Discriminating pineapple batches for susceptibility to postharvest internal browning

Abstract

Postharvest internal browning (PIB) is a fruit disorder caused by low temperature storage that negatively impacts the logistics of pineapple (Ananas comosus L. Merr.) export. Weather conditions during the growing period affects fruit biochemical composition and physiological properties at harvest, thereby influencing the severity of PIB in pineapple. In this work, a model was developed to predict the severity of PIB based on fruit attributes measured at harvest and the weather conditions during the growing period. Batches of fruit from 70 harvests were sampled on 11-13 occasions throughout the year from eight commercial orchards in seven agroecological regions. Fruit with no internal browning were classified as ‘no PIB’, fruit with <40% browning were classified as having ‘mild PIB’, and the rest were classified as showing ‘severe PIB’. Stepwise discriminant analysis (SDA) was performed to develop a model with optimally selected classifying variables. The overall classification accuracy of the SDA model for predicting PIB was 71.4%, i.e., 6 out of 13 for ‘no PIB’ pineapple batches, 23 out of 31 for ‘mild PIB’ and 21 out of 26 for ‘severe PIB’. The discriminant model was validated using another 18 batches of pineapples and the classification accuracy was similar, i.e., 72.2%. Batches of pineapples with low ascorbic acid, high TSS, low calcium at harvest, and more rain in the 60 days preceding harvest, tended to develop more severe PIB symptoms. On the other hand, batches of pineapples with high growing degree days, high ascorbic acid, and high calcium at harvest, tended to be free of PIB. Complicated relationships between preharvest environmental factors, fruit chemical composition, and PIB of pineapple fruit were revealed. This model will be beneficial for packinghouses to prioritize fruit selection for export markets.