Abnormal eggs detection based on spectroscopy technology and multiple classifier fusion.

The aim of the research was to improve detection accuracy of the blood spots and meat spots in eggs, which can provide consumers with high-quality eggs. Spectroscopy technology and multiple classifier fusion for abnormal egg detection were investigated. Micro fiber spectrometer (Ocean Optics company, USB2000+) was used to collect the transmittance spectroscopy of both normal and abnormal eggs, which were from Hubei Shendan Healthy Food Co., Ltd. After outliers detection and elimination, there were 336 eggs in all, which were randomly assigned to training set and test set; among the 336 eggs, 224 (about two-thirds of the total) were assigned to the training set, and the remaining 112 (about one-third of the total) were assigned to the test set. Before multiple classifier fusion, all data collected from micro fiber spectrometer was preprocessed including the methods of SNV (standard normal variate), smoothing and MSC (multiplicative scatter correction). usion of multiple classifiers was used to detect the foreign bodies of eggs on the basis of single-classifier. Firstly, five single-classifiers which was inclusive of Naive Bayes classifier, Mahalanobis distance classifier, PLS-DA (partial least squares-discriminate analysis) classifier, AdaBoost (adaptive boosting) classifier and SVM (support vector machine) classifier were all trained, and five groups of classification results were attained. In order to choose suitable one from these five single-classifiers, according to diversity measure, output disagreement measure and error agreement measure were introduced and used, and Naive Bayes, AdaBoost and SVM classifiers were selected as the single-classifiers; then, on the basis of these three selected single-classifiers, through feature level fusion, 21 base-classifiers were obtained. In order to get the final base-classifiers which were used to accomplish the multiple classifier fusion, in a similar way, through output disagreement measure and error agreement measure again, 5 base-classifiers were chosen from these 21 base-classifiers. Finally, 5 basic-classifiers were fused by weight vote strategy on the decision level. The weight vote strategy was that each base-classifier was allocated a weight value according to its accuracy rate, and the higher accuracy rate a base-classifier had, the larger weight value it would be allocated, because it was more trusted. Detection accuracy rate of ensemble classifier, which was formed after multiple classifier fusion, was 92.86% and 91.07%, respectively for normal eggs and abnormal eggs. As a contrast, among all the single-classifiers and base-classifiers, the highest detection accuracy of normal eggs in the test set was 91.07%, which came from AdaBoost (500-600 nm), and the highest detection accuracy of abnormal eggs in the test set was 89.29%, which came from SVM (550-600 nm). The experiment results showed that the model established by multiple classifier fusion could take full advantage of the information which came from each single-classifier or base-classifier, and in the aspect of the detection accuracy of either normal eggs or abnormal eggs, the model established by multiple classifier fusion was indeed superior to the model established by each single-classifier or base-classifier. Even though the detection accuracy was enhanced by a small margin, considering that a large number of either normal eggs or abnormal eggs were being produced and being detected in lots of companies that were involved in eggs, in this meaning, the slight promotion of detection accuracy was of great significance. [ABSTRACT FROM AUTHOR]