Linear and Non-linear Pattern Recognition Models for Classi cation of Fruit from Visible-Near Infrared Spectra

| Environment and genotype a ect the composition, quality, storability and sensory properties of plant based-products. Visible-near infrared (NIR) spectral measurements is used increasingly to monitor fruit properties such as maturity, sensory properties and storability non-destructively both prior to harvest and during storage. To explore this problem, at harvest and after storage, visible-NIR spectra containing 1024 individual data points were measured on kiwifruit berries sourced from six pre-harvest fruit management treatments. These raw spectra were processed by principal component analysis, or by Fourier, Hartley, Haar, Hurst, range renormalisation or polar coordinate transforms in order to extract a smaller set of features selected independently of treatment. In order to reduce their dimensionality further, the extracted features were processed by canonical variate (cv) analysis. The ability of various connectionist and linear discrimination pattern recognition models to predict the treatment source of unknown fruit on the basis of these features were evaluated. Thus far, this work