Detection of early core browning in pears based on statistical features in vibro-acoustic signals

Abstract

The purpose of this work was to achieve non-destructive detection of early core browning in Korla pear using the vibro-acoustic method. The response signals of a piezoelectric sensor were analyzed to extract 18 statistical features. Three types of feature sets were then designed: time-domain feature set (11 features), frequency-domain feature set (7 features), and combined feature set (18 features). The minimum number of features in each feature set was selected using distance evaluation technology to train classifiers based on support vector machine. Two classifiers were constructed for disorder detection: a browning classifier for moderate disorder and a slight browning classifier for the mild disorder. The browning classifier obtained a high overall accuracy of 93.9% with three time-domain features and one frequency-domain feature. The overall accuracy of the slight browning classifier was 86.4% using two time-domain features and one frequency-domain feature. For these two classifiers, the F1 values from confusion matrix analysis were 85.9% and 93.5%, respectively. Therefore, our constructed classifiers could be used in the application of the vibro-acoustic method for the detection of internal browning in pears.