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Bitter pit and soft scald development during storage of unconditioned and conditioned ‘Honeycrisp’ apples in relation to mineral contents and harvest indices
Postharvest Biology and Technology 160 (2020) 111044
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Bitter pit and soft scald development during storage of unconditioned and conditioned ‘Honeycrisp’ apples in relation to mineral contents and harvest indices
T
Yosef Al Shoffea, Jacqueline F. Nocka, Tara Auxt Baugherb, Richard P. Marinic, Christopher B. Watkinsa,* a b c
Horticulture Section, School of Integrative Plant Science, Plant Science Building, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY 14853, USA The Pennsylvania State University, Cooperative Extension in Adams County, 670 Old Harrisburg Road, Gettysburg, PA 17325, USA Department of Plant Science, The Pennsylvania State University, 119 Tyson Building, University Park, PA 16802, USA
ARTICLE INFO
ABSTRACT
Keywords: Malus × domestica Bitter pit Soft scald Minerals Maturity Prediction models
‘Honeycrisp’ apple fruit are highly susceptible to development of soft scald and bitter pit during storage. However, the commercial postharvest treatment of conditioning fruit at 10 °C for 7 d before storage at 3 °C to reduce soft scald development can increase bitter pit incidence. Prediction of these physiological disorders would enable storage operators to modify management techniques to reduce fruit losses due to both disorders. To develop prediction tools, harvest indices and mineral concentrations of fruit were analyzed from orchard blocks in Pennsylvania (PA) for three years, the Hudson Valley region (HV) for four years, the Champlain region for two years, and Western New York (WNY) for five years. Fruit were stored at 3 °C, without or with conditioning, and stored for 2 - 5 months in 2013- 2017. Fruit were also stored at 0.5 °C without or with conditioning in 2013, 2015, 2016. Multivariate analysis described significant relationships that were different for unconditioned and conditioned fruit. In unconditioned fruit, bitter pit incidence was negatively correlated with increasing internal ethylene concentration (IEC) and starch pattern indices (lower starch content), positively with higher chlorophyll content as indicated by the index of absorbance difference and with all minerals except N, as well as mineral ratios. In conditioned fruit, bitter pit incidence was correlated negatively with IEC, Ca, and positively with firmness, and all mineral ratios. Soft scald incidence in fruit stored at 0.5 °C was positively correlated with IEC and firmness, and all fruit mineral ratios except N/Ca and P/Ca, and negatively with Ca and Mg. For conditioned and unconditioned fruit stored at 3 °C, harvest indices predicted 27-28 % and 21-26 % bitter pit, respectively, while minerals and mineral ratios predicted 22-55 % and 18-54 % bitter pit, respectively. Harvest indices predicted 29-57 % soft scald, while minerals and mineral ratios predicted 29-49 for % soft scald for fruit stored at 0.5 °C. Correlations of bitter pit against P, K, and Mg were higher, and Ca and all mineral ratios lower, in conditioned fruit stored at 3 °C as opposed to those stored unconditioned at 3 °C. Nonlinear iterative partial least square algorithms based on variable importance plots vs coefficients showed that the regression of determination was affected by postharvest treatment in relation to harvest indices, minerals and their ratios. A negative correlation of bitter pit incidence against soft scald incidence was found for a region with high bitter pit and soft scald development.
1. Introduction ‘Honeycrisp’ apples have unique flavor and texture at harvest that is maintained during storage (Harb et al., 2012; Mann et al., 2005; Schaeffer et al., 2016; Wargo et al., 2004; Yue and Tong, 2011). The cultivar is highly profitable, but also very susceptible to soft scald, soggy breakdown, and bitter bit development during storage (Al Shoffe
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and Watkins, 2018; Baugher et al., 2017; DeEll et al., 2016; Watkins et al., 2004) in addition to a range of newer disorders such as leather blotch and wrinkly skin (Al Shoffe et al., 2016; Watkins and Mattheis, 2019). The risk of soft scald and soggy breakdown development can be reduced or eliminated by conditioning fruit at 10 °C for one week followed by storage at 3 °C (Moran et al., 2010; Watkins and Rosenberger, 1999; Watkins et al., 2004). However, while conditioning is a widely
Corresponding author. E-mail address: [email protected] (C.B. Watkins).
https://doi.org/10.1016/j.postharvbio.2019.111044 Received 26 June 2019; Received in revised form 2 September 2019; Accepted 13 October 2019 Available online 16 November 2019 0925-5214/ © 2019 Elsevier B.V. All rights reserved.
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applied commercial treatment, it can increase bitter pit incidence (Al Shoffe et al., 2019; Moran et al., 2010; Watkins et al., 2004). Prediction of bitter pit before harvest would reduce losses due to disorders as storage operators could make management decisions such as short-term storage of fruit without conditioning. Fruit maturity is a critical factor affecting incidence of physiological disorders in apple fruit (Watkins and Mattheis, 2019; Wilkinson and Fidler, 1973). For ‘Honeycrisp’, early harvest is associated with higher bitter pit incidence (DeLong et al., 2014), while later harvests are associated with higher risk of soft scald, soggy breakdown, senescent breakdown, and other flesh browning disorders (Moran et al., 2010; Prange et al., 2015; Tong et al., 2003; Watkins et al., 2005). Development of bitter pit occurs rapidly after harvest, typically within the first month, followed by that of soft scald (Al Shoffe et al., 2016). Preharvest 1-methylcyclopropene (1-MCP) treatment delayed fruit maturity and reduced soft scald development in ‘Honeycrisp’ apples (DeEll and Ehsani-Moghaddam, 2010). Bitter pit is a complex disorder involving orchard factors such as mineral composition and management factors such as crop load and application of calcium products in addition to fruit maturity (Bramlage, 1993; Drake et al., 2008; Ferguson and Watkins, 1989; Jemrić et al., 2016; Watkins and Mattheis, 2019). High bitter pit incidence is related to a high Mg/ Ca ratio in ‘Fuji’ apples (do Amarante et al., 2013), Mg + K + N/ Ca in ‘Honeycrisp’ apples (Baugher et al., 2017), and Mg + K/ Ca in ‘Granny Smith’ apples (de Freitas et al., 2010). Relationships can be improved by incorporation of factors such as shoot length (Baugher et al., 2017). Non-mineral methods in which fruit collected before commercial harvest are kept at 20 °C without or with ethylene and magnesium dips have also been investigated as predictors of bitter pit risk in stored fruit (Al Shoffe et al., 2019). The mechanism of soft scald development is still not well understood. In a previous study we found that a conditioning treatment increased internal ethylene concentration (IEC) and hastened metabolism, resulting in lower acetaldehyde and ethanol concentrations in the fruit (Al Shoffe et al., 2018). Soft scald incidence is related to oxidation of unsaturated fatty acids in the surface lipids and elevated hexanol concentration in the fruit (Hopkirk and Wills, 1981). Many factors affect its development including fruit maturity, region and climate (Lachapelle et al., 2013; Moran et al., 2009; Moran et al., 2010; Tong et al., 2003). Leisso et al. (2019) found that weather station data and fruit quality assessment at harvest for ‘Honeycrisp’ apples are not reliable indicators for soft scald development in storage. Recently, multivariate analysis has been used for nondestructive studies of fruit and vegetable quality based on near infrared spectroscopy to distinguish the relationships between different factors. Du et al. (2017) used the multivariate statistical approach of principle component analysis (PCA) using correlations on protein abundance resulting from 1-methylcyclopropene (1-MCP) and diphenylamine treatments to reveal protein changes in relation to superficial scald development in apple fruit. The variable importance plot (VIP) method, which graphs values for each X variable and measures by modeling in both the X and Y direction, was used to distinguish variables with a small coefficient and a small VIP to be candidates for deletion from the model (Wold, 1995). Also, partial least square regression (PLS) and nonlinear iterative partial least square (NIPALS) algorithms are stable statistical approaches (Wold, 1995; Wold et al., 2001), that have been used for prediction models for different quality studies in fruit and vegetables (Adebayo et al., 2017; Córdova et al., 2019; Nicolaï et al., 2007; Rivera et al., 2017; Zhang et al., 2019). The objective of this study was to investigate the relationships between mineral concentrations, harvest indices and incidences of bitter pit and soft scald in the ‘Honeycrisp’ apple. We have applied bivariate, multivariate, and NIPALS algorithms to dissect these relationships.
