ANALYSIS OF GRAPE QUALITY USING NEURAL NETWORK

摘要

Prediction of non-linear properties of fruit samples, such as total anthocyanin concentration in grapes, Brix or pH. A set of data such as near infrared spectroscopic data is obtained from a training set of fruit samples, and from that data a reduced set of variables is derived which have co-correlation with the non-linear property. The reduced set of variables can be partial least squares (PLS) scores obtained by applying a PLS regression, and/or wavelength specific portions of the raw data, determined to have co-correlation with the non-linear property. A feed-forward back-propagation artificial neural network (ANN) is trained by using the reduced set of variables as inputs to the ANN. The ANN, once calibrated, is used to predict the non-linear property in data obtained from a prediction set of fruit samples.