Introduction Directory UMM :Data Elmu:jurnal:P:Postharvest Biology and Technology:Vol19.Issue3.Jul2000:

Postharvest Biology and Technology 19 2000 211 – 220 Empirical modelling of postharvest changes in the firmness of kiwifruit Jason R. Benge a, , H. Nihal De Silva b , Nigel H. Banks a , Peter B. Jeffery a a Institute of Food, Nutrition and Human Health, College of Sciences, Massey Uni6ersity, Pri6ate Bag 11222 , Palmerston North, New Zealand b The Horticulture and Food Research Institute of New Zealand, Pri6ate Bag 11030 , Palmerston North, New Zealand Received 29 November 1999; accepted 21 February 2000 Abstract Several different types of empirical mathematical models were used to characterise the softening behaviour of ‘Hayward’ kiwifruit Actinidia deliciosa A. Chev C.F. Liang et A.R. Ferguson during storage at 0°C. Our purpose was to determine whether or not the softening behaviour of fruit conformed to a limited number of patterns. If this were so, such models, coupled with measurements made in a short period after harvest, might form the basis of a quantitative tool that would allow the industry to segregate batches of fruit with differing storage potentials. Initially, three simple models were used to characterise firmness data: a Complementary Michaelis – Menten type CMM, Exponential EXP, and Complementary Gompertz CG. However, these were unable to characterise firmness changes with sufficient accuracy, either in the early, middle or latter stages of storage. Instead, the firmness data were better characterised by two more complex models that were identified during the course of the study: a segmented Jointed Michaelis – Menten type JMM and Inverse Exponential Polynomial IEP. With the JMM model, different functional relationships were assumed for different regions of the time domain although its parameters were difficult to estimate accurately when the number of data points for a region of the time domain was limited. The IEP model best characterised the firmness data although its ability to predict the softening behaviour of fruit was poor given a limited window of data. None of the equations that were studied provided a standard curve that could be useful as a predictive model for firmness in storage. Nevertheless, the more complex equations did accurately characterise our firmness data and have potential value for comparing treatment effects in experimental programmes. © 2000 Elsevier Science B.V. All rights reserved. Keywords : Actinidia deliciosa; Flesh firmness; Prediction; Segmented models; Storage potential www.elsevier.comlocatepostharvbio

1. Introduction

Firmness is a key criterion by which the re- maining storage potential of kiwifruit is assessed. At harvest, the flesh firmness of kiwifruit is gener- ally in the range of 60 – 110 N Newtons but they Corresponding author. Present address: 19 Hassard St., Manaia, Taranaki, New Zealand. Tel.: + 64-6-3569099. E-mail address : j.r.bengemassey.ac.nz J.R. Benge. 0925-521400 - see front matter © 2000 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 5 - 5 2 1 4 0 0 0 0 0 9 1 - 0 are not eating-ripe until the firmness is in the range of about 4 – 8 N MacRae et al., 1990. Therefore, a large decrease in firmness occurs after harvest before the fruit is ready to eat. Generally, fruit softening accelerates over the first period of stor- age. Subsequently, the rate of softening slows, with the inflection point between the two phases occur- ring at a time after storage when firmness drops to about 40 N. In some instances, the rate of softening may increase again towards the end of storage while in others, there may be a lag phase after harvest where there is very little fruit softening MacRae et al., 1989, 1990. Although the general nature of softening in kiwifruit is well established, very little has been published concerning the modelling of kiwifruit softening. This is surprising given the potential of modelling to provide objective comparisons of treatment effects on fruit softening in experimental studies. Furthermore, if fruit softening could be characterised by a limited number of simple rela- tionships with time in storage, there could be potential to predict subsequent storage behaviour from measurements made soon after harvest. This would mean that fruit that are likely to soften excessively might be separated from other fruit early in storage and sent to market earlier, thereby facilitating inventory management and reducing fruit losses. In this paper, a number of models were fitted to firmness data for batches of kiwifruit from different growers and seasons. Our purpose was to determine whether or not a model with robust parameter estimates could be identified that would characterise the softening behaviour of fruit from different sources.

2. Material and methods