2. Materials and methods 2.1. Fruit material ‘Honeycrisp’ fruit (Malus × domestica Borkh) were harvested at the start of commercial harvest in each region (Table S1); in 2013 from 4, 6, and 12 orchard blocks in Pennsylvania (PA), Hudson Valley (HV), and Western New York (WNY), respectively. In 2014, fruit were harvested from 2, 3, 3 And 2 orchard blocks in PA, HV, Champlain (CH) and WNY, respectively; in 2015, from 4, 3, 3, and 3 orchard blocks in PA, HV, CH and WNY, respectively; and in 2016, from 2 orchard blocks in WNY. In 2017, fruit were harvested from 6 orchard blocks in each of HV and WNY. Each replicate was a crate of approximately 70 fruit. There were three replicates per orchard block in all years except in 2016, when 4 replicates per orchard were used. Fruit were stored at 3 °C without or with one week of conditioning at 10 °C for 4 months in 2013 and 2017, 5 months in 2014 and 2015, and for 2 months in 2016. Also, another set of fruit were stored at 0.5 °C with or without one week of conditioning at 10 °C in 2013 from WNY, in 2015 from PA, HV, CH, and WNY, and in 2016 from WNY. Fruit were assessed after storage plus 4 d at 20 °C. The experiment was carried out as a 2 × 2 factorial (2 levels of conditioning (conditioned and unconditioned) and 2 storage temperatures (0.5 or 3 °C)) in a randomized block design, where orchard/year combinations were considered blocks. Ten fruit per replicate were used at harvest for measurement of harvest indices and mineral concentrations. 2.2. Harvest indices Harvest indices were measured using methods described by Al Shoffe et al. (2018) and Zhang et al. (2019). The IEC of each fruit was measured on 1 mL samples taken from the core using gas chromatography. Flesh firmness was measured on opposite peeled sides of each fruit using an automatic pressure tester (Guss Manufacturing (Pty) Ltd., Strand, South Africa) fitted with an 11.1 mm diameter probe. The expressed juice was used for measurement of the soluble solids concentration (SSC) with a refractometer (PR-100, Atago Co. Ltd., Tokyo, Japan). The titratable acidity (TA) was measured by titrating the juice to pH 8.1 with 0.1 M NaOH using a Mettler FL 12 Titrator (Hightstown, NJ, USA). The starch pattern index (SPI) of each fruit cut at the equator was assessed using the Cornell generic chart where 1 = 100% stained starch and 8 = 0% stained starch (Blanpied and Silsby, 1992). The index of absorbance difference (IAD) was measured on blushed and unblushed sides of each fruit using a Delta Absorbance (DA) meter (TR Turoni srl, Forli, Italy). 2.3. Mineral analyses Mineral contents were measured in fruit peel taken from the calyxend of 10 fruit per replicate as previously described (Al Shoffe et al., 2019). Peel tissues were dried to a constant weight, and then ground to a fine powder. The samples were analyzed at the agricultural analytical laboratory at Pennsylvania State University in 2013, 2014, 2015, and 2016, while the samples were measured at the nutrient analysis laboratory at Cornell University in 2017. Powdered peel tissues (0.50 g) were weighed into a 50 ml Teflon container and digested with nitric and perchloric acid using an automated Vulcan 84 digestion unit (Questron Technologies, Mississauga, Canada). The digested samples were analyzed for their elemental concentration using a Spectro Arcos axial viewed ICP-OES (SPECTRO Analytical Instruments Inc. AMETEK®, Kleve, Germany,). The results are expressed as g kg-1 on a dry weight basis. 2.4. Physiological disorder assessment Fruit were assessed after storage for incidence of external physiological disorders then fruit were cut transversely at least five times from 2
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Fig. 1. Bivariate analysis of bitter pit incidence (%) in ‘Honeycrisp’ apples that were unconditioned or conditioned for 7 d at 10 °C, and stored at 3 °C (A, B, C, D, E, F) or 0.5 °C (G, H, I, J). Fruit were harvested from different orchard blocks in Champlain, Hudson Valley (HV), Pennsylvania (PA) and Western New York (WNY) over several years. NS; P > 0.05. N; total number of samples (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
the calyx to the stem end for presence or absence of internal physiological disorders. Incidence of each disorder was calculated as a percentage of the total fruit number in each replicate.
2.5. Statistical analysis The Tukey HSD test was used to compare means at the 5% 3
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confidence level. The VIP method was used to distinguish variables with a small coefficient and a small VIP to be candidates for deletion from the model (Wold, 1995). A value of 0.8 was considered to be a small VIP (Eriksson et al., 2006). NIPALS models were used based on the VIP. PLS is an extension from multiple liner regression analysis that describes the liner relationships between the dependent variable Y and a set of predictor variable Xs (Miller and Miller, 2018). Means, standard deviation (SD), and standard error (SE) were used to present data in figures. All statistical analyses were carried out using the JMP statistical program (JMP Pro 14.INK). Percentage data were arcsine transformed for analysis, and presented as back-transformed means.
Table 1 Prediction formulas and the calculated predicted range of bitter pit (%) in ‘Honeycrisp’ apples after storage at 3 °C, with or without one week of conditioning at 10 °C, based on significant factors in Table 4 of harvest indices, minerals, and mineral ratios for five years. Actual bitter pit ranges were 0-57 % and 0-88 % for unconditioned and conditioned fruit. Treatment
Factor
Prediction formulas
Predicted bitter pit range (%)
No conditioning
IEC (μL L-1) SPI (1-8) IAD index
19 – (0.2 × IEC) 36 – (3 × SPI) 10 + (10 × IAD index) 5 + (128 × P0 2 + (18 × K) 30 – (399 × Ca) 3 + (156 × Mg) −8 + (10 × Mg/Ca) 0.02 + (1.5 × N/Ca) 15 + (0.2 × P/Ca) 11+(0.3 × K/Ca) −2 + (0.7 × (Mg + K)/Ca) 0.4 + (0.4 × (Mg + K+N)/Ca) −7 + 0.6 × (N + P+K + Mg)/ Ca 29 – (0.3 × IEC) −52 + (1.2 × Firmness) 13 + (133 × P) 9.1 + (21 × K) 49 – (689 × Ca) 4.1 + (258 × Mg) −15 + (16 × Mg/ Ca) −4 + (2.5 × N/Ca) 4.8 + (7.6 × P/ Ca) −0.2 + (1.1 × K/Ca) −1.9 + (1.1 × (Mg + K)/Ca) −7.4 + (0.9 × (Mg + K+N)/Ca) −7 + (0.8 × (N + P + K + Mg)/ Ca)
4-19 13-26 12-24
P K Ca Mg Mg/Ca N/Ca P/Ca K/Ca (Mg + K)/Ca (Mg + K+N)/Ca (N + P+K + Mg)/Ca Conditioning
IEC (μL L-1) Firmness (N) P K Ca Mg Mg/Ca N/Ca P/Ca K/Ca (Mg + K)/Ca (Mg + K+N)/Ca (N + P+K + Mg)/Ca
3. Results and discussion
10-27 8-29 7-24 10-25 2-31 7-31 15-25 11-23 5-32
3.1. Disorder incidence To minimize risk of soft scald development, the recommended storage temperature for ‘Honeycrisp’ apples is 3 °C after conditioning of fruit at 10 °C for a week, but conditioning increases bitter pit incidence (Al Shoffe et al., 2019; Moran et al., 2010; Watkins et al., 2004). In the current study, bitter pit development was also higher after conditioning than without conditioning either at 3 °C (Fig. 1 A-F) or at 0.5 °C (Fig. 1 G-J). Bitter pit was higher with conditioning +3 °C, overall being 88 % compared with 57 % at 3 °C, over five years (Table 1). The correlations between bitter pit from conditioning +3 °C against bitter pit from continuous 3 °C were significant for individual or combined years. However, the correlation between bitter pit from the conditioning +0.5 °C treatment against bitter pit from continuous 0.5 °C was not significant. Bitter pit incidence remained low during storage at continuous 0.5 °C as shown by Watkins et al. (2004). A large variation in bitter pit incidence was found in fruit from different orchards in all years (Fig. 1). Such variation is common, being associated with several preharvest factors. These include soil pH, cultivar, rootstock, vegetative growth, water status of the trees, fruit mineral concentrations, fruit maturity, crop load, and fruit location on the tree (DeLong et al., 2014; Ferguson and Triggs, 1990; Ferguson and Watkins, 1992; Jemrić et al., 2016; Watkins et al., 2005; Watkins and Mattheis, 2019; Wünsche and Ferguson, 2005). In our study, the variation between bitter pit developments over five years was consistent with the wide variability in fruit maturity, minerals, and mineral ratios at harvest (Table S2). Soft scald developed at 0.5 °C with or without conditioning, but was highest without conditioning compared with other treatments (Table 2). Incidence was lowest at 3 °C. The disorder is associated with low temperature storage and can be reduced by conditioning, although not as effectively as at 0.5 °C as at 3 °C (Watkins et al., 2004). Incidence was the highest in fruit from WNY compared with the other regions, and absent in PA fruit (Table 2). Climate factors in susceptibility to soft scald appear important but are not well characterized (Tong et al., 2003; Lachapelle et al., 2013; Leisso et al., 2019). Interestingly, high incidences of soft scald and bitter pit were found in fruit from the WNY region for which complete data for both storage temperatures and conditioning are available. Bitter pit incidence of fruit stored at 3 °C after conditioning was negatively correlated with soft scald incidence (R2 = 0.44) of fruit from the same orchards and
