An optimization of the MOS electronic nose sensor array for the detection of Chinese pecan quality. (June 2017)

• Record Type: Journal Article
• Authors: Xu, Keming; Wang, Jun; Wei, Zhenbo; Deng, Fanfei; Wang, Yongwei; Cheng, Shaoming
• Is Part Of: Journal of food engineering. Volume 203(2017)
• Page Start: 25
• Page End: 31
• Publication Date: 2017-06
• Subjects: Array optimization -- e-nose -- Feature matrix -- PCA -- PLSR -- BPNN; Food industry and trade -- Periodicals; Food -- Analysis -- Periodicals; Aliments -- Industrie et commerce -- Périodiques; Aliments -- Analyse -- Périodiques; Aliments -- Recherche -- Périodiques; 664.005
• DOI: 10.1016/j.jfoodeng.2017.01.023
• Languages: English
• ISSNs: 0260-8774

Abstract

Abstract: In this research, an embedded metal oxide semiconductor (MOS) electronic nose (e-nose) was designed to detect Chinese pecan quality. To improve the performance of e-nose, three types of features were extracted to form initial feature matrix, including mean-differential coefficient value, stable value, and response area value. Furthermore, followed by the non-search feature selection strategy, optimized feature matrix was obtained through the procedure of mean analysis, variation coefficient analysis, cluster analysis and correlation analysis. It was observed that pecans were better classified after the optimization of initial feature matrix, shown by principal component analysis (PCA) score plot. And also the regression models of optimized feature matrix established by partial least squares regression (PLSR) (R 2 = 0.9377) and back propagation neural networks (BPNN) (R 2 = 0.9787) presented a better prediction capacity than these of initial one (PLSR: R 2 = 0.8887; BPNN: R 2 = 0.9093). In conclusion, the optimization method not only reduced data dimensionality but also improved electronic nose performance. Highlights: Four batches of aged pecans are discriminated successfully using optimized array. Non-searching feature selection method is used for sensor array optimization. Three feature types are extracted to generate the initial feature matrix. Sensors corresponding to the optimized feature matrix are chosen.