and descriptive analysis including the mean value, minimum value (min), maximum value (max) and coefficient of variation (CV) of 35 preharvest andpostharvest variables determined in three different seasons at 42 experimental sites growing ‘Hass’ avocado in central Chile.

Variables Abbreviation Units Mean Min Max CV

Climate/environmentSeasonal cumulative degree days DD13 Degree day 984.0 91.3 2,318.0 45.7Seasonal mean maximum temperature TMAX ◦C 23.4 18.3 28.4 8.9Seasonal mean minimum temperature TMIN ◦C 7.9 5.5 13.0 17.9Seasonal mean relative humidity RH % 72.5 62.0 85.0 8.9Seasonal mean solar radiation per day SOLARAD W m−2 376.6 325.0 410.0 5.6

Plant nutritionFruit nitrogen content FN g kg−1 1.1 0.59 2.0 22.0Fruit potassium content FK g kg−1 1.9 1.0 3.2 21.9Fruit calcium content FCa g kg−1 0.06 0.02 0.10 24.9Fruit magnesium content FMg g kg−1 0.09 0.05 0.15 23.2Fruit boron FB mg kg−1 70.2 12.7 185.3 55.8Fruit nitrogen/calcium FN/Ca ratio 21.0 8.3 60.6 41.7Fruit calcium/boron FCa/B ratio 1.2E-3 2.3E-4 3.9E-3 65.7Fruit calcium/potassium FCa/K ratio 0.04 0.01 0.3 73.6Fruit potassium/magnesium FK/Mg ratio 23.8 7.4 43.7 43.7Leaf phosphorus LP mg kg−1 0.14 0.09 0.21 19.1Leaf zinc LZn mg kg−1 41.1 15.0 200.0 75.9

Planting characterizationAltitude over sea level ALT m 417.0 89.3 1103.0 62.8Planting slope SLOPE % 12.0 0 45.5 83.6Soil macroporous content MACROPOROUS % 17.7 9.2 40.3 34.0Plants per hectare PLANTHECT count 560.3 143.0 1111.0 45.2Plant age PLANTAGE d 3,357.8 1095 12,775 48.3

Irrigation managementIrrigation management at bloom BLH2O % 68.0 0.0 186 53.3Irrigation management at fruit set FSH2O % 88.0 32.0 160.0 29.7Irrigation management during fruit development FDH2O % 108.0 0.0 256.0 38.2Seasonal mean irrigation management MEANH2O % 95.0 43.0 157.0 26.7

Physiology and productionLeaf area index LAI m2 m−2 2.6 0.7 10.3 82.7Trunk diameter TRUNKDIAM mm 64.6 27.7 149.3 33.8Number of fruits per tree FRNUM count 122.2 50 266.7 39.6Mean fruit weight FWEIGHT g 196.5 126.6 299.9 15.5

Fruit qualityDry matter content DM % 25.8 20.4 30.9 9.7Harvest fruit firmness HFIRM N 265.9 185.8 337.5 8.8Fruit age FRUITAGE d 268.1 198.9 332.5 8.3

Postharvest ripening behavior−1

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Softening rate SOFTRATE

Peel color change COLOR35

Days to ripening RTE35

ariability in planting altitude and slope indicate important dif-erences in relation to planting design among avocado growersn Chile. Furthermore, parameters such as irrigation management,utrient content and leaf area index demonstrated important dif-

erences in agronomic management and physiological responsemong planting sites and seasons. Unlike those parameters, theolar radiation per day (CV = 5.6%), seasonal mean maximum tem-erature (CV = 8.9%), seasonal mean relative humidity (CV = 8.9%)nd seasonal mean minimum temperature (CV = 17.9%) exhibitedhe lowest coefficients of variation.

Regarding fruit-ripening behavior, among all variables, thehange in peel color (CV = 134.0%) showed the highest coefficientf variation among sampling sites (Table 1). Indeed, due to differ-nce in color among fruits in a box or pallet at the destinationountry, ‘Hass’ avocados from Chile are described as having acheckerboard appearance’. Despite a lack of major differences inruit firmness at harvest among sites and seasons, the variabilityn the fruit-softening rate during cold storage differed consider-bly (Table 2). Moreover, storage conditions were the same among

easons in terms of storage temperature, relative humidity andO2 and O2 concentrations; however, the variability in the soft-ning rate differed among seasons, exhibiting a mean and CV of

