Discrimination of Apple Essences Based on Spectral Data Fusion Combined with Pattern Recognition Algorithm

doi:10.7506/spkx1002-6630-201622029

Abstract

Abstract: In this paper, Raman spectroscopy and ion mobility spectrometry (IMS) were used in combination to characterize nine kinds of apple essences from different producers. A discrimination model was built using spectral data fusion combined with principal component analysis (PCA) and nearest neighbor (NN) algorithm. First, apple essences were diluted with ultra-pure water. Then, Raman and IMS fingerprint databases were established by Raman and IMS analyses, respectively. The single spectral data combined with PCA-NN models and the data fusion of Raman and IMS combined with PCA-NN model were used to distinguish apple essences, respectively. It was shown that the identification accuracy rate of the Raman-IMS combined with PCA-NN model for nine kinds of apple essences was 98.35%, which was higher than that of the Raman spectra combined with PCA-NN model (94.18%) and the IMS spectra combined with PCA-NN model (78.14%). Aqueous dilution technique was simple and fast, caused neither side effect nor pollution, and could retain all substances in the sample, ensuring the reliability and stability of the experimental results. Both Raman and IMS had the advantages of easy operation and quick analysis. The results from this study demonstrated that the Raman-IMS combined with PCA-NN model can be used as a reliable, stable and fast new method to discriminate among apple essences.

Key words: apple essence, Raman spectroscopy, ion mobility spectrometry (IMS), data fusion, principal component analysis (PCA), nearest neighbor (NN) algorithm, discrimination

CLC Number:

TS201.6

SHA Min, SONG Chao, ZHANG Zhengyong, WANG Suyu, LIU Jun, WANG Haiyan,*. Discrimination of Apple Essences Based on Spectral Data Fusion Combined with Pattern Recognition Algorithm[J]. FOOD SCIENCE, 2016, 37(22): 192-197.

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