7-31 3-34 5-29 10-35 17-36 16-37 9-38 15-35 2-51 8-49 14-50 10-50 9-50 6-54 6-52
Table 2 Soft scald (%) in ‘Honeycrisp’ apples after storage at 0.5 or 3 °C with or without conditioning (C) for 7 d at 10 °C. Fruit were harvested in Western New York (WNY), Pennsylvania (PA) or Hudson Valley (HV), and stored for 4 months in 2013, 5 months in 2015, and 2 months in 2016. Treatment
0.5 °C C +0.5 °C 3 °C C +3 °C P value
2013
2015
2016
WNY
PA
HV
24.6 2.4 0.0 0.0 < .0001
0.0 0.0 0.0 0.0
1.3 0.4 0.0 0.0
Champlain 5.1 2.9 0.0 0.0 < .0001
WNY
WNY
36.0 1.9 0.0 0.0
6.8 0.5 0.1 0.0 0.02
Table 3 Physiological disorders in ‘Honeycrisp’ apples after storage at 0.5 or 3 °C with or without one week of conditioning (C) at 10 °C in 2013, 2015, and 2016.. Treatment
Bitter pit (%)
Soft scald (%)
Soggy breakdown (%)
Senescent browning (%)
Cavities (%)
Wrinkly skin (%)
Flesh browning (%)
Decay (%)
0.5 °C C +0.5 °C 3 °C C +3 °C P value
2.2 13.0 10.6 20.6 < .0001
7.6 0.9 0.0 0.0 < .0001
0.9 0.7 0.1 0.0 0.0006
0.3 0.7 1.3 1.1 0.09
0.2 0.1 0.2 0.1 0.6
6.4 4.9 0.0 0.0 < .0001
0.8 0.2 0.0 0.0 0.0006
0.5 0.9 3.4 1.6 0.0002
4
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Table 4 Multivariate analysis for bitter pit (%) in ‘Honeycrisp’ apples after storage at 3 °C with or without one week of conditioning (C) at 10 °C against fruit harvest indices, minerals, and mineral ratios at harvest for five years, and soft scald (%) after storage at 0.5 °C against fruit harvest indices, minerals, and mineral ratios at harvest for three years... Bitter pit (%)
Soft scald (%)
3 °C Factor -1
IEC (μL L ) Firmness (N) SSC (%) TA (%) SPI (1-8) IAD index N P K Ca Mg Mg/Ca N/Ca P/Ca K/Ca (Mg + K)/Ca (Mg + K + N)/Ca (N + P + K + Mg)/ Ca
C +3 °C
0.5 °C
R
P value
R
P value
R
P value
−0.17 0.13 −0.06 0.13 −0.23 0.15 0.03 0.26 0.30 −0.28 0.20 0.46 0.33 0.16 0.24 0.45 0.40 0.48
0.02 0.09 0.42 0.10 0.0001 0.04 0.65 0.0005 < .0001 0.0001 0.006 < .0001 < .0001 0.04 0.001 < .0001 < .0001 < .0001
−0.22 0.24 0.03 −0.11 0.02 0.00 −0.07 0.19 0.23 −0.36 0.19 0.54 0.40 0.41 0.47 0.48 0.50 0.50
0.01 0.01 0.76 0.22 0.84 0.97 0.47 0.03 0.01 < .0001 0.03 < .0001 < .0001 < .0001 < .0001 < .0001 < .0001 < .0001
−0.43 −0.50 −0.06 −0.09 −0.10 0.18 −0.09 −0.03 0.26 −0.36 −0.27 0.26 0.39 0.27 0.48 0.48 0.48 0.47
0.001 0.0002 0.7 0.5 0.5 0.2 0.5 0.8 0.06 0.008 0.048 0.06 0.004 0.05 0.0003 0.0003 0.0003 0.0004
trees that were stored at 0.5 °C without conditioning (Fig. 2). The negative correlation between the two disorders could be used to make decisions about storage management, for example, not condition fruit with high bitter pit potential if soft scald risk is low. However, more research is needed. Incidences of other physiological disorders at 3 °C, with or without conditioning, through all years in this study were minor with a maximum of 2 % soft scald, 5 % senescent breakdown and 4 % wrinkly skin in any single year.. Soggy breakdown, core browning, wrinkly skin, flesh browning, and cavities, which are typically associated with carbon dioxide injury, occurred only in unconditioned fruit. Incidences of disorder at 0.5 and 3 °C without and with one week of conditioning are shown in Table 3. Soft scald, soggy breakdown, wrinkly skin and flesh browning were higher and decay was lower at 0.5 °C than at 3 °C with or without conditioning, while bitter pit was the lowest at 0.5 °C without conditioning. The occurrence of soft scald, soggy breakdown, wrinkly skin, and flesh browning at 0.5 °C confirm that these disorders are associated with low temperature storage. The effect of the conditioning treatment on increasing the bitter pit is most likely associated with faster metabolism, which is known to aggravate the disorder (Al Shoffe et al., 2019; Ferguson and Watkins, 1992). The factors involved in the wrinkly skin disorder found in ‘Honeycrisp’ are not well known (Watkins and Mattheis, 2019). The presence of carbon dioxide injury in fruit stored in air is unusual, but can occur and its incidence is known to decrease with conditioning (DeEll et al., 2016).
Fig. 2. Bivariate analysis for soft scald after storage at 0.5 °C against bitter pit after conditioning for 7 d at 10 °C followed by storage at 3 °C for ‘Honeycrisp’ apples harvested from WNY in three years. Soft scald (%) at 0.5 °C = 44.8 (0.6 × BP (%) for conditioning +3 °C. Table 5 Prediction formula and prediction range of soft scald (%) in ‘Honeycrisp’ apples for storage at 0.5 °C based on significant factors in Table 4 for harvest indices, minerals, and mineral ratios for three years. The actual soft scald incidence range for stored fruit was 0-69 %. Factor
Prediction formula
Predicted soft scald range (%)
3.2. Multivariate analysis with harvest indices
IEC (μL L-1) Firmness (N) Ca Mg N/Ca K/Ca (Mg + K)/Ca (Mg + K+N)/Ca
20.6 - (0.4 × IEC) - (1.8 × Firmness) 32 - (602 × Ca) 42 - (381× Mg) −13.7 + (2.3 × N/Ca) −9 + (0.9 × K/Ca) −9.9 + (0.9 × (Mg + K)/Ca) −13.7 + 0.7 × (Mg + K+N)/Ca −13.2 + 0.7 × (N + P + K + Mg)/ Ca
0-20 0-39 0-23 5-25 0-27 0-35 0-34 0-32
Fruit used in these experiments were harvested at the beginning of commercial harvest. ‘Honeycrisp’ fruit are picked largely on the basis of percentage red color and ‘bright’ appearance, because of limited usefulness of harvest indices used for other cultivars (Wargo and Watkins, 2004). Therefore, differences in orchard blocks, growing regions reflect actual commercial maturity for the cultivar in each year. Multivariate analysis for bitter pit at 3 °C, with and without conditioning, against maturity indices at harvest are presented in Table 4. Relationships between bitter pit and harvest indices, while significant, were weak. For fruit stored at 3 °C without conditioning, bitter pit was correlated with IEC, SPI, and the IAD index, the correlation coefficients being - 0.17, - 0.23, and 0.15, respectively. For conditioning +3 °C
(N + P+K + Mg)/Ca
0-32
5
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Fig. 3. Nonlinear iterative partial least square (NIPALS) algorithms based on the variable importance plot (VIP) for bitter pit (%) in ‘Honeycrisp’ apples after storage against harvest indices for five years, X- variables (maturity indices at harvest) and Y- variable (bitter pit incidence after storage). Fruit were stored at 3 °C without (A, B) or with conditioning for 7 d at 10 °C (C, D) (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
fruit, bitter pit was correlated with IEC and firmness, with correlation coefficients of – 0.22 and 0.24, respectively. Fruit from each region were harvested on one date each year, and an association with fruit maturity may be stronger if fruit were sampled over time. Bitter pit incidence can decrease with advancing harvest date (Prange et al., 2011; Watkins et al., 1989). In ‘Honeycrisp’ apples this decrease was associated with decreasing IAD indices (DeLong et al., 2014), and with increasing IEC and SPI, and decreasing firmness (DeLong et al., 2014; Prange et al., 2011; Wargo and Watkins, 2004; Watkins et al., 2005). Multivariate analysis of soft scald at 0.5 °C with harvest indices (Table 4) indicated that it was negatively correlated with IEC and firmness being -0.43 and -0.50, respectively. It is known that soft scald correlates with advanced fruit maturity. Soft scald correlated negatively with IEC in our study, consistent with Ehsani-Moghaddam and DeEll (2013) who also found a significant negative correlation between IEC at harvest and soft scald development after storage of ‘Honeycrisp’ apples. On the other hand, soft scald development in ‘Honeycrisp’ apples from late harvests differed from different locations but did not correlate with ethylene production rates (Tong et al., 2003). ‘Honeycrisp’ apples do not show the autocatalytic ethylene production found in other cultivars (Wargo and Watkins, 2004; Watkins et al., 2004; Moran et al., 2010; Harb et al., 2012; Prange et al. 2015), and therefore it is unlikely that IEC would be a useful harvest index for this cultivar .