N d 4.5 0.21 8.1 48.8% 22.1 0.0 100.0 134.0d 3.2 0.7 10.1 48.3

6.1 N d−1 and 29.1%, 4.7 N d−1 and 28.1%, and 2.6 N d−1 and 78.5%for the 2008/2009, 2009/2010 and 2010/2011 seasons, respectively(Table 2). Therefore, the estimated variability in the softening rateduring storage could be derived from the differences observed ingrowing and seasonal characteristics among the sites (Table 1). Thispattern was also found for the time required for the fruits to ripen;for the 2010/2011 season, a delay in ripening of 2.0 d at 20 ◦C wasobserved in comparison with the 2008/2009 and 2009/2010 sea-sons (Table 2). Moreover, there were large differences in peel colorchange. The mean percentage of fruits with a color between 3 and5 for the 2008/2009 season was 46.8%, though it was less than 1%for the 2010/2011 season (Table 2).

3.2. Relationship between preharvest parameters andpostharvest fruit quality attributes

The principal component (PC) analysis performed using datafor 35 preharvest and postharvest variables obtained from 42

experimental sites in three consecutive growing seasons (126observations per variable) showed that 4 PCs could explain 47%of the total variability, with 9 PCs explaining 72.0%. However, onlythe analyses of PC1 (15.4%) and PC2 (13.8%) are shown in Fig. 1. In

S.A. Rivera et al. / Scientia Horticulturae 216 (2017) 29–37 33

Table 2Descriptive analysis including the mean and coefficient of variation of the quality and ripening indicators of ‘Hass’ avocado determined using 42 experimental sites locatedin central Chile for the 2008/2009, 2009/2010 and 2010/2011 seasons.

Fruit quality and postharvest ripening behavior Season

2008/2009 2009/2010 2010/2011

mean CV (%) mean CV (%) mean CV (%)

Dry matter content (%) 26.1 9.1 26.4 8.7 24.8 10.6Harvest fruit firmness (N) 278.8 6.8 273.8 6.8 250.8 7.6Softening rate (N d−1) 6.1 29.1 4.7 28.1 2.6 78.5Peel color change (%) 46.8 71.2 17.2 115.9 0.66 363.3Days to ready to eat stage (d) 2.5 52.1 2.6 18.1 4.6 36.5

F is: 29v s (126

tmtnomscbtaiaFsdrdepc

ig. 1. Loading plot of the first two principal components (biplot PC1 and PC2 axariables obtained from 42 experimental sites in three consecutive growing season

he loading plot, PC1 ordered the variables into two groups, and theost influential variables for PC1 are shown on both extremes of

he loading plot (Fig. 1). On the extreme left side are the fruit mag-esium content, seasonal mean relative humidity, and fruit age;n the opposite side (extreme right side) of PC1 are the seasonalean maximum temperature, fruit potassium/magnesium ratio,

eason degree days, fruit firmness at harvest, and fruit potassiumontent. PC2 ordered the most influential variables at the top andottom of the loading plot (Fig. 1); the number of plants per hectare,ime at 20 ◦C to ripen, and planting slope are shown at the top,nd the trunk diameter, fruit nitrogen/calcium ratio, fruit soften-ng rate during cold storage, change in fruit color during storagend planting age are shown at the bottom (negative coordinate).rom this analysis, it was possible to observe significant relation-hips between the preharvest variables. The fruit age (number ofays between fruit set and harvest) showed a significant inverseelationship with the planting altitude (r = −0.53; p < 0.0001) and

egree day (r = −0.42; p < 0.0001). Additionally, the trunk diam-ter showed a significant relationship with the number of fruitser plant (r = 0.31; p = 0.0001), and the fruit weight was positivelyorrelated with the leaf area index (r = 0.24; p = 0.007).

.2%) obtained by Principal Component Analysis of 35 preharvest and postharvest observations). See Table 1 for the definitions of the abbreviations.

Regarding the relationship between preharvest variables andfruit-ripening indicators, multivariate analysis showed that theseasonal mean minimum air temperature, seasonal degree days,trunk diameter and fruit firmness at harvest exhibited a propor-tional relationship with the postharvest softening rate (SOFTRATE)and change in peel color during storage (COLOR35) and an inverseand significant relationship with the days required to reach theready-to-eat stage (RTE35) (Fig. 1 and Table 3). In contrast, the leafarea index, number of plants per hectare, and irrigation manage-ment at the bloom stage showed a proportional relationship withRTE35 and an inverse relationship with SOFTRATE and COLOR35.(Fig. 1 and Table 3). Moreover, Fig. 2 shows the Partial Least SquaresRegression (PLS) analysis modeling of the effect of preharvestparameters on postharvest fruit-ripening indicators during storage.The PLS model explained 53.5% of the total variability of the fruit-ripening indicators (R2Y), and cumulative, overall cross-validatedQ2 was 45.8%. This analysis indicated that the seasonal mean min-

imum air temperature (TMIN), degree days (DD13), fruit firmnessat harvest (HFIRM), dry matter content (DM), irrigation manage-ment at bloom (BLH2O), leaf area index (LAI), number of plantsper hectare (PLANTHECT), and fruit calcium content (FCa) were

