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Comparisons of mineral and non-mineral prediction methods for bitter pit in ‘Honeycrisp’ apples
Scientia Horticulturae 254 (2019) 116–123
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Comparisons of mineral and non-mineral prediction methods for bitter pit in ‘Honeycrisp’ apples Yosef Al Shoffea, Jacqueline F. Nocka, Yiyi Zhanga, Li-wu Zhua,b, Christopher B. Watkinsa, a b
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Horticulture Section, School of Integrative Plant Science, Plant Science Building, Cornell University, Ithaca, NY 14853, USA Key Laboratory of Pomology, Anhui Agricultural University Hefei, Anhui, 230036, China
ARTICLE INFO
ABSTRACT
Keywords: Malus × domestica Borkh Bitter pit Minerals Prediction methods Postharvest management
Bitter pit is a physiological disorder that can develop during storage and result in major economic losses of susceptible apple cultivars such as ‘Honeycrisp’. Prediction methods to determine the risk of disorder development have focused primarily on mineral composition of the fruit, but there has also been interest in nonmineral based methods. In this study, we have compared non-mineral methods with mineral analyses for bitter pit prediction. Fruit of ‘Honeycrisp’ were harvested three weeks before anticipated commercial harvest and at commercial harvest. Mineral contents in peel from the calyx-end were measured in fruit at both harvest times. In year 1, fruit were kept at 20 °C (passive method), dipped in 2000 mg L−1 2-chloroethylphosphonic acid (ethylene method) or 0.8 M MgCl2 (Mg method). Treated fruit were then kept at 20 °C for three weeks. At commercial harvest, fruit were stored at 0.5 or 3 °C with and without 1 week of conditioning at 10 °C. The Mg method showed toxicity on the fruit, which was hard to distinguish from bitter pit. Therefore, in year 2, only the passive and ethylene methods were used, and fruit from the commercial harvest were stored at 3 °C with or without conditioning. In year 3, only the passive method was used because of its simplicity and lack of registration of ethephon for this purpose. Fruit from the commercial harvest of 38 orchards in three growing regions were stored at 3 °C after conditioning. Fruit were stored for two months in year 1 and four months in years 2 and 3, and assessed after 4 d at 20 °C. Multivariate analysis shows that the passive and ethylene methods for fruit harvested three weeks before the anticipated harvest had higher or similar correlations with the actual bitter pit after cold storage than those from minerals. Although actual bitter pit incidence was higher than that predicted from the passive method, the method results in predictions that are similar to mineral-based predictions, and also is more straightforward for commercial application.
1. Introduction Bitter pit is a physiological disorder of susceptible apple cultivars that develops as necrotic pits, primarily in the calyx-end of the fruit, but which can extend throughout cortical tissues when severe (Ferguson and Watkins, 1989; Jemrić et al., 2016; Watkins and Mattheis, 2019). The disorder can be observed on fruit in the orchard, but usually develops during storage. Bitter pit has received research attention because it can cause high economic losses in key cultivars, depending on growing region, as in ‘Gala’ apples in Brazil (Amarante et al., 2005), ‘Golden Smoothee’ in Europe (Torres et al., 2015), and ‘Cox’s Orange Pippin’ in New Zealand (Ferguson and Watkins, 1992). In North America, ‘Honeycrisp’ is an apple cultivar that is highly profitable for growers due to consumer demand for its flavor and crisp texture. However, the cultivar is susceptible to a large number of physiological
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disorders (Al Shoffe et al., 2016), the most problematic being soft scald, soggy breakdown and bitter pit (Al Shoffe et al., 2018; Al Shoffe and Watkins, 2018; DeEll et al., 2016). These losses can be devastating because fruit placed into storage are free of disorders. In ‘Honeycrisp’, losses due to bitter pit are exacerbated because of the commercial conditioning treatment of 7 d at 10 °C used to reduce risk of soft scald and soggy breakdown development, as well as recommended storage temperature of 3 °C (Watkins et al., 2004). Increased bitter pit incidence due to conditioning averages about 58% compared with that found in fruit without conditioning (unpublished data). The relationship between low Ca contents in fruit and higher bitter pit risk has long been recognized, and foliar application of Ca products reduce bitter pit development in susceptible cultivars (Ferguson and Watkins, 1989; Jemrić et al., 2016), including ‘Honeycrisp’ (Biggs and Peck, 2015; Rosenberger et al., 2004). These relationships have also led
Corresponding author. E-mail address: [email protected] (C.B. Watkins).
https://doi.org/10.1016/j.scienta.2019.04.073 Received 20 February 2019; Received in revised form 25 April 2019; Accepted 26 April 2019 Available online 09 May 2019 0304-4238/ © 2019 Elsevier B.V. All rights reserved.
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to development of prediction models for various cultivars; models have been developed based on Ca alone, but usually in relation to other minerals including Mg, K and N (Bangerth, 1979; de Freitas and Mitcham, 2012d; Kalcsits, 2016; Perring, 1986). Bitter pit incidence is related to high ratios of Mg/ Ca in ‘Fuji’ apples (do Amarante et al., 2013d), Mg + K + N/ Ca in ‘Honeycrisp’ apples (Baugher et al., 2017), Mg + K/ Ca in ‘Cox Orange Pippin’ (Van Der Boon, 2015) and ‘Granny Smith’ apples (de Freitas et al., 2010d), and Mg/ Ca and K/ Ca in ‘Catarina’ apples (do Amarante et al., 2018d). Regression coefficients vary among studies however, and can range from as low as 0.4 to 0.9 (Autio et al., 1986; Baugher et al., 2017; Ferguson and Watkins, 1989; Peryea et al., 2007; Torres et al., 2017). Many factors affect reliability of mineral analysis to predict bitter pit. Examples include higher bitter pit incidence and lower Ca and higher K concentrations in fruit from light crop load compared with heavy crop load trees, and in fruit from the upper canopy of the tree compared with those in the lower canopy (Ferguson and Triggs, 1990; Ferguson and Watkins, 1992; Musacchi and Serra, 2018; Wünsche and Ferguson, 2005). In addition to mineral based methods for bitter pit prediction, a number of non-mineral methods for prediction of bitter pit have been described. These methods include treatments with Mg salts (Amarante et al., 2010; Burmeister and Dilley, 1994; Retamales et al., 2000; Torres et al., 2015). Vacuum infiltration with 0.05- 0.1 M MgCl2 for 2 min. in ‘Braeburn’ apples was used to predict bitter pit incidence of fruit after 90 d of storage at 2 °C + 10 d at 18 °C (Retamales et al., 2001). Ethylene was used to accelerate ripening either by dipping to 2000 mg L−1 ethephon or fumigation with 10,000 mg L−1 of acetylene gas (Eksteen et al., 1977; Lötze and Theron, 2006; Lötze et al., 2010). A passive method where fruit are kept at warm temperatures (e.g. 20 °C) to allow bitter pit to develop before harvest has also been developed (England and Larsen, 1973), and shown to be reliable for prediction of bitter pit development of ‘Golden Smoothee’ apples when applied at 20, 40, and 60 d before harvest. (Torres and Alegre, 2012; Torres et al., 2015). Torres et al. (2015) noted that the passive and ethylene methods in different commercial orchards over two years had similar regression coefficients. The authors concluded that the passive method is reliable for assessing bitter pit risk in ‘Golden Smoothee’ apples, but that it needs to be assessed for different cultivars and growing regions. In addition, there was no comparison of mineral-based prediction methods in that study. The objective of our study was to evaluate non-mineral prediction methods used to induce bitter pit in ‘Honeycrisp’ apples, and compare the usefulness of these methods with those based on mineral analyses. In year 1, we investigated the effects of three non-mineral methods, passive, ethylene and Mg, as inducers of bitter pit before commercial harvest. Based on these results the passive and ethylene methods were further investigatied in year 2. Because the results of the two methods were similar, the ease of use for application of the passive method, and absence of registration of ethephon for this purpose, we chose the passive method for evaluation across a large number of orchards across three growing regions in year 3.
