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

Comparative Analysis of Statistical and Supervised Learning Models for Freshness Assessment of... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Food Analytical Methods Springer Journals

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

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References (73)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
ISSN
1936-9751
eISSN
1936-976X
DOI
10.1007/s12161-021-02161-7
Publisher site
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Abstract

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.

Journal

Food Analytical Methods – Springer Journals

Published: Apr 1, 2022

Keywords: Oyster mushroom; Major and minor feature; Freshness class; Artificial neural network (ANN); Food safety

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