34 S.A. Rivera et al. / Scientia Horticulturae 216 (2017) 29–37

Table 3Spearman correlation coefficients between 35 variables (climate/environment, plant nutrition, planting characterization, irrigation management, physiology and production,fruit quality and postharvest ripening indicators) and softening rate during cold storage, fruit external color after 35 d at 5 ◦C and days at 20 ◦C to reach to the ready-to-eatstage in ‘Hass’ avocados obtained from 3 seasons at 42 experimental sites in central Chile.

Softening rate (N d−1) Peel color change (%) Days to ready to eat (d)

ra P r P r P

Climate/environmentDegree Days 0.34 *** 0.30 *** −0.34 ***

Seasonal mean maximum temperature (◦C) 0.12 n.s.b 0.05 n.s. −0.07 n.s.Seasonal mean minimum temperature (◦C) 0.41 **** 0.48 **** −0.45 ****

Seasonal mean relative humidity (%) −0.07 n.s. 0.04 n.s. −0.02 n.s.Seasonal mean solar radiation (W m-2) −0.01 n.s. −0.04 n.s. −0.09 n.s.

Plant NutritionFruit nitrogen content (g kg-1) 0.04 n.s. 0.01 n.s. −0.05 n.s.Fruit potassium content (g kg-1) 0.24 ** 0.10 n.s. −0.16 n.s.Fruit calcium content (g kg-1) −0.19 * −0.04 n.s. 0.15 n.s.Fruit magnesium content (g kg-1) −0.14 n.s −0.06 n.s. 0.15 n.s.Fruit boron (mg kg-1) 0.005 n.s. 0.09 n.s. −0.07 n.s.Fruit nitrogen/calcium (ratio) 0.18 n.s. 0.06 n.s. −0.15 n.s.Fruit calcium/boron (ratio) −0.14 n.s. −0.14 n.s. 0.16 n.s.Fruit calcium/potassium (ratio) −0.24 ** −0.04 n.s. 0.18 n.s.Fruit potassium/magnesium (ratio) 0.19 * 0.07 n.s. −0.16 *

Leaf phosphorus (mg kg-1) 0.06 n.s. 0.01 n.s. −0.06 n.s.Leaf Zinc (mg kg−1) −0.05 n.s. −0.01 n.s. −0.06 n.s.

Planting characterizationPlantation altitude over sea level (m) −0.10 n.s. −0.15 n.s. 0.12 n.s.Planting slope (%) −0.10 n.s. −0.11 n.s. 0.13 n.s.Soil macroporus content (%) −0.08 n.s. −0.03 n.s. −0.03 n.s.Plant per hectares (count) −0.37 **** −0.36 *** 0.34 ****

Plant age (d) 0.18 n.s. 0.15 n.s. −0.12 n.s.

Irrigation managementIrrigation management at bloom (%) −0.31 *** −0.27 ** 0.22 *

Irrigation management at fruit set (%) −0.02 n.s. −0.09 n.s. 0.02 n.s.Irrigation management at fruit developing (%) 0.11 n.s. 0.06 n.s. −0.02 n.s.Season mean irrigation (%) 0.14 n.s. 0.07 n.s. −0.04 n.s.

Physiology and productionLeaf area index (m2 m−2) −0.23 * −0.31 *** 0.25 **

Mean trunk diameter (cm) 0.22 * 0.19 * −0.19 *

Fruit number per tree (count) −0.11 n.s. −0.10 n.s. 0.17 n.s.Mean fruit weight (g) −0.06 n.s. −0.10 n.s. −0.07 n.s.

