Identification of preharvest factors determining postharvest ripening behaviors in ‘Hass’ avocado under long term storage

Scientia Horticulturae 216 (2017) 29–37

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Identification of preharvest factors determining postharvest ripening behaviors in ‘Hass’ avocado under long term storage Sebastián A. Rivera a , Raúl Ferreyra b , Paula Robledo a , Gabriel Selles b , Mary Lu Arpaia c , Jorge Saavedra d , Bruno G. Defilippi a,∗ a

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

a r t i c l e

i n f o

Article history: Received 17 August 2016 Accepted 23 December 2016 Keywords: Fruit variability Dry mater content Fruit quality Multivariate 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 fruitripening behavior, especially considering the significant variability in quality that can be found within a box or pallet of the fruit. The broad range of conditions under which trees are grown, particularly with regard to climate, soil and agronomical management, can influence this ripening variability. The aims of this study were (i) to determine the variability in fruit ripening among ‘Hass’ avocado grown under 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 were evaluated at 42 experimental sites in Chile during three consecutive seasons. In addition, avocados with over 21% dry matter were collected at each site during each season and stored for 35 d at 5 ◦ C under normal atmospheric conditions before being ripened at 20 ◦ C. Indicators of ripening behavior, such as the softening rate (SOFTRATE), change in peel color (COLO35) and days at 20 ◦ C necessary to reach the readyto-eat stage (RTE35), were evaluated. As expected, high fruit variability in terms of ripening behavior was observed among the experimental sites and seasons. Multivariate analysis showed that the seasonal mean minimum air temperature, seasonal degree-days, trunk diameter and fruit firmness at harvest had a proportional relationship with postharvest SOFTRATE and COLOR35 during storage and a significant inverse relationship with RTE35. Conversely, the leaf area index, number of plants per hectare, and irrigation management at the bloom stage had a proportional relationship with RTE35 and an inverse relationship with SOFTRATE and COLOR35. Moreover, all of the three postharvest ripening behavior indicators were 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 a misleading simplification of reality because several factors, including climate/environmental, agronomic management and physiological variables, influence the ripening behavior of ‘Hass’ avocado fruit. © 2016 Elsevier B.V. All rights reserved.

1. Introduction Chile is an important worldwide producer of avocado, exporting nearly 60% of production, mainly to Europe and the U.S.A. Avocado cv ‘Hass’ is the most important cultivar in Chile comprising more than 90% of the fresh avocado exports and reaching 133,415 t in the 2013–2014 export season (www.asoex.com). However, the increased production both in Chile (39,303 ha) and in other avo-

∗ Corresponding author at: Santa Rosa 11610, La Pintana, Santiago, Chile. E-mail address: bdefi[email protected] (B.G. Defilippi). http://dx.doi.org/10.1016/j.scienta.2016.12.024 0304-4238/© 2016 Elsevier B.V. All rights reserved.

cado exporting countries such as Mexico and Peru may result in a price reduction due to simultaneous high market volumes. Therefore, an important issue to be considered is arrival at market of a high-quality product even after several days of shipping and storage (longer than 30–40 d). Fruit softening during cold storage is considered to be an indicator of postharvest ripening behavior in avocado 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 avocado consumers can easily discriminate a soft, ready-to-eat fruit from an unripe fruit, a major challenge for the Chilean industry is

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

to provide a homogenous product in terms of quality and ripening attributes. However, high variability in terms of fruit origin, quality and ripeness can be found within a pallet of the fruit, a situation that is critical because it complicates prediction of the postharvest shelf life and the time necessary for ripening. Indeed, prediction of ripening behavior at harvest is considered to be a major challenge because both exogenous and endogenous factors affect fruit ripening (Blakey et al., 2009). For example, a proportion of the observed variability in fruit quality and ripening is due to the broad range of conditions in which the trees are grown, especially with respect to environmental factors such as the temperature during the growing season (Arpaia, 1994; Sams, 1999: Woolf and Ferguson, 2000) and level of sun exposure (Woolf et al., 1999; Woolf et al., 2000). In addition, fruit-ripening behavior and postharvest quality can be associated with different cultural practices, including irrigation management (Bower, 1988), and also with the composition of macronutrients and micronutrients, such as calcium (Witney et al., 1990; Thorp et al., 1997), nitrogen (Arpaia et al., 1996) and zinc (Vorster and Bezuidenhout, 1988), within the fruit. Furthermore, some metabolites of photosynthesis, such as the seven-carbon sugars mannoheptulose and perseitol, have been linked to the ripening process (Liu et al., 2002; Blakey et al., 2012). It is generally recognized that the dry matter content can be used as a maturity indicator of fruit at harvest (OECD, 2004). However, we suggest that utilization of the dry matter concentration for predicting fruit-ripening behavior must be accompanied by other preharvest variables. Moreover, the combined effect of several preharvest variables on postharvest fruit-ripening behavior has not yet been reported. Therefore, this study was conducted (i) to determine the variability in fruit ripening among different ‘Hass’ avocado growing conditions in Chile and (ii) to understand the postharvest ripening behaviors of avocados stored under regular air due to the combined effect of several preharvest variables (i.e., climate/environment, planting design, plant nutrition, irrigation management and plant physiology and production parameters). 2. Material and methods 2.1. Experimental sites and fruit material During three consecutive seasons (2008/2009; 2009/2010; and 2010/2011), ‘Hass’ avocados were harvested from 42 experimental sites of 1000 m2 located in central Chile; the trees were grown under different climate, planting design and agricultural management conditions. Of these sites, twenty are located in the Aconcagua Valley, six in the Maipo Valley and sixteen in La Ligua Valley. Green fruits of homogenous size that were free of external defects were harvested from each experimental site based on the dry matter (DM) content, considering fruit with a DM ≥ 20.0%. After harvest, the fruit was transported to the Postharvest Unit on the same day for storage at 4.8 ± 0.4 ◦ C for 35 d under regular air (0.04% CO2 and 21.0% O2 ) and 91 ± 5% RH. After storage, the fruit was exposed to a temperature of 20 ◦ C (19 ± 0.5 ◦ C) and an RH of 35 ± 4% (shelf life) until ripening.