fruit were harvested from six orchards in each of HV and WNY (commercial harvest dates of September 5 and September 13, respectively). In 2018 (year 3), fruit were harvested from 12 orchards in HV, 18 orchards in WNY, and 8 orchards in Champlain NY (commercial harvest dates of September 6, September 13, and September 18, respectively). In 2016 and 2017, fruit were harvested from 24 trees in three different rows, while in year 3, fruit were harvested from 10 to 12 trees per orchard block. In all years, trees were selected randomly, but were representative of the orchard block in terms of tree vigor and crop load. Trees were tagged so that fruit for prediction studies and storage were harvested from the same trees. In 2016 and 2017, 70 fruit per replicate were collected per orchard using 40 fruit for storage, 20 fruit for minerals and 10 fruit for maturity indices. Fruit from the early harvest were assessed twice weekly in year one and once weekly in year two for three weeks to study the dynamic of bitter pit development from the prediction methods. In 2018, 120 fruit were collected per orchard using 100 fruit for storage and 20 fruit for minerals and maturity indices at each harvest date. Fruit were transported to Ithaca on the day of harvest, and harvest indices assessed within 24 h. 2.2. Mineral analyses Mineral contents were measured in fruit peel taken from the calyxend of 20 fruit per orchard block at each harvest time after washing fruit in Palmolive clear dish detergent followed by rinsing in distilled water. Peel samples were kept in aluminum foil inside paper bags, dried to a constant weight, and then ground to a fine powder. The powder was kept in plastic bags in glass jars containing anhydrous calcium sulfate until further use. The samples were analyzed at the nutrient analysis laboratory at Cornell University; 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 content using a Spectro Arcos axial viewed ICP-OES (SPECTRO Analytical Instruments Inc. AMETEK®, Kleve, Germany,). The results were expressed as g 100 g−1 on a dry weight basis. 2.3. Harvest indices The IEC of each fruit was measured by injecting 1 mL of gas sample taken from the core cavity as described by Watkins et al. (2000). Samples were injected into a Hewlett-Packard 5890 series II gas chromatograph (Wilmington, DE) equipped with a flame ionization detector and 3394 A integrator. Temperatures were 230 °C and 245 °C for the injector and detector with the oven run isothermally at 160 °C. The stainless steel column (2 m x 3 mm id) was packed with 60/80 mesh Alumina F-1. Flow rates were 30 ml min−1 for the nitrogen carrier gas, while the detector was supplied with 30 ml min−1 hydrogen and 230 ml min−1 compressed air. Firmness was measured on opposite peeled sides of each fruit using an 11.1 mm diameter probe (Guss Manufacturing (Pty) Ltd., Strand, South Africa) and the expressed juice 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 (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 IAD index was measured using a Delta Absorbance (DA) meter (TR Turoni srl, Forli, Italy).
2. Materials and methods 2.1. Fruit source ‘Honeycrisp’ apples (Malus × domestica Borkh) used for these trials were obtained from the three major growing regions in New York (NY), Western NY (WNY), Hudson Valley (HV) and the Champlain Valley (Champlain), depending on year. The fruit were harvested at three weeks before anticipated harvest and at commercial harvest. In 2016 (year 1), fruit were harvested from two orchards in WNY (commercial harvest date of September 21), and in the 2017 (year 2) harvest season,
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2.4. Non-mineral treatments to induce bitter pit development
of bitter pit development with the three prediction methods over time were also studied. In year 2, passive and ethylene methods were compared using several orchards, and then in year 3, the passive method was selected for large scale testing across orchards and growing regions. In year 1, fruit from two orchard blocks were used (Table 1). Bitter pit increased over time at 20 °C in the passive and ethylene methods (Fig. 1). The effects of Mg over time were similar (data not shown) but toxicity from Mg, probably because fruit were not washed after treatment, made it difficult to distinguish bitter pit from toxicity symptoms on the skin. However, we found that bitter pit incidence in Mg-treated fruit was similar to that in the passive and ethylene treatments when fruit were assessed at week 3 (Fig. 2). In one of two orchards the passive treatment under-estimated bitter pit incidence compared with the other methods and with that found in fruit after storage (Fig. 2). The dynamics of bitter pit development using predictive methods are comparable with those of England and Larsen (1973) who found that fruit harvested prior to commercial harvest developed bitter pit during 1–3 weeks in ‘Goldspur’ and ‘Wellspur’ apples, and Torres et al. (2015) found that 10–30 d before anticipated harvest is a reliable time to predict the disorder in ‘Golden Smoothee’ apples. Burmeister and Dilley (1994) found high correlations with bitter pit incidence after storage in air at 5 °C or at 3 °C in air or CA for ‘Northern Spy’ apples when fruit were infiltrated with Mg 10 d before commercial harvest. The correlations between the predicted bitter pit from the non-mineral methods against the actual bitter pit after harvest overall were 0.88, 0.92 and 0.88 for the Mg, ethylene, and passive method, respectively, being similar to those of Torres et al.(2015) who found the respective R2 values were 0.81, 0.89, and 0.79 from fruit harvested 20 d before anticipated harvest. In our study, we focused on prediction based on bitter pit that developed in storage after the commercially used conditioning period of 10 °C for 7 d followed by storage at 3 °C. However, bitter pit development at 3 °C without condition, and 0.5 °C with and without conditioning, was also evaluated. The lowest bitter pit incidence of 6% occurred in the continuous 0.5 °C treatment compared with 38% in continuous 3 °C treatment, and 33% in conditioned fruit stored at 0.5 °C. Nevertheless, the correlations between actual bitter pit from 3 °C with or without conditioning and 0.5 °C with conditioning with predicted bitter pit from the three non-mineral methods were significant. These ranged between 0.75-0.92, while the correlation of bitter pit from continuous 0.5 °C and the non-mineral methods was 0.32, 0.11, and 0.23 for passive, ethylene, and Mg, respectively (Table 2). Also, the three non-mineral prediction methods did not show significant differences in the bitter pit incidence, with 28, 39 and 37% for passive, ethylene and Mg methods, respectively, either between each other or compared with actual bitter pit from continuous 3 °C or conditioned 0.5 and 3 °C. The passive method showed a wider range of bitter pit compared with ethylene, which ranged between 8–51%, while the Mg
Fruit picked three weeks before the commercial harvest date were subjected to three non-mineral prediction methods in 2016. For the passive method, the fruit were kept at 20 °C. For the magnesium method, fruit were dipped into 0.18 M MgCl2 (≥ 98%, Sigma-Aldrich, USA) solution + 0.05% silicon surfactant (Widespread Max) for 2 min. For the ethylene method, fruit were dipped into 2000 mg L−1 ethephon (Motivate™, Fine Americas, Inc, Walnut Greek, CA, USA) (9.22 mL L−1) + 0.05% silicon surfactant (Widespread Max) for 5 min. 2.5. Fruit storage In 2016, four sets of four replicates of 40 fruit from the two harvest dates were stored at 0.5 and 3 °C with and without one week of conditioning at 10 °C for two months. In 2017, two sets of fruit in three replicates of 40 fruit were stored at 3 °C with and without one week of conditioning at 10 °C for 4 months. In 2018, 100 fruit per orchard were stored at 3 °C after one week of conditioning at 10 °C for 4 months. 2.6. Assessment of physiological disorders External and internal physiological disorders were assessed visually. Fruit were cut transversely at least 5 times from the calyx end. The disorders were calculated as a percentage of fruit with disorders from the total fruit number. Bitter pit was the only consistent disorder in this study, soft scald and soggy breakdown occurring in year 1 when the fruit were stored at 0.5 °C with or without conditioning (data not shown). Therefore, only bitter pit incidence is shown in this paper. 2.7. Statistical analysis Tukey and Student tests were used to compare means at the 5% confidence level. Multivariate and bivariate analyses were used for the coefficient of correlation, the correlation probability, and the coefficient of determination among factors. A neural network, which is a powerful computational data model that is able to capture and represent complex input/output relationships (Noh and Lu, 2007), was also used. All statistics were carried out using the JMP statistical program (JMP Pro 12.INK). Percentage data were arcsine transformed for analysis, and presented as back-transformed means. 3. Results and discussion 3.1. Non-mineral methods for bitter pit prediction Non-mineral methods were evaluated over three seasons. In year 1, passive and treatments with ethylene or Mg were tested. The dynamics
Table 1 Harvest indices of ‘Honeycrisp’ apples from 2 orchards in WNY in 2016 (Year 1), 6 orchards in each of HV and WNY in 2017 (Year 2), and 38 orchards from HV, WNY, and Champlain in 2018 (Year 3). Not sigificant (NS) at P < 0.05. Year 1 2
Region
WNY HV WNY Significance 3 HV WNY Champlain Significance Correlation with actual bitter pit (3 years) Significance
IEC (μl L−1)
Firmness (N)
SSC (%)
TA (%)
SPI
IAD index
1.05 11.1 3.4 0.001 7.2 10.1 15.7 NS −0.37 0.0007
70.6 64.7 65.8 NS 64.8 66.5 60.4 0.0004 0.42 < .0001
12.9 10.8 11.1 NS 10.6 10.9 11.3 NS −0.03 NS
0.71 0.63 0.59 0.04 0.59 0.62 0.55 NS 0.29 0.0072
4.9 5.3 5.7 NS 4.2 3.9 6.1 0.0001 −0.45 < .0001
0.53 0.91 0.84 NS 1.06 0.92 0.96 NS 0.04 NS
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Fig. 1. Dynamic of bitter pit development from fruit harvested three weeks before anticipated harvest, Passive method (A), or Ethylene method (B), from two orchards in WNY in 2016. Data are presented as means ± SE.
