ATR-FTIR spectroscopy and machine/deep learning models for detecting adulteration in coconut water with sugars, sugar alcohols, and artificial sweeteners.

Autor: Teklemariam TA; Canadian Food Inspection Agency, Greater Toronto Area Laboratory, 2301 Midland Avenue, Toronto, ON M1P 4R7, Canada. Electronic address: thomas.teklemariam@inspection.gc.ca., Chou F; Canadian Food Inspection Agency, 1400 Merivale Road, Ottawa, ON K1A 0Y9, Canada., Kumaravel P; University of Guelph, Molecular and Cellular Biology, Guelph, ON N1G 2W1, Canada., Van Buskrik J; Canadian Food Inspection Agency, Greater Toronto Area Laboratory, 2301 Midland Avenue, Toronto, ON M1P 4R7, Canada.
Jazyk: angličtina
Zdroj: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy [Spectrochim Acta A Mol Biomol Spectrosc] 2024 Dec 05; Vol. 322, pp. 124771. Date of Electronic Publication: 2024 Jul 03.
DOI: 10.1016/j.saa.2024.124771
Abstrakt: Packaged coconut water offers various options, from pure to those with added sugars and other additives. While the purity of coconut water is esteemed for its health benefits, its popularity also exposes it to potential adulteration and misrepresentation. To address this concern, our study combines Fourier transform infrared spectroscopy (FTIR) and machine learning techniques to detect potential adulterants in coconut water through classification models. The dataset comprises infrared spectra from coconut water samples spiked with 15 different types of potential sugar substitutes, including: sugars, artificial sweeteners, and sugar alcohols. The interaction of infrared light with molecular bonds generates unique molecular fingerprints, forming the basis of our analysis. Departing from previous research predominantly reliant on linear-based chemometrics for adulterant detection, our study explored linear, non-linear, and combined feature extraction models. By developing an interactive application utilizing principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), non-targeted sugar adulterant detection was streamlined through enhanced visualization and pattern recognition. Targeted analysis using ensemble learning random forest (RF) and deep learning 1-dimensional convolutional neural network (1D CNN) achieved higher classification accuracies (95% and 96%, respectively) compared to sparse partial least squares discriminant analysis (sPLS-DA) at 77% and support vector machine (SVM) at 88% on the same dataset. The CNN's demonstrated classification accuracy is complemented by exceptional efficiency through its ability to train and test on raw data.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024. Published by Elsevier B.V.)
Databáze: MEDLINE
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