Exposing adulteration of Muscatel wines and assessing its distribution chain with fluorescence via intelligent and chaotic networks. (December 2020)

• Record Type: Journal Article
• Authors: Cancilla, John C.; Izquierdo, Manuel; Semenikhina, Anastasiia; González-Flores, Ester; Lastra-Mejías, Miguel; Torrecilla, José S.
• Is Part Of: Food control. Volume 118(2020)
• Publication Date: 2020-12
• Subjects: Muscatel wine -- Adulteration -- Distribution chain -- Chaotic parameters -- Artificial neural networks -- Laser diode; Food -- Quality -- Periodicals; Food -- Analysis -- Periodicals; Food handling -- Periodicals; Food industry and trade -- Quality control -- Periodicals; Aliments -- Industrie et commerce -- Qualité -- Contrôle -- Périodiques; Aliments -- Qualité -- Périodiques; Aliments -- Analyse -- Périodiques; Hygiène alimentaire -- Périodiques; Food -- Analysis; Food handling; Food -- Quality; Periodicals; Electronic journals; 664.07
• DOI: 10.1016/j.foodcont.2020.107428
• Languages: English
• ISSNs: 0956-7135

Abstract

Abstract: In this work, intelligent algorithms and fluorescent measurements have been integrated into a portable system to evaluate the storage conditions and detect adulterations of wines such as water and ethanol. More than 700 spectra derived from the analysis of three types of either large-scale or artisanal Muscatel wines of European origin (French and Russian) were collected. Different sets of independent variables were extracted and used to train neural network models. Two classes were employed including variables extracted via feature selection directly from the fluorescent emission spectra and others calculated in the form of chaotic parameters. To reach the two proposed objectives, more than 77, 500 neural networks have been developed to optimize the tools. The main results are that the integration between fluorescence and intelligent algorithms, whether based on chaotic parameters or direct emission data, are capable of detecting anomalous storage conditions and, above all, of locating the presence of adulterants such as water or ethanol in the wines tested. Highlights: The quality of three Muscatel wines assessed via fluorescence and neural networks. Chaotic parameters extract descriptive data of the wines from spectral profiles. Distribution chain evaluated by determining temperature experienced by wine. Adulterants such as water and ethanol accurately detected and quantified. Cost-effective and real-time approach to protect wine producers and consumers.