an application, but expanded to 38 orchards across the three major growing regions in NY (Table 1). Means for individual orchards are provided in (Table S1). The coefficient of correlation for the passive method with the actual bitter pit from fruit conditioned and then stored at 3 °C was 0.91. 3.2. Relationships between harvest indices and bitter pit development Bitter pit after storage correlated negatively with IEC and SPI and positively with firmness and TA at harvest, but not with SSC or IAD index (Table 1). Bitter pit incidence and senescent breakdown in ‘Honeycrisp’ apples is associated with early and later harvests, respectively (Prange et al., 2015). These associations were also related with the IAD index (DeLong et al., 2014). The negative correlation for bitter pit with IEC and SPI is consistent with lower bitter pit incidence with greater maturity, but not found for the IAD index. Our study was limited to first commercial harvest in each region, and therefore the range of fruit maturities was limited. Although we have not found close associations between harvest indices and bitter pit development under New York conditions (Watkins et al., 2005), further research is needed.
Fig. 2. Bitter pit in 'Honeycrisp' apples from two orchards (O1 and O2), predicted in fruit harvested 3 weeks before anticipated harvest and untreated (passive), or treated with ethylene or Mg, and kept at 20 °C for 3 weeks, compared with bitter pit in fruit stored at 3 °C after one week of conditioning at 10 °C for 2 months (Actual), different lower case letters indicate a significant difference at the P ≤ 0.05 level (Tukey’s test). Data are presented as means ± SE.
3.3. Relationships between minerals and bitter pit development
method showed the highest estimation for bitter pit compared with the other non-mineral methods, with a value of 65% (Table S2). The difficulty of assessing damage compared with bitter pit limits the utility of the Mg method, and vacuum infiltration is not an easy method to apply commercially. Therefore, in year 2, we focused on the passive and ethylene methods. Fruit from six orchards in each of two regions were used (Table 1). Overall, fruit maturity was similar in both regions, except that IECs and TA were higher in fruit from the HV region than from WNY (Table 1), and sometimes they were variable within a region (Table S1). The coefficient of correlation values for passive and ethylene methods were similar at 0.81 and 0.84, respectively. In year 3, we chose to further restrict analysis of prediction methods to the passive one alone, given lack of registration of ethylene for such
The effects of region on concentrations and ratios of minerals were variable between years 1, 2 and 3 (Tables 3, S2, S3, and S4). In year 2, the concentrations of Ca, K, and Mg were lower and their ratios were higher in WNY compared with HV fruit. In year 3, HV fruit had higher P, K, and mineral ratios compared with WNY and Champlain regions, whereas Mg was higher in Champlain. The effects of harvest time were more consistent, however, with lower concentrations at commercial harvest than 3 weeks before commercial harvest date (Table 3) as would be expected because of increasing fruit size over this time period. Multivariate analyses of mineral concentrations and ratios were carried out for each year (Table 4). For fruit harvested three weeks before commercial harvest, bitter pit incidence was positively associated with P and K concentrations, negatively with Ca concentrations, but no association with those of Mg were detected. However, the ratios
Table 2 Multivariate analysis for actual bitter pit in 'Honeycrisp' apples stored at 0.5 or 3 °C after one week of conditioning (C) at 10 °C, predicted bitter pit from fruit harvested 3 weeks before anticipated harvest and dipped or not in 2000 mg L−1 ethephon or 0.18 M MgCl2 and kept at 20 °C for 3 weeks in year 1. Actual bitter pit (%) 0.5 °C
C + 0.5 °C
3 °C
C+ 3 °C
Method
R
Significance
R
Significance
R
Significance
R
Significance
Passive Ethylene Magnesium
0.34 0.11 0.23
NS NS NS
0.77 0.75 0.80
0.03 0.03 0.02
0.80 0.87 0.82
0.02 0.01 0.01
0.88 0.92 0.88
0.004 0.001 0.004
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Table 3 Mineral concentrations (g 100 g−1) and their ratios for 'Honeycrisp' apples picked at 3 weeks before anticipated harvest (3WBH) or at commercial harvest (H) for 2 orchards in WNY in 2016 (Year 1), 6 orchards in each of HV and WNY in 2017 (Year 2), and 38 orchards from HV, WNY, and Champlain in 2018 (Year 3). Not significant (NS) at P < 0.05. Year 1
Region Harvest
2
Region Harvest
3
Region
Harvest
WNY 3WBH H Significance HV WNY Significance 3WBH H Significance HV WNY Champlain Significance 3WBH H Significance
P
K
Ca
Mg
Mg/Ca
K/Ca
P/Ca
(K + Mg)/Ca
(K + Mg + P)/Ca
0.15 0.15 0.15 NS 0.11 0.10 NS 0.12 0.09 0.001 0.09 0.08 0.07 0.0002 0.08 0.08 NS
1.19 1.25 1.14 0.02 0.91 0.84 0.009 0.98 0.77 < 0.0001 0.72 0.61 0.53 < 0.0001 0.73 0.50 < 0.0001
0.04 0.04 0.03 NS 0.05 0.03 < 0.0001 0.05 0.04 < 0.0001 0.04 0.05 0.05 NS 0.05 0.05 NS
0.09 0.09 0.08 0.02 0.10 0.09 < 0.0001 0.11 0.08 < 0.0001 0.09 0.09 0.10 0.008 0.10 0.09 0.02
2.5 2.5 2.4 NS 2.0 2.8 < 0.0001 2.5 2.3 NS 2.3 2.1 2.3 NS 2.3 2.1 NS
34.0 34.8 33.2 NS 17.7 27.1 < 0.0001 23.2 21.6 NS 17.8 14.1 11.8 0.0004 17.6 11.5 < 0.0001
4.2 4.0 4.4 NS 2.2 3.3 < 0.0001 2.8 2.7 NS 2.3 1.9 1.6 0.008 2.0 1.9 NS
36.5 37.3 35.6 NS 19.7 29.9 < 0.0001 25.7 23.9 NS 20.1 16.2 14.1 0.001 19.9 13.6 < 0.0001
40.7 41.4 40.0 NS 21.9 33.2 < 0.0001 28.5 26.6 NS 22.4 18.2 15.7 0.001 22.0 15.5 < 0.0001
of the minerals were highly and consistently associated with bitter pit incidence in both years. In contrast, the associations between mineral ratios and bitter pit were less consistent in fruit analyzed at harvest, especially in year 2, for which correlation coefficients and P values were lower than for the earlier sampling. The correlation between fruit minerals and their ratios with bitter pit development in apple fruit has been investigated for several cultivars (Baugher et al., 2017; Biggs and Peck, 2015; Fallahi et al., 2010; Ferguson and Watkins, 1989; Rosenberger et al., 2004; Telias et al., 2006). As an example, bitter pit was associated with lower Ca and higher K and Mg concentrations (Baugher et al., 2017; Perring, 1986). Also, the regression coefficient between bitter pit at harvest and Ca was the highest at 60 days after full bloom in ‘Golden Smoothee’ apples with variation between years ranging from 0.1- 0.74 (Torres et al., 2017).