radiation per day (SOLARAD) were calculated using data obtained from experimental meteorological stations (Decagon Devices Inc., WA) located at each of the 42 experimental sites. The meteorological stations were equipped with a five-channel data logger (model Em50, Decagon Devices Inc.) that recorded weather conditions every 60 min. Degree days were calculated using a minimum threshold temperature of 13 ◦ C. 2.4. Plant nutrition The fruit nitrogen (FN), potassium (FK), calcium (FCa), magnesium (FMg), and boron (FB) contents and the fruit nitrogen/calcium (FN/Ca), calcium/boron (FCa/B), calcium/potassium (FCa/K), and potassium/magnesium (FK/Mg) ratios were estimated from 3 subsamples of 5 fruits at harvest maturity per experimental site and season. In addition, the leaf phosphorus (LP) and zinc (LZn) contents were estimated using 3 sub-samples of 60–80 fully mature and expanded leaves for each experimental site and season. Spring leaves that were 5–6 months old were obtained from summergrowth branches that had stopped developing. Fruit and leaf sampling was performed randomly from 6 trees per experimental site. The nutrient contents of the fruits and leaves were analyzed according to the methodologies proposed by Sadzawka et al. (2007). The nitrogen content was analyzed using the standard Kjeldahl method (Vapodest 505 Gerhardt, Germany). Calcium, magnesium and zinc were estimated by atomic absorption spectrophotometry (Analist 200, Perkin Elmer, CA), and potassium was estimated by 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), macroporous content in the first soil horizon (MACROPOROUS), plants per hectare (PLANTHECT) and plant age (PLANTAGE) were determined at each of the 42 experimental sites. The planting slope was calculated using a global positioning system (Nuvi 205, Garmin, KS), and the macroporous content was calculated following the methodology proposed by Ball and Smith (1991). 2.6. Plant physiology and production parameters The trunk diameter (TRUNKDIAM), leaf area index (LAI), number of fruits per tree (FRNUM) and individual fruit weight (FRUITWEIGHT) were determined using six healthy, productive trees per experimental site. The trunk diameter was measured for the rootstock using a digital caliper (Mitutoyo, Japan) at 10 cm below the graft during the fruit-set stage. The LAI was estimated during the fruit-set stage on the basis of the photosynthetic active radiation (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 for each of the six trees per experimental site.

2.2. Determination of preharvest and postharvest variables

2.7. Irrigation management

In total, 33 preharvest variables and 3 postharvest variables were assessed during the three consecutive seasons (2008/2009; 2009/2010; and 2010/2011) at the 42 experimental sites.

The percentage of applied water in relation to crop evapotranspiration (ETc) at the bloom (BLH2O), fruit-set (FSH2O), and fruit-development (FDH2O) stages were calculated based on the applied water determined using a volumetric meter installed with the irrigation equipment at each experimental site. In addition, the amount of precipitation obtained from the experimental meteorological stations was considered when estimating the applied water. ETc was calculated using LAI at different fruit stages, and the reference evapotranspiration (ET0) was calculated from

2.3. Climate/environment The degree days (DD13), mean seasonal maximum air temperature (TMAX), mean seasonal minimum air temperature (TMIN), mean seasonal relative humidity (RH), and mean seasonal solar

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

the Penman-Monteith equation using data obtained from the experimental meteorological stations. Finally, the mean irrigation percentage (MEANH20) was determined considering the monthly irrigation management for each season. 2.8. Fruit quality at harvest From each experimental site, 3 sub-samples of 20 fruits were used for quality determination at harvest, considering fruit firmness at harvest (HFIRM), dry matter content (DM) and fruit age (FRUITAGE). The flesh firmness was measured using a penetrometer (Effegi, Milan, Italy) equipped with a 4 mm plunger tip. The fruit age was determined considering the number of days from fruit set until harvest. The dry matter content was estimated by oven drying, as follows: half of each vertically sliced fruit was peeled, the seed coat was removed, and the flesh was chopped and weighed; each sample was dried for 24 h at 103 ◦ C until a constant weight was reached. 2.9. Postharvest ripening Postharvest ripening indicators were determined in 3 subsamples of 20 fruits harvested from six healthy trees per experimental site. The ripening parameters were measured during cold storage at 5 ◦ C and during exposure at 20 ◦ C until each fruit ripened. The fruit-softening rate (SOFTRATE) and change in peel color (COLOR35) were measured during 35 d of storage at 5 ◦ C. The softening rate was calculated using the flesh firmness at harvest and during storage, as follows: Softeningrate =

