Rapid Identification of Rainbow Trout Adulteration in Atlantic Salmon by Raman Spectroscopy Combined with Machine Learning
This study intends to evaluate the utilization potential of the combined Raman spectroscopy and machine learning approach to quickly identify the rainbow trout adulteration in Atlantic salmon. The adulterated samples contained various concentrations (0–100% w/w at 10% intervals) of rainbow trout mix...
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ftpubmed:oai:pubmedcentral.nih.gov:6696069 2023-05-15T15:28:48+02:00 Rapid Identification of Rainbow Trout Adulteration in Atlantic Salmon by Raman Spectroscopy Combined with Machine Learning Chen, Zeling Wu, Ting Xiang, Cheng Xu, Xiaoyan Tian, Xingguo 2019-08-06 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6696069/ http://www.ncbi.nlm.nih.gov/pubmed/31390746 https://doi.org/10.3390/molecules24152851 en eng MDPI http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6696069/ http://www.ncbi.nlm.nih.gov/pubmed/31390746 http://dx.doi.org/10.3390/molecules24152851 © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). CC-BY Article Text 2019 ftpubmed https://doi.org/10.3390/molecules24152851 2019-09-08T00:32:05Z This study intends to evaluate the utilization potential of the combined Raman spectroscopy and machine learning approach to quickly identify the rainbow trout adulteration in Atlantic salmon. The adulterated samples contained various concentrations (0–100% w/w at 10% intervals) of rainbow trout mixed into Atlantic salmon. Spectral preprocessing methods, such as first derivative, second derivative, multiple scattering correction (MSC), and standard normal variate, were employed. Unsupervised algorithms, such as recursive feature elimination, genetic algorithm (GA), and simulated annealing, and supervised K-means clustering (KM) algorithm were used for selecting important spectral bands to reduce the spectral complexity and improve the model stability. Finally, the performances of various machine learning models, including linear regression, nonlinear regression, regression tree, and rule-based models, were verified and compared. The results denoted that the developed GA–KM–Cubist machine learning model achieved satisfactory results based on MSC preprocessing. The determination coefficient (R(2)) and root mean square error of prediction sets (RMSEP) in the test sets were 0.87 and 10.93, respectively. These results indicate that Raman spectroscopy can be used as an effective Atlantic salmon adulteration identification method; further, the developed model can be used for quantitatively analyzing the rainbow trout adulteration in Atlantic salmon. Text Atlantic salmon PubMed Central (PMC) Molecules 24 15 2851 |
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Article Chen, Zeling Wu, Ting Xiang, Cheng Xu, Xiaoyan Tian, Xingguo Rapid Identification of Rainbow Trout Adulteration in Atlantic Salmon by Raman Spectroscopy Combined with Machine Learning |
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description |
This study intends to evaluate the utilization potential of the combined Raman spectroscopy and machine learning approach to quickly identify the rainbow trout adulteration in Atlantic salmon. The adulterated samples contained various concentrations (0–100% w/w at 10% intervals) of rainbow trout mixed into Atlantic salmon. Spectral preprocessing methods, such as first derivative, second derivative, multiple scattering correction (MSC), and standard normal variate, were employed. Unsupervised algorithms, such as recursive feature elimination, genetic algorithm (GA), and simulated annealing, and supervised K-means clustering (KM) algorithm were used for selecting important spectral bands to reduce the spectral complexity and improve the model stability. Finally, the performances of various machine learning models, including linear regression, nonlinear regression, regression tree, and rule-based models, were verified and compared. The results denoted that the developed GA–KM–Cubist machine learning model achieved satisfactory results based on MSC preprocessing. The determination coefficient (R(2)) and root mean square error of prediction sets (RMSEP) in the test sets were 0.87 and 10.93, respectively. These results indicate that Raman spectroscopy can be used as an effective Atlantic salmon adulteration identification method; further, the developed model can be used for quantitatively analyzing the rainbow trout adulteration in Atlantic salmon. |
format |
Text |
author |
Chen, Zeling Wu, Ting Xiang, Cheng Xu, Xiaoyan Tian, Xingguo |
author_facet |
Chen, Zeling Wu, Ting Xiang, Cheng Xu, Xiaoyan Tian, Xingguo |
author_sort |
Chen, Zeling |
title |
Rapid Identification of Rainbow Trout Adulteration in Atlantic Salmon by Raman Spectroscopy Combined with Machine Learning |
title_short |
Rapid Identification of Rainbow Trout Adulteration in Atlantic Salmon by Raman Spectroscopy Combined with Machine Learning |
title_full |
Rapid Identification of Rainbow Trout Adulteration in Atlantic Salmon by Raman Spectroscopy Combined with Machine Learning |
title_fullStr |
Rapid Identification of Rainbow Trout Adulteration in Atlantic Salmon by Raman Spectroscopy Combined with Machine Learning |
title_full_unstemmed |
Rapid Identification of Rainbow Trout Adulteration in Atlantic Salmon by Raman Spectroscopy Combined with Machine Learning |
title_sort |
rapid identification of rainbow trout adulteration in atlantic salmon by raman spectroscopy combined with machine learning |
publisher |
MDPI |
publishDate |
2019 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6696069/ http://www.ncbi.nlm.nih.gov/pubmed/31390746 https://doi.org/10.3390/molecules24152851 |
genre |
Atlantic salmon |
genre_facet |
Atlantic salmon |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6696069/ http://www.ncbi.nlm.nih.gov/pubmed/31390746 http://dx.doi.org/10.3390/molecules24152851 |
op_rights |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
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https://doi.org/10.3390/molecules24152851 |
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