to each other. In year 2, multivariate analyses resulted in correlation coefficient values of 0.81 and 0.84 for the passive and ethylene methods, respectively, and in year 3, 0.91 for passive alone. These values are consistently higher than those found for mineral concentrations and similar to or higher than those of their ratios (Table 4). Based on the strength of the correlation for the passive method from the multivariate analysis, we ran bivariate analyses to obtain the determination coefficient for the percentage variation in actual bitter pit that is explained by predicted bitter pit (Fig. 3A–C). Bivariate analysis examine the relationship between two variables and shows percentage variation in which y is explained by all the x variables together. 3.5. Development of models of bitter pit prediction using the passive method The bivariate analysis of the predicted bitter pit using the passive method against the actual bitter pit after storage from the two regions showed R2 of 0.66, and adjusted R2 of 0.65 (Fig. 3A). The multivariate analyses in year 3 also showed the highest correlation coefficient (0.91) for the passive method against actual bitter pit
3.4. Comparison between non-mineral and mineral methods for bitter pit prediction Multivariate analysis shows how several continuous variables relate
Table 4 Multivariate analysis for minerals and ratios taken 3 weeks before harvest (3WBH) and at harvest (H) against bitter pit in 'Honeycrisp' apples stored at 3 °C after one week of conditioning at 10 °C. Not significant (NS) at p < 0.05. Year 2 R 3WBH
H
P K Ca Mg Mg/Ca K/Ca P/Ca (K + Mg)/Ca (K + Mg + P)/ca N P K Ca Mg Mg/Ca K/Ca P/Ca N/Ca (K + Mg)/Ca (K + Mg + P)/ca (K + Mg + P+N)/Ca
0.35 0.58 −0.48 0.16 0.75 0.75 0.69 0.75 0.76 −0.34 0.23 0.19 −0.41 0.15 0.55 0.49 0.43 0.37 0.49 0.15 0.49
Year 3 Significance 0.0382 0.0002 0.0031 NS < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.0313 NS NS 0.0132 NS 0.0005 0.0024 0.0094 0.0276 0.002 NS 0.0025
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R 0.36 0.50 −0.67 0.06 0.84 0.75 0.72 0.77 0.77 0.02 0.34 0.27 −0.59 0.19 0.66 0.64 0.62 0.36 0.66 0.68 0.62
Two years Significance 0.0281 0.0014 < 0.001 NS < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 NS 0.0347 0.0979 0.0001 NS < 0.0001 < 0.0001 < 0.0001 0.028 < 0.0001 < 0.0001 < 0.0001
R 0.16 0.23 −0.57 0.03 0.73 0.62 0.54 0.63 0.63 −0.19 0.23 −0.01 −0.40 0.23 0.53 0.21 0.35 0.17 0.23 0.25 0.24
Significance NS 0.0466 < 0.0001 NS < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 NS NS NS 0.0004 0.046 < 0.0001 NS 0.0025 NS 0.0447 0.0315 0.0384
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Fig. 3. Bivariate analyses for actual bitter pit for fruit stored at 3 °C after one week of conditioning at 10 °C for 4 m + 4 d at 20 °C against predicted bitter pit from passive method in 2017 (A), 2018 (B), and three successive years (C). The dashed line represents a 1:1 ratio of actual and predicted bitter pit.
Fig. 4. Neural analyses for actual bitter pit for fruit stored at 3 °C after one week of conditioning at 10 °C for 4 m + 4 d at 20 °C against predicted bitter pit from passive method in three successive years, training (A) and validation (B). The dashed line represents a 1:1 ratio of actual and predicted bitter pit.
compared with the minerals at two harvest dates. The bivariate analyses for predicted bitter pit from the passive method against the actual bitter pit resulted in a R2 of 0.82 (Fig. 3B). We observed a consistent range of bitter pit development from the passive method of 8–51, 0–50, and 1–55% for year 1, 2, and 3, respectively (Tables S2, S3, S4). The combined data from years 2 and 3 show that the bitter pit from the passive method had the highest correlation against the actual bitter pit after storage compared with those from minerals at two harvest dates (Table 4). Also, the bivariate analyses showed that the R2 for the bitter pit from the passive method against the actual bitter pit from three years data was 0.68 (Fig. 3C). Based on the stability of the results from the passive method in three years, a neural network model was used for validation of the method. The R2 of this model was 0.78 (Fig. 4). The predicted bitter pit was 0, 14, 28, 42, and 55% against 6, 22, 35, 53, and 65% for the actual bitter pit. The bivariate analyses shows a R2 of 0.99 (Fig. 5) with the following prediction formula:
Actual bitter pit (%) = 6 + Predicted bitter pit (%) In conclusion, non-mineral methods showed as good or higher correlation with actual bitter pit as that found with mineral analysis. Of the three methods investigated in this study, the passive method showed consistent results in different orchards, in different regions, and over successive years. Its simplicity makes the passive method a powerful and reliable method that can used easily by growers or storage operators to predict of bitter pit in ‘Honeycrisp’ apples. Ethylene is not labelled for use as a postharvest ripening agent, while Mg application is relatively cumbersome to use. The passive method does under-estimate the incidence of bitter pit in fruit after storage, but this is more acceptable than over-estimation from an industry perspective. The nonchemical method eliminates the need to prepare tissue samples and carry out mineral analyses.
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Factors involved in fruit calcium deficiency disorders. Hortic. Rev. 40, 107–146. de Freitas, S.T., Amarante, C.V.T.do, Labavitch, J.M., Mitcham, E.J., 2010d. Cellular approach to understand bitter pit development in apple fruit. Postharvest Biol. Technol. 57, 6–13. https://doi.org/10.1016/j.postharvbio.2010.02.006. DeEll, J.R., Lum, G.B., Ehsani-Moghaddam, B., 2016. Effects of delayed controlled atmosphere storage on disorder development in ‘Honeycrisp’ apples. Can. J. Plant Sci. 96, 621–629. https://doi.org/10.1139/cjps-2016-0031. DeLong, J., Prange, R., Harrison, P., Nichols, D., Wright, H., 2014. Determination of optimal harvest boundaries for Honeycrisp™ fruit using a new chlorophyll meter. Can. J. Plant Sci. 94, 361–369. https://doi.org/10.4141/cjps2013-241. do Amarante, C.V.T., Miqueloto, A., Freitas, S.T., Steffens, C.A., Silveira, J.P.G., Corrêa, T.R., 2013d. 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Fig. 5. Bivariate analyses for actual bitter pit for fruit stored at 3 °C after one week of conditioning at 10 °C for 4 m + 4 d at 20 °C against predicted bitter pit from passive method in three successive years after validation. The dashed line represents a 1:1 ratio of actual and predicted bitter pit.
Declarations of interest None. Acknowledgments This research was supported in part by the NY Apple Research and Development Program, and the USDA National Institute of Food and Agriculture, Hatch project 2013-14-483, Improving Quality and Reducing Losses in Specialty Fruit Crops through Storage Technologies (NE-1336). Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the National Institute of Food and Agriculture (NIFA) or the United States Department of Agriculture (USDA). We would like to thank Dan Donahue and Mike Basedow, Eastern New York Commercial Horticulture Program, for assistance with coordination of fruit harvest. Li-wu Zhu was funded by Foundation of the Foreign Visiting Scholars of Anhui Higher education Revitalization (gxfxZD 2016023). 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.scienta.2019.04.073. References 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. 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., 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. Amarante, C.V.T., Steffens, C.A., Ernani, P.R., 2010. Preharvest identification of bitter pit risk in’ Gala’ apples by fruit infiltration with magnesium and analysis of fruit contents of calcium and nitrogen. Rev. Bras. Frutic. 32, 027–034. https://doi.org/10.1590/ s0100-29452010005000015. Amarante, C.V.T., Ernani, P.R., Chaves, D.V., 2005. Fruit infiltration with magnesium Is a feasible way to predict bitter pit susceptibility in ´Gala´ apples grown in southern Brazil. Acta Hortic. 682, 1271–1274. https://doi.org/10.17660/ActaHortic.2005. 682.169. Autio, W., Bramlage, W., Weis, S., 1986. Predicting poststorage disorders of’Cox’s Orange Pippin’and’Bramley’s Seedling’apples by regression equations. J. Am. Soc. Hortic. Sci. 111, 738–742. Bangerth, F., 1979. Calcium-related physiological disorders of plants. Annu. Rev.