Fruit qualityDry matter content (%) 0.30 *** 0.22 * −0.20 *

Fruit age (d) −0.08 n.s. −0.01 n.s. −0.03 n.s.Harvest fruit firmness (N) 0.50 **** 0.44 **** −0.44 ****

Postharvest ripening behaviorSoftening rate (N d-1) 1.0 0.72 **** −0.66 ****

Peel color change (%) 1.0 −0.69 ****

Days to ready to eat stage (d) 1.0

a r = Spearman correlation coefficient.b n.s. = not significant (P > 0.05).* = P < 0.05.

i(pwv

h

** = P < 0.01.*** = P < 0.001.

**** = P < 0.0001.

mportant variables influencing the overall fruit-ripening behaviorFig. 2). Finally, Ridge Regression considering the most significantreharvest variables (X) for each of the 3 ripening indicators (Y)as performed. The softening rate was fitted against the preharvest

ariables by the following significant regression model (R2 = 0.668):

SOFTRATE = −6.76 + 0.03 · HFIRM + 0.3 · TMIN + 0.001 · DD13 + 0.127 · DM−0.19 · LAI + 0.07 · FCa/K − 0.9 · BLH2O − 0.001 · PLANTHECT − 0.12 · Fk/Mg+1.62 · FK + 1.19 · FDH2O − 28.32 · FMg − 2.82 · FCa − 0.05 · MACROPORUS−0.005 · FRNUM − 0.008 · LZn + 0.033 · FN/Ca

In addition, the time at 20 ◦C to ripen was fitted against the pre-arvest variables by the significant regression model (R2 = 0.479).

RTE35 = 6.74 + 0.0008 · PLANTHECT + 0.117 · LAI − 0.235 · TMIN − 0.007 ·HFIRM − 0.0006 · DD13 + 0.891 · FCa/K − 0.022 · DM + 0.472 · BLH2O − 0.074 ·FK + 0.003 · FK/Mg + 0.0001 · PLANTAGE + 1.43 · FCa − 0.007 · TRUNKDIAM+2.6 · FMg − 0.011 · FN/Ca − 0.007 · SLOPE

Finally, the change in fruit skin color after 35 d at 5 ◦C was fittedagainst the preharvest variables by the following significant model(R2 = 0.654):

COLOR35 = −32.2 + 1.8 · TMIN + 0.04 · DD13 + 0.03 · PLANTHECT + 0.1 ·HFIRM − 12.0 · BLH2O − 1.2 · LAI + 64.5 · FCa/K + 0.06 · TRUNKDIAM + 0.04 ·FN/Ca − 0.002 · PLANTAGE − 0.42 · SLOPE + 0.05 · FB − 0.02 · ALT − 14.8 ·FSH2O + 30.9 · FDH2O + 5.1 · FK − 484.5 · FCa + 378.0 · FMg − 12.4 · FN

Considering these models, the softening rate, color change andtime at 20 ◦C to ripen were accurately and successfully predicted(Fig. 3).

S.A. Rivera et al. / Scientia Horticulturae 216 (2017) 29–37 35

Fig. 2. Loading plot obtained by a Partial Least Squares Regression analysis using 32 preharvest variables as predictors (triangle) and 3 postharvest variables as dependentvariables (square) obtained from 42 experimental sites in three consecutive growing seasons (366 observations). See Table 1 for the definitions of the abbreviations.

F e pre(

3f

iascsrthSa2a

ig. 3. Observed softening rate, color change, and time to ripening plotted against thB) and time to ripening (C). See Table 1 for the definitions of the abbreviations.

.3. Understanding the influence of preharvest variables onruit-ripening behavior

Softening behavior during storage has a very high impact dur-ng the value chain. Thus, the softening rate and time to ripeningre considered to be estimators of the timing of avocado con-umption maturity. Among the plant nutrient contents, the calciumontent and its relationships with other nutrients such as potas-ium and nitrogen significantly affected the postharvest softeningate and thereby the fruit firmness after 35 d of storage. The rela-ionship between the fruit calcium content and ripening behavioras been extensively studied in avocado (Witney et al., 1990;aucedo-Hernandez et al., 2005; Wills and Tirmazi, 1982) as well

s other fruit crops such as apple (Ortiz et al., 2011; Casero et al.,004), kiwifruit (Hopkirk et al., 1990), and pear (Gerasopoulosnd Richardson, 1997). The relevance of this relationship is due

dicted values obtained by regression models for the softening rate (A), color change

to the effect of Ca2+ on the stabilization of pectin components forstrengthening of the plant cell wall (Sams, 1999; Balic et al., 2014).Witney et al. (1990) showed a significant proportional relationship(r = 0.92; p = 0.01) between the fruit calcium concentration and daysto ripening in avocado. Moreover, postharvest treatment with cal-cium chloride infiltration in avocado fruits has been shown to beeffective at delaying the ripening process compared with untreatedfruit (Wills and Tirmazi, 1982; Yuen et al., 1994). It is generallyaccepted that calcium transport occurs via xylem and that thelargest uptake of calcium in avocado fruit occurs during the first7–8 weeks after fruit set (Bower, 1985). Therefore, as was showedby our results, irrigation management during the early stages offruit development could influence the calcium content of the fruit

