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

Abstract

The visible/nearinfrared spectra of 300 chicken livers were analyzed to explore the feasibility of usingspectroscopy to separate septicemic livers from normal livers. Three strategies involving offset, second difference, andfunctional link methods were applied to preprocess the spectra, while principal component analysis (PCA) was utilized toreduce the input data dimensions. PCA scores were fed into a feedforward backpropagation neural network forclassification. The results showed no obvious difference in classification accuracy between offset and nonoffset data whenno other preprocessing method was applied. The full 4002498 nm wavelength region produced better results than the400700 nm, 4001098 nm, and 11022498 nm subregions when more than 30 PCA scores were used. In general, theclassification accuracy was improved by increasing the number of scores of input data, but too many scores diminishedperformance. The functional link test showed that using functionallink spectra selected at every third point with 60 scoresachieved the same classification accuracy as that obtained when using all the data points with 90 scores. The bestclassification model used offset correction followed by second difference (g = 31) and 60 scores. It achieved a classificationaccuracy of 98% for normal and 94% for septicemic livers.