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Y. Al Shoffe, et al. Torres, E., Alegre, S., 2012. Predicting bitter pit in’ Golden Smoothee’ apples. Acta Hortic. 934, 861–864. https://doi.org/10.17660/ActaHortic.2012.934.114. Torres, E., Recasens, I., Peris, J.M., Alegre, S., 2015. Induction of symptoms pre-harvest using the ‘passive method’: an easy way to predict bitter pit. Postharvest Biol. Technol. 101, 66–72. https://doi.org/10.1016/j.postharvbio.2014.11.002. Torres, E., Recasens, I., Àvila, G., Lordan, J., Alegre, S., 2017. Early stage fruit analysis to detect a high risk of bitter pit in ‘Golden Smoothee’. Sci. Hortic. 219, 98–106. https:// doi.org/10.1016/j.scienta.2017.03.003. Van Der Boon, J., 2015. Prediction and control of bitter pit in apples. I. Prediction based on mineral leaf composition, cropping levels and summer temperatures. J. Hortic. Sci. 55, 307–312. https://doi.org/10.1080/00221589.1980.11514939. Watkins, C.B., Mattheis, J.P., 2019. Apples. In: de Freitas, S.T., Pareek, S. (Eds.), Postharvest Physiological Disorders in Fruits and Vegetables. CRC Press, pp. 165–206 Chapter 8, ISBN 9781138035508.
Watkins, C.B., Nock, J.F., Whitaker, B.D., 2000. Responses of early, mid and late season apple cultivars to postharvest application of 1-methylcyclopropene (1-MCP) under air and controlled atmosphere storage conditions. Postharvest Biol. Technol. 19, 17–32. https://doi.org/10.1016/S0925-5214(00)00070-3. Watkins, C.B., Nock, J.F., Weis, S.A., Jayanty, S., Beaudry, R.M., 2004. Storage temperature, diphenylamine, and pre-storage delay effects on soft scald, soggy breakdown and bitter pit of ‘Honeycrisp’ apples. Postharvest Biol. Technol. 32, 213–221. https://doi.org/10.1016/j.postharvbio.2003.11.003. Watkins, C.B., Erkan, M., Nock, J.F., Iungerman, K.A., Beaudry, R.M., Moran, R.E., 2005. Harvest date effects on maturity, quality, and storage disorders of’ Honeycrisp’ apples. HortScience 40, 164–169. https://doi.org/10.21273/HORTSCI.40.1.164. Wünsche, J.N., Ferguson, I.B., 2005. Crop load interactions in apple. Hort. Rev 31, 231–290. https://www.wiley.com/en-us/Horticultural+Reviews%2C+Volume +31-p-9780470650875.
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Comparisons of mineral and non-mineral prediction methods for bitter pit in ‘Honeycrisp’ apples Yosef Al Shoffea, Jacqueline F. Nocka, Yiyi Zhanga, Li-wu Zhua,b, Christopher B. Watkinsa, a b
T
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Horticulture Section, School of Integrative Plant Science, Plant Science Building, Cornell University, Ithaca, NY 14853, USA Key Laboratory of Pomology, Anhui Agricultural University Hefei, Anhui, 230036, China
ARTICLE INFO
ABSTRACT
Keywords: Malus × domestica Borkh Bitter pit Minerals Prediction methods Postharvest management
Bitter pit is a physiological disorder that can develop during storage and result in major economic losses of susceptible apple cultivars such as ‘Honeycrisp’. Prediction methods to determine the risk of disorder development have focused primarily on mineral composition of the fruit, but there has also been interest in nonmineral based methods. In this study, we have compared non-mineral methods with mineral analyses for bitter pit prediction. Fruit of ‘Honeycrisp’ were harvested three weeks before anticipated commercial harvest and at commercial harvest. Mineral contents in peel from the calyx-end were measured in fruit at both harvest times. In year 1, fruit were kept at 20 °C (passive method), dipped in 2000 mg L−1 2-chloroethylphosphonic acid (ethylene method) or 0.8 M MgCl2 (Mg method). Treated fruit were then kept at 20 °C for three weeks. At commercial harvest, fruit were stored at 0.5 or 3 °C with and without 1 week of conditioning at 10 °C. The Mg method showed toxicity on the fruit, which was hard to distinguish from bitter pit. Therefore, in year 2, only the passive and ethylene methods were used, and fruit from the commercial harvest were stored at 3 °C with or without conditioning. In year 3, only the passive method was used because of its simplicity and lack of registration of ethephon for this purpose. Fruit from the commercial harvest of 38 orchards in three growing regions were stored at 3 °C after conditioning. Fruit were stored for two months in year 1 and four months in years 2 and 3, and assessed after 4 d at 20 °C. Multivariate analysis shows that the passive and ethylene methods for fruit harvested three weeks before the anticipated harvest had higher or similar correlations with the actual bitter pit after cold storage than those from minerals. Although actual bitter pit incidence was higher than that predicted from the passive method, the method results in predictions that are similar to mineral-based predictions, and also is more straightforward for commercial application.
1. Introduction Bitter pit is a physiological disorder of susceptible apple cultivars that develops as necrotic pits, primarily in the calyx-end of the fruit, but which can extend throughout cortical tissues when severe (Ferguson and Watkins, 1989; Jemrić et al., 2016; Watkins and Mattheis, 2019). The disorder can be observed on fruit in the orchard, but usually develops during storage. Bitter pit has received research attention because it can cause high economic losses in key cultivars, depending on growing region, as in ‘Gala’ apples in Brazil (Amarante et al., 2005), ‘Golden Smoothee’ in Europe (Torres et al., 2015), and ‘Cox’s Orange Pippin’ in New Zealand (Ferguson and Watkins, 1992). In North America, ‘Honeycrisp’ is an apple cultivar that is highly profitable for growers due to consumer demand for its flavor and crisp texture. However, the cultivar is susceptible to a large number of physiological
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disorders (Al Shoffe et al., 2016), the most problematic being soft scald, soggy breakdown and bitter pit (Al Shoffe et al., 2018; Al Shoffe and Watkins, 2018; DeEll et al., 2016). These losses can be devastating because fruit placed into storage are free of disorders. In ‘Honeycrisp’, losses due to bitter pit are exacerbated because of the commercial conditioning treatment of 7 d at 10 °C used to reduce risk of soft scald and soggy breakdown development, as well as recommended storage temperature of 3 °C (Watkins et al., 2004). Increased bitter pit incidence due to conditioning averages about 58% compared with that found in fruit without conditioning (unpublished data). The relationship between low Ca contents in fruit and higher bitter pit risk has long been recognized, and foliar application of Ca products reduce bitter pit development in susceptible cultivars (Ferguson and Watkins, 1989; Jemrić et al., 2016), including ‘Honeycrisp’ (Biggs and Peck, 2015; Rosenberger et al., 2004). These relationships have also led
Corresponding author. E-mail address: [email protected] (C.B. Watkins).
https://doi.org/10.1016/j.scienta.2019.04.073 Received 20 February 2019; Received in revised form 25 April 2019; Accepted 26 April 2019 Available online 09 May 2019 0304-4238/ © 2019 Elsevier B.V. All rights reserved.
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to development of prediction models for various cultivars; models have been developed based on Ca alone, but usually in relation to other minerals including Mg, K and N (Bangerth, 1979; de Freitas and Mitcham, 2012d; Kalcsits, 2016; Perring, 1986). Bitter pit incidence is related to high ratios of Mg/ Ca in ‘Fuji’ apples (do Amarante et al., 2013d), Mg + K + N/ Ca in ‘Honeycrisp’ apples (Baugher et al., 2017), Mg + K/ Ca in ‘Cox Orange Pippin’ (Van Der Boon, 2015) and ‘Granny Smith’ apples (de Freitas et al., 2010d), and Mg/ Ca and K/ Ca in ‘Catarina’ apples (do Amarante et al., 2018d). Regression coefficients vary among studies however, and can range from as low as 0.4 to 0.9 (Autio et al., 1986; Baugher et al., 2017; Ferguson and Watkins, 1989; Peryea et al., 2007; Torres et al., 2017). Many factors affect reliability of mineral analysis to predict bitter pit. Examples include higher bitter pit incidence and lower Ca and higher K concentrations in fruit from light crop load compared with heavy crop load trees, and in fruit from the upper canopy of the tree compared with those in the lower canopy (Ferguson and Triggs, 1990; Ferguson and Watkins, 1992; Musacchi and Serra, 2018; Wünsche and Ferguson, 2005). In addition to mineral based methods for bitter pit prediction, a number of non-mineral methods for prediction of bitter pit have been described. These methods include treatments with Mg salts (Amarante et al., 2010; Burmeister and Dilley, 1994; Retamales et al., 2000; Torres et al., 2015). Vacuum infiltration with 0.05- 0.1 M MgCl2 for 2 min. in ‘Braeburn’ apples was used to predict bitter pit incidence of fruit after 90 d of storage at 2 °C + 10 d at 18 °C (Retamales et al., 2001). Ethylene was used to accelerate ripening either by dipping to 2000 mg L−1 ethephon or fumigation with 10,000 mg L−1 of acetylene gas (Eksteen et al., 1977; Lötze and Theron, 2006; Lötze et al., 2010). A passive method where fruit are kept at warm temperatures (e.g. 20 °C) to allow bitter pit to develop before harvest has also been developed (England and Larsen, 1973), and shown to be reliable for prediction of bitter pit development of ‘Golden Smoothee’ apples when applied at 20, 40, and 60 d before harvest. (Torres and Alegre, 2012; Torres et al., 2015). Torres et al. (2015) noted that the passive and ethylene methods in different commercial orchards over two years had similar regression coefficients. The authors concluded that the passive method is reliable for assessing bitter pit risk in ‘Golden Smoothee’ apples, but that it needs to be assessed for different cultivars and growing regions. In addition, there was no comparison of mineral-based prediction methods in that study. The objective of our study was to evaluate non-mineral prediction methods used to induce bitter pit in ‘Honeycrisp’ apples, and compare the usefulness of these methods with those based on mineral analyses. In year 1, we investigated the effects of three non-mineral methods, passive, ethylene and Mg, as inducers of bitter pit before commercial harvest. Based on these results the passive and ethylene methods were further investigatied in year 2. Because the results of the two methods were similar, the ease of use for application of the passive method, and absence of registration of ethephon for this purpose, we chose the passive method for evaluation across a large number of orchards across three growing regions in year 3.
