Connectionist methods for classification of fruit populations based on visible-near infrared spectrophotometry data

Abstract

Variation in fruit maturation can influence harvest timing and duration, post-harvest fruit attributes and consumer acceptability. Present methods of managing and identifying lines of fruit with specific attributes both in commercial fruit production systems and breeding programs are limited by a lack of suitable tools to characterise fruit attributes at different stages of development in order to predict fruit behaviour at harvest, during storage or in relation to consumer acceptance. With visible-near infrared (VNIR) reflectance spectroscopy a vast array of analytical information is collected rapidly with a minimum of sample pre-treatment. VNIR spectra contain information about the amount and the composition of constituents within fruit. This information can be obtained from intact fruit at different stage of development. Spectroscopic data is processed using chemometrics techniques such as principal component analysis (PCA), discriminant analysis and/or connectionist approaches in order to extract qualitative and quantitative information for classification and predictive purposes. In this paper, we will illustrate the effectiveness of a model, connectionist and hybrid approaches, for fruit quality classification problems.