Cargando…

Predicting sensory evaluation of spinach freshness using machine learning model and digital images

The visual perception of freshness is an important factor considered by consumers in the purchase of fruits and vegetables. However, panel testing when evaluating food products is time consuming and expensive. Herein, the ability of an image processing-based, nondestructive technique to classify spi...

Descripción completa

Detalles Bibliográficos
Autores principales: Koyama, Kento, Tanaka, Marin, Cho, Byeong-Hyo, Yoshikawa, Yusaku, Koseki, Shige
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7978266/
https://www.ncbi.nlm.nih.gov/pubmed/33739969
http://dx.doi.org/10.1371/journal.pone.0248769
_version_ 1783667174829719552
author Koyama, Kento
Tanaka, Marin
Cho, Byeong-Hyo
Yoshikawa, Yusaku
Koseki, Shige
author_facet Koyama, Kento
Tanaka, Marin
Cho, Byeong-Hyo
Yoshikawa, Yusaku
Koseki, Shige
author_sort Koyama, Kento
collection PubMed
description The visual perception of freshness is an important factor considered by consumers in the purchase of fruits and vegetables. However, panel testing when evaluating food products is time consuming and expensive. Herein, the ability of an image processing-based, nondestructive technique to classify spinach freshness was evaluated. Images of spinach leaves were taken using a smartphone camera after different storage periods. Twelve sensory panels ranked spinach freshness into one of four levels using these images. The rounded value of the average from all twelve panel evaluations was set as the true label. The spinach image was removed from the background, and then converted into a gray scale and CIE-Lab color space (L*a*b*) and Hue, Saturation and Value (HSV). The mean value, minimum value, and standard deviation of each component of color in spinach leaf were extracted as color features. Local features were extracted using the bag-of-words of key points from Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elementary Features). The feature combinations selected from the spinach images were used to train machine learning models to recognize freshness levels. Correlation analysis between the extracted features and the sensory evaluation score showed a positive correlation (0.5 < r < 0.6) for four color features, and a negative correlation (‒0.6 < r < ‒0.5) for six clusters in the local features. The support vector machine classifier and artificial neural network algorithm successfully classified spinach samples with overall accuracy 70% in four-class, 77% in three-class and 84% in two-class, which was similar to that of the individual panel evaluations. Our findings indicate that a model using support vector machine classifiers and artificial neural networks has the potential to replace freshness evaluations currently performed by non-trained panels.
format Online
Article
Text
id pubmed-7978266
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-79782662021-03-30 Predicting sensory evaluation of spinach freshness using machine learning model and digital images Koyama, Kento Tanaka, Marin Cho, Byeong-Hyo Yoshikawa, Yusaku Koseki, Shige PLoS One Research Article The visual perception of freshness is an important factor considered by consumers in the purchase of fruits and vegetables. However, panel testing when evaluating food products is time consuming and expensive. Herein, the ability of an image processing-based, nondestructive technique to classify spinach freshness was evaluated. Images of spinach leaves were taken using a smartphone camera after different storage periods. Twelve sensory panels ranked spinach freshness into one of four levels using these images. The rounded value of the average from all twelve panel evaluations was set as the true label. The spinach image was removed from the background, and then converted into a gray scale and CIE-Lab color space (L*a*b*) and Hue, Saturation and Value (HSV). The mean value, minimum value, and standard deviation of each component of color in spinach leaf were extracted as color features. Local features were extracted using the bag-of-words of key points from Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elementary Features). The feature combinations selected from the spinach images were used to train machine learning models to recognize freshness levels. Correlation analysis between the extracted features and the sensory evaluation score showed a positive correlation (0.5 < r < 0.6) for four color features, and a negative correlation (‒0.6 < r < ‒0.5) for six clusters in the local features. The support vector machine classifier and artificial neural network algorithm successfully classified spinach samples with overall accuracy 70% in four-class, 77% in three-class and 84% in two-class, which was similar to that of the individual panel evaluations. Our findings indicate that a model using support vector machine classifiers and artificial neural networks has the potential to replace freshness evaluations currently performed by non-trained panels. Public Library of Science 2021-03-19 /pmc/articles/PMC7978266/ /pubmed/33739969 http://dx.doi.org/10.1371/journal.pone.0248769 Text en © 2021 Koyama et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Koyama, Kento
Tanaka, Marin
Cho, Byeong-Hyo
Yoshikawa, Yusaku
Koseki, Shige
Predicting sensory evaluation of spinach freshness using machine learning model and digital images
title Predicting sensory evaluation of spinach freshness using machine learning model and digital images
title_full Predicting sensory evaluation of spinach freshness using machine learning model and digital images
title_fullStr Predicting sensory evaluation of spinach freshness using machine learning model and digital images
title_full_unstemmed Predicting sensory evaluation of spinach freshness using machine learning model and digital images
title_short Predicting sensory evaluation of spinach freshness using machine learning model and digital images
title_sort predicting sensory evaluation of spinach freshness using machine learning model and digital images
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7978266/
https://www.ncbi.nlm.nih.gov/pubmed/33739969
http://dx.doi.org/10.1371/journal.pone.0248769
work_keys_str_mv AT koyamakento predictingsensoryevaluationofspinachfreshnessusingmachinelearningmodelanddigitalimages
AT tanakamarin predictingsensoryevaluationofspinachfreshnessusingmachinelearningmodelanddigitalimages
AT chobyeonghyo predictingsensoryevaluationofspinachfreshnessusingmachinelearningmodelanddigitalimages
AT yoshikawayusaku predictingsensoryevaluationofspinachfreshnessusingmachinelearningmodelanddigitalimages
AT kosekishige predictingsensoryevaluationofspinachfreshnessusingmachinelearningmodelanddigitalimages