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Detection and Classification of Saffron Adulterants by Vis-Nir Imaging, Chemical Analysis, and Soft Computing

Saffron (Crocus sativus L.) is the most expensive spice in the world, known for its unique aroma and coloring in the food industry. Hence, its high price is frequently adulterated. In the current study, a variety of soft computing methods, including classifiers (i.e., RBF, MLP, KNN, SVM, SOM, and LV...

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Autores principales: Alighaleh, Pejman, Pakdel, Reyhaneh, Ghanei Ghooshkhaneh, Narges, Einafshar, Soodabeh, Rohani, Abbas, Saeidirad, Mohammad Hossein
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10252310/
https://www.ncbi.nlm.nih.gov/pubmed/37297436
http://dx.doi.org/10.3390/foods12112192
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author Alighaleh, Pejman
Pakdel, Reyhaneh
Ghanei Ghooshkhaneh, Narges
Einafshar, Soodabeh
Rohani, Abbas
Saeidirad, Mohammad Hossein
author_facet Alighaleh, Pejman
Pakdel, Reyhaneh
Ghanei Ghooshkhaneh, Narges
Einafshar, Soodabeh
Rohani, Abbas
Saeidirad, Mohammad Hossein
author_sort Alighaleh, Pejman
collection PubMed
description Saffron (Crocus sativus L.) is the most expensive spice in the world, known for its unique aroma and coloring in the food industry. Hence, its high price is frequently adulterated. In the current study, a variety of soft computing methods, including classifiers (i.e., RBF, MLP, KNN, SVM, SOM, and LVQ), were employed to classify four samples of fake saffron (dyed citrus blossom, safflower, dyed fibers, and mixed stigma with stamens) and three samples of genuine saffron (dried by different methods). RGB and spectral images (near-infrared and red bands) were captured from prepared samples for analysis. The amount of crocin, safranal, and picrocrocin were measured chemically to compare the images’ analysis results. The comparison results of the classifiers indicated that KNN could classify RGB and NIR images of samples in the training phase with 100% accuracy. However, KNN’s accuracy for different samples in the test phase was between 71.31% and 88.10%. The RBF neural network achieved the highest accuracy in training, test, and total phases. The accuracy of 99.52% and 94.74% was obtained using the features extracted from RGB and spectral images, respectively. So, soft computing models are helpful tools for detecting and classifying fake and genuine saffron based on RGB and spectral images.
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spelling pubmed-102523102023-06-10 Detection and Classification of Saffron Adulterants by Vis-Nir Imaging, Chemical Analysis, and Soft Computing Alighaleh, Pejman Pakdel, Reyhaneh Ghanei Ghooshkhaneh, Narges Einafshar, Soodabeh Rohani, Abbas Saeidirad, Mohammad Hossein Foods Article Saffron (Crocus sativus L.) is the most expensive spice in the world, known for its unique aroma and coloring in the food industry. Hence, its high price is frequently adulterated. In the current study, a variety of soft computing methods, including classifiers (i.e., RBF, MLP, KNN, SVM, SOM, and LVQ), were employed to classify four samples of fake saffron (dyed citrus blossom, safflower, dyed fibers, and mixed stigma with stamens) and three samples of genuine saffron (dried by different methods). RGB and spectral images (near-infrared and red bands) were captured from prepared samples for analysis. The amount of crocin, safranal, and picrocrocin were measured chemically to compare the images’ analysis results. The comparison results of the classifiers indicated that KNN could classify RGB and NIR images of samples in the training phase with 100% accuracy. However, KNN’s accuracy for different samples in the test phase was between 71.31% and 88.10%. The RBF neural network achieved the highest accuracy in training, test, and total phases. The accuracy of 99.52% and 94.74% was obtained using the features extracted from RGB and spectral images, respectively. So, soft computing models are helpful tools for detecting and classifying fake and genuine saffron based on RGB and spectral images. MDPI 2023-05-30 /pmc/articles/PMC10252310/ /pubmed/37297436 http://dx.doi.org/10.3390/foods12112192 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Alighaleh, Pejman
Pakdel, Reyhaneh
Ghanei Ghooshkhaneh, Narges
Einafshar, Soodabeh
Rohani, Abbas
Saeidirad, Mohammad Hossein
Detection and Classification of Saffron Adulterants by Vis-Nir Imaging, Chemical Analysis, and Soft Computing
title Detection and Classification of Saffron Adulterants by Vis-Nir Imaging, Chemical Analysis, and Soft Computing
title_full Detection and Classification of Saffron Adulterants by Vis-Nir Imaging, Chemical Analysis, and Soft Computing
title_fullStr Detection and Classification of Saffron Adulterants by Vis-Nir Imaging, Chemical Analysis, and Soft Computing
title_full_unstemmed Detection and Classification of Saffron Adulterants by Vis-Nir Imaging, Chemical Analysis, and Soft Computing
title_short Detection and Classification of Saffron Adulterants by Vis-Nir Imaging, Chemical Analysis, and Soft Computing
title_sort detection and classification of saffron adulterants by vis-nir imaging, chemical analysis, and soft computing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10252310/
https://www.ncbi.nlm.nih.gov/pubmed/37297436
http://dx.doi.org/10.3390/foods12112192
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