concentrations and their ratios except for N (Table 4). Correlation coefficients for Ca and all mineral ratios were higher with conditioned fruit than without, reflecting higher bitter pit incidence in those fruit (Fig. 1). That bitter pit is associated with low fruit Ca concentration is well known (Ferguson and Watkins, 1992; Jemrić et al., 2016; Watkins and Mattheis, 2019), but relationships are often improved when ratios between minerals are taken into account (Al Shoffe et al., 2019; de Freitas et al., 2010; do Amarante et al., 2013; Kalcsits, 2016). Soft scald from fruit stored at 0.5 °C correlated negatively with Ca and Mg concentrations and positively with all mineral ratios except Mg/ Ca and P/ Ca (Table 4). In contrast, Tong et al. (2003) found that Mg concentration was high in ‘Honeycrisp’ fruit that were susceptible to soft scald. High Ca concentrations are generally associated with lower incidences of physiological disorders (Watkins and Mattheis, 2019), but little information relationships between minerals and soft scald is available. 3.4. Efficiency of harvest indices, minerals, and their ratios in prediction of bitter pit and soft scald Using selected variables that were significant from the results of multivariate analyses in Table 4, prediction ranges were calculated using partial least square analysis (PLS) for bitter pit (Table 1) and soft scald (Table 5). Bivariate analysis of the classified data from the output grid table was used to show a correlation of determination that equals one, which helps to identify the potential of every individual variable in prediction of the actual bitter pit or soft scald. The predicted bitter pit from maturity indices ranged from 4-26 % compared with 0-57 % for
3.3. Multivariate analysis with mineral concentrations Bitter pit correlated negatively with Ca for fruit stored at 3 °C with or without conditioning, and positively with the other mineral 6
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Fig. 4. Nonlinear iterative partial least square (NIPALS) algorithm based on the variable importance plot (VIP) for bitter pit (%) in ‘Honeycrisp’ apples after storage against minerals from peel tissues and their ratios at harvest for five years, where X- (minerals and their ratios at harvest) and Y- variable (bitter pit incidence after storage). Fruit were stored at 3 °C without (A, B) or with conditioning for 7 d at 10 °C (C, D) (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
based on the NIPALS algorithm to show the correlation between bitter pit and soft scald against different variables. IEC, firmness, SPI, and the IAD index had the highest VIP values in correlation with bitter pit after storage at 3 °C (Fig. 3 A). IEC and firmness had the highest VIP values in correlation with bitter pit in fruit with conditioning +3 °C (Fig. 3 C). The correlation loading plot for the actual bitter pit against the above threshold variables had R2 values of 0.08 and 0.12 for the correlation between bitter bit and the selected harvest indices from fruit stored at 3 °C without and with conditioning, respectively (Fig. 3 B, D). Both IEC and firmness were correlated with bitter pit in conditioned or unconditioned fruit compared with other maturity indices. Mineral ratios showed the highest VIP values against bitter pit in fruit stored at 3 °C (Fig. 4 A). Ca and mineral ratios had the highest VIP values against bitter pit from fruit stored at 3 °C after conditioning (Fig. 4 C). The correlation of determination for bitter pit against the above threshold variables was 0.26 and 0.31 from unconditioned and conditioned fruit, respectively (Fig. 4 B, D). NIPALS results show that Ca only predicted bitter bit when the disorder was high, such as in fruit stored +3 °C after conditioning. For soft scald incidence, IEC, firmness, and SSC had the highest VIP values compared with the others harvest indices (Fig. 5 A). These indices correlated negatively with soft scald, with a coefficient of determination of 0.39. All minerals and mineral ratios except N had VIP values higher than 0.8 (Fig. 5 C). Soft scald correlated negatively with Ca and Mg and positively with the other threshold minerals and mineral ratios with coefficient of determination of 0.31 (Fig. 5 D). The negative correlation between soft scald development and SSC at harvest might be partially due to the variation in SSC between orchards and years, where
actual bitter pit from the fruit stored at 3 °C and the range was 5-35 % for predicted compared with 0-88 % for actual incidence for conditioning +3 °C fruit. For mineral concentrations and their ratios, the predicted bitter pit ranged between 2-34 % compared with the actual bitter bit from the unconditioned fruit while the predicted bitter pit ranged from 2-51 % compared with the range of actual bitter pit from conditioned fruit. The correlation with harvest indices, mineral concentrations and their ratios were greater at 3 °C with than without conditioning because of higher bitter pit incidence. The harvest indices predicted 27-28 % and 21-26 % actual bitter pit, while the prediction of bitter pit by minerals and mineral ratios were 22-55 % and 18-54 % with and without conditioning, respectively (Table 1). These results indicate that there was a wider range for bitter pit prediction based on mineral concentrations and their ratios than those based on harvest indices. Predictions for soft scald based on harvest indices ranged from 039%, while those based on mineral concentrations and their ratios ranged from 0-35 % compared with 0-69 % for actual disorder incidence (Table 5). The harvest indices predicted 29-57 % soft scald, while minerals and mineral ratios predicted 29-49 % soft scald (Table 5). Overall, harvest indices had a slightly wider range of prediction for soft scald compared with mineral concentrations and their ratios. 3.5. NIPALS models for bitter pit and soft scald in relation to harvest indices, fruit minerals and their ratios Variables were classified by VIP vs coefficients to build models 7
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Fig. 5. Nonlinear iterative partial least square (NIPALS) algorithm based on the variable importance plot (VIP) for soft scald (%) in ‘Honeycrisp’ apples after storage at 0.5 °C against harvest indices and minerals from peel tissues and their ratios at harvest for three years, where X- (harvest indices or minerals and their ratios at harvest) and Y- variable (soft scald incidence after storage). (A, B) soft scald against harvest indices, (C, D) soft scald against minerals and their ratios at harvest.
SSC ranged from 10-16% over five years (Table S2). In conclusion, IEC and firmness were the only harvest indices that correlated with bitter pit development at 3 °C either with or without conditioning. Correlations of bitter pit against minerals and mineral ratios were higher at 3 °C after conditioning compared with unconditioned fruit. According to NIPALS analysis, Ca only satisfactorily correlated bitter bit at 3 °C in conditioned fruit compared with unconditioned fruit. The bivariate (individual variable) and NIPALS (combined variables) analyses confirm that harvest indices had higher correlations, while minerals and their ratios had lower correlations with soft scald compared with bitter pit development. A negative correlation between soft scald and bitter pit might be a good indicator to predict soft scald based on the bitter pit prediction but more research is needed.
Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.postharvbio.2019. 111044. References Adebayo, S.E., Hashim, N., Hass, R., Reich, O., Regen, C., Münzberg, M., Abdan, K., Hanafi, M., Zude-Sasse, M., 2017. Using absorption and reduced scattering coefficients for non-destructive analyses of fruit flesh firmness and soluble solids content in pear (Pyrus communis ‘Conference’)—An update when using diffusion theory. Postharv. Biol. Technol. 130, 56–63. https://doi.org/10.1016/j.postharvbio.2017.04. 004. Al Shoffe, Y., Nock, J.F., Baugher, T.A., Watkins, C.B., 2016. Honeycrisp–to condition or not condition. New York Fruit Q. 24, 19–23. http://nyshs.org/wp-content/uploads/ 2016/10/Watkins-Pages-19-24-NYFQ-Book-Summer-2016.pdf. Al Shoffe, Y., Nock, J.F., Zhang, Y., Zhu, L., Watkins, C.B., 2019. Comparisons of mineral and non-mineral prediction methods for bitter pit in ‘Honeycrisp’ apples. Sci. Hortic. 254, 116–123. https://doi.org/10.1016/j.scienta.2019.04.073. Al Shoffe, Y., Shah, A.S., Nock, J.F., Watkins, C.B., 2018. Acetaldehyde and ethanol metabolism during conditioning and air storage of’ Honeycrisp’ apples. HortScience 53, 1347–1351. https://doi.org/10.21273/hortsci13167-18. Al Shoffe, Y., Watkins, C.B., 2018. Initial short-term storage at 33 °F reduces physiological disorder development in’ Honeycrisp’ apples. HortTechnology 28, 481–484. https:// doi.org/10.21273/horttech04102-18. Baugher, T.A., Marini, R., Schupp, J.R., Watkins, C.B., 2017. Prediction of bitter pit in’ Honeycrisp’ apples and best management implications. HortScience 52, 1368–1374. https://doi.org/10.21273/hortsci12266-17. Blanpied, G.D., Silsby, K.J., 1992. Predicting harvest date windows for apples. Cornell Cooper. Extension. https://ecommons.cornell.edu/handle/1813/3299. Bramlage, W.J., 1993. Interactions of orchard factors and mineral nutrition on quality of pome fruit. Acta Hortic. 326, 15–28. https://doi.org/10.17660/ActaHortic.1993.
