Modelling of corn kernel pre-treatment, drying and processing for ethanol production using artificial neural networks

Voća, Neven; Pezo, Lato; Peter, Anamarija; Šuput, Danijela; Lončar, Biljana; Krička, Tajana

doi:10.1016/j.indcrop.2021.113293

Cited by 13 publications

(16 citation statements)

References 38 publications

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“…This means that the ANN model developed for each of investigated outputs should provide accurate results when cycling through the algorithm routine for the next optimization step. The “goodness of fit” tests parameters for the developed ANN model ( Supplementary Table S6 ) were calculated according to Voca et al [ 28 ] and showed good prediction of ANN model.…”

Section: Resultsmentioning

confidence: 99%

“…The artificial neural network (ANN) was employed to investigate the impact of extrusion process variables (M, SS, and P) on output variables (EI, BD, D, V, L*, a*, b*, ΔE, DG, Ci and Hardness). Input and output data were normalized (min-max normalization method) prior ANN calculation [ 28 ]. The training process of the network (in the form of Multilayer Perceptron-MLP) using 20 data points (every point refers to one sample) was repeated 100,000 times, changing the various ANN topologies, with a different number of neurons in the hidden and the output layers (5–20), different activation functions, using random initial values of weight coefficients and biases.…”

Section: Methodsmentioning

confidence: 99%

“…The optimization of the ANN structure was performed by minimizing the validation error, and generated ANN predicates 11 output parameters simultaneously. The Broyden–Fletcher–Goldfarb–Shanno algorithm (BFGS) was used during the ANN modelling, according to Voca et al [ 28 ], to expedite the computation and maintain the solution’s convergence. The performance of created ANN models was tested through the R 2 value (coefficient of determination).…”

Section: Methodsmentioning

confidence: 99%

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Development of a Novel Rice-Based Snack Enriched with Chicory Root: Physicochemical and Sensory Properties

Bokić

Kojić

Krulj

et al. 2022

Foods

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A novel rice-based snack enriched with chicory root flour (CRF) was developed by twin-screw extrusion. Chicory (Cichorium intybus L.) is one of the promising medicinal plants for the development of innovative food and may be considered a functional food ingredient. Central composite design (CCD) was employed to generate snack formulations by varying feed moisture (M, 16.3–22.5%), screw speed (SS, 500–900 rpm) and CRF content (20–40%). The optimization according to artificial neural network modeling and a genetic algorithm was applied to define optimal process conditions (17.6% moisture, 820 rpm and 24.1% of CRF) for obtaining the product with the highest expansion (3.34), crispiness (3.22 × 10−3), volume (2040 m3), degree of gelatinization (69.70%) and good color properties. Bulk density (110.33 g/L), density (250 kg/m3), and hardness (98.74 N) resulted in low values for the optimal sample. The descriptive sensory analysis evaluated low hardness and bitterness, with high crispiness for the optimal extrudate. This study points to the possibility of a novel chicory enriched extrudate production with desirable physicochemical and sensory properties.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Development of a Novel Rice-Based Snack Enriched with Chicory Root: Physicochemical and Sensory Properties

Bokić

Kojić

Krulj

et al. 2022

Foods

Self Cite

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“…The multilayer perceptron (MLP) creates its experiences with acquired information from samples of a dataset and by making appropriate decisions for similar issues (Vo ca et al, 2021). The 16-8-3-5 architecture used in this study is given in Figure 6.…”

Section: Multilayer Perceptronmentioning

confidence: 99%

Varietal classification of maize seeds using computer vision and machine learning techniques

Yang

Zeng

et al. 2021

J Food Process Engineering

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In agriculture, seed sorting is critical for production and marketing purposes. Low‐quality seeds can cause poor plant growth and lead to problems such as disease and low yields. This study uses machine vision and machine learning to develop a rapid detection and classification method for maize (Zea mays L.) seeds based on variety purity. A computer vision system was designed to recognize five varieties of maize seeds. Halogen lamps were applied for illumination and a high‐resolution RGB camera was used to acquire images of 8,080 maize seeds in the laboratory. An image processing algorithm was proposed to extract 16 important features (12 dimensional and 4 of shape) from the maize seed images, and a user‐friendly interface was developed using a MATLAB graphical user interface (GUI). Multilayer perceptron (MLP), decision tree (DT), linear discrimination (LDA), Naive Bayes (NB), support vector machine (SVM), k‐nearest neighbors (KNN), and AdaBoost algorithm were used to develop the varietal classification model. The optimal model parameters were obtained with 10‐fold cross‐validation, and the performance metrics were compared. The names of the maize varieties were marked in the GUI. The overall classification accuracy was determined as 96.26, 94.95, 95.97, 93.97, 96.46, 95.59, and 95.31% for MLP, DT, LDA, NB, SVM, KNN, and AdaBoost, respectively. The SVM classification model obtained the highest accuracy for BaoQiu, ShanCu, XinNuo, LiaoGe, and KouXian varieties, which reached 93.07, 98.95, 96.15, 89.65, and 99.22%, respectively. The classification results satisfy the needs of producers and consumers.

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“…Response surface methodology and artificial neural network could optimize the extraction of polysaccharides and polyphenols from blackcurrant fruit (Bu et al., 2021 ). The ANN model was used to predict and optimize the main quality parameters of corn for ethanol production (Voca et al., 2021 ). The ANN model has been shown to be an effective and reliable forecasting tool in many studies (Azarmdel et al., 2020 ; Banga et al., 2020 ; Huang et al., 2021 ; Kumar et al., 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Fruit quality prediction based on soil mineral element content in peach orchard

Sun

Huang

Chen

et al. 2022

Food Science & Nutrition

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Mineral nutrition of orchard soil is critical for the growth of fruit trees and improvement of fruit quality. In the present study, the effects of soil mineral nutrients on peach fruit quality were studied by using artificial neural network model. The results showed that the four established ANN models had the highest prediction accuracy (R2 = .9735, .9607, .9036, and .9440, respectively). The results of prediction model sensitivity analysis showed that available B, Ca, N, and K in the soil had the greatest influence on the single fruit weight, available Fe, K, B, and Ca in the soil had the greatest effect on fruit soluble solid content, available Ca, N, B, and K in the soil had the greatest influence on the fruit titratable acid content, and available Ca, Fe, N, and Mn in the soil had the greatest effect on fruit edible rate. The response surface methodology analysis determined the optimal range of these mineral elements, which is critical for guiding precision fertilization in peach orchards and improving peach fruit quality.

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Modelling of corn kernel pre-treatment, drying and processing for ethanol production using artificial neural networks

Cited by 13 publications

References 38 publications

Development of a Novel Rice-Based Snack Enriched with Chicory Root: Physicochemical and Sensory Properties

Development of a Novel Rice-Based Snack Enriched with Chicory Root: Physicochemical and Sensory Properties

Varietal classification of maize seeds using computer vision and machine learning techniques

Fruit quality prediction based on soil mineral element content in peach orchard

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