3.3.1 At-harvest
To explore change in fruit quality characteristics as influenced by progression through the season, SSC, DM and firmness were assessed by the day of arrival (ISO d). Fruit for each GL was received within two days of harvest and we had no control over fruit storage between harvest and day of arrival.
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3.3.2 Optimal storage
Firmness data from optimal storage were used to define the soft fractile (SF, 9th lowest firmness value of approximately 300 fruit, Table 3.1). SF is developed from the threshold limit of 1.3% failure, which means if 3% of population of 300 fruit have firmness value less than 1 kgf, so it can be tolerated (Section 2.6). SF is a common form of expressing firmness data. SF are also used to understand and define the firmness trend and variability of GLs in industry (Adams et al., 2010). Time to reach SF of 1 kgf is a common method to compare GLs for storage performance (Burdon et al., 2013). So, SF of GLs was defined over the storage period to estimate storage life. Storage life of a GL was the time (days) to reach a SF less than 1 kgf. As firmness data were recorded at 21 d intervals, storage life was estimated by using linear interpolation between two data points (former and the first value when SF < 1 kgf), to approximate a day when SF was less than 1 kgf. From storage life, seasonal life of each GL was also calculated in ISO days. Seasonal life was defined as day of year (in ISO days), when each GL reached SF less than 1 kgf. Calculating seasonal life of each GL rescaled storage life to the expected failure time during the year. Seasonal life may assign the same ISO day of year to GLs with different harvest times and storage life but it enables prioritisation of distribution decisions on a real time scale. Therefore, seasonal life of GLs was used for further data processing.
3.3.3 Accelerated fruit library (AFL)
Initial manipulation was performed to extract summary statistics and other parameters i.e. export threshold, distribution descriptors, average, variability and defect count; of AFL data sets (Figure 3.2, Table 3.1). Export threshold comprises a widely used parameter of SF, which is 9th softest fruit in a population of 300 fruit. Industry also uses distribution descriptors to assess and compare different batches quality during storage. Therefore, distribution descriptors like count of fruit with firmness less than 1, 2 or 3 kgf, fruit at eating soft (~ 0.6 - 1 kgf) and below eating soft (~ <0.6 kgf) stage, and firmness value at 1st and 3rd quartile were also calculated. Distribution patterns of firmness values of a sample population were also assessed by skewness and kurtosis. Parameters related to averages comprise mean and median firmness while variability in observed firmness was evaluated by highest, lowest firmness, range (highest minus
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lowest), and standard deviation. Counts of fruit with rots, soft patches, bruise and defects were also recorded to evaluate losses in fruit quality other than softening during AFL.
Table 3.1: Parameters derived from AFL data at each measurement occasion. Each population was obtained from 3 MB packs of count 36 ‘Hayward’ kiwifruit
resulting in approximately 300 individuals.
Category Parameters Description
Export threshold
Soft fractile
(SF, kgf) 9th lowest value of firmness from 300 fruit
Distribution descriptors
Number of fruit <1 kgf Number of fruit <2 kgf Number of fruit <3 kgf
Number of fruit within eating window (0.6 - 1 kgf) Number of fruit below eating window (< 0.6 kgf) 1st quartile
(kgf) Firmness value below which 25% of the firmness values are lying 3rd quartile
(kgf)
Firmness value below which 75% of the firmness values are lying
Skewness Measure of symmetry Kurtosis Measure of peakedness Average
Mean
(kgf) Average firmness
Median
(kgf) Middle firmness value
Variability
Minimum value
(kgf) Minimum firmness value Maximum value
(kgf) Maximum firmness value Range
(kgf)
Difference between highest and lowest fruit firmness
Standard deviation
(kgf) Measure of firmness variability Defect
41 Firmness (kgf) 0 1 2 3 4 5 6 7 8 9 10 N umbe r of frui t 0 50 100 150 200 250 300 NoF <0.6 NoF <2 NoF <3 NoF <1 Mean Minimum Maximum Kurtosis 1st quartile 3rd quartile Skewness
Figure 3.2: An hypothetical cumulative frequency graph to show different parameters of AFL firmness data. NoF: number of fruit.
Simplicity and ease of measurement was the main purpose to evaluate these AFL parameters. This was to make interpretation of results easy to follow at industrial scale with minimum calculation effort. AFL parameters were extracted for all measurement times separately with the propose to demonstrate storage potential of different GLs. Particular attention was given to parameters that had good prediction power at several adjacent measurement times (termed consistent parameters), because this should increase the likelihood of finding the same predictions in any data set. SSC:firmness ratio was also calculated from mean SSC and firmness values at each measurement occasion to follow the comparative change in both quality attributes during AFL.
3.3.4 Relating at-harvest and AFL to optimal storage
To investigate the usefulness of at-harvest and AFL monitoring data for generating information about storage potential of GLs in a season, calculated parameters were correlated with seasonal life (Figure 3.3). For this purpose, three different linear correlation techniques were followed. Firstly, AFL parameters were correlated to the observed seasonal life (Pearson correlation). Secondly, both AFL parameters and seasonal life were converted from the continuous ranges to ranked data sets ranging from 1 - 20, with one being the smallest value and 20 being the highest value recorded.
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Ranked parameters were then correlated with ranked seasonal life (Spearman correlation). Thirdly, continuously variable AFL parameters were correlated with ranked seasonal life. Linear correlation gave an advantage that value or rank of GL in AFL monitoring could reflect value or rank of GL in optimal storage. This technique would be quick and easy to perform in an industrial environment. Obtaining a correlation from AFL parameters to ranked seasonal life of GL was advantageous because a simple continuous range of AFL parameters could reflect the rank of GLs throughout a season.
Figure 3.3: Scheme of data manipulation and calculation of correlation coefficients (r) for at-harvest and AFL parameters with storage performance of kiwifruit GLs.
Highlight correlation coefficient (r) of ≥|0.5| Arrange correlation coefficient values in tables
Optimal storage AFL
At-harvest
Data arrangement
At-harvest and AFL parameters
Soft fractile from optimal storage
Seasonal life
Calculate correlation coefficient (Section 3.4.4) i) At-harvest and AFL parameters with seasonal life
ii) Ranked at-harvest and AFL parameters with ranked seasonal life iii)At-harvest and AFL parameters with ranked seasonal life
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