Classification of 1-methylcyclopropene treated apples by fluorescence fingerprint using partial least squares discriminant analysis with stepwise selectivity ratio variable selection method

• Published in: Chemometrics and intelligent laboratory systems Vol. 175; pp. 30 - 36
• Main Authors: Trivittayasil, Vipavee, Tsuta, Mizuki, Kasai, Satoshi, Matsuo, Yosuke, Sekiyama, Yasuyo, Shoji, Toshihiko, Aiyama, Ryoko, Kokawa, Mito, Sugiyama, Junichi
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.04.2018
• Subjects: 1-MethylCyclopropene; Apple; Fluorescence fingerprint; Partial least squares discriminant analysis; Selectivity ratio; Fluorescence fingerprint; Apple; Partial least squares discriminant analysis; Selectivity ratio; 1-MethylCyclopropene
• ISSN: 0169-7439; 1873-3239
• DOI: 10.1016/j.chemolab.2018.02.004

In this study, we investigated the potential of using fluorescence fingerprint (FF) for nondestructive identification of apples treated with 1-methylcyclopropene (1-MCP). In total, 442 apples of two cultivars (Fuji and Orin) and different storage times (0, 4, 5, 6, and 8 months) were assessed. The classification model used in this study was built using partial least squares discriminant analysis (PLSDA) with the stepwise selectivity ratio (SR) method. The stepwise SR method is a recursive variable selection method proposed in this study. FF was capable of classifying 1-MCP-treated apples with accuracies of 91.23%, 89.74%, and 90.17% for calibration, cross-validation, and validation results, respectively. PLSDA with the stepwise SR method could identify four aggregations of wavelength conditions, which are important to the classification. In addition, a non-targeted approach was taken to screen the metabolites characterizing 1-MCP-treated and control apples by liquid chromatography-mass spectrometry (LC/MS) and nuclear magnetic resonance (NMR) spectroscopy. The observed difference in metabolic profiles may contribute to the difference in the fluorescence profiles of 1-MCP treated and control apples. •Fluorescence fingerprints (FF) of control and 1-MCP treated apples (442 samples).•PLSDA models based on FF with stepwise SR method for 1-MCP apple discrimination.•Validation accuracy of 90.17% with reduced number of variables from 2438 to 58.•Data fusion with LC/MS and NMR for the relationship between metabolites and FF.