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Qualitative and quantitative analysis of Chinese pecans (Carya cathayensis) during storage using MOS E-nose combined with chemometrics methods

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2016 ASABE Annual International Meeting  162460018.(doi:10.13031/aim.20162460018)
Authors:   Shui Jiang, Jun Wang, Shaoming Cheng, Yongwei Wang
Keywords:   Chinese pecans, Electronic nose, Fatty acids, Nondestructive detection, Voting method.

Abstract. Due to the high contents of unsaturated fatty acids, Chinese pecans (Carya cathayensis) are prone to rancidity during storage. Because of the hard shells, it is difficult to detect the internal quality nondestructively by traditional ways, such as spectroscopy technology, physicochemical method, and sensory evaluation. The different constituents cause the changes of volatile compounds of nut meat, and microporous structure of shells allows volatile compounds to emit from the pecans. According to the volatilization mechanism, an electronic nose (E-nose) equipped with an array of metal oxide semiconductor (MOS) sensors was applied to detect pecans nondestructively for qualitative and quantitative analysis. A voting model (VM), composed of 5 principle component analysis (PCA) results, was used as qualitative approach based on different features (i.e., the 10th second values, the 75th second values, the area values, the maximum values and the minimum values). VM showed a satisfactory qualitative classification result with 96% accuracy rate. For quantitative analysis, a multi-target back propagation neural networks (BPNN) model was built to simultaneously predict the contents of six fatty acids based pm E-nose. The prediction indexes were accurately predicted by the BPNN model, and coefficient of determination (R2) between predicted values and measured values were satisfactory (R2 > 0.95 in calibration sets and R2 > 0.88 in validation sets). E-nose was proved as a potential nondestructive technique to classify different Chinese pecans, and to predict fatty acids contents accurately.

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