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

The peel color was assessed visually using a hedonic scale with scores from 1 to 5: 1 = green over 100% of the peel surface; 2 = 20% colored black/purple on green; 3 = 60% colored black/purple on green; 4 = purple over 100% of the peel surface; 5 = black over 100% of the peel surface. The percentage of fruit with peel color change from green to black was calculated considering the percentage of fruits with a score between 3 and 5 at the end of the storage period. The number of days at 20 ◦ C necessary to reach the ripe stage after 35 d of cold storage (RTE35) was measured for 3 sub-samples of 20 fruits for each of the 42 experimental sites after 35 d at 5 ◦ C and the 20 ◦ C incubation period until a fruit firmness between 4.0 and 8.0 N was reached. Additionally, external and internal fruit physiological disorders and other damage were assessed at the ripe stage using hedonic scales from 1 to 5: 1 = no occurrence; 2 = slight damage; 3 = moderate damage; 4 = moderately severe damage; 5 = severe damage. 2.10. Data management and statistical analysis To determine the variability in preharvest parameters and fruit quality attributes, the data collected over the three sampling years were analyzed using an exploratory descriptive analysis, and the coefficient of variation (CV) was calculated as follows: CV =

Standard Deviation x100 Mean

Multivariate analysis was performed to study the relationship between the 32 preharvest and 3 postharvest ripening variables. Because of the importance of multivariate measurements in chemistry, Principal Component Analysis (PCA) is likely the most widespread multivariate chemometric technique, and it is certainly the most widespread technique used in multivariate exploratory data analyses (Brereton, 2009). PCA is an unsupervised exploratory tool that processes a matrix array, such as variables per case that can

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display the main variations between samples and sample groups and the relationships between samples and variables in orthogonal planes 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, by identifying relationships between different observations that comprise the scores matrix; and second, by determining relationships between different variables of the data set, known as the loadings matrix (Saavedra and Cordova, 2011). The axes derived from the analysis represent the maximum variance directions, with the first principal component (PC1) located along the direction of maximum variance of the data set, the second component (PC2) disposed along the direction of the second greatest variance, and so on. All PCs are simultaneously orthogonal to each other, and there is no covariance among them. In addition, the line projected by each PC is the best fit to all points simultaneously through least-squares optimization (Eriksson et al., 2006; Saavedra et al., 2013). In this study, PCA was performed considering the 35 variables obtained from the 42 experimental sites during the three consecutive seasons. The data was previously standardized for the PCA analysis. Partial Least Squares Regression (PLS), a latent structure projection method using partial least squares, i.e., a multivariate analysis, was performed to model the effect of preharvest parameters on postharvest fruit-ripening indicators. This multivariate linear regression method predicts one set of data from another and treats both separately (Ballabio et al., 2006), and the goal of PLS is to search for directions that maximize the covariance between the matrix of predictor variables (Z) and the matrix of response variables (Y). PLS relies on analysis of two variable sets that represent the predictor and response variable set; a linear parametric relationship exists between these variable sets (Kruger and Xie, 2012). The multivariate analyses were based on the nonlinear iterative partial least squares (NIPALS) algorithm (Wold et al., 2001). All data were centered and scaled prior to the analysis and validated by a full cross-validation routine, minimizing the predicted residual sum of squares function (PRESS) to avoid overfitting of the models (Cen et al., 2007). Additionally, based on this analysis, a correlation matrix was performed considering the relationships between each of the 42 preharvest and postharvest variables, and the Spearman correlation coefficient for each relationship was calculated. The postharvest ripening attributes (SOFTRATE, COLOR35 and RTE35) were used as dependent variables, and the 33 preharvest variables were used as predictor variables. Finally, a Ridge Regression Analysis (RRA) was performed. RRA is an alternative to ordinary least squares and is one of several biased regression estimators proposed in the literature. It is an alternative to deleting the regressor due to the presence of collinearity or multicollinearity (Ryan, 2009). The statistical analyses were performed with the software InfoStat (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 postharvest ripening behavior The descriptive analysis showed wide variability in the preharvest parameters and postharvest ripening behavior among the 42 planting sites and the three sampling seasons (Table 1). The planting slope (CV = 83.6%), leaf area index (CV = 82.7%), leaf zinc content (CV = 75.9%), fruit calcium/potassium ratio (CV = 73.6%), fruit calcium/boron ratio (CV = 65.7%), altitude over sea level (CV = 62.8%), fruit boron content (CV = 55.8%) and irrigation management at the bloom stage (CV = 53.3%) showed the highest coefficients of variation among the preharvest variables. Therefore, the observed

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

Abbreviation

Units

Mean

Min

Max

CV

Climate/environment Seasonal cumulative degree days Seasonal mean maximum temperature Seasonal mean minimum temperature Seasonal mean relative humidity Seasonal mean solar radiation per day

