A critical review on the applications of artificial neural networks in winemaking technology

Moldes, O.; Mejuto, J. C.; Rial-Otero, R.; Simal-Gándara, Jesús

doi:10.1080/10408398.2015.1078277

Cited by 32 publications

(17 citation statements)

References 59 publications

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“…Three classes of NNs (MLP, RBF, and WNN) including their structure and training process concept was assessed. Among them, the MLP and RBF are two well‐known conventional NNs that details of their structures, training algorithms, and modeling are available in the literature (Alianna, Halston, & Pap, ; Kohonen, ; Krogh, Hertz, & Palmer, ; Moldes, Mejuto, Rial‐Otero, & Simal‐Gandara, ; Moody & Darken, ; Nematollahi, Farid, Hematiyan, & Safavi, ; Rojas, ). Therefore, a brief and useful explanation related to these types of NNs is provided here.…”

Section: Methodsmentioning

confidence: 99%

Neural network prediction of friction coefficients of rosemary leaves

Nematollahi

Khaneghah

2019

J Food Process Engineering

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In the current study, the static friction coefficient (SFC) and dynamic friction coefficient (DFC) of rosemary leaves were predicted. In this regard, a wavelet‐based neural network (WNN) was proposed as a tool for mapping of the inputs including moisture content (3.7–23 d. b.%), contact surface (aluminum, rubber, glass, galvanized steel, and plywood), and sliding velocity (1.25–16.5 cm/s) and output data (friction coefficients of rosemary leaves) which were obtained as a result of laboratory activities. Also, 80% and 20% of the total data set were used for training and testing the WNN, respectively, while the statistical parameters for test data set of the proposed NN (MAPE = 6.1310%, RMSE = 0.0468, R2 = 0.956) were compared with those obtained from conventional NNs, such as multilayer perceptron (MLP) (MAPE = 4.1787%, RMSE = 0.0248, R2 = 0.968) and radial basis function (RBF) (MAPE = 10.0311%, RMSE = 0.0591, R2 = 0.831). The results indicated that the accuracy of the MLP NN was slightly better than that of the proposed WNN; however, the learning time of WNN was much less than the MLP NN. The high capability of the proposed NN in the prediction of the SFC and DFC of the rosemary leaves was demonstrated by our findings. Practical applications Data on the frictional characteristics of agricultural products may be useful for the design of specific processing equipment, and laboratory measurements may also provide some interesting results that can guide designers. This study deals with the static friction coefficient (SFC) and dynamic friction coefficient (DFC) prediction of rosemary leaves with a view of performing predictive tools useful in the design of postharvest equipment. Rosemary has been widely cultivated as an aromatic and medicinal plant. The extracted essential oil from rosemary has several applications due to its chemical composition with beneficial properties. The prediction of friction coefficients is performed by a wavelet‐based neural network (WNN). The proposed NN has a multiresolution nature that enables accurate modeling and also fast training. The results showed that the proposed NN is a powerful tool to predict the friction coefficients of rosemary leaves.

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Section: Methodsmentioning

confidence: 99%

Neural network prediction of friction coefficients of rosemary leaves

Nematollahi

Khaneghah

2019

J Food Process Engineering

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“…Other advantages of ANN include fast network training that makes it suitable for on‐line forecasting, ability to predict multi‐responses using only a single process (Youssefi, Emam‐Djomeh, & Mousavi, ) and better utilization of resources like time and raw materials. However, being a black‐box model by nature, ANN cannot give direct insights into the system like RSM (Moldes, Mejuto, Rial‐Otero, & Simal‐Gandara, ). But there are several sensitivity analysis methods available for ANN making it an equally capable method (Pilkington, Preston, & Gomes, ).…”

Section: Introductionmentioning

confidence: 99%

Comparison of optimization approaches (response surface methodology and artificial neural network‐genetic algorithm) for a novel mixed culture approach in soybean meal fermentation

Mukherjee

Chakraborty

Dutta

2019

J Food Process Engineering

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A mixed culture of Bacillus coagulans and Lactobacillus johnsonii is utilized to ferment soybean meal (SBM) and the effects of the fermentation conditions (temperature, time, and initial inoculums level) on the different antioxidative properties are measured. Both response surface methodology (RSM) and artificial neural network (ANN) are applied to the experimental data to suggest a better approach for modeling. It is observed that the fermentation conditions have significant effects on antioxidant activity, and by following a mixed culture approach, these properties can be increased efficiently. Although both ANN and RSM fitted to the experimental data with accuracy, it is evident from root mean squared error, R 2 , and average absolute deviation values that ANN is superior to RSM when predicting the responses. The results thus point to the advantage of using a properly trained ANN in cases of nonlinear fermentative systems and also for prediction of multiple responses simultaneously. Practical applicationsConversion of low-cost by-products into value-added products has always been the main focus of fermentation industry and currently its scope is being expanded to include the use of probiotics to ferment various by-products apart from dairy products. SBM is a promising by-product that has not yet reached its full potential use and should be explored further to widen its range of applications. The purpose to develop optimized fermentation processes needs a suitable mathematical model that has the ability to mimic the complex nonlinearity of mixed culture fermentations without compromising its prediction capabilities. The results of this study will be further used to assess the possibility of development of a fermented-SBM-based supplement.

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“…Wine adulterations such as water dilution or mixed with cheaper wine, are a common practice even since ancient Greece and Rome (Moldes, Mejuto, Rial-Otero and Simal-Gandara, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, the quality and the commercial value are linked to elaboration procedures and geographical places (Moldes, Mejuto, Rial-Otero and Simal-Gandara, 2017), as for example, Tempranillo red wine from D.O (Denominación de Origen) Toro (Spain), where the wine authenticity is a key factor in terms of differentiation, which has a significant influence on the final sale price (Moldes, Mejuto, Rial-Otero and Simal-Gandara, 2017).…”

Section: Introductionmentioning

confidence: 99%

Prediction Models to Control Aging Time in Red Wine

Astray

Mejuto

Martínez-Martínez

et al. 2019

Preprint

Self Cite

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A combination of physical-chemical analysis has been used to monitor the aging of red wines from D.O. Toro (Spain). The changes in the chemical composition of wines that occur along aging time can be permitted to discriminate wine samples collected after one, four, seven and ten months of aging. Different computational models were used to develop a good authenticity tool to certificate wines. In this research different models have developed: Artificial Neural Network models (ANNs), Support Vector Machine (SVM) and Random Forest (RF) models. The results obtained for the ANN model developed with sigmoidal function in the output neuron and the RF model permit to determine the aging time, with an average absolute percentage deviation below 1% and it can conclude that these two models have demonstrated its capacity as a valid tool to predict the wine age.

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A critical review on the applications of artificial neural networks in winemaking technology

Cited by 32 publications

References 59 publications

Neural network prediction of friction coefficients of rosemary leaves

Neural network prediction of friction coefficients of rosemary leaves

Comparison of optimization approaches (response surface methodology and artificial neural network‐genetic algorithm) for a novel mixed culture approach in soybean meal fermentation

Prediction Models to Control Aging Time in Red Wine

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