Feature Extraction of Citrus Juice During Storage for Electronic Nose Based on Cellular Neural Network

Abstract

Aroma is one of the most important factors affecting the quality of citrus fruit and its processed products. We use electronic nose (E-nose) to detect and analyze volatile components in citrus. An E-nose is an artificial intelligence system with strong independence and fast detection speed. It combines an array of gas sensors and intelligent algorithms designed to analyze gas. Moreover, it has the ability to detect and analyze volatile components. Feature extraction is the first step of sensor signal processing and plays an important role in subsequent pattern recognition. Cellular neural network (CNN) is a real-time high-speed parallel array processor and a locally connected network, which has mature applications in the field of image processing. Previous researches have shown that CNN has an outstanding impact on image feature extractio. In this paper, the traditional CNN is improved and a template for dynamic feature extraction of the E-nose response curve is proposed. In addition, we provide users with single-template and multi-template solutions which can be applied in different environments. To free up the computational power of occupancy, the effect of the single-template version of CNN is not as effective as the multi-template version, but it still has good feature extraction ability. These two solutions prove that CNN is sensitive to dynamic features. In order to make the results more representative, we choose several traditional feature extraction methods for comparison.