Estimation of intramuscular level of marbling among Whiteheaded Mutton Sheep lambs

Przybylak, Andrzej; Boniecki, P.; Koszela, Krzysztof; Ludwiczak, Agnieszka; Zaborowicz, Maciej; Lisiak, D.; Stanisz, Marek; Śłósarz, P.

doi:10.1016/j.jfoodeng.2015.07.035

Cited by 39 publications

(11 citation statements)

References 26 publications

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“…It was performed in the contrast to the MLP type network, in which a PNN-type network was obtained: 13-404-5-1. The issue related to the estimation of intramuscular level of marbling among white headed mutton sheep lambsu, obtained RMS error for a set of validation was at the level of 0.12 (Przybylak et al 2016). Small values of RMS error may indicate that both MLP neural networks possess an appropriate ability to generalize knowledge in classification issues, Equation (1).…”

Section: Resultsmentioning

confidence: 99%

Computer vision and artificial neural network techniques for classification of damage in potatoes during the storage process

Przybył¹,

Boniecki²,

Koszela³

et al. 2019

Czech J. Food Sci.

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The research methodology consists of several stages to develop a noninvasive method of identifying the turgor of potato tubers during the storage. During the first stage, a graphic database (set of training data) has been created for selected varieties of potatoes. As a next step, special proprietary software called ’PID system’ was used together with a commercial MATLAB package to extract parameters defining the digital image descriptors. This included: hue space models, shape coefficient and image texture. Thirdly, Artificial Neural Network (ANN) training was conducted with the use of Statistica and MATLAB tools. As a result of the analysis, a neural model has been obtained, which had the greatest classification features.

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Section: Resultsmentioning

confidence: 99%

Computer vision and artificial neural network techniques for classification of damage in potatoes during the storage process

Przybył¹,

Boniecki²,

Koszela³

et al. 2019

Czech J. Food Sci.

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“…The most frequently used measure of the performance of an artificial neural network is the total error known as the RMS ( Root Mean Square ) error created by the generated model on the training file (training, testing, and validation) [ 29 ]. It is determined by summing the squares of individual errors, dividing the obtained sum by the number of used cases, and then determining the square root from the obtained quotient as below:

…”

Section: Resultsmentioning

confidence: 99%

Neural Reduction of Image Data in Order to Determine the Quality of Malting Barley

Boniecki

Raba

Pilarska

et al. 2021

Sensors

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Image analysis using neural modeling is one of the most dynamically developing methods employing artificial intelligence. The feature that caused such widespread use of this technique is mostly the ability of automatic generalization of scientific knowledge as well as the possibility of parallel analysis of the empirical data. A properly conducted learning process of artificial neural network (ANN) allows the classification of new, unknown data, which helps to increase the efficiency of the generated models in practice. Neural image analysis is a method that allows extracting information carried in the form of digital images. The paper focuses on the determination of imperfections such as contaminations and damages in the malting barley grains on the basis of information encoded in the graphic form represented by the digital photographs of kernels. This choice was dictated by the current state of knowledge regarding the classification of contamination that uses undesirable features of kernels to exclude them from use in the malting industry. Currently, a qualitative assessment of kernels is carried by malthouse-certified employees acting as experts. Contaminants are separated from a sample of malting barley manually, and the percentages of previously defined groups of contaminations are calculated. The analysis of the problem indicates a lack of effective methods of identifying the quality of barley kernels, such as the use of information technology. There are new possibilities of using modern methods of artificial intelligence (such as neural image analysis) for the determination of impurities in malting barley. However, there is the problem of effective compression of graphic data to a form acceptable for ANN simulators. The aim of the work is to develop an effective procedure of graphical data compression supporting the qualitative assessment of malting barley with the use of modern information technologies. Image analysis can be implemented into dedicated software.

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“…The cuttingedge researches indicate adverse health issues are related to consumption of spoiled mushroom (Adebo et al 2021). Thus, reliable scientific methods for on-site mushroom quality evaluation are a need in recent days (Przybylak et al 2016). The conventional fruit quality evaluation is generally offline and destructive in nature.…”

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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Estimation of intramuscular level of marbling among Whiteheaded Mutton Sheep lambs

Cited by 39 publications

References 26 publications

Computer vision and artificial neural network techniques for classification of damage in potatoes during the storage process

Computer vision and artificial neural network techniques for classification of damage in potatoes during the storage process

Neural Reduction of Image Data in Order to Determine the Quality of Malting Barley

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

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