(Bower, 1985). In relation to the nitrogen content, Arpaia et al.(1996) showed a significant reduction in the time to ripening asthe nitrogen leaf content increased. However, in our study, we did

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6 S.A. Rivera et al. / Scientia

ot observe a significant relationship between the nitrogen contentnd the softening rate.

The irrigation management applied at the flowering stagesSeptember in Chile) and the nutritional content of the fruit atarvest were also shown to be relevant variables affecting posthar-est ripening behavior. These results indicate the importance ofhe cultural practices during the early stages of fruit developmentor achieving a high postharvest fruit quality. Moreover, Blakeyt al. (2009) reported that the level of water stress in fruit couldnfluence postharvest ripening patterns due to differences in ABAoncentration among fruits. However, irrigation management dur-ng the growing season showed a positive relationship with the leafrea index (LAI) and inversely influenced the softening rate duringtorage. LAI could be an indirect estimator of the photosynthesisotential. In avocado, the non-structural seven-carbon sugars man-oheptulose and perseitol are synthesized during photosynthesis,nd these sugars have been postulated as being partly responsibleor inhibiting fruit ripening, largely because of the notable declinen the content of these sugars in avocado fruits during ripeningLiu et al., 2002). To a certain extent, a reduction in these seven-arbon sugars could result in the initiation of fruit ripening (Liut al., 2002). Moreover, Blakey et al. (2012) observed a proportionalorrelation between the concentrations of seven-carbon sugarsnd the postharvest fruit firmness, indicating that slow-ripeningvocados have higher concentrations of D-mannoheptulose thanast-ripening fruits. Conversely, Pedreschi et al. (2014) observed

non-significant correlation between total seven-carbon sugarsnd the number of days to ripening. These authors postulated thathe ripening heterogeneity observed among fruits could be betterorrelated with key metabolites from primary metabolism, suchs different amino acids and fatty acids, rather than seven-carbonugars.

The dry matter content has been accepted worldwide as anndicator of harvest maturity, and a minimum percentage of DM,tandardized for each avocado cultivar, will ensure a continuationf the ripening process after harvest (OECD, 2004). The percentagef dry matter is correlated strongly and proportionally with theercentage of oil in ‘Hass’ avocado (Lee et al., 1983). Moreover, theM content has been linked with the potential marketability and

ntention to buy avocado fruits (Ranney, 1991; Gamble et al., 2010).lakey et al. (2009) found that more mature fruits with a lowerater content ripened sooner than less mature fruits. In our study,M showed a significant (p < 0.05) and proportional relationshipith the softening rate, and the DM content was included in therediction models of softening rate and days to ripening (Table 2nd Fig. 3). However, this result is in contrast to that of Pedreschit al. (2014), who did not observe a significant relationship betweenhe dry matter content and days to ripening. Moreover, Hofmant al. (2000) observed a poor relationship between the DM con-ent and fruit quality in late-harvested avocados with a DM contentetween 28.8% and 31.4%. In our study, the observed relationshipetween DM and ripening patterns was constructed based on threeears of fruit sampling from 42 sites with the DM at harvest rangingetween 20.4% and 30.9%.

Environmental temperatures during the growing season cannfluence plant metabolism, affecting cellular structure and otheromponents related to fruit texture (Sams, 1999). The mean mini-um temperature and cumulative degree days during the growing

eason were found to proportionally affect the softening rate duringtorage (Fig. 2). The relationship between preharvest tempera-ures and fruit ripening has been studied in several fruit cropsuch as pears, apples and avocados (Villalobos-Acuna and Mitcham,