DD13 TMAX TMIN RH SOLARAD

Degree day ◦ C ◦ C % W m−2

984.0 23.4 7.9 72.5 376.6

91.3 18.3 5.5 62.0 325.0

2,318.0 28.4 13.0 85.0 410.0

45.7 8.9 17.9 8.9 5.6

Plant nutrition Fruit nitrogen content Fruit potassium content Fruit calcium content Fruit magnesium content Fruit boron Fruit nitrogen/calcium Fruit calcium/boron Fruit calcium/potassium Fruit potassium/magnesium Leaf phosphorus Leaf zinc

FN FK FCa FMg FB FN/Ca FCa/B FCa/K FK/Mg LP LZn

g kg−1 g kg−1 g kg−1 g kg−1 mg kg−1 ratio ratio ratio ratio mg kg−1 mg kg−1

1.1 1.9 0.06 0.09 70.2 21.0 1.2E-3 0.04 23.8 0.14 41.1

0.59 1.0 0.02 0.05 12.7 8.3 2.3E-4 0.01 7.4 0.09 15.0

2.0 3.2 0.10 0.15 185.3 60.6 3.9E-3 0.3 43.7 0.21 200.0

22.0 21.9 24.9 23.2 55.8 41.7 65.7 73.6 43.7 19.1 75.9

Planting characterization Altitude over sea level Planting slope Soil macroporous content Plants per hectare Plant age

ALT SLOPE MACROPOROUS PLANTHECT PLANTAGE

m % % count d

417.0 12.0 17.7 560.3 3,357.8

89.3 0 9.2 143.0 1095

1103.0 45.5 40.3 1111.0 12,775

62.8 83.6 34.0 45.2 48.3

Irrigation management Irrigation management at bloom Irrigation management at fruit set Irrigation management during fruit development Seasonal mean irrigation management

BLH2O FSH2O FDH2O MEANH2O

% % % %

68.0 88.0 108.0 95.0

0.0 32.0 0.0 43.0

186 160.0 256.0 157.0

53.3 29.7 38.2 26.7

Physiology and production Leaf area index Trunk diameter Number of fruits per tree Mean fruit weight

LAI TRUNKDIAM FRNUM FWEIGHT

m2 m−2 mm count g

2.6 64.6 122.2 196.5

0.7 27.7 50 126.6

10.3 149.3 266.7 299.9

82.7 33.8 39.6 15.5

Fruit quality Dry matter content Harvest fruit firmness Fruit age

DM HFIRM FRUITAGE

% N d

25.8 265.9 268.1

20.4 185.8 198.9

30.9 337.5 332.5

9.7 8.8 8.3

Postharvest ripening behavior Softening rate Peel color change Days to ripening

SOFTRATE COLOR35 RTE35

N d−1 % d

4.5 22.1 3.2

0.21 0.0 0.7

8.1 100.0 10.1

48.8 134.0 48.3

variability in planting altitude and slope indicate important differences in relation to planting design among avocado growers in Chile. Furthermore, parameters such as irrigation management, nutrient content and leaf area index demonstrated important differences in agronomic management and physiological response among planting sites and seasons. Unlike those parameters, the solar radiation per day (CV = 5.6%), seasonal mean maximum temperature (CV = 8.9%), seasonal mean relative humidity (CV = 8.9%) and seasonal mean minimum temperature (CV = 17.9%) exhibited the lowest coefficients of variation. Regarding fruit-ripening behavior, among all variables, the change in peel color (CV = 134.0%) showed the highest coefficient of variation among sampling sites (Table 1). Indeed, due to difference in color among fruits in a box or pallet at the destination country, ‘Hass’ avocados from Chile are described as having a ‘checkerboard appearance’. Despite a lack of major differences in fruit firmness at harvest among sites and seasons, the variability in the fruit-softening rate during cold storage differed considerably (Table 2). Moreover, storage conditions were the same among seasons in terms of storage temperature, relative humidity and CO2 and O2 concentrations; however, the variability in the softening rate differed among seasons, exhibiting a mean and CV of

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 rate during storage could be derived from the differences observed in growing and seasonal characteristics among the sites (Table 1). This pattern 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 was observed in comparison with the 2008/2009 and 2009/2010 seasons (Table 2). Moreover, there were large differences in peel color change. The mean percentage of fruits with a color between 3 and 5 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 and postharvest fruit quality attributes The principal component (PC) analysis performed using data for 35 preharvest and postharvest variables obtained from 42 experimental sites in three consecutive growing seasons (126 observations per variable) showed that 4 PCs could explain 47% of the total variability, with 9 PCs explaining 72.0%. However, only the 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

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

Season 2008/2009

Dry matter content (%) Harvest fruit firmness (N) Softening rate (N d−1 ) Peel color change (%) Days to ready to eat stage (d)

2009/2010

2010/2011

mean

CV (%)

mean

CV (%)

mean

CV (%)

26.1 278.8 6.1 46.8 2.5

9.1 6.8 29.1 71.2 52.1

26.4 273.8 4.7 17.2 2.6

8.7 6.8 28.1 115.9 18.1

24.8 250.8 2.6 0.66 4.6

10.6 7.6 78.5 363.3 36.5

Fig. 1. Loading plot of the first two principal components (biplot PC1 and PC2 axis: 29.2%) obtained by Principal Component Analysis of 35 preharvest and postharvest variables obtained from 42 experimental sites in three consecutive growing seasons (126 observations). See Table 1 for the definitions of the abbreviations.