fruit were harvested from six orchards in each of HV and WNY (commercial harvest dates of September 5 and September 13, respectively). In 2018 (year 3), fruit were harvested from 12 orchards in HV, 18 orchards in WNY, and 8 orchards in Champlain NY (commercial harvest dates of September 6, September 13, and September 18, respectively). In 2016 and 2017, fruit were harvested from 24 trees in three different rows, while in year 3, fruit were harvested from 10 to 12 trees per orchard block. In all years, trees were selected randomly, but were representative of the orchard block in terms of tree vigor and crop load. Trees were tagged so that fruit for prediction studies and storage were harvested from the same trees. In 2016 and 2017, 70 fruit per replicate were collected per orchard using 40 fruit for storage, 20 fruit for minerals and 10 fruit for maturity indices. Fruit from the early harvest were assessed twice weekly in year one and once weekly in year two for three weeks to study the dynamic of bitter pit development from the prediction methods. In 2018, 120 fruit were collected per orchard using 100 fruit for storage and 20 fruit for minerals and maturity indices at each harvest date. Fruit were transported to Ithaca on the day of harvest, and harvest indices assessed within 24 h. 2.2. Mineral analyses Mineral contents were measured in fruit peel taken from the calyxend of 20 fruit per orchard block at each harvest time after washing fruit in Palmolive clear dish detergent followed by rinsing in distilled water. Peel samples were kept in aluminum foil inside paper bags, dried to a constant weight, and then ground to a fine powder. The powder was kept in plastic bags in glass jars containing anhydrous calcium sulfate until further use. The samples were analyzed at the nutrient analysis laboratory at Cornell University; 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 content using a Spectro Arcos axial viewed ICP-OES (SPECTRO Analytical Instruments Inc. AMETEK®, Kleve, Germany,). The results were expressed as g 100 g−1 on a dry weight basis. 2.3. Harvest indices The IEC of each fruit was measured by injecting 1 mL of gas sample taken from the core cavity as described by Watkins et al. (2000). Samples were injected into a Hewlett-Packard 5890 series II gas chromatograph (Wilmington, DE) equipped with a flame ionization detector and 3394 A integrator. Temperatures were 230 °C and 245 °C for the injector and detector with the oven run isothermally at 160 °C. The stainless steel column (2 m x 3 mm id) was packed with 60/80 mesh Alumina F-1. Flow rates were 30 ml min−1 for the nitrogen carrier gas, while the detector was supplied with 30 ml min−1 hydrogen and 230 ml min−1 compressed air. Firmness was measured on opposite peeled sides of each fruit using an 11.1 mm diameter probe (Guss Manufacturing (Pty) Ltd., Strand, South Africa) and the expressed juice 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 (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 IAD index was measured using a Delta Absorbance (DA) meter (TR Turoni srl, Forli, Italy).
2. Materials and methods 2.1. Fruit source ‘Honeycrisp’ apples (Malus × domestica Borkh) used for these trials were obtained from the three major growing regions in New York (NY), Western NY (WNY), Hudson Valley (HV) and the Champlain Valley (Champlain), depending on year. The fruit were harvested at three weeks before anticipated harvest and at commercial harvest. In 2016 (year 1), fruit were harvested from two orchards in WNY (commercial harvest date of September 21), and in the 2017 (year 2) harvest season,
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2.4. Non-mineral treatments to induce bitter pit development
of bitter pit development with the three prediction methods over time were also studied. In year 2, passive and ethylene methods were compared using several orchards, and then in year 3, the passive method was selected for large scale testing across orchards and growing regions. In year 1, fruit from two orchard blocks were used (Table 1). Bitter pit increased over time at 20 °C in the passive and ethylene methods (Fig. 1). The effects of Mg over time were similar (data not shown) but toxicity from Mg, probably because fruit were not washed after treatment, made it difficult to distinguish bitter pit from toxicity symptoms on the skin. However, we found that bitter pit incidence in Mg-treated fruit was similar to that in the passive and ethylene treatments when fruit were assessed at week 3 (Fig. 2). In one of two orchards the passive treatment under-estimated bitter pit incidence compared with the other methods and with that found in fruit after storage (Fig. 2). The dynamics of bitter pit development using predictive methods are comparable with those of England and Larsen (1973) who found that fruit harvested prior to commercial harvest developed bitter pit during 1–3 weeks in ‘Goldspur’ and ‘Wellspur’ apples, and Torres et al. (2015) found that 10–30 d before anticipated harvest is a reliable time to predict the disorder in ‘Golden Smoothee’ apples. Burmeister and Dilley (1994) found high correlations with bitter pit incidence after storage in air at 5 °C or at 3 °C in air or CA for ‘Northern Spy’ apples when fruit were infiltrated with Mg 10 d before commercial harvest. The correlations between the predicted bitter pit from the non-mineral methods against the actual bitter pit after harvest overall were 0.88, 0.92 and 0.88 for the Mg, ethylene, and passive method, respectively, being similar to those of Torres et al.(2015) who found the respective R2 values were 0.81, 0.89, and 0.79 from fruit harvested 20 d before anticipated harvest. In our study, we focused on prediction based on bitter pit that developed in storage after the commercially used conditioning period of 10 °C for 7 d followed by storage at 3 °C. However, bitter pit development at 3 °C without condition, and 0.5 °C with and without conditioning, was also evaluated. The lowest bitter pit incidence of 6% occurred in the continuous 0.5 °C treatment compared with 38% in continuous 3 °C treatment, and 33% in conditioned fruit stored at 0.5 °C. Nevertheless, the correlations between actual bitter pit from 3 °C with or without conditioning and 0.5 °C with conditioning with predicted bitter pit from the three non-mineral methods were significant. These ranged between 0.75-0.92, while the correlation of bitter pit from continuous 0.5 °C and the non-mineral methods was 0.32, 0.11, and 0.23 for passive, ethylene, and Mg, respectively (Table 2). Also, the three non-mineral prediction methods did not show significant differences in the bitter pit incidence, with 28, 39 and 37% for passive, ethylene and Mg methods, respectively, either between each other or compared with actual bitter pit from continuous 3 °C or conditioned 0.5 and 3 °C. The passive method showed a wider range of bitter pit compared with ethylene, which ranged between 8–51%, while the Mg
Fruit picked three weeks before the commercial harvest date were subjected to three non-mineral prediction methods in 2016. For the passive method, the fruit were kept at 20 °C. For the magnesium method, fruit were dipped into 0.18 M MgCl2 (≥ 98%, Sigma-Aldrich, USA) solution + 0.05% silicon surfactant (Widespread Max) for 2 min. For the ethylene method, fruit were dipped into 2000 mg L−1 ethephon (Motivate™, Fine Americas, Inc, Walnut Greek, CA, USA) (9.22 mL L−1) + 0.05% silicon surfactant (Widespread Max) for 5 min. 2.5. Fruit storage In 2016, four sets of four replicates of 40 fruit from the two harvest dates were stored at 0.5 and 3 °C with and without one week of conditioning at 10 °C for two months. In 2017, two sets of fruit in three replicates of 40 fruit were stored at 3 °C with and without one week of conditioning at 10 °C for 4 months. In 2018, 100 fruit per orchard were stored at 3 °C after one week of conditioning at 10 °C for 4 months. 2.6. Assessment of physiological disorders External and internal physiological disorders were assessed visually. Fruit were cut transversely at least 5 times from the calyx end. The disorders were calculated as a percentage of fruit with disorders from the total fruit number. Bitter pit was the only consistent disorder in this study, soft scald and soggy breakdown occurring in year 1 when the fruit were stored at 0.5 °C with or without conditioning (data not shown). Therefore, only bitter pit incidence is shown in this paper. 2.7. Statistical analysis Tukey and Student tests were used to compare means at the 5% confidence level. Multivariate and bivariate analyses were used for the coefficient of correlation, the correlation probability, and the coefficient of determination among factors. A neural network, which is a powerful computational data model that is able to capture and represent complex input/output relationships (Noh and Lu, 2007), was also used. All statistics were carried out using the JMP statistical program (JMP Pro 12.INK). Percentage data were arcsine transformed for analysis, and presented as back-transformed means. 3. Results and discussion 3.1. Non-mineral methods for bitter pit prediction Non-mineral methods were evaluated over three seasons. In year 1, passive and treatments with ethylene or Mg were tested. The dynamics
