Neural Modeling of the Distribution of Protein, Water and Gluten in Wheat Grains during Storage

Szwedziak, K.; Polańczyk, E.; Grzywacz, Żaneta; Niedbała, Gniewko; Wojtkiewicz, Wiktoria

doi:10.3390/su12125050

Cited by 11 publications

(8 citation statements)

References 29 publications

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“…Many applications of empirical modelling in the food industry were reported in prior literature. The ANN technique was useful for the determining changes in the water content, protein, and gluten in stored wheat [24], for accurate and rapid prediction of the moisture and fat content of tofu [25], for the development of a crispness prediction model of crunchy food [26], or the estimation of sugar concentration in food products [27]. Chauchard et al [28] proposed the sensor for acidity prediction in grapes based on NIR spectroscopy and Least-Squared Support Vector Machine regression.…”

Section: Introductionmentioning

confidence: 99%

Modelling of Mechanical Properties of Fresh and Stored Fruit of Large Cranberry Using Multiple Linear Regression and Machine Learning

et al. 2022

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The study investigated the selected mechanical properties of fresh and stored large cranberries. The analyses focused on changes in the energy requirement up to the breaking point and aimed to identify the apparent elasticity index of the fruit of the investigated large cranberry fruit varieties relating to harvest time, water content, as well as storage duration and conditions. After 25 days in storage, the fruit of the investigated varieties were found with a decrease in mean acidity, from 1.56 g·100 g−1 to 1.42 g·100 g−1, and mean water content, from 89.71% to 87.95%. The findings showed a decrease in breaking energy; there was also a change in the apparent modulus of elasticity, its mean value in the fresh fruit was 0.431 ± 0.07 MPa, and after 25 days of storage it decreased to 0.271 ± 0.08 MPa. The relationships between the cranberry varieties, storage temperature, duration of storage, x, y, and z dimensions of the fruits, and their selected mechanical parameters were modeled with the use of multiple linear regression, artificial neural networks, and support vector machines. Machine learning techniques outperformed multiple linear regression.

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Section: Introductionmentioning

confidence: 99%

Modelling of Mechanical Properties of Fresh and Stored Fruit of Large Cranberry Using Multiple Linear Regression and Machine Learning

et al. 2022

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“…The chosen network returned good error values: learning, below 0.001; testing, 0.015; and validation, 0.008. The obtained results and their interpretation are an important element in the warehouse industry [27]. The research allowed improving the research methodology used in the grain warehouse and optimize management, achieving savings resulting from the time necessary for goods receipt and further production process.…”

Section: Analysis and Discussion Of Resultsmentioning

confidence: 99%

Optimization of Management Processes in Assessing the Quality of Stored Grain Using Vision Techniques and Artificial Neural Networks

et al. 2020

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The paper presents the method of using vision techniques and artificial neural networks to assess the degree of contamination of cereal during grain reception. The aim of the work is to optimize the management of the contaminant evaluation process of grain mass in warehouse and during purchase using vision techniques based on computer image analysis in order to expedite laboratory work. The obtained photographs of wheat seed samples were analyzed using the “Agropol V06” computer application and neural analysis of the obtained empirical results was performed. The application of computer image analysis reduced the time necessary for the quality assessment of the examined material compared to traditional methods. The generated models were characterized by good parameters and high quality, obtaining a high R2 coefficient at the level of 0.999. As part of the investment project, savings resulting from the time of goods receipt and further production process were made. Profitability was estimated at 191.43% per day. The analysis was made without taking into account other costs related to the business activity. The straight payback period is 3 years.

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“…1 Maize, due to the high nutritional value and moisture content of it, is especially susceptible to mold during processing and storage. 2 Mold contamination not only reduces the nutritional value and appearance of the maize but also produces harmful secondary metabolites. 3 Aspergillus flavus, a major mold species affecting maize, can penetrate the maize shell 4 and produce various complex and stable toxins.…”

Section: Introductionmentioning

confidence: 99%

Detection of Maize Mold Based on a Nanocomposite Colorimetric Sensor Array under Different Substrates

Lin,

Chen,

Solomon Adade

et al. 2024

J. Agric. Food Chem.

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This study developed a novel nanocomposite colorimetric sensor array (CSA) to distinguish between fresh and moldy maize. First, the headspace solid-phase microextraction gas chromatography−mass spectrometry (HS-SPME-GC/MS) method was used to analyze volatile organic compounds (VOCs) in fresh and moldy maize samples. Then, principal component analysis and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used to identify 2-methylbutyric acid and undecane as key VOCs associated with moldy maize. Furthermore, colorimetric sensitive dyes modified with different nanoparticles were employed to enhance the dye properties used in the nanocomposite CSA analysis of key VOCs. This study focused on synthesizing four types of nanoparticles: polystyrene acrylic (PSA), porous silica nanospheres (PSNs), zeolitic imidazolate framework-8 (ZIF-8), and ZIF-8 after etching. Additionally, three types of substrates, qualitative filter paper, polyvinylidene fluoride film, and thin-layer chromatography silica gel, were comparatively used to fabricate nanocomposite CSA combining with linear discriminant analysis (LDA) and K-nearest neighbor (KNN) models for real sample detection. All moldy maize samples were correctly identified and prepared to characterize the properties of the CSA. Through initial testing and nanoenhancement of the chosen dyes, four nanocomposite colorimetric sensitive dyes were confirmed. The accuracy rates for LDA and KNN models in this study reached 100%. This work shows great potential for grain quality control using CSA methods.

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Neural Modeling of the Distribution of Protein, Water and Gluten in Wheat Grains during Storage

Cited by 11 publications

References 29 publications

Modelling of Mechanical Properties of Fresh and Stored Fruit of Large Cranberry Using Multiple Linear Regression and Machine Learning

Modelling of Mechanical Properties of Fresh and Stored Fruit of Large Cranberry Using Multiple Linear Regression and Machine Learning

Optimization of Management Processes in Assessing the Quality of Stored Grain Using Vision Techniques and Artificial Neural Networks

Detection of Maize Mold Based on a Nanocomposite Colorimetric Sensor Array under Different Substrates

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