SEPARATING SEPTICEMIC AND NORMAL CHICKEN LIVERS BY VISIBLE/NEAR–INFRARED SPECTROSCOPY AND BACK–PROPAGATION NEURAL NETWORKS

The visible/near–infrared spectra of 300 chicken livers were analyzed to explore the feasibility of using spectroscopy to separate septicemic livers from normal livers. Three strategies involving offset, second difference, and functional link methods were applied to preprocess the spectra, while principal component analysis (PCA) was utilized to reduce the input data dimensions. PCA scores were fed into a feed–forward back–propagation neural network for classification. The results showed no obvious difference in classification accuracy between offset and non–offset data when no other preprocessing method was applied. The full 400–2498 nm wavelength region produced better results than the 400–700 nm, 400–1098 nm, and 1102–2498 nm sub–regions when more than 30 PCA scores were used. In general, the classification accuracy was improved by increasing the number of scores of input data, but too many scores diminished performance. The functional link test showed that using functional–link spectra selected at every third point with 60 scores achieved the same classification accuracy as that obtained when using all the data points with 90 scores. The best classification model used offset correction followed by second difference (g = 31) and 60 scores. It achieved a classification accuracy of 98% for normal and 94% for septicemic livers.