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...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0248769&type=printable |
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| author | Kento Koyama Marin Tanaka Byeong-Hyo Cho Yusaku Yoshikawa Shige Koseki |
| author_facet | Kento Koyama Marin Tanaka Byeong-Hyo Cho Yusaku Yoshikawa Shige Koseki |
| author_sort | Kento Koyama |
| collection | DOAJ |
| 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 | Article |
| id | doaj-art-18e12f91c6a14efcb267637ae67740c0 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-18e12f91c6a14efcb267637ae67740c02025-08-20T02:17:50ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01163e024876910.1371/journal.pone.0248769Predicting sensory evaluation of spinach freshness using machine learning model and digital images.Kento KoyamaMarin TanakaByeong-Hyo ChoYusaku YoshikawaShige KosekiThe 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.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0248769&type=printable |
| spellingShingle | Kento Koyama Marin Tanaka Byeong-Hyo Cho Yusaku Yoshikawa Shige Koseki Predicting sensory evaluation of spinach freshness using machine learning model and digital images. PLoS ONE |
| 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 |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0248769&type=printable |
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