Declaration of Competing Interest None. Acknowledgments This work was supported by the New York Apple Research and Development Program, and the National Institute of Food and Agriculture, U.S. Department of Agriculture, Multistate under 1001075, NE-1836, Improving Quality and Reducing Losses in Specialty Fruit Crops through Storage Technologies. 8
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Bitter pit and soft scald development during storage of unconditioned and conditioned ‘Honeycrisp’ apples in relation to mineral contents and harvest indices
T
Yosef Al Shoffea, Jacqueline F. Nocka, Tara Auxt Baugherb, Richard P. Marinic, Christopher B. Watkinsa,* a b c
Horticulture Section, School of Integrative Plant Science, Plant Science Building, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY 14853, USA The Pennsylvania State University, Cooperative Extension in Adams County, 670 Old Harrisburg Road, Gettysburg, PA 17325, USA Department of Plant Science, The Pennsylvania State University, 119 Tyson Building, University Park, PA 16802, USA
ARTICLE INFO
ABSTRACT
Keywords: Malus × domestica Bitter pit Soft scald Minerals Maturity Prediction models
‘Honeycrisp’ apple fruit are highly susceptible to development of soft scald and bitter pit during storage. However, the commercial postharvest treatment of conditioning fruit at 10 °C for 7 d before storage at 3 °C to reduce soft scald development can increase bitter pit incidence. Prediction of these physiological disorders would enable storage operators to modify management techniques to reduce fruit losses due to both disorders. To develop prediction tools, harvest indices and mineral concentrations of fruit were analyzed from orchard blocks in Pennsylvania (PA) for three years, the Hudson Valley region (HV) for four years, the Champlain region for two years, and Western New York (WNY) for five years. Fruit were stored at 3 °C, without or with conditioning, and stored for 2 - 5 months in 2013- 2017. Fruit were also stored at 0.5 °C without or with conditioning in 2013, 2015, 2016. Multivariate analysis described significant relationships that were different for unconditioned and conditioned fruit. In unconditioned fruit, bitter pit incidence was negatively correlated with increasing internal ethylene concentration (IEC) and starch pattern indices (lower starch content), positively with higher chlorophyll content as indicated by the index of absorbance difference and with all minerals except N, as well as mineral ratios. In conditioned fruit, bitter pit incidence was correlated negatively with IEC, Ca, and positively with firmness, and all mineral ratios. Soft scald incidence in fruit stored at 0.5 °C was positively correlated with IEC and firmness, and all fruit mineral ratios except N/Ca and P/Ca, and negatively with Ca and Mg. For conditioned and unconditioned fruit stored at 3 °C, harvest indices predicted 27-28 % and 21-26 % bitter pit, respectively, while minerals and mineral ratios predicted 22-55 % and 18-54 % bitter pit, respectively. Harvest indices predicted 29-57 % soft scald, while minerals and mineral ratios predicted 29-49 for % soft scald for fruit stored at 0.5 °C. Correlations of bitter pit against P, K, and Mg were higher, and Ca and all mineral ratios lower, in conditioned fruit stored at 3 °C as opposed to those stored unconditioned at 3 °C. Nonlinear iterative partial least square algorithms based on variable importance plots vs coefficients showed that the regression of determination was affected by postharvest treatment in relation to harvest indices, minerals and their ratios. A negative correlation of bitter pit incidence against soft scald incidence was found for a region with high bitter pit and soft scald development.
1. Introduction ‘Honeycrisp’ apples have unique flavor and texture at harvest that is maintained during storage (Harb et al., 2012; Mann et al., 2005; Schaeffer et al., 2016; Wargo et al., 2004; Yue and Tong, 2011). The cultivar is highly profitable, but also very susceptible to soft scald, soggy breakdown, and bitter bit development during storage (Al Shoffe
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and Watkins, 2018; Baugher et al., 2017; DeEll et al., 2016; Watkins et al., 2004) in addition to a range of newer disorders such as leather blotch and wrinkly skin (Al Shoffe et al., 2016; Watkins and Mattheis, 2019). The risk of soft scald and soggy breakdown development can be reduced or eliminated by conditioning fruit at 10 °C for one week followed by storage at 3 °C (Moran et al., 2010; Watkins and Rosenberger, 1999; Watkins et al., 2004). However, while conditioning is a widely
Corresponding author. E-mail address: [email protected] (C.B. Watkins).
https://doi.org/10.1016/j.postharvbio.2019.111044 Received 26 June 2019; Received in revised form 2 September 2019; Accepted 13 October 2019 Available online 16 November 2019 0925-5214/ © 2019 Elsevier B.V. All rights reserved.
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applied commercial treatment, it can increase bitter pit incidence (Al Shoffe et al., 2019; Moran et al., 2010; Watkins et al., 2004). Prediction of bitter pit before harvest would reduce losses due to disorders as storage operators could make management decisions such as short-term storage of fruit without conditioning. Fruit maturity is a critical factor affecting incidence of physiological disorders in apple fruit (Watkins and Mattheis, 2019; Wilkinson and Fidler, 1973). For ‘Honeycrisp’, early harvest is associated with higher bitter pit incidence (DeLong et al., 2014), while later harvests are associated with higher risk of soft scald, soggy breakdown, senescent breakdown, and other flesh browning disorders (Moran et al., 2010; Prange et al., 2015; Tong et al., 2003; Watkins et al., 2005). Development of bitter pit occurs rapidly after harvest, typically within the first month, followed by that of soft scald (Al Shoffe et al., 2016). Preharvest 1-methylcyclopropene (1-MCP) treatment delayed fruit maturity and reduced soft scald development in ‘Honeycrisp’ apples (DeEll and Ehsani-Moghaddam, 2010). Bitter pit is a complex disorder involving orchard factors such as mineral composition and management factors such as crop load and application of calcium products in addition to fruit maturity (Bramlage, 1993; Drake et al., 2008; Ferguson and Watkins, 1989; Jemrić et al., 2016; Watkins and Mattheis, 2019). High bitter pit incidence is related to a high Mg/ Ca ratio in ‘Fuji’ apples (do Amarante et al., 2013), Mg + K + N/ Ca in ‘Honeycrisp’ apples (Baugher et al., 2017), and Mg + K/ Ca in ‘Granny Smith’ apples (de Freitas et al., 2010). Relationships can be improved by incorporation of factors such as shoot length (Baugher et al., 2017). Non-mineral methods in which fruit collected before commercial harvest are kept at 20 °C without or with ethylene and magnesium dips have also been investigated as predictors of bitter pit risk in stored fruit (Al Shoffe et al., 2019). The mechanism of soft scald development is still not well understood. In a previous study we found that a conditioning treatment increased internal ethylene concentration (IEC) and hastened metabolism, resulting in lower acetaldehyde and ethanol concentrations in the fruit (Al Shoffe et al., 2018). Soft scald incidence is related to oxidation of unsaturated fatty acids in the surface lipids and elevated hexanol concentration in the fruit (Hopkirk and Wills, 1981). Many factors affect its development including fruit maturity, region and climate (Lachapelle et al., 2013; Moran et al., 2009; Moran et al., 2010; Tong et al., 2003). Leisso et al. (2019) found that weather station data and fruit quality assessment at harvest for ‘Honeycrisp’ apples are not reliable indicators for soft scald development in storage. Recently, multivariate analysis has been used for nondestructive studies of fruit and vegetable quality based on near infrared spectroscopy to distinguish the relationships between different factors. Du et al. (2017) used the multivariate statistical approach of principle component analysis (PCA) using correlations on protein abundance resulting from 1-methylcyclopropene (1-MCP) and diphenylamine treatments to reveal protein changes in relation to superficial scald development in apple fruit. The variable importance plot (VIP) method, which graphs values for each X variable and measures by modeling in both the X and Y direction, was used to distinguish variables with a small coefficient and a small VIP to be candidates for deletion from the model (Wold, 1995). Also, partial least square regression (PLS) and nonlinear iterative partial least square (NIPALS) algorithms are stable statistical approaches (Wold, 1995; Wold et al., 2001), that have been used for prediction models for different quality studies in fruit and vegetables (Adebayo et al., 2017; Córdova et al., 2019; Nicolaï et al., 2007; Rivera et al., 2017; Zhang et al., 2019). The objective of this study was to investigate the relationships between mineral concentrations, harvest indices and incidences of bitter pit and soft scald in the ‘Honeycrisp’ apple. We have applied bivariate, multivariate, and NIPALS algorithms to dissect these relationships.
