Artificial neural networks to model the production of blood protein hydrolysates for plant fertilisation

Pérez‐Gálvez, Raúl; Espejo-Carpio, F. Javier; Guadix, Emilia M.; Guadix, Antonio

doi:10.1002/jsfa.7083

Cited by 6 publications

(7 citation statements)

References 24 publications

(51 reference statements)

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“…Modelling the enzyme deactivation is challenging (Gálvez et al, 2016) due to the complexity of the reactions involved. Response surface methodology has been used to model enzymatic hydrolysis, but for biological systems, polynomials fail to model, in some cases, the complete system (Morales-Medina et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Modelling of the kinetics of red tilapia (Oreochromis spp.) viscera enzymatic hydrolysis using mathematical and neural network models

Montoya¹,

Rincón²,

Montoya³

2022

IFRJ

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The present work modelled the enzymatic hydrolysis of red tilapia (Oreochromis spp.) viscera with Alcalase® 2.4 L in both 0.5 and 5 L reactors. The best conditions for the enzymatic hydrolysis were 60°C and pH 10. The product inhibited the enzymatic hydrolysis, and the enzyme deactivated following second-order reaction. K_M and K_p from a secondary plot of K_M^app as a function of inhibitor concentration, and k_2, p, and k_3 were found by non-linear regression. While the obtained parameters modelled the 0.5 L reactor well, it did not model the 5 L reactor, probably because of unconsidered fluid dynamics in the model. To have a better modelling, a neural network (tensorflow.keras.models module) was built and trained. The neural network modelled the enzymatic hydrolysis of red tilapia at several concentrations of substrate and enzyme. This result proved that neural networks are a powerful tool for modelling biological processes.

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Section: Introductionmentioning

confidence: 99%

“…With some specific algorithms, ANN are capable of learning any mathematical function with sufficient training data (Aggarwal, 2018). They have been applied in the modelling of biological systems (Castro et al, 2010;Das et al, 2015;Gálvez et al, 2016;Morales-Medina et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Modelling of the kinetics of red tilapia (Oreochromis spp.) viscera enzymatic hydrolysis using mathematical and neural network models

Montoya¹,

Rincón²,

Montoya³

2022

IFRJ

View full text Add to dashboard Cite

show abstract

“…They are also used with data processing models and exhibit strong pattern recognition and data fitting capabilities. ANNs are widely used for multivariate data analysis in a wide range of areas, including for data mapping, regression, modeling and classification . Data analysis using ANNs is different from other analysis methods and does not require that the original or input data exhibit a specific distribution.…”

Section: Introductionmentioning

confidence: 99%

Prediction of benzo[a]pyrene content of smoked sausage using back‐propagation artificial neural network

Chen

Cai

et al. 2018

J Sci Food Agric

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“…In addition, image processing techniques and artificial intelligence techniques (AITs) are used in combination to increase classification accuracy [1][2]. Neural networks such as artificial neural network (ANN), support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS) and decision tree (DT), K-nearest neighbors (KNN), Naive Bayes (NB) and discriminant analysis (DA) are the most utilized with IPT for classifying agricultural products [3][4][5][6][7]. Over a last decade ANN which is widely used artificial intelligence technique model adopts remarkable importance in classification of agricultural grains due to its fast and accurate modelling.…”

Section: Introductionmentioning

confidence: 99%

An Application of ANN Trained by ABC Algorithm for Classification of Wheat Grains

Kayabaşı

2018

ijisae

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Artificial Neural Networks (ANNs) have emerged as an important tool for classification problem. This paper presents an application of ANN model trained by artificial bee colony (ABC) optimization algorithm for classification the wheat grains into bread and durum. ABC algorithm is used to optimize the weights and biases of three-layer multilayer perceptron (MLP) based ANN. The classification is carried out through data of wheat grains (#200) acquired using image-processing techniques (IPTs). The data set includes five grain's geometric parameters: length, width, area, perimeter and fullness. The ANN-ABC model input with the geometric parameters are trained through 170 wheat grain data and their accuracies are tested via 30 data. The ANN-ABC model numerically calculate the outputs with mean absolute error (MAE) of 0.0034 and classify the grains with accuracy of 100% for the testing process. The results of ANN-ABC model are compared with other ANN models trained by 4 different learning algorithms. These results point out that the ANN trained by ABC optimization algorithm can be successfully applied to classification of wheat grains.

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Artificial neural networks to model the production of blood protein hydrolysates for plant fertilisation

Abstract: ANN modelling was a useful tool to model enzymatic reactions and was successfully employed to optimise the degree of hydrolysis.

Cited by 6 publications

References 24 publications

Modelling of the kinetics of red tilapia (Oreochromis spp.) viscera enzymatic hydrolysis using mathematical and neural network models

Modelling of the kinetics of red tilapia (Oreochromis spp.) viscera enzymatic hydrolysis using mathematical and neural network models

Prediction of benzo[a]pyrene content of smoked sausage using back‐propagation artificial neural network

An Application of ANN Trained by ABC Algorithm for Classification of Wheat Grains

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