the loading plot, PC1 ordered the variables into two groups, and the most influential variables for PC1 are shown on both extremes of the loading plot (Fig. 1). On the extreme left side are the fruit magnesium content, seasonal mean relative humidity, and fruit age; on the opposite side (extreme right side) of PC1 are the seasonal mean maximum temperature, fruit potassium/magnesium ratio, season degree days, fruit firmness at harvest, and fruit potassium content. PC2 ordered the most influential variables at the top and bottom of the loading plot (Fig. 1); the number of plants per hectare, time at 20 ◦ C to ripen, and planting slope are shown at the top, and the trunk diameter, fruit nitrogen/calcium ratio, fruit softening rate during cold storage, change in fruit color during storage and planting age are shown at the bottom (negative coordinate). From this analysis, it was possible to observe significant relationships between the preharvest variables. The fruit age (number of days between fruit set and harvest) showed a significant inverse relationship with the planting altitude (r = −0.53; p < 0.0001) and degree day (r = −0.42; p < 0.0001). Additionally, the trunk diameter showed a significant relationship with the number of fruits per plant (r = 0.31; p = 0.0001), and the fruit weight was positively correlated with the leaf area index (r = 0.24; p = 0.007).

Regarding the relationship between preharvest variables and fruit-ripening indicators, multivariate analysis showed that the seasonal mean minimum air temperature, seasonal degree days, trunk diameter and fruit firmness at harvest exhibited a proportional relationship with the postharvest softening rate (SOFTRATE) and change in peel color during storage (COLOR35) and an inverse and significant relationship with the days required to reach the ready-to-eat stage (RTE35) (Fig. 1 and Table 3). In contrast, the leaf area index, number of plants per hectare, and irrigation management at the bloom stage showed a proportional relationship with RTE35 and an inverse relationship with SOFTRATE and COLOR35. (Fig. 1 and Table 3). Moreover, Fig. 2 shows the Partial Least Squares Regression (PLS) analysis modeling of the effect of preharvest parameters on postharvest fruit-ripening indicators during storage. The PLS model explained 53.5% of the total variability of the fruitripening indicators (R2 Y), and cumulative, overall cross-validated Q2 was 45.8%. This analysis indicated that the seasonal mean minimum air temperature (TMIN), degree days (DD13), fruit firmness at harvest (HFIRM), dry matter content (DM), irrigation management at bloom (BLH2O), leaf area index (LAI), number of plants per hectare (PLANTHECT), and fruit calcium content (FCa) were

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Table 3 Spearman 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-eat stage in ‘Hass’ avocados obtained from 3 seasons at 42 experimental sites in central Chile. Softening rate (N d−1 )

Peel color change (%)

ra

P

r

P

r

P

Climate/environment Degree Days Seasonal mean maximum temperature (◦ C) Seasonal mean minimum temperature (◦ C) Seasonal mean relative humidity (%) Seasonal mean solar radiation (W m-2 )

0.34 0.12 0.41 −0.07 −0.01

***

0.30 0.05 0.48 0.04 −0.04

***

***

n.s. n.s.

−0.34 −0.07 −0.45 −0.02 −0.09

Plant Nutrition Fruit nitrogen content (g kg-1 ) Fruit potassium content (g kg-1 ) Fruit calcium content (g kg-1 ) Fruit magnesium content (g kg-1 ) Fruit boron (mg kg-1 ) Fruit nitrogen/calcium (ratio) Fruit calcium/boron (ratio) Fruit calcium/potassium (ratio) Fruit potassium/magnesium (ratio) Leaf phosphorus (mg kg-1 ) Leaf Zinc (mg kg−1 )

0.04 0.24 −0.19 −0.14 0.005 0.18 −0.14 −0.24 0.19 0.06 −0.05

n.s.

0.01 0.10 −0.04 −0.06 0.09 0.06 −0.14 −0.04 0.07 0.01 −0.01

n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s.

−0.05 −0.16 0.15 0.15 −0.07 −0.15 0.16 0.18 −0.16 −0.06 −0.06

n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s.

Planting characterization Plantation altitude over sea level (m) Planting slope (%) Soil macroporus content (%) Plant per hectares (count) Plant age (d)

−0.10 −0.10 −0.08 −0.37 0.18

n.s. n.s. n.s.

−0.15 −0.11 −0.03 −0.36 0.15

n.s. n.s. n.s.

0.12 0.13 −0.03 0.34 −0.12

n.s. n.s. n.s.

Irrigation management Irrigation management at bloom (%) Irrigation management at fruit set (%) Irrigation management at fruit developing (%) Season mean irrigation (%)

−0.31 −0.02 0.11 0.14

***

−0.27 −0.09 0.06 0.07

**

0.22 0.02 −0.02 −0.04

*

Physiology and production Leaf area index (m2 m−2 ) Mean trunk diameter (cm) Fruit number per tree (count) Mean fruit weight (g)

−0.23 0.22 −0.11 −0.06

*

−0.31 0.19 −0.10 −0.10

***

0.25 −0.19 0.17 −0.07

**

Fruit quality Dry matter content (%) Fruit age (d) Harvest fruit firmness (N)

0.30 −0.08 0.50

***

0.22 −0.01 0.44

*

−0.20 −0.03 −0.44

*

0.72 1.0

****

−0.66 −0.69 1.0

****

Postharvest ripening behavior Softening rate (N d-1 ) Peel color change (%) Days to ready to eat stage (d) a b * ** *** ****

1.0

n.s.b ****

n.s. n.s.