2. Materials and methods 2.1. Fruit material ‘Honeycrisp’ fruit (Malus × domestica Borkh) were harvested at the start of commercial harvest in each region (Table S1); in 2013 from 4, 6, and 12 orchard blocks in Pennsylvania (PA), Hudson Valley (HV), and Western New York (WNY), respectively. In 2014, fruit were harvested from 2, 3, 3 And 2 orchard blocks in PA, HV, Champlain (CH) and WNY, respectively; in 2015, from 4, 3, 3, and 3 orchard blocks in PA, HV, CH and WNY, respectively; and in 2016, from 2 orchard blocks in WNY. In 2017, fruit were harvested from 6 orchard blocks in each of HV and WNY. Each replicate was a crate of approximately 70 fruit. There were three replicates per orchard block in all years except in 2016, when 4 replicates per orchard were used. Fruit were stored at 3 °C without or with one week of conditioning at 10 °C for 4 months in 2013 and 2017, 5 months in 2014 and 2015, and for 2 months in 2016. Also, another set of fruit were stored at 0.5 °C with or without one week of conditioning at 10 °C in 2013 from WNY, in 2015 from PA, HV, CH, and WNY, and in 2016 from WNY. Fruit were assessed after storage plus 4 d at 20 °C. The experiment was carried out as a 2 × 2 factorial (2 levels of conditioning (conditioned and unconditioned) and 2 storage temperatures (0.5 or 3 °C)) in a randomized block design, where orchard/year combinations were considered blocks. Ten fruit per replicate were used at harvest for measurement of harvest indices and mineral concentrations. 2.2. Harvest indices Harvest indices were measured using methods described by Al Shoffe et al. (2018) and Zhang et al. (2019). The IEC of each fruit was measured on 1 mL samples taken from the core using gas chromatography. Flesh firmness was measured on opposite peeled sides of each fruit using an automatic pressure tester (Guss Manufacturing (Pty) Ltd., Strand, South Africa) fitted with an 11.1 mm diameter probe. The expressed juice was used for measurement of the soluble solids concentration (SSC) with a refractometer (PR-100, Atago Co. Ltd., Tokyo, Japan). The titratable acidity (TA) was measured by titrating the juice to pH 8.1 with 0.1 M NaOH using a Mettler FL 12 Titrator (Hightstown, NJ, USA). The starch pattern index (SPI) of each fruit cut at the equator was assessed using the Cornell generic chart where 1 = 100% stained starch and 8 = 0% stained starch (Blanpied and Silsby, 1992). The index of absorbance difference (IAD) was measured on blushed and unblushed sides of each fruit using a Delta Absorbance (DA) meter (TR Turoni srl, Forli, Italy). 2.3. Mineral analyses Mineral contents were measured in fruit peel taken from the calyxend of 10 fruit per replicate as previously described (Al Shoffe et al., 2019). Peel tissues were dried to a constant weight, and then ground to a fine powder. The samples were analyzed at the agricultural analytical laboratory at Pennsylvania State University in 2013, 2014, 2015, and 2016, while the samples were measured at the nutrient analysis laboratory at Cornell University in 2017. Powdered peel tissues (0.50 g) were weighed into a 50 ml Teflon container and digested with nitric and perchloric acid using an automated Vulcan 84 digestion unit (Questron Technologies, Mississauga, Canada). The digested samples were analyzed for their elemental concentration using a Spectro Arcos axial viewed ICP-OES (SPECTRO Analytical Instruments Inc. AMETEK®, Kleve, Germany,). The results are expressed as g kg-1 on a dry weight basis. 2.4. Physiological disorder assessment Fruit were assessed after storage for incidence of external physiological disorders then fruit were cut transversely at least five times from 2
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Fig. 1. Bivariate analysis of bitter pit incidence (%) in ‘Honeycrisp’ apples that were unconditioned or conditioned for 7 d at 10 °C, and stored at 3 °C (A, B, C, D, E, F) or 0.5 °C (G, H, I, J). Fruit were harvested from different orchard blocks in Champlain, Hudson Valley (HV), Pennsylvania (PA) and Western New York (WNY) over several years. NS; P > 0.05. N; total number of samples (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
the calyx to the stem end for presence or absence of internal physiological disorders. Incidence of each disorder was calculated as a percentage of the total fruit number in each replicate.
2.5. Statistical analysis The Tukey HSD test was used to compare means at the 5% 3
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confidence level. The VIP method was used to distinguish variables with a small coefficient and a small VIP to be candidates for deletion from the model (Wold, 1995). A value of 0.8 was considered to be a small VIP (Eriksson et al., 2006). NIPALS models were used based on the VIP. PLS is an extension from multiple liner regression analysis that describes the liner relationships between the dependent variable Y and a set of predictor variable Xs (Miller and Miller, 2018). Means, standard deviation (SD), and standard error (SE) were used to present data in figures. All statistical analyses were carried out using the JMP statistical program (JMP Pro 14.INK). Percentage data were arcsine transformed for analysis, and presented as back-transformed means.
Table 1 Prediction formulas and the calculated predicted range of bitter pit (%) in ‘Honeycrisp’ apples after storage at 3 °C, with or without one week of conditioning at 10 °C, based on significant factors in Table 4 of harvest indices, minerals, and mineral ratios for five years. Actual bitter pit ranges were 0-57 % and 0-88 % for unconditioned and conditioned fruit. Treatment
Factor
Prediction formulas
Predicted bitter pit range (%)
No conditioning
IEC (μL L-1) SPI (1-8) IAD index
19 – (0.2 × IEC) 36 – (3 × SPI) 10 + (10 × IAD index) 5 + (128 × P0 2 + (18 × K) 30 – (399 × Ca) 3 + (156 × Mg) −8 + (10 × Mg/Ca) 0.02 + (1.5 × N/Ca) 15 + (0.2 × P/Ca) 11+(0.3 × K/Ca) −2 + (0.7 × (Mg + K)/Ca) 0.4 + (0.4 × (Mg + K+N)/Ca) −7 + 0.6 × (N + P+K + Mg)/ Ca 29 – (0.3 × IEC) −52 + (1.2 × Firmness) 13 + (133 × P) 9.1 + (21 × K) 49 – (689 × Ca) 4.1 + (258 × Mg) −15 + (16 × Mg/ Ca) −4 + (2.5 × N/Ca) 4.8 + (7.6 × P/ Ca) −0.2 + (1.1 × K/Ca) −1.9 + (1.1 × (Mg + K)/Ca) −7.4 + (0.9 × (Mg + K+N)/Ca) −7 + (0.8 × (N + P + K + Mg)/ Ca)
4-19 13-26 12-24
P K Ca Mg Mg/Ca N/Ca P/Ca K/Ca (Mg + K)/Ca (Mg + K+N)/Ca (N + P+K + Mg)/Ca Conditioning
IEC (μL L-1) Firmness (N) P K Ca Mg Mg/Ca N/Ca P/Ca K/Ca (Mg + K)/Ca (Mg + K+N)/Ca (N + P+K + Mg)/Ca
3. Results and discussion
10-27 8-29 7-24 10-25 2-31 7-31 15-25 11-23 5-32
3.1. Disorder incidence To minimize risk of soft scald development, the recommended storage temperature for ‘Honeycrisp’ apples is 3 °C after conditioning of fruit at 10 °C for a week, but conditioning increases bitter pit incidence (Al Shoffe et al., 2019; Moran et al., 2010; Watkins et al., 2004). In the current study, bitter pit development was also higher after conditioning than without conditioning either at 3 °C (Fig. 1 A-F) or at 0.5 °C (Fig. 1 G-J). Bitter pit was higher with conditioning +3 °C, overall being 88 % compared with 57 % at 3 °C, over five years (Table 1). The correlations between bitter pit from conditioning +3 °C against bitter pit from continuous 3 °C were significant for individual or combined years. However, the correlation between bitter pit from the conditioning +0.5 °C treatment against bitter pit from continuous 0.5 °C was not significant. Bitter pit incidence remained low during storage at continuous 0.5 °C as shown by Watkins et al. (2004). A large variation in bitter pit incidence was found in fruit from different orchards in all years (Fig. 1). Such variation is common, being associated with several preharvest factors. These include soil pH, cultivar, rootstock, vegetative growth, water status of the trees, fruit mineral concentrations, fruit maturity, crop load, and fruit location on the tree (DeLong et al., 2014; Ferguson and Triggs, 1990; Ferguson and Watkins, 1992; Jemrić et al., 2016; Watkins et al., 2005; Watkins and Mattheis, 2019; Wünsche and Ferguson, 2005). In our study, the variation between bitter pit developments over five years was consistent with the wide variability in fruit maturity, minerals, and mineral ratios at harvest (Table S2). Soft scald developed at 0.5 °C with or without conditioning, but was highest without conditioning compared with other treatments (Table 2). Incidence was lowest at 3 °C. The disorder is associated with low temperature storage and can be reduced by conditioning, although not as effectively as at 0.5 °C as at 3 °C (Watkins et al., 2004). Incidence was the highest in fruit from WNY compared with the other regions, and absent in PA fruit (Table 2). Climate factors in susceptibility to soft scald appear important but are not well characterized (Tong et al., 2003; Lachapelle et al., 2013; Leisso et al., 2019). Interestingly, high incidences of soft scald and bitter pit were found in fruit from the WNY region for which complete data for both storage temperatures and conditioning are available. Bitter pit incidence of fruit stored at 3 °C after conditioning was negatively correlated with soft scald incidence (R2 = 0.44) of fruit from the same orchards and