008; Woolf et al., 2000; Woolf and Ferguson, 2000). In pears,nvironmental growing conditions such as air temperature influ-nced the ethylene production rate and therefore fruit firmnessuring postharvest (Villalobos-Acuna and Mitcham, 2008). Agar

ulturae 216 (2017) 29–37

et al. (1999) observed that fruits harvested at earlier time pointsin warmer locations were firmer than fruits harvested at later timepoints in cooler locations, showing a higher ethylene productionrate during ripening. Although postharvest ethylene productionwas not assessed in our study, the softening rate showed aproportional relationship with the seasonal mean minimum airtemperature (Fig. 2). Therefore, these results appear to indicate adifferent pattern for the ripening of avocado fruits compared withpears. In studies using avocado fruits, differences in ripening behav-ior have been observed between fruits exposed and not exposed todirect sunlight, and these differences could be related to the tem-perature regime tested at each level of sunlight exposure (Woolfet al., 1999; 2000). Accordingly, Woolf et al. (2000) postulated thatsoftening rates follow an inverse pattern in relation to the peaktemperature experienced by the fruits during development. Fruittemperature was not measured in our study, but the air maximumtemperature ranged from 18.3 to 28.4 ◦C, corresponding to a similarvalue of air temperature in sun-exposed fruits with temperaturesover 35 ◦C reported by Woolf et al. (1999). In our study, there wasno discrimination between sun-exposed and shaded fruit on thetrees during the harvest, though the cumulative degree days and amean minimum temperature ranging between 5.5 and 13 ◦C appearto be more useful for determining the ripening behavior of ‘Hass’avocado.

The change in peel color from green to black was stronglyinfluenced by climate/environmental conditions; for example, thegrowing temperature, such as the seasonal minimum temperatureand cumulative degree days, were significant parameters in defin-ing the potential color change of ‘Hass’ avocado during cold storage.The relationship between avocado skin coloration and growingtemperature and other environmental stresses was previously pro-posed by Cox et al. (2004), who postulated the influence of thegrowing temperature on the anthocyanin (cyanidin-3-O-glucoside)and chlorophyll contents in fruit skin. The relationship betweengrowing temperature and anthocyanin biosynthesis in fruit skinshas been reviewed in other fruit crops such as grapes (Downeyet al., 2006) and apples (Ubi, 2004). In addition, variables relatedto plant vigor, photosynthesis, and sugar distribution balance havebeen related to anthocyanin biosynthesis in the fruit skin of cropssuch as grapes (Downey et al., 2006) and apples (Ubi, 2004). In ourstudy, variables such as LAI, irrigation management, and the fruitnitrogen content were significant for predicting the change in peelcolor during storage (Fig. 3).

4. Conclusions

Based on our findings, we can postulate that predicting thepostharvest behavior of ‘Hass’ avocado using a single preharvestvariable such as dry matter or calcium content could be a mis-leading simplification of reality because several factors, includingclimate/environment, agronomical management, and physiologi-cal variables, directly (e.g., fruit mineral content) or indirectly (e.g.,metabolites from photosynthesis process) influence the ripeningbehavior of ‘Hass’ avocado fruits. Our study showed that the cumu-lative degree days, dry matter content, fruit calcium content, fruitfirmness at harvest, irrigation management at bloom, mean min-imum temperature, and LAI are important variables significantlyinfluencing the overall ripening behavior of ‘Hass’ avocado fruits.Therefore, the next step will be to identify quantitative thresholds

for each preharvest variable affecting the attributes determin-ing quality, which would a major contribution to growers andexporters for guarantying an optimum and consistent avocado atconsumer’s level.

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Woolf, A.B., Wexler, A., Prusky, D., Kobiler, E., Lurie, S., 2000. Direct sunlightinfluence postharvest temperature responses and ripening of five avocadocultivars. J. Amer. Soc. Hort. Sci. 125, 370–376.

Yuen, C.M.C., Caffin, N., Boonyakiat, D., 1994. Effect of calcium infiltration on

S.A. Rivera et al. / Scientia

cknowledgments

This study was financially supported by Innova Project11CEII-568, and Conicyt/Fondecyt Regular1130107.

eferences

gar, T., Biasi, W.V., Mitcham, E.J., 1999. Exogenous ethylene accelerates ripeningresponses in Bartlett pears regardless of maturity or growing region.Postharvest Biol. Technol. 17, 67–78.

rpaia, M.L., Meyer, J.L., Witney, G.W., Bender, G.S., Stottlemeyer, D.S., Robinson,P.R., 1996. The Chasin Creek Nitrogen Fertilizer Trial –What Did We Learn?, 80.California Avocado Society, pp. 85–98 (1996 Yearbook).

rpaia, M.L., 1994. Preharvest factors influencing postharvest quality of tropicaland subtropical fruit. HortScience 29, 982–985.

