Application of artificial neural networks (ANN) for predicting the effect of processing on the digestibility of foods

Sandoval, L.A. Espinosa; Polanía, Anna María; Florez, L. Castañeda; Figueroa, Alexis García

doi:10.1016/b978-0-323-85513-6.00011-6

Cited by 5 publications

(7 citation statements)

References 88 publications

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“…The cavitation-produced free radicals could cause the bacterial cells to suffer damage and stop functioning. Since heat may denature and deteriorate bacterial DNA, protein, and other parts of cells, the temperature increase that takes place during TS may also help in the deactivation of bacteria [44].…”

Section: Processing's Effect On Sohshang Juices' Microbial Countsmentioning

confidence: 99%

“…In addition, a rise in temperature may lead to protein denaturation to occur which might inactivate microorganisms. By combining these effects, treated juice may experience a decrease in the amount of yeast and mold [44]. Alongside TS, more antimicrobial agents or high-pressure The topology of a well-developed ANN model for TSSJ.…”

Section: Processing's Effect On Sohshang Juices' Microbial Countsmentioning

confidence: 99%

“…W: weight; b: bias; ANN: artificial neural network; TSSJ: thermosonicated sohshang juice; TSS: total soluble solid; TA: titratable acidity; AA: ascorbic acid; AOA: antioxidant activity; TPC: total phenolic content; TFC: total flavonoid content; TVC: total viable content; YM: yeast and mold count. As previously reported in studies, a variety of criteria, including MAE, R 2 , and error histogram, were used to identify the ideal variables spanning the ANN [28,44]. A network structure with a concealed layer of ten neural connections was discovered to produce the most effective outcomes after evaluating a number of topologies of the network.…”

Section: Processing's Effect On Sohshang Juices' Microbial Countsmentioning

confidence: 99%

“…Predicted network outputs from ANN modelling1 . Journal of Food Processing and Preservation a good step forward for the food processing sector and could lead to the development of improved and more effective approaches to processing[28,44,46,47].…”

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confidence: 99%

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Computational Modelling for Optimization of Thermosonicated Sohshang (Elaeagnus latifolia) Fruit Juice Using Artificial Neural Networks

Das,

Nayak,

Sharma

et al. 2024

Journal of Food Processing and Preservation

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The study involved subjecting sohshang (Elaeagnus latifolia) juice (SJ) to thermosonications (TS), a process integrating ultrasound and heat, with a range of independent variables. Specifically, three explored distinct amplitudes (30%, 40%, and 50%) alongside three temperature settings (30°C, 40°C, and 50°C) and four treatment durations (15, 30, 45, and 60 minutes) were used in the experiment. A variety of quality parameters were analyzed such as antioxidant activity (AOA), ascorbic acid (AA), total flavonoid content (TFC), total phenolic content (TPC), yeast and mold count (YMC), and total viable count (TVC). Thermosonicated sohshang juices (TSSJ) successfully achieved highest content of AA (69.15±0.99 mg/100 ml), AOA (72.93±1.62%), TPC (122.03±4.23 mg GAE/ml), and TFC (116.14±3.29 mg QE)/ml) under ideal circumstances. Also, minimal TVC and YMC in these juices have been observed. The best results for AA and TFC were observed at 40°C with 40% and 50% amplitude over processing times of 45 and 60 min. To optimize the extraction processes with various objectives, artificial neural network (ANN) was established with an original experimental planning methodology. Overall, the investigation demonstrated that TS is an effective method to enhance the nutritional and microbiological qualities of sohshang fruit juice. The use of ANN in the optimization process is particularly valuable in achieving desirable outcomes. As the food and pharmaceutical industries seek natural and bioactive substances, TSSJ holds great potential for various applications.

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Section: Processing's Effect On Sohshang Juices' Microbial Countsmentioning

confidence: 99%

Section: Processing's Effect On Sohshang Juices' Microbial Countsmentioning

confidence: 99%

Section: Processing's Effect On Sohshang Juices' Microbial Countsmentioning

confidence: 99%

mentioning

confidence: 99%

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Computational Modelling for Optimization of Thermosonicated Sohshang (Elaeagnus latifolia) Fruit Juice Using Artificial Neural Networks

Das,

Nayak,

Sharma

et al. 2024

Journal of Food Processing and Preservation

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“…Recently, ANNs have been increasingly used in modeling food product formulations, particularly through the efficiency of multilayer perceptron (MLP-ANN) in regression applications [ 14 , 15 ]. ANNs, characterized by their structure, learning algorithm, and activation function, effectively fit and generalize data based on selected input parameters to achieve desired output values [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Cookie Formulations with Combined Dehydrated Peach: A Machine Learning Approach for Technological Quality Assessment and Optimization

Lončar,

Pezo,

Knežević

et al. 2024

Foods

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This study focuses on predicting and optimizing the quality parameters of cookies enriched with dehydrated peach through the application of Support Vector Machine (SVM) and Artificial Neural Network (ANN) models. The purpose of the study is to employ advanced machine learning techniques to understand the intricate relationships between input parameters, such as the presence of dehydrated peach and treatment methods (lyophilization and lyophilization with osmotic pretreatment), and output variables representing various quality aspects of cookies. For each of the 32 outputs, including the parameters of the basic chemical compositions of the cookie samples, selected mineral contents, moisture contents, baking characteristics, color properties, sensorial attributes, and antioxidant properties, separate models were constructed using SVMs and ANNs. Results showcase the efficiency of ANN models in predicting a diverse set of quality parameters with r2 up to 1.000, with SVM models exhibiting slightly higher coefficients of determination for specific variables with r2 reaching 0.981. The sensitivity analysis underscores the pivotal role of dehydrated peach and the positive influence of osmotic pretreatment on specific compositional attributes. Utilizing established Artificial Neural Network models, multi-objective optimization was conducted, revealing optimal formulation and factor values in cookie quality optimization. The optimal quantity of lyophilized peach with osmotic pretreatment for the cookie formulation was identified as 15%.

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Recent advances in delivery systems optimization using machine learning approaches

Yakoubi

Kobayashi

Uemura

et al. 2023

Chemical Engineering and Processing - Process Intensification

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Application of artificial neural networks (ANN) for predicting the effect of processing on the digestibility of foods

Cited by 5 publications

References 88 publications

Computational Modelling for Optimization of Thermosonicated Sohshang (Elaeagnus latifolia) Fruit Juice Using Artificial Neural Networks

Computational Modelling for Optimization of Thermosonicated Sohshang (Elaeagnus latifolia) Fruit Juice Using Artificial Neural Networks

Enhancing Cookie Formulations with Combined Dehydrated Peach: A Machine Learning Approach for Technological Quality Assessment and Optimization

Recent advances in delivery systems optimization using machine learning approaches

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