nondestructive quality evaluation of abbot kiwifruit using electronic nose

currently, nondestructive quality evaluation of various agricultural products are being developed and used. in this study, application of an electronic nose system for the quality assessment of kiwifruit (abbot variety) is used. electronic nose system coupled with artificial neural network (ann) and principal component analysis (pca) techniques was able to classify unripe, half-ripe, ripe, over-ripe and spoiled kiwifruit cultivars successfully. the two main components & , contains about 99% of variance without overlapping. the success rate for ann was found to be 100%. the minimum and maximum mean square error was obtained for the half-ripe and spoiled samples as 0.02523 & 0.00198, respectively. in this paper, stiffness as a quality indicator for kiwi was measured and them predicted by electronic nose data. analysis of the results showed that the kiwifruit firmness after harvest (unripe, half-ripe, ripe and overripe) has significant difference at 5%. using artificial neural network, the firmness prediction of abbot variety of kiwifruit through ripening stages aroma determined with the coefficient electronic nose system can be considered as a reliable tool for the monitoring of kiwifruits in storage conditions.