** *

n.s n.s. n.s. n.s. ** *

n.s. n.s.

****

n.s.

n.s. n.s. n.s.

*

n.s. n.s.

n.s. ****

Days to ready to eat (d)

n.s. ****

***

n.s.

n.s. n.s. n.s.

*

n.s. n.s.

n.s. ****

n.s. ****

n.s. n.s.

*

n.s. n.s.

****

n.s.

n.s. n.s. n.s.

*

n.s. n.s.

n.s. ****

****

r = Spearman correlation coefficient. n.s. = not significant (P > 0.05). = P < 0.05. = P < 0.01. = P < 0.001. = P < 0.0001.

important variables influencing the overall fruit-ripening behavior (Fig. 2). Finally, Ridge Regression considering the most significant preharvest variables (X) for each of the 3 ripening indicators (Y) was performed. The softening rate was fitted against the preharvest variables 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 preharvest 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 fitted against 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 and time 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 dependent variables (square) obtained from 42 experimental sites in three consecutive growing seasons (366 observations). See Table 1 for the definitions of the abbreviations.

Fig. 3. Observed softening rate, color change, and time to ripening plotted against the predicted values obtained by regression models for the softening rate (A), color change (B) and time to ripening (C). See Table 1 for the definitions of the abbreviations.

3.3. Understanding the influence of preharvest variables on fruit-ripening behavior Softening behavior during storage has a very high impact during the value chain. Thus, the softening rate and time to ripening are considered to be estimators of the timing of avocado consumption maturity. Among the plant nutrient contents, the calcium content and its relationships with other nutrients such as potassium and nitrogen significantly affected the postharvest softening rate and thereby the fruit firmness after 35 d of storage. The relationship between the fruit calcium content and ripening behavior has been extensively studied in avocado (Witney et al., 1990; Saucedo-Hernandez et al., 2005; Wills and Tirmazi, 1982) as well as other fruit crops such as apple (Ortiz et al., 2011; Casero et al., 2004), kiwifruit (Hopkirk et al., 1990), and pear (Gerasopoulos and Richardson, 1997). The relevance of this relationship is due

to the effect of Ca2+ on the stabilization of pectin components for strengthening 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 days to ripening in avocado. Moreover, postharvest treatment with calcium chloride infiltration in avocado fruits has been shown to be effective at delaying the ripening process compared with untreated fruit (Wills and Tirmazi, 1982; Yuen et al., 1994). It is generally accepted that calcium transport occurs via xylem and that the largest uptake of calcium in avocado fruit occurs during the first 7–8 weeks after fruit set (Bower, 1985). Therefore, as was showed by our results, irrigation management during the early stages of fruit 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 as the nitrogen leaf content increased. However, in our study, we did

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not observe a significant relationship between the nitrogen content and the softening rate. The irrigation management applied at the flowering stages (September in Chile) and the nutritional content of the fruit at harvest were also shown to be relevant variables affecting postharvest ripening behavior. These results indicate the importance of the cultural practices during the early stages of fruit development for achieving a high postharvest fruit quality. Moreover, Blakey et al. (2009) reported that the level of water stress in fruit could influence postharvest ripening patterns due to differences in ABA concentration among fruits. However, irrigation management during the growing season showed a positive relationship with the leaf area index (LAI) and inversely influenced the softening rate during storage. LAI could be an indirect estimator of the photosynthesis potential. In avocado, the non-structural seven-carbon sugars mannoheptulose and perseitol are synthesized during photosynthesis, and these sugars have been postulated as being partly responsible for inhibiting fruit ripening, largely because of the notable decline in the content of these sugars in avocado fruits during ripening (Liu et al., 2002). To a certain extent, a reduction in these sevencarbon sugars could result in the initiation of fruit ripening (Liu et al., 2002). Moreover, Blakey et al. (2012) observed a proportional correlation between the concentrations of seven-carbon sugars and the postharvest fruit firmness, indicating that slow-ripening avocados have higher concentrations of D-mannoheptulose than fast-ripening fruits. Conversely, Pedreschi et al. (2014) observed a non-significant correlation between total seven-carbon sugars and the number of days to ripening. These authors postulated that the ripening heterogeneity observed among fruits could be better correlated with key metabolites from primary metabolism, such as different amino acids and fatty acids, rather than seven-carbon sugars. The dry matter content has been accepted worldwide as an indicator of harvest maturity, and a minimum percentage of DM, standardized for each avocado cultivar, will ensure a continuation of the ripening process after harvest (OECD, 2004). The percentage of dry matter is correlated strongly and proportionally with the percentage of oil in ‘Hass’ avocado (Lee et al., 1983). Moreover, the DM content has been linked with the potential marketability and intention to buy avocado fruits (Ranney, 1991; Gamble et al., 2010). Blakey et al. (2009) found that more mature fruits with a lower water content ripened sooner than less mature fruits. In our study, DM showed a significant (p < 0.05) and proportional relationship with the softening rate, and the DM content was included in the prediction models of softening rate and days to ripening (Table 2 and Fig. 3). However, this result is in contrast to that of Pedreschi et al. (2014), who did not observe a significant relationship between the dry matter content and days to ripening. Moreover, Hofman et al. (2000) observed a poor relationship between the DM content and fruit quality in late-harvested avocados with a DM content between 28.8% and 31.4%. In our study, the observed relationship between DM and ripening patterns was constructed based on three years of fruit sampling from 42 sites with the DM at harvest ranging between 20.4% and 30.9%. Environmental temperatures during the growing season can influence plant metabolism, affecting cellular structure and other components related to fruit texture (Sams, 1999). The mean minimum temperature and cumulative degree days during the growing season were found to proportionally affect the softening rate during storage (Fig. 2). The relationship between preharvest temperatures and fruit ripening has been studied in several fruit crops ˜ and Mitcham, such as pears, apples and avocados (Villalobos-Acuna 2008; Woolf et al., 2000; Woolf and Ferguson, 2000). In pears, environmental growing conditions such as air temperature influenced the ethylene production rate and therefore fruit firmness ˜ and Mitcham, 2008). Agar during postharvest (Villalobos-Acuna