alic, I., Ejsmentewicz, T., Sanhueza, D., Silva, C., Peredo, T., Olmedo, P., Barros, M.,Verdonk, J.C., Paredes, R., Meneses, C., Prieto, H., Orellana, A., Defilippi, B.G.,Campos-Vargas, R., 2014. Biochemical and physiological study of the firmnessof table grape berries. Postharvest Biol. Technol. 93, 15–23.

all, B., Smith, K., 1991. Gas movement. In: Smith, K., Mullins, Ch. (Eds.), SoilAnalysis. Marcel Dekker, New York, USA, pp. 511–550.

allabio, D., Mauri, A., Todeschini, R., Buratti, S., 2006. Geographical classificationof wine and olive oil by means of classification and influence matrix analysis(CAIMAN). Anal. Chim. Acta 570, 249–258.

lakey, R.J., Bower, J.P., Bertlin, g.I., 2009. Influence of water and ABA supply on theripening pattern of avocado (Persea americana Mill.) fruit and the prediction ofwater content using Near Infrared Spectroscopy. Postharvest Biol. Technol. 53,72–76.

lakey, R.J., Tesfay, S.Z., Bertling, I., Bower, J.P., 2012. Changes in sugars: totalprotein and oil in ‘Hass’ avocado (Persea americana Mill.) fruit during ripening.J. Hortic. Sci. Biotechnol. 87, 381–387.

ower, J.P., 1985. The calcium accumulation pattern in avocado fruit as influencedby long-term irrigation regime. South Afr. Avocado Grow. Assoc. Yearb. 8,97–99.

ower, J.P., 1988. Pre and postharvest measures for long-term storage of avocados.S. Afr. Avocado Grow. Assoc. Yearb. 11, 68–72.

rereton, R., 2009. Chemometrics for Pattern Recognition. John Wiley and Sons,Bristol, UK, pp. 522.

asero, T., Benavides, A., Puy, J., Recasens, I., 2004. Relationship between leaf andfruit nutrients and fruit quality attributes in golden smoothee apples usingmultivariate regression techniques. J. Plant Nutr. 27, 313–324.

en, H., He, Y., Huang, M., 2007. Combination and comparison of multivariateanalysis for the identification of oranges varieties using visible and nearinfrared reflectance spectroscopy. Eur. Food Res. Technol. 225, 699–705.

ox, K.A., McGhie, T.K., White, A., Woolf, A.B., 2004. Skin colour and pigmentchanges during ripening of Hass avocado fruit. Postharvest Biol. Technol. 31,287–294.

owney, M.O., Dokoozlian, N.K., Krstic, M.P., 2006. Cultural practice andenvironmental impacts on the flavonoid composition of grapes and wine: areview of recent research. Am. J. Enol. Vitic. 57, 257–268.

riksson, L., Johansson, E., Kettaneh-Wold, N., Trygg, J., Wikstrom, C., Wold, S.,2006. Multi and Megavariate Data Analysis, Part I: Basic Principles andApplications, 2th ed. Umetrics AB, Sweeden (pp, 425).

amble, J., Harker, F.R., Jaeger, S.R., White, A., Bava, C., Beresford, M., Stubbings, B.,Wohlers, M., Hofman, P.J., Marques, R., Woolf, A.B., 2010. The impact of drymatter, ripeness and internal defects on consumer perceptions of avocadoquality and intentions to purchase. Postharvest Biol. Technol. 57, 35–43.

erasopoulos, D., Richardson, D.G., 1997. Fruit maturity and calcium affect chillingrequirements and ripening of ‘Dànjou’ pears. HortScience 32, 911–913.

ofman, P., Jobin-Décor, M., Giles, J., 2000. Percentage of dry matter and oilcontent are not reliable indicators of fruit maturity or quality in late-harvested‘Hass’ avocado. Postharvest Biol. Technol. 35, 694–695.

opkirk, G., Harker, F.R., Harman, J.E., 1990. Calcium and the firmness of kiwifruit.N. Z. J. Crop Hort. Sci. 18, 215–219.

ruger, U., Xie, L., 2012. Advances in Statistical Monitoring of ComplexMultivariate Process: with Applications in Industrial Process Control. JohnWiley & Sons, Ltd., United Kingdom.

ulturae 216 (2017) 29–37 37

Lee, S.K., Young, R.E., Schiffman, P.M., Coggings, C.W., 1983. Maturity studies ofavocado fruit based on picking dates and dry weight. J. Amer. Soc. Hort. Sci.108, 390–394.

Liu, X., Sievert, J., Arpaia, M.L., Madore, M., 2002. Postulated physiological roles ofthe seven?carbon sugars, mannoheptulose, and perseitol in avocado. J. Amer.Soc. Hort. Sci 127, 108–114.

