Non-destructive Determination of Sugar Content in Apple by Near Infrared Spectroscopy with Similarity Evaluation Combined with Local Regression Method

A novel local regression method combined with similarity evaluation for near infrared spectra was proposed.In this method,the similarity evaluation based on Bayesian statistics was utilized to compare the NIR spectra.The calibration subsets were then selected to construct the model by partial least square method according to the similarity.The sugar content of apple samples was predicted with the model.The mean values of the standard errors of validation(SEV) and prediction(SEP) for the partial least square local regression method based on Bayesian statistics(B-PLS) were 0.57 and 0.61,respectively.Those for the partial least square local regression method based on Mahalanobis distance(M-PLS) were 0.59 and 0.64,respectively,and those for the partial least square global regression method(G-PLS) were 0.65 and 0.70,respectively.The results showed that B-PLS could accurately predict the sugar content in apple and its performance was superior to that of G-PLS and a little superior to that of M-PLS.Thus the proposed method possesses higher accuracy compared with G-PLS and could be widely applied in the rapid and nondestructive analysis of internal qualities such as the sugar content of apple.Furthermore,the method could provide a new tool for near infrared analysis.