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

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. KeywordsOyster mushroom • Major and minor feature • Freshness class • Artificial neural network (ANN) • Food safety

Recent Advance of Intelligent Packaging Aided by Artificial Intelligence for Monitoring Food Freshness

“…This method verifies that the freshness of eggs can be determined by density and has the potential to become an important measurement system for the poultry industry in the future. Kingshuk [100] used two different freshness assessment models, employing statistical methods such as principal component analysis (PCA) and supervised learning algorithms such as artificial neural networks (ANN) to investigate the different characteristics of mushroom images and classify them into fresh and spoiled categories. Observations show that the supervised learning models outperform the statistical models in terms of classification accuracy.…”

“…This development by Khaled [100] of an online classification system using deep convolutional neural networks (DCNN) is also considered to be the latest technology in the field of machine vision-based classification. Bhargava et al [102] dealt with various methods of pre-processing, segmentation, feature extraction and classification of fruit and vegetable quality based on color, texture, size, shape and defects, using computer vision to accomplish various classification and grading algorithms that can be used to detect the freshness of fruits and vegetables very well.…”

An innovative approach to detecting the freshness of fruits and vegetables through the integration of convolutional neural networks and bidirectional long short-term memory network

“…In order to reduce labor costs and improve the efficiency of fruit and vegetable freshness detection, some scholars have used manual feature extraction combined with machine learning to achieve automatic detection of fruit and vegetable freshness ( Sarkar et al, 2021 ; Zhang et al, 2021a ). Existing research shows that the core work of this fruit and vegetable freshness detection method is concentrated in the manual feature extraction stage, and constructing a set of feature vectors that can accurately represent the freshness of fruits and vegetables has become the focus of research ( Koyama et al, 2021 ).…”

Near-infrared spectroscopy of Chinese soy sauce for quality evaluation

“…However, this process is time-consuming and costly, and the results are highly subjective. Sensory evaluation can Quality Assurance and Safety of Crops & Foods 15 (1) Chen et al also be influenced by environmental factors (Xu et al 2013), and it cannot be used for rapid testing of product sensory quality (Sarkar et al, 2022a). Therefore, establishment of an objective, effective, and rapid quality evaluation model is currently considered to be the most critical aspect in quality testing of soy sauce.…”