et al. (1999) observed that fruits harvested at earlier time points in warmer locations were firmer than fruits harvested at later time points in cooler locations, showing a higher ethylene production rate during ripening. Although postharvest ethylene production was not assessed in our study, the softening rate showed a proportional relationship with the seasonal mean minimum air temperature (Fig. 2). Therefore, these results appear to indicate a different pattern for the ripening of avocado fruits compared with pears. In studies using avocado fruits, differences in ripening behavior have been observed between fruits exposed and not exposed to direct sunlight, and these differences could be related to the temperature regime tested at each level of sunlight exposure (Woolf et al., 1999; 2000). Accordingly, Woolf et al. (2000) postulated that softening rates follow an inverse pattern in relation to the peak temperature experienced by the fruits during development. Fruit temperature was not measured in our study, but the air maximum temperature ranged from 18.3 to 28.4 ◦ C, corresponding to a similar value of air temperature in sun-exposed fruits with temperatures over 35 ◦ C reported by Woolf et al. (1999). In our study, there was no discrimination between sun-exposed and shaded fruit on the trees during the harvest, though the cumulative degree days and a mean minimum temperature ranging between 5.5 and 13 ◦ C appear to be more useful for determining the ripening behavior of ‘Hass’ avocado. The change in peel color from green to black was strongly influenced by climate/environmental conditions; for example, the growing temperature, such as the seasonal minimum temperature and cumulative degree days, were significant parameters in defining the potential color change of ‘Hass’ avocado during cold storage. The relationship between avocado skin coloration and growing temperature and other environmental stresses was previously proposed by Cox et al. (2004), who postulated the influence of the growing temperature on the anthocyanin (cyanidin-3-O-glucoside) and chlorophyll contents in fruit skin. The relationship between growing temperature and anthocyanin biosynthesis in fruit skins has been reviewed in other fruit crops such as grapes (Downey et al., 2006) and apples (Ubi, 2004). In addition, variables related to plant vigor, photosynthesis, and sugar distribution balance have been related to anthocyanin biosynthesis in the fruit skin of crops such as grapes (Downey et al., 2006) and apples (Ubi, 2004). In our study, variables such as LAI, irrigation management, and the fruit nitrogen content were significant for predicting the change in peel color during storage (Fig. 3).

4. Conclusions Based on our findings, we can postulate that predicting the postharvest behavior of ‘Hass’ avocado using a single preharvest variable such as dry matter or calcium content could be a misleading simplification of reality because several factors, including climate/environment, agronomical management, and physiological variables, directly (e.g., fruit mineral content) or indirectly (e.g., metabolites from photosynthesis process) influence the ripening behavior of ‘Hass’ avocado fruits. Our study showed that the cumulative degree days, dry matter content, fruit calcium content, fruit firmness at harvest, irrigation management at bloom, mean minimum temperature, and LAI are important variables significantly influencing 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 determining quality, which would a major contribution to growers and exporters for guarantying an optimum and consistent avocado at consumer’s level.

