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

Koyama, Kento; Tanaka, Marin; Cho, Byeong-Hyo; Yoshikawa, Yusaku; Koseki, Shige

doi:10.1371/journal.pone.0248769

Cited by 46 publications

(14 citation statements)

References 48 publications

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“…However, the accuracy of the three-factor models was not significantly improved. These findings were similar to those of the previous studies that used machine learning models and digital images to predict fresh spinach stages [ 35 ]. The results of the present study showed that the more combination factors there were, the lower the accuracy of the model and the overall prediction effect.…”

Section: Discussionsupporting

confidence: 91%

Prediction of the Maturity of Greenhouse Grapes Based on Imaging Technology

Wei

et al. 2022

Plant Phenomics

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To predict grape maturity in solar greenhouses, a plant phenotype-monitoring platform (Phenofix, France) was used to obtain RGB images of grapes from expansion to maturity. Horizontal and longitudinal diameters, compactness, soluble solid content (SSC), titratable acid content, and the SSC/acid of grapes were measured and evaluated. The color values (R,G,B,H,S,and I) of the grape skin were determined and subjected to a back-propagation neural network algorithm (BPNN) to predict grape maturity. The results showed that the physical and chemical properties (PCP) of the three varieties of grapes changed significantly during the berry expansion stage and the color-changing maturity stage. According to the normalized rate of change of the PCP indicators, the ripening process of the three varieties of grapes could be divided into two stages: an immature stage (maturity coefficient Mc<0.7) and a mature stage (after which color changes occurred) (0.7≤Mc<1). When predicting grape maturity based on the R,G,B,H,I,and S color values, the R,G,and I as well as G,H,and I performed well for Drunk Incense, Muscat Hamburg, and Xiang Yue grape maturity prediction. The GPI ranked in the top three (up to 0.87) when the above indicators were used in combination with BPNN to predict the grape Mc by single-factor and combined-factor analysis. The results showed that the prediction accuracy (RG and HI) of the two-factor combination was better for Drunk Incense, Muscat Hamburg, and Xiang Yue grapes (with recognition accuracies of 79.3%, 78.2%, and 79.4%, respectively), and all of the predictive values were higher than those of the single-factor predictions. Using a confusion matrix to compare the accuracy of the Mc’s predictive ability under the two-factor combination method, the prediction accuracies were in the following order: Xiang Yue (88%) > Muscat Hamburg (81.3%) > Drunk Incense (76%). The results of this study provide an effective way to predict the ripeness of grapes in the greenhouse.

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Section: Discussionsupporting

confidence: 91%

Prediction of the Maturity of Greenhouse Grapes Based on Imaging Technology

Wei

et al. 2022

Plant Phenomics

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“…The various kinds of electrochemical sensors used for e-nose are metal oxide gas sensors, fiber-optic gas sensors, etc. (Koyama et al, 2021;Miguel and Laura, 2009;Yakubu et al, 2021). Finally, the third element is the data analysis and processing system helping in interpretation of the data generated by the detection system (Tan et al, 2019).…”

Section: Elements Of Sensory Analytical Toolsmentioning

confidence: 99%

“…It deals with the visual inspection of quality characteristics of food products by capturing, processing, and analyzing images. Due to its cost-effectiveness, superior speed, accuracy, and consistency, e-eye has been successfully used for quality analysis of meat, fish, pizza, cheese, bread, and cereal grains (Koyama et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Artificial senses and their fusion as a booming technique in food quality assessment

Kaur

Kiran²,

Aggarwal³

2022

Qual. Assur. Saf. Crops Foods

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Sensory science has witnessed increased adoption of technological advancements in recent years. Food analysis using human senses severely impacted the evaluation responses due to errors and the complexity of the assessment methods. Hence, the adoption of tools capable of mimicking human senses is considered a more viable approach. This article provides a critical demonstration of the developments in sensory science detailing the technology behind the construction and working of the electronic tongue, electronic nose, and electronic eye. The paper also attempts to brief the industrial applications of artificial senses and the fusion technique in monitoring as well as assessment of food quality.

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“…Though the freshness of food materials is prospective, it regulates the consumer preference most. Freshness depends on the variety of the fruit, and on the experience of the evaluator (Koyama et al 2021). Objective indices and consumer evaluation has been combined to follow the freshness concept in terms of consumer perception (Koyama et al 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Freshness depends on the variety of the fruit, and on the experience of the evaluator (Koyama et al 2021). Objective indices and consumer evaluation has been combined to follow the freshness concept in terms of consumer perception (Koyama et al 2021). Quantifiable standards like colour, texture and shape perform as a critical indicator during interpretation of sensory evaluation.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Statistical and Supervised Learning Models for Freshness Assessment of Oyster Mushrooms

Sarkar

Mukherjee²,

Chatterjee³

et al. 2021

Food Anal. Methods

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Automatic assessment of the quality of fruits and vegetables is a growing field of research in this modern era in order to enable faster processing of good quality foods. In this work, we have analyzed ten major colour variant features of two sets of oyster mushrooms in terms of histograms of each layer of the red-green-blue colourmap, hue-saturation-vital component colourmap, luminance-chrominance colourmap and the greyscale image. Besides, texture analysis has been carried out using entropy window filtering. Apart from that, five other minor features, such as mean, standard deviation, entropy, kurtosis and skewness of each of these layers, and four other greyscale features, such as contrast, correlation, energy and homogeneity are analyzed in this work. Two different freshness assessment models employing statistical methods like principal component analysis (PCA) and supervised learning algorithms such as artificial neural network (ANN) have been used here to investigate the different features of the mushroom images and classify the same into fresh and deteriorated classes. Analysis revealed that the ANN classifier outperforms the PCA threshold classifier with almost all the features. The highest classifier accuracy is obtained as 94.4% using the ANN model and 93.3% using the PCA threshold freshness detector. Most importantly, the use of smartphones ensures portability, as well as the possibility of widespread application of the proposed models. KeywordsOyster mushroom • Major and minor feature • Freshness class • Artificial neural network (ANN) • Food safety

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

Cited by 46 publications

References 48 publications

Prediction of the Maturity of Greenhouse Grapes Based on Imaging Technology

Prediction of the Maturity of Greenhouse Grapes Based on Imaging Technology

Artificial senses and their fusion as a booming technique in food quality assessment

Comparative Analysis of Statistical and Supervised Learning Models for Freshness Assessment of Oyster Mushrooms

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