Table 1 Harvest indices of ‘Honeycrisp’ apples from 2 orchards in WNY in 2016 (Year 1), 6 orchards in each of HV and WNY in 2017 (Year 2), and 38 orchards from HV, WNY, and Champlain in 2018 (Year 3). Not sigificant (NS) at P < 0.05. Year 1 2
Region
WNY HV WNY Significance 3 HV WNY Champlain Significance Correlation with actual bitter pit (3 years) Significance
IEC (μl L−1)
Firmness (N)
SSC (%)
TA (%)
SPI
IAD index
1.05 11.1 3.4 0.001 7.2 10.1 15.7 NS −0.37 0.0007
70.6 64.7 65.8 NS 64.8 66.5 60.4 0.0004 0.42 < .0001
12.9 10.8 11.1 NS 10.6 10.9 11.3 NS −0.03 NS
0.71 0.63 0.59 0.04 0.59 0.62 0.55 NS 0.29 0.0072
4.9 5.3 5.7 NS 4.2 3.9 6.1 0.0001 −0.45 < .0001
0.53 0.91 0.84 NS 1.06 0.92 0.96 NS 0.04 NS
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Fig. 1. Dynamic of bitter pit development from fruit harvested three weeks before anticipated harvest, Passive method (A), or Ethylene method (B), from two orchards in WNY in 2016. Data are presented as means ± SE.
an application, but expanded to 38 orchards across the three major growing regions in NY (Table 1). Means for individual orchards are provided in (Table S1). The coefficient of correlation for the passive method with the actual bitter pit from fruit conditioned and then stored at 3 °C was 0.91. 3.2. Relationships between harvest indices and bitter pit development Bitter pit after storage correlated negatively with IEC and SPI and positively with firmness and TA at harvest, but not with SSC or IAD index (Table 1). Bitter pit incidence and senescent breakdown in ‘Honeycrisp’ apples is associated with early and later harvests, respectively (Prange et al., 2015). These associations were also related with the IAD index (DeLong et al., 2014). The negative correlation for bitter pit with IEC and SPI is consistent with lower bitter pit incidence with greater maturity, but not found for the IAD index. Our study was limited to first commercial harvest in each region, and therefore the range of fruit maturities was limited. Although we have not found close associations between harvest indices and bitter pit development under New York conditions (Watkins et al., 2005), further research is needed.
Fig. 2. Bitter pit in 'Honeycrisp' apples from two orchards (O1 and O2), predicted in fruit harvested 3 weeks before anticipated harvest and untreated (passive), or treated with ethylene or Mg, and kept at 20 °C for 3 weeks, compared with bitter pit in fruit stored at 3 °C after one week of conditioning at 10 °C for 2 months (Actual), different lower case letters indicate a significant difference at the P ≤ 0.05 level (Tukey’s test). Data are presented as means ± SE.
3.3. Relationships between minerals and bitter pit development
method showed the highest estimation for bitter pit compared with the other non-mineral methods, with a value of 65% (Table S2). The difficulty of assessing damage compared with bitter pit limits the utility of the Mg method, and vacuum infiltration is not an easy method to apply commercially. Therefore, in year 2, we focused on the passive and ethylene methods. Fruit from six orchards in each of two regions were used (Table 1). Overall, fruit maturity was similar in both regions, except that IECs and TA were higher in fruit from the HV region than from WNY (Table 1), and sometimes they were variable within a region (Table S1). The coefficient of correlation values for passive and ethylene methods were similar at 0.81 and 0.84, respectively. In year 3, we chose to further restrict analysis of prediction methods to the passive one alone, given lack of registration of ethylene for such
The effects of region on concentrations and ratios of minerals were variable between years 1, 2 and 3 (Tables 3, S2, S3, and S4). In year 2, the concentrations of Ca, K, and Mg were lower and their ratios were higher in WNY compared with HV fruit. In year 3, HV fruit had higher P, K, and mineral ratios compared with WNY and Champlain regions, whereas Mg was higher in Champlain. The effects of harvest time were more consistent, however, with lower concentrations at commercial harvest than 3 weeks before commercial harvest date (Table 3) as would be expected because of increasing fruit size over this time period. Multivariate analyses of mineral concentrations and ratios were carried out for each year (Table 4). For fruit harvested three weeks before commercial harvest, bitter pit incidence was positively associated with P and K concentrations, negatively with Ca concentrations, but no association with those of Mg were detected. However, the ratios
Table 2 Multivariate analysis for actual bitter pit in 'Honeycrisp' apples stored at 0.5 or 3 °C after one week of conditioning (C) at 10 °C, predicted bitter pit from fruit harvested 3 weeks before anticipated harvest and dipped or not in 2000 mg L−1 ethephon or 0.18 M MgCl2 and kept at 20 °C for 3 weeks in year 1. Actual bitter pit (%) 0.5 °C
C + 0.5 °C
3 °C
C+ 3 °C
Method
R
Significance
R
Significance
R
Significance
R
Significance
Passive Ethylene Magnesium
0.34 0.11 0.23
NS NS NS
0.77 0.75 0.80
0.03 0.03 0.02
0.80 0.87 0.82
0.02 0.01 0.01
0.88 0.92 0.88
0.004 0.001 0.004
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Table 3 Mineral concentrations (g 100 g−1) and their ratios for 'Honeycrisp' apples picked at 3 weeks before anticipated harvest (3WBH) or at commercial harvest (H) for 2 orchards in WNY in 2016 (Year 1), 6 orchards in each of HV and WNY in 2017 (Year 2), and 38 orchards from HV, WNY, and Champlain in 2018 (Year 3). Not significant (NS) at P < 0.05. Year 1
Region Harvest
2
Region Harvest
3
Region
Harvest
WNY 3WBH H Significance HV WNY Significance 3WBH H Significance HV WNY Champlain Significance 3WBH H Significance
P
K
Ca
Mg
Mg/Ca
K/Ca
P/Ca
(K + Mg)/Ca
(K + Mg + P)/Ca
0.15 0.15 0.15 NS 0.11 0.10 NS 0.12 0.09 0.001 0.09 0.08 0.07 0.0002 0.08 0.08 NS
1.19 1.25 1.14 0.02 0.91 0.84 0.009 0.98 0.77 < 0.0001 0.72 0.61 0.53 < 0.0001 0.73 0.50 < 0.0001
0.04 0.04 0.03 NS 0.05 0.03 < 0.0001 0.05 0.04 < 0.0001 0.04 0.05 0.05 NS 0.05 0.05 NS
0.09 0.09 0.08 0.02 0.10 0.09 < 0.0001 0.11 0.08 < 0.0001 0.09 0.09 0.10 0.008 0.10 0.09 0.02
2.5 2.5 2.4 NS 2.0 2.8 < 0.0001 2.5 2.3 NS 2.3 2.1 2.3 NS 2.3 2.1 NS
34.0 34.8 33.2 NS 17.7 27.1 < 0.0001 23.2 21.6 NS 17.8 14.1 11.8 0.0004 17.6 11.5 < 0.0001
4.2 4.0 4.4 NS 2.2 3.3 < 0.0001 2.8 2.7 NS 2.3 1.9 1.6 0.008 2.0 1.9 NS
36.5 37.3 35.6 NS 19.7 29.9 < 0.0001 25.7 23.9 NS 20.1 16.2 14.1 0.001 19.9 13.6 < 0.0001
40.7 41.4 40.0 NS 21.9 33.2 < 0.0001 28.5 26.6 NS 22.4 18.2 15.7 0.001 22.0 15.5 < 0.0001
of the minerals were highly and consistently associated with bitter pit incidence in both years. In contrast, the associations between mineral ratios and bitter pit were less consistent in fruit analyzed at harvest, especially in year 2, for which correlation coefficients and P values were lower than for the earlier sampling. The correlation between fruit minerals and their ratios with bitter pit development in apple fruit has been investigated for several cultivars (Baugher et al., 2017; Biggs and Peck, 2015; Fallahi et al., 2010; Ferguson and Watkins, 1989; Rosenberger et al., 2004; Telias et al., 2006). As an example, bitter pit was associated with lower Ca and higher K and Mg concentrations (Baugher et al., 2017; Perring, 1986). Also, the regression coefficient between bitter pit at harvest and Ca was the highest at 60 days after full bloom in ‘Golden Smoothee’ apples with variation between years ranging from 0.1- 0.74 (Torres et al., 2017).
