Modelling batch variability in softening of 鈥楬ayward鈥?kiwifruit from at-harvest maturity measures

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• 作者：Abdul Jabbar ; Andrew R. East ; Geoff Jones ; David J. Tanner ; Julian A. Heyes
• 关键词：Actinidia deliciosa ; Firmness ; Non-linear mixed effects ; Complementary Gompertz ; Time shift
• 刊名：Postharvest Biology and Technology
• 出版年：April, 2014
• 年：2014
• 卷：90
• 期：Complete
• 页码：7-14
• 全文大小：889 K

文摘

Firmness of kiwifruit (Actinidia deliciosa (A. Chev) C.F. Liang et A.R. Ferguson cv. Hayward) is an important determinant of quality. Batches of fruit vary not only in firmness at time of harvest but also in time to reach eating ripeness (0.5-1.0 kg_f). Failure to identify batches with rapid rate of firmness breakdown results in economic loss to the industry. Understanding variability in softening rate and its relation with at-harvest measures may lead to opportunities for industry to segregate batches for storage potential. The objective of this paper was to model batch-specific softening behaviour of kiwifruit and investigate if predictive models could be determined from at-harvest maturity measures. Data for 鈥楬ayward鈥?kiwifruit softening at 20 掳C were collected over 21 d for 108 batches across two seasons (2011 and 2012). In model creation, application of both Complementary Gompertz (CG) equation and a time shift (TSCG) alternative version resulted in a mean absolute error (MAE) of 0.11-1.55 kg_f and 0.14-1.44 kg_f respectively for 54 batches. Model parameters were fitted using a non-linear mixed effects procedure. The resulting batch-dependent softening description parameters (B, 魏 and 蟿) were best associated with at-harvest firmness and the SSC:firmness ratio. For prediction validation, at-harvest quality indicators of an alternative set of 54 batches were used to predict softening descriptive model parameters and subsequent batch-dependent softening behaviour at 20 掳C. When B and 魏 were predicted from firmness and the SSC:firmness ratio respectively in the validation batches, MAE of firmness prediction by CG ranged from 0.17 to 2.75 kg_f with 46% of the batches having MAE of less than 0.5 kg_f. Likewise, when 蟿 was predicted from firmness, MAE of firmness prediction by TSCG ranged from 0.17 to 2.78 kg_f and approximately 30% of batches had MAE less than 0.5 kg_f. This paper demonstrates the potential for predicting softening variability of kiwifruit batches from at-harvest fruit maturity measures. Future work is required to ascertain if a similar modelling protocol may enable prediction of kiwifruit softening at commercial storage conditions (0 掳C).