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ATR-FTIR spectroscopy and machine/deep learning models for detecting adulteration in coconut water with sugars, sugar alcohols, and artificial sweeteners.
Teklemariam, Thomas A; Chou, Faith; Kumaravel, Pavisha; Van Buskrik, Jeremy.
Afiliação
  • 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.
Spectrochim Acta A Mol Biomol Spectrosc ; 322: 124771, 2024 Dec 05.
Article em En | MEDLINE | ID: mdl-39032237
ABSTRACT
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Edulcorantes / Cocos / Açúcares / Aprendizado Profundo Idioma: En Revista: Spectrochim Acta A Mol Biomol Spectrosc Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Edulcorantes / Cocos / Açúcares / Aprendizado Profundo Idioma: En Revista: Spectrochim Acta A Mol Biomol Spectrosc Ano de publicação: 2024 Tipo de documento: Article