Application of Statistical Features in Vibro-acoustic Signals to Detect Early Browning Disorder in Pears Compared with Food Chemistry Method

Abstract

- Browning in pears is one of the most serious diseases in pear fruit, which is caused by Alternaria alternata. The browning process is accompanied by changes in the chemical properties of the fruit, which affect its taste and nutritional composition. Additionally, as a typical postharvest disease, internal browning in pears can cause fruit tissue decay during storage that can reduce the shelf stability of fruit, and bring serious losses to sellers. Because it is difficult to identify the browning pears by appearance, a non-destructive detection technology is highly desirable to correctly discriminate a pear at the early stage of browning for increasing the market value. Firstly, 11 and 7 statistical features were calculated from the time-domain and frequency-domain, respectively. Then, sensitive features in time-domain set, frequency-domain set and combined feature set were selected by the distance evaluation technology, respectively. These selected features were used to train classifier based on K-nearest neighbor algorithm under different K-values. With the selected combined features adopted, the constructed KNN classifier performed the best classification performance. It allowed a high overall accuracy of 91.8 % to classify the healthy and browning pears. Also, the F1 value of 92.6 % indicated that the classifier can be successfully generalized. Therefore, the classification model established in this study is effective for identifying the early browning disorder in pear fruit.