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Acknowledgments This study was financially supported by Innova Project11CEII9568, and Conicyt/Fondecyt Regular1130107. References Agar, T., Biasi, W.V., Mitcham, E.J., 1999. Exogenous ethylene accelerates ripening responses in Bartlett pears regardless of maturity or growing region. Postharvest Biol. Technol. 17, 67–78. Arpaia, 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). Arpaia, M.L., 1994. Preharvest factors influencing postharvest quality of tropical and subtropical fruit. HortScience 29, 982–985. Balic, 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 firmness of table grape berries. Postharvest Biol. Technol. 93, 15–23. Ball, B., Smith, K., 1991. Gas movement. In: Smith, K., Mullins, Ch. (Eds.), Soil Analysis. Marcel Dekker, New York, USA, pp. 511–550. Ballabio, D., Mauri, A., Todeschini, R., Buratti, S., 2006. Geographical classification of wine and olive oil by means of classification and influence matrix analysis (CAIMAN). Anal. Chim. Acta 570, 249–258. Blakey, R.J., Bower, J.P., Bertlin, g.I., 2009. Influence of water and ABA supply on the ripening pattern of avocado (Persea americana Mill.) fruit and the prediction of water content using Near Infrared Spectroscopy. Postharvest Biol. Technol. 53, 72–76. Blakey, R.J., Tesfay, S.Z., Bertling, I., Bower, J.P., 2012. Changes in sugars: total protein and oil in ‘Hass’ avocado (Persea americana Mill.) fruit during ripening. J. Hortic. Sci. Biotechnol. 87, 381–387. Bower, J.P., 1985. The calcium accumulation pattern in avocado fruit as influenced by long-term irrigation regime. South Afr. Avocado Grow. Assoc. Yearb. 8, 97–99. Bower, J.P., 1988. Pre and postharvest measures for long-term storage of avocados. S. Afr. Avocado Grow. Assoc. Yearb. 11, 68–72. Brereton, R., 2009. Chemometrics for Pattern Recognition. John Wiley and Sons, Bristol, UK, pp. 522. Casero, T., Benavides, A., Puy, J., Recasens, I., 2004. Relationship between leaf and fruit nutrients and fruit quality attributes in golden smoothee apples using multivariate regression techniques. J. Plant Nutr. 27, 313–324. Cen, H., He, Y., Huang, M., 2007. Combination and comparison of multivariate analysis for the identification of oranges varieties using visible and near infrared reflectance spectroscopy. Eur. Food Res. Technol. 225, 699–705. Cox, K.A., McGhie, T.K., White, A., Woolf, A.B., 2004. Skin colour and pigment changes during ripening of Hass avocado fruit. Postharvest Biol. Technol. 31, 287–294. Downey, M.O., Dokoozlian, N.K., Krstic, M.P., 2006. Cultural practice and environmental impacts on the flavonoid composition of grapes and wine: a review of recent research. Am. J. Enol. Vitic. 57, 257–268. Eriksson, L., Johansson, E., Kettaneh-Wold, N., Trygg, J., Wikstrom, C., Wold, S., 2006. Multi and Megavariate Data Analysis, Part I: Basic Principles and Applications, 2th ed. Umetrics AB, Sweeden (pp, 425). Gamble, 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 dry matter, ripeness and internal defects on consumer perceptions of avocado quality and intentions to purchase. Postharvest Biol. Technol. 57, 35–43. Gerasopoulos, D., Richardson, D.G., 1997. Fruit maturity and calcium affect chilling requirements and ripening of ‘Dànjou’ pears. HortScience 32, 911–913. Hofman, P., Jobin-Décor, M., Giles, J., 2000. Percentage of dry matter and oil content are not reliable indicators of fruit maturity or quality in late-harvested ‘Hass’ avocado. Postharvest Biol. Technol. 35, 694–695. Hopkirk, G., Harker, F.R., Harman, J.E., 1990. Calcium and the firmness of kiwifruit. N. Z. J. Crop Hort. Sci. 18, 215–219. Kruger, U., Xie, L., 2012. Advances in Statistical Monitoring of Complex Multivariate Process: with Applications in Industrial Process Control. John Wiley & Sons, Ltd., United Kingdom.

37

Lee, S.K., Young, R.E., Schiffman, P.M., Coggings, C.W., 1983. Maturity studies of avocado 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 of the 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-destructive methods 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 cell wall-modifying enzyme activities and delay cell wall disassembly at commercial 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 of postharvest ripening heterogeneity of Hass Avocadoes. Postharvest Biol. Technol. 92, 172–179. Ranney, C., 1991. Relationship between physiological maturity and percent dry matter 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 in soft-drink process using 3-way PLS approach. Procedia Food Sci. 1, 1181–1187. Saavedra, J., Córdova, A., Gálvez, L., Quezada, C., Navarro, R., 2013. Principal component 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 de Investigaciones Agropecuarias (INIA) La Platina, Santiago, Chile, pp, 140 (in Spanish). 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 sprays in the ripening and chilling injury of ‘Fuerte’ avocado. Rev. Chapingo Serie Hortic. 11, 149–157 (In Spanish). Thorp, T.G., Hutching, D., Lowe, T., Marsh, K.B., 1997. Survey of fruit mineral concentrations 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. Food Agric. Environ. 2, 65–70. ˜ M.G., Mitcham, E.J., 2008. Ripening of European pears: the Villalobos-Acuna, chilling 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 vigour and 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 of chemometrics. Chemom. Intell. Lab. Syst. 58, 109–130. Woolf, A.B., Ferguson, I.B., 2000. Postharvest responses to high fruit temperatures in 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. Revista Chapingo Serie Horticultura 5, 353–358. Woolf, A.B., Wexler, A., Prusky, D., Kobiler, E., Lurie, S., 2000. Direct sunlight influence postharvest temperature responses and ripening of five avocado cultivars. J. Amer. Soc. Hort. Sci. 125, 370–376. Yuen, C.M.C., Caffin, N., Boonyakiat, D., 1994. Effect of calcium infiltration on ripening of avocados of different maturities. Aust. J. Exp. Agric. 34, 123–126.