7-31 3-34 5-29 10-35 17-36 16-37 9-38 15-35 2-51 8-49 14-50 10-50 9-50 6-54 6-52
Table 2 Soft scald (%) in ‘Honeycrisp’ apples after storage at 0.5 or 3 °C with or without conditioning (C) for 7 d at 10 °C. Fruit were harvested in Western New York (WNY), Pennsylvania (PA) or Hudson Valley (HV), and stored for 4 months in 2013, 5 months in 2015, and 2 months in 2016. Treatment
0.5 °C C +0.5 °C 3 °C C +3 °C P value
2013
2015
2016
WNY
PA
HV
24.6 2.4 0.0 0.0 < .0001
0.0 0.0 0.0 0.0
1.3 0.4 0.0 0.0
Champlain 5.1 2.9 0.0 0.0 < .0001
WNY
WNY
36.0 1.9 0.0 0.0
6.8 0.5 0.1 0.0 0.02
Table 3 Physiological disorders in ‘Honeycrisp’ apples after storage at 0.5 or 3 °C with or without one week of conditioning (C) at 10 °C in 2013, 2015, and 2016.. Treatment
Bitter pit (%)
Soft scald (%)
Soggy breakdown (%)
Senescent browning (%)
Cavities (%)
Wrinkly skin (%)
Flesh browning (%)
Decay (%)
0.5 °C C +0.5 °C 3 °C C +3 °C P value
2.2 13.0 10.6 20.6 < .0001
7.6 0.9 0.0 0.0 < .0001
0.9 0.7 0.1 0.0 0.0006
0.3 0.7 1.3 1.1 0.09
0.2 0.1 0.2 0.1 0.6
6.4 4.9 0.0 0.0 < .0001
0.8 0.2 0.0 0.0 0.0006
0.5 0.9 3.4 1.6 0.0002
4
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Table 4 Multivariate analysis for bitter pit (%) in ‘Honeycrisp’ apples after storage at 3 °C with or without one week of conditioning (C) at 10 °C against fruit harvest indices, minerals, and mineral ratios at harvest for five years, and soft scald (%) after storage at 0.5 °C against fruit harvest indices, minerals, and mineral ratios at harvest for three years... Bitter pit (%)
Soft scald (%)
3 °C Factor -1
IEC (μL L ) Firmness (N) SSC (%) TA (%) SPI (1-8) IAD index N P K Ca Mg Mg/Ca N/Ca P/Ca K/Ca (Mg + K)/Ca (Mg + K + N)/Ca (N + P + K + Mg)/ Ca
C +3 °C
0.5 °C
R
P value
R
P value
R
P value
−0.17 0.13 −0.06 0.13 −0.23 0.15 0.03 0.26 0.30 −0.28 0.20 0.46 0.33 0.16 0.24 0.45 0.40 0.48
0.02 0.09 0.42 0.10 0.0001 0.04 0.65 0.0005 < .0001 0.0001 0.006 < .0001 < .0001 0.04 0.001 < .0001 < .0001 < .0001
−0.22 0.24 0.03 −0.11 0.02 0.00 −0.07 0.19 0.23 −0.36 0.19 0.54 0.40 0.41 0.47 0.48 0.50 0.50
0.01 0.01 0.76 0.22 0.84 0.97 0.47 0.03 0.01 < .0001 0.03 < .0001 < .0001 < .0001 < .0001 < .0001 < .0001 < .0001
−0.43 −0.50 −0.06 −0.09 −0.10 0.18 −0.09 −0.03 0.26 −0.36 −0.27 0.26 0.39 0.27 0.48 0.48 0.48 0.47
0.001 0.0002 0.7 0.5 0.5 0.2 0.5 0.8 0.06 0.008 0.048 0.06 0.004 0.05 0.0003 0.0003 0.0003 0.0004
trees that were stored at 0.5 °C without conditioning (Fig. 2). The negative correlation between the two disorders could be used to make decisions about storage management, for example, not condition fruit with high bitter pit potential if soft scald risk is low. However, more research is needed. Incidences of other physiological disorders at 3 °C, with or without conditioning, through all years in this study were minor with a maximum of 2 % soft scald, 5 % senescent breakdown and 4 % wrinkly skin in any single year.. Soggy breakdown, core browning, wrinkly skin, flesh browning, and cavities, which are typically associated with carbon dioxide injury, occurred only in unconditioned fruit. Incidences of disorder at 0.5 and 3 °C without and with one week of conditioning are shown in Table 3. Soft scald, soggy breakdown, wrinkly skin and flesh browning were higher and decay was lower at 0.5 °C than at 3 °C with or without conditioning, while bitter pit was the lowest at 0.5 °C without conditioning. The occurrence of soft scald, soggy breakdown, wrinkly skin, and flesh browning at 0.5 °C confirm that these disorders are associated with low temperature storage. The effect of the conditioning treatment on increasing the bitter pit is most likely associated with faster metabolism, which is known to aggravate the disorder (Al Shoffe et al., 2019; Ferguson and Watkins, 1992). The factors involved in the wrinkly skin disorder found in ‘Honeycrisp’ are not well known (Watkins and Mattheis, 2019). The presence of carbon dioxide injury in fruit stored in air is unusual, but can occur and its incidence is known to decrease with conditioning (DeEll et al., 2016).
Fig. 2. Bivariate analysis for soft scald after storage at 0.5 °C against bitter pit after conditioning for 7 d at 10 °C followed by storage at 3 °C for ‘Honeycrisp’ apples harvested from WNY in three years. Soft scald (%) at 0.5 °C = 44.8 (0.6 × BP (%) for conditioning +3 °C. Table 5 Prediction formula and prediction range of soft scald (%) in ‘Honeycrisp’ apples for storage at 0.5 °C based on significant factors in Table 4 for harvest indices, minerals, and mineral ratios for three years. The actual soft scald incidence range for stored fruit was 0-69 %. Factor
Prediction formula
Predicted soft scald range (%)
3.2. Multivariate analysis with harvest indices
IEC (μL L-1) Firmness (N) Ca Mg N/Ca K/Ca (Mg + K)/Ca (Mg + K+N)/Ca
20.6 - (0.4 × IEC) - (1.8 × Firmness) 32 - (602 × Ca) 42 - (381× Mg) −13.7 + (2.3 × N/Ca) −9 + (0.9 × K/Ca) −9.9 + (0.9 × (Mg + K)/Ca) −13.7 + 0.7 × (Mg + K+N)/Ca −13.2 + 0.7 × (N + P + K + Mg)/ Ca
0-20 0-39 0-23 5-25 0-27 0-35 0-34 0-32
Fruit used in these experiments were harvested at the beginning of commercial harvest. ‘Honeycrisp’ fruit are picked largely on the basis of percentage red color and ‘bright’ appearance, because of limited usefulness of harvest indices used for other cultivars (Wargo and Watkins, 2004). Therefore, differences in orchard blocks, growing regions reflect actual commercial maturity for the cultivar in each year. Multivariate analysis for bitter pit at 3 °C, with and without conditioning, against maturity indices at harvest are presented in Table 4. Relationships between bitter pit and harvest indices, while significant, were weak. For fruit stored at 3 °C without conditioning, bitter pit was correlated with IEC, SPI, and the IAD index, the correlation coefficients being - 0.17, - 0.23, and 0.15, respectively. For conditioning +3 °C
(N + P+K + Mg)/Ca
0-32
5
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Fig. 3. Nonlinear iterative partial least square (NIPALS) algorithms based on the variable importance plot (VIP) for bitter pit (%) in ‘Honeycrisp’ apples after storage against harvest indices for five years, X- variables (maturity indices at harvest) and Y- variable (bitter pit incidence after storage). Fruit were stored at 3 °C without (A, B) or with conditioning for 7 d at 10 °C (C, D) (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
fruit, bitter pit was correlated with IEC and firmness, with correlation coefficients of – 0.22 and 0.24, respectively. Fruit from each region were harvested on one date each year, and an association with fruit maturity may be stronger if fruit were sampled over time. Bitter pit incidence can decrease with advancing harvest date (Prange et al., 2011; Watkins et al., 1989). In ‘Honeycrisp’ apples this decrease was associated with decreasing IAD indices (DeLong et al., 2014), and with increasing IEC and SPI, and decreasing firmness (DeLong et al., 2014; Prange et al., 2011; Wargo and Watkins, 2004; Watkins et al., 2005). Multivariate analysis of soft scald at 0.5 °C with harvest indices (Table 4) indicated that it was negatively correlated with IEC and firmness being -0.43 and -0.50, respectively. It is known that soft scald correlates with advanced fruit maturity. Soft scald correlated negatively with IEC in our study, consistent with Ehsani-Moghaddam and DeEll (2013) who also found a significant negative correlation between IEC at harvest and soft scald development after storage of ‘Honeycrisp’ apples. On the other hand, soft scald development in ‘Honeycrisp’ apples from late harvests differed from different locations but did not correlate with ethylene production rates (Tong et al., 2003). ‘Honeycrisp’ apples do not show the autocatalytic ethylene production found in other cultivars (Wargo and Watkins, 2004; Watkins et al., 2004; Moran et al., 2010; Harb et al., 2012; Prange et al. 2015), and therefore it is unlikely that IEC would be a useful harvest index for this cultivar .