to each other. In year 2, multivariate analyses resulted in correlation coefficient values of 0.81 and 0.84 for the passive and ethylene methods, respectively, and in year 3, 0.91 for passive alone. These values are consistently higher than those found for mineral concentrations and similar to or higher than those of their ratios (Table 4). Based on the strength of the correlation for the passive method from the multivariate analysis, we ran bivariate analyses to obtain the determination coefficient for the percentage variation in actual bitter pit that is explained by predicted bitter pit (Fig. 3A–C). Bivariate analysis examine the relationship between two variables and shows percentage variation in which y is explained by all the x variables together. 3.5. Development of models of bitter pit prediction using the passive method The bivariate analysis of the predicted bitter pit using the passive method against the actual bitter pit after storage from the two regions showed R2 of 0.66, and adjusted R2 of 0.65 (Fig. 3A). The multivariate analyses in year 3 also showed the highest correlation coefficient (0.91) for the passive method against actual bitter pit
3.4. Comparison between non-mineral and mineral methods for bitter pit prediction Multivariate analysis shows how several continuous variables relate
Table 4 Multivariate analysis for minerals and ratios taken 3 weeks before harvest (3WBH) and at harvest (H) against bitter pit in 'Honeycrisp' apples stored at 3 °C after one week of conditioning at 10 °C. Not significant (NS) at p < 0.05. Year 2 R 3WBH
H
P K Ca Mg Mg/Ca K/Ca P/Ca (K + Mg)/Ca (K + Mg + P)/ca N P K Ca Mg Mg/Ca K/Ca P/Ca N/Ca (K + Mg)/Ca (K + Mg + P)/ca (K + Mg + P+N)/Ca
0.35 0.58 −0.48 0.16 0.75 0.75 0.69 0.75 0.76 −0.34 0.23 0.19 −0.41 0.15 0.55 0.49 0.43 0.37 0.49 0.15 0.49
Year 3 Significance 0.0382 0.0002 0.0031 NS < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.0313 NS NS 0.0132 NS 0.0005 0.0024 0.0094 0.0276 0.002 NS 0.0025
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R 0.36 0.50 −0.67 0.06 0.84 0.75 0.72 0.77 0.77 0.02 0.34 0.27 −0.59 0.19 0.66 0.64 0.62 0.36 0.66 0.68 0.62
Two years Significance 0.0281 0.0014 < 0.001 NS < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 NS 0.0347 0.0979 0.0001 NS < 0.0001 < 0.0001 < 0.0001 0.028 < 0.0001 < 0.0001 < 0.0001
R 0.16 0.23 −0.57 0.03 0.73 0.62 0.54 0.63 0.63 −0.19 0.23 −0.01 −0.40 0.23 0.53 0.21 0.35 0.17 0.23 0.25 0.24
Significance NS 0.0466 < 0.0001 NS < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 NS NS NS 0.0004 0.046 < 0.0001 NS 0.0025 NS 0.0447 0.0315 0.0384
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Fig. 3. Bivariate analyses for actual bitter pit for fruit stored at 3 °C after one week of conditioning at 10 °C for 4 m + 4 d at 20 °C against predicted bitter pit from passive method in 2017 (A), 2018 (B), and three successive years (C). The dashed line represents a 1:1 ratio of actual and predicted bitter pit.
Fig. 4. Neural analyses for actual bitter pit for fruit stored at 3 °C after one week of conditioning at 10 °C for 4 m + 4 d at 20 °C against predicted bitter pit from passive method in three successive years, training (A) and validation (B). The dashed line represents a 1:1 ratio of actual and predicted bitter pit.
compared with the minerals at two harvest dates. The bivariate analyses for predicted bitter pit from the passive method against the actual bitter pit resulted in a R2 of 0.82 (Fig. 3B). We observed a consistent range of bitter pit development from the passive method of 8–51, 0–50, and 1–55% for year 1, 2, and 3, respectively (Tables S2, S3, S4). The combined data from years 2 and 3 show that the bitter pit from the passive method had the highest correlation against the actual bitter pit after storage compared with those from minerals at two harvest dates (Table 4). Also, the bivariate analyses showed that the R2 for the bitter pit from the passive method against the actual bitter pit from three years data was 0.68 (Fig. 3C). Based on the stability of the results from the passive method in three years, a neural network model was used for validation of the method. The R2 of this model was 0.78 (Fig. 4). The predicted bitter pit was 0, 14, 28, 42, and 55% against 6, 22, 35, 53, and 65% for the actual bitter pit. The bivariate analyses shows a R2 of 0.99 (Fig. 5) with the following prediction formula:
Actual bitter pit (%) = 6 + Predicted bitter pit (%) In conclusion, non-mineral methods showed as good or higher correlation with actual bitter pit as that found with mineral analysis. Of the three methods investigated in this study, the passive method showed consistent results in different orchards, in different regions, and over successive years. Its simplicity makes the passive method a powerful and reliable method that can used easily by growers or storage operators to predict of bitter pit in ‘Honeycrisp’ apples. Ethylene is not labelled for use as a postharvest ripening agent, while Mg application is relatively cumbersome to use. The passive method does under-estimate the incidence of bitter pit in fruit after storage, but this is more acceptable than over-estimation from an industry perspective. The nonchemical method eliminates the need to prepare tissue samples and carry out mineral analyses.
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Fig. 5. Bivariate analyses for actual bitter pit for fruit stored at 3 °C after one week of conditioning at 10 °C for 4 m + 4 d at 20 °C against predicted bitter pit from passive method in three successive years after validation. The dashed line represents a 1:1 ratio of actual and predicted bitter pit.
Declarations of interest None. Acknowledgments This research was supported in part by the NY Apple Research and Development Program, and the USDA National Institute of Food and Agriculture, Hatch project 2013-14-483, Improving Quality and Reducing Losses in Specialty Fruit Crops through Storage Technologies (NE-1336). Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the National Institute of Food and Agriculture (NIFA) or the United States Department of Agriculture (USDA). We would like to thank Dan Donahue and Mike Basedow, Eastern New York Commercial Horticulture Program, for assistance with coordination of fruit harvest. Li-wu Zhu was funded by Foundation of the Foreign Visiting Scholars of Anhui Higher education Revitalization (gxfxZD 2016023). 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.scienta.2019.04.073. References 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. 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., 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. Amarante, C.V.T., Steffens, C.A., Ernani, P.R., 2010. Preharvest identification of bitter pit risk in’ Gala’ apples by fruit infiltration with magnesium and analysis of fruit contents of calcium and nitrogen. Rev. Bras. Frutic. 32, 027–034. https://doi.org/10.1590/ s0100-29452010005000015. Amarante, C.V.T., Ernani, P.R., Chaves, D.V., 2005. Fruit infiltration with magnesium Is a feasible way to predict bitter pit susceptibility in ´Gala´ apples grown in southern Brazil. Acta Hortic. 682, 1271–1274. https://doi.org/10.17660/ActaHortic.2005. 682.169. Autio, W., Bramlage, W., Weis, S., 1986. Predicting poststorage disorders of’Cox’s Orange Pippin’and’Bramley’s Seedling’apples by regression equations. J. Am. Soc. Hortic. Sci. 111, 738–742. Bangerth, F., 1979. Calcium-related physiological disorders of plants. Annu. Rev.
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