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Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication

Authentication of meat floss origin has been highly critical for its consumers due to existing potential risks of having allergic diseases or religion perspective related to pork-containing foods. Herein, we developed and assessed a compact portable electronic nose (e-nose) comprising gas sensor arr...

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Autores principales: Putri, Linda Ardita, Rahman, Iman, Puspita, Mayumi, Hidayat, Shidiq Nur, Dharmawan, Agus Budi, Rianjanu, Aditya, Wibirama, Sunu, Roto, Roto, Triyana, Kuwat, Wasisto, Hutomo Suryo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10275922/
https://www.ncbi.nlm.nih.gov/pubmed/37328497
http://dx.doi.org/10.1038/s41538-023-00205-2
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author Putri, Linda Ardita
Rahman, Iman
Puspita, Mayumi
Hidayat, Shidiq Nur
Dharmawan, Agus Budi
Rianjanu, Aditya
Wibirama, Sunu
Roto, Roto
Triyana, Kuwat
Wasisto, Hutomo Suryo
author_facet Putri, Linda Ardita
Rahman, Iman
Puspita, Mayumi
Hidayat, Shidiq Nur
Dharmawan, Agus Budi
Rianjanu, Aditya
Wibirama, Sunu
Roto, Roto
Triyana, Kuwat
Wasisto, Hutomo Suryo
author_sort Putri, Linda Ardita
collection PubMed
description Authentication of meat floss origin has been highly critical for its consumers due to existing potential risks of having allergic diseases or religion perspective related to pork-containing foods. Herein, we developed and assessed a compact portable electronic nose (e-nose) comprising gas sensor array and supervised machine learning with a window time slicing method to sniff and to classify different meat floss products. We evaluated four different supervised learning methods for data classification (i.e., linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbors (k-NN), and random forest (RF)). Among them, an LDA model equipped with five-window-extracted feature yielded the highest accuracy values of >99% for both validation and testing data in discriminating beef, chicken, and pork flosses. The obtained e-nose results were correlated and confirmed with the spectral data from Fourier-transform infrared (FTIR) spectroscopy and gas chromatography–mass spectrometry (GC-MS) measurements. We found that beef and chicken had similar compound groups (i.e., hydrocarbons and alcohol). Meanwhile, aldehyde compounds (e.g., dodecanal and 9-octadecanal) were found to be dominant in pork products. Based on its performance evaluation, the developed e-nose system shows promising results in food authenticity testing, which paves the way for ubiquitously detecting deception and food fraud attempts.
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spelling pubmed-102759222023-06-18 Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication Putri, Linda Ardita Rahman, Iman Puspita, Mayumi Hidayat, Shidiq Nur Dharmawan, Agus Budi Rianjanu, Aditya Wibirama, Sunu Roto, Roto Triyana, Kuwat Wasisto, Hutomo Suryo NPJ Sci Food Article Authentication of meat floss origin has been highly critical for its consumers due to existing potential risks of having allergic diseases or religion perspective related to pork-containing foods. Herein, we developed and assessed a compact portable electronic nose (e-nose) comprising gas sensor array and supervised machine learning with a window time slicing method to sniff and to classify different meat floss products. We evaluated four different supervised learning methods for data classification (i.e., linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbors (k-NN), and random forest (RF)). Among them, an LDA model equipped with five-window-extracted feature yielded the highest accuracy values of >99% for both validation and testing data in discriminating beef, chicken, and pork flosses. The obtained e-nose results were correlated and confirmed with the spectral data from Fourier-transform infrared (FTIR) spectroscopy and gas chromatography–mass spectrometry (GC-MS) measurements. We found that beef and chicken had similar compound groups (i.e., hydrocarbons and alcohol). Meanwhile, aldehyde compounds (e.g., dodecanal and 9-octadecanal) were found to be dominant in pork products. Based on its performance evaluation, the developed e-nose system shows promising results in food authenticity testing, which paves the way for ubiquitously detecting deception and food fraud attempts. Nature Publishing Group UK 2023-06-16 /pmc/articles/PMC10275922/ /pubmed/37328497 http://dx.doi.org/10.1038/s41538-023-00205-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Putri, Linda Ardita
Rahman, Iman
Puspita, Mayumi
Hidayat, Shidiq Nur
Dharmawan, Agus Budi
Rianjanu, Aditya
Wibirama, Sunu
Roto, Roto
Triyana, Kuwat
Wasisto, Hutomo Suryo
Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication
title Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication
title_full Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication
title_fullStr Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication
title_full_unstemmed Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication
title_short Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication
title_sort rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10275922/
https://www.ncbi.nlm.nih.gov/pubmed/37328497
http://dx.doi.org/10.1038/s41538-023-00205-2
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