concentrations and their ratios except for N (Table 4). Correlation coefficients for Ca and all mineral ratios were higher with conditioned fruit than without, reflecting higher bitter pit incidence in those fruit (Fig. 1). That bitter pit is associated with low fruit Ca concentration is well known (Ferguson and Watkins, 1992; Jemrić et al., 2016; Watkins and Mattheis, 2019), but relationships are often improved when ratios between minerals are taken into account (Al Shoffe et al., 2019; de Freitas et al., 2010; do Amarante et al., 2013; Kalcsits, 2016). Soft scald from fruit stored at 0.5 °C correlated negatively with Ca and Mg concentrations and positively with all mineral ratios except Mg/ Ca and P/ Ca (Table 4). In contrast, Tong et al. (2003) found that Mg concentration was high in ‘Honeycrisp’ fruit that were susceptible to soft scald. High Ca concentrations are generally associated with lower incidences of physiological disorders (Watkins and Mattheis, 2019), but little information relationships between minerals and soft scald is available. 3.4. Efficiency of harvest indices, minerals, and their ratios in prediction of bitter pit and soft scald Using selected variables that were significant from the results of multivariate analyses in Table 4, prediction ranges were calculated using partial least square analysis (PLS) for bitter pit (Table 1) and soft scald (Table 5). Bivariate analysis of the classified data from the output grid table was used to show a correlation of determination that equals one, which helps to identify the potential of every individual variable in prediction of the actual bitter pit or soft scald. The predicted bitter pit from maturity indices ranged from 4-26 % compared with 0-57 % for
3.3. Multivariate analysis with mineral concentrations Bitter pit correlated negatively with Ca for fruit stored at 3 °C with or without conditioning, and positively with the other mineral 6
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Fig. 4. Nonlinear iterative partial least square (NIPALS) algorithm based on the variable importance plot (VIP) for bitter pit (%) in ‘Honeycrisp’ apples after storage against minerals from peel tissues and their ratios at harvest for five years, where X- (minerals and their ratios at harvest) and Y- variable (bitter pit incidence after storage). Fruit were stored at 3 °C without (A, B) or with conditioning for 7 d at 10 °C (C, D) (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
based on the NIPALS algorithm to show the correlation between bitter pit and soft scald against different variables. IEC, firmness, SPI, and the IAD index had the highest VIP values in correlation with bitter pit after storage at 3 °C (Fig. 3 A). IEC and firmness had the highest VIP values in correlation with bitter pit in fruit with conditioning +3 °C (Fig. 3 C). The correlation loading plot for the actual bitter pit against the above threshold variables had R2 values of 0.08 and 0.12 for the correlation between bitter bit and the selected harvest indices from fruit stored at 3 °C without and with conditioning, respectively (Fig. 3 B, D). Both IEC and firmness were correlated with bitter pit in conditioned or unconditioned fruit compared with other maturity indices. Mineral ratios showed the highest VIP values against bitter pit in fruit stored at 3 °C (Fig. 4 A). Ca and mineral ratios had the highest VIP values against bitter pit from fruit stored at 3 °C after conditioning (Fig. 4 C). The correlation of determination for bitter pit against the above threshold variables was 0.26 and 0.31 from unconditioned and conditioned fruit, respectively (Fig. 4 B, D). NIPALS results show that Ca only predicted bitter bit when the disorder was high, such as in fruit stored +3 °C after conditioning. For soft scald incidence, IEC, firmness, and SSC had the highest VIP values compared with the others harvest indices (Fig. 5 A). These indices correlated negatively with soft scald, with a coefficient of determination of 0.39. All minerals and mineral ratios except N had VIP values higher than 0.8 (Fig. 5 C). Soft scald correlated negatively with Ca and Mg and positively with the other threshold minerals and mineral ratios with coefficient of determination of 0.31 (Fig. 5 D). The negative correlation between soft scald development and SSC at harvest might be partially due to the variation in SSC between orchards and years, where
actual bitter pit from the fruit stored at 3 °C and the range was 5-35 % for predicted compared with 0-88 % for actual incidence for conditioning +3 °C fruit. For mineral concentrations and their ratios, the predicted bitter pit ranged between 2-34 % compared with the actual bitter bit from the unconditioned fruit while the predicted bitter pit ranged from 2-51 % compared with the range of actual bitter pit from conditioned fruit. The correlation with harvest indices, mineral concentrations and their ratios were greater at 3 °C with than without conditioning because of higher bitter pit incidence. The harvest indices predicted 27-28 % and 21-26 % actual bitter pit, while the prediction of bitter pit by minerals and mineral ratios were 22-55 % and 18-54 % with and without conditioning, respectively (Table 1). These results indicate that there was a wider range for bitter pit prediction based on mineral concentrations and their ratios than those based on harvest indices. Predictions for soft scald based on harvest indices ranged from 039%, while those based on mineral concentrations and their ratios ranged from 0-35 % compared with 0-69 % for actual disorder incidence (Table 5). The harvest indices predicted 29-57 % soft scald, while minerals and mineral ratios predicted 29-49 % soft scald (Table 5). Overall, harvest indices had a slightly wider range of prediction for soft scald compared with mineral concentrations and their ratios. 3.5. NIPALS models for bitter pit and soft scald in relation to harvest indices, fruit minerals and their ratios Variables were classified by VIP vs coefficients to build models 7
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Fig. 5. Nonlinear iterative partial least square (NIPALS) algorithm based on the variable importance plot (VIP) for soft scald (%) in ‘Honeycrisp’ apples after storage at 0.5 °C against harvest indices and minerals from peel tissues and their ratios at harvest for three years, where X- (harvest indices or minerals and their ratios at harvest) and Y- variable (soft scald incidence after storage). (A, B) soft scald against harvest indices, (C, D) soft scald against minerals and their ratios at harvest.
SSC ranged from 10-16% over five years (Table S2). In conclusion, IEC and firmness were the only harvest indices that correlated with bitter pit development at 3 °C either with or without conditioning. Correlations of bitter pit against minerals and mineral ratios were higher at 3 °C after conditioning compared with unconditioned fruit. According to NIPALS analysis, Ca only satisfactorily correlated bitter bit at 3 °C in conditioned fruit compared with unconditioned fruit. The bivariate (individual variable) and NIPALS (combined variables) analyses confirm that harvest indices had higher correlations, while minerals and their ratios had lower correlations with soft scald compared with bitter pit development. A negative correlation between soft scald and bitter pit might be a good indicator to predict soft scald based on the bitter pit prediction but more research is needed.
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Declaration of Competing Interest None. Acknowledgments This work was supported by the New York Apple Research and Development Program, and the National Institute of Food and Agriculture, U.S. Department of Agriculture, Multistate under 1001075, NE-1836, Improving Quality and Reducing Losses in Specialty Fruit Crops through Storage Technologies. 8
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