Magwaza, L.S., Tesfay, S.Z., 2015. A review of destructive and non-destructivemethods for determining avocado fruit maturity. Food Bioprocess Technol. 8,1995–2011.

Organization for Economic Co-operation and Development (OECD), 2004.International Standardization of Fruit and Vegetables, Avocados. http://www.oecd.org/tad/code/46590985.pdf.

Ortiz, A., Graell, J., Lara, I., 2011. Preharvest calcium applications inhibit some cellwall-modifying enzyme activities and delay cell wall disassembly atcommercial harvest of ‘Fuji Kiku-8’ apples. Postharvest Biol. Technol. 62,161–167.

Pedreschi, R., Munoz, P., Robledo, P., Becerra, C., Defilippi, B.G., van Eekelen, H.,Mumm, R., Westra, E., de Vos, R. Ch., 2014. Metabolomics analysis ofpostharvest ripening heterogeneity of Hass Avocadoes. Postharvest Biol.Technol. 92, 172–179.

Ranney, C., 1991. Relationship between physiological maturity and percent drymatter of avocados. California Avocado Soc.Yearb. 75, 71–85.

Ryan, T., 2009. Modern Regression Methods. John Wiley and Sons, Inc, NY, USA(672 pp.).

Saavedra, J., Cordova, A., 2011. Multivariate process control by transition scheme insoft-drink process using 3-way PLS approach. Procedia Food Sci. 1, 1181–1187.

Saavedra, J., Córdova, A., Gálvez, L., Quezada, C., Navarro, R., 2013. Principalcomponent analysis as an exploration tool for kinetic modeling of food quality:a case study of a dried apple cluster snack. J. Food Eng. 119, 229–235.

Sadzawka, A., Carrasco, M., Demanet, R., Flores, H., Grez, R., Mora, M., Neaman, A.,2007. Métodos De Análisis De Tejidos Vegetales, Segunda Edición. Instituto deInvestigaciones Agropecuarias (INIA) La Platina, Santiago, Chile, pp, 140 (inSpanish).

Sams, C.E., 1999. Preharvest factors affecting postharvest texture. Postharvest Biol.Technol. 15, 249–254.

Saucedo-Hernandez, L., Martinez-Damián, M.T., Colinas-León, M.T.,Barrientos-Priego, A.F., Aguilar-Melchor, J.J., 2005. Calcium nitrate foliar spraysin the ripening and chilling injury of ‘Fuerte’ avocado. Rev. Chapingo SerieHortic. 11, 149–157 (In Spanish).

Thorp, T.G., Hutching, D., Lowe, T., Marsh, K.B., 1997. Survey of fruit mineralconcentrations and postharvest quality of New Zealand-grown Hass avocado(Persea americana Mill.). N. Z. J. Crop Hort. Sci. 25, 251–260.

Ubi, B., 2004. External stimulation of anthocyanin biosynthesis in apple fruit. FoodAgric. Environ. 2, 65–70.

Villalobos-Acuna, M.G., Mitcham, E.J., 2008. Ripening of European pears: thechilling dilemma. Postharvest Biol. Technol. 49, 187–200.

Vorster, L., Bezuidenhout, J.J., 1988. Does zinc play role in reducing pulp spot? S.Afr. Avocado Grow. Assoc. Yearb. 11, 60.

Wills, R.B.H., Tirmazi, D.I.H., 1982. Inhibition of ripening of avocados with calcium.Sci. Hort. 16, 323–330.

Witney, G.W., Hofman, P.J., Wolstenholme, B.N., 1990. Effect of cultivar, tree vigourand fruit position on calcium accumulation in avocado fruits. Sci. Hort. 44,269–278.

Wold, S., Sjödtröm, M., Eriksson, L., 2001. PLS-regression: a basic tool ofchemometrics. Chemom. Intell. Lab. Syst. 58, 109–130.

Woolf, A.B., Ferguson, I.B., 2000. Postharvest responses to high fruit temperaturesin the field. Postharvest Biol. Technol. 21, 7–20.

Woolf, A.B., Fergunson, I.B., Requejo-Tapia, L., Boyd, L., Laing, W.A., White, A., 1999.Impact of sun exposure on harvest quality of Hass avocado fruit. RevistaChapingo Serie Horticultura 5, 353–358.

ripening of avocados of different maturities. Aust. J. Exp. Agric. 34, 123–126.