Drying temperature-dependent profile of bioactive compounds and prediction of antioxidant capacity of cashew apple pomace using coupled Gaussian Process Regression and Support Vector Regression (GPR–SVR) model

Luka, Bobby Shekarau; Yuguda, Taitiya Kenneth; Adnouni, Meriem; Zakka, Riyang; Abdulhamid, Ibrahim; Gargea, Bumbyerga  G.

doi:10.1016/j.heliyon.2022.e10461

Cited by 12 publications

(6 citation statements)

References 25 publications

(30 reference statements)

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“…The SVM is a kernel‐based model, and its complexity and capacity to map nonlinearity in response prediction have been defined by the kernel function, which was the SVM component that was heavily emphasized throughout the design 30 . For all the optimization techniques, the R 2 value ranges from 0.9999 to 1.00, with RMSE of 0.0012 to 0.0021 and MAE of 0.0007 to 0.002.…”

Section: Resultsmentioning

confidence: 99%

“…A linear regression model may contain an unknown error. This method uses a machine‐learning model based on kernels 30 . The GPR distribution's data distribution curve resembles the normal distribution curve in shape (bell curve).…”

Section: Methodsmentioning

confidence: 99%

“…This method uses a machine-learning model based on kernels. 30 The GPR distribution's data distribution curve resembles the normal distribution curve in shape (bell curve). The Gaussian distribution is therefore explained by the mean and variance, which are its two basic parameters.…”

Section: Gaussian Process Regressionmentioning

confidence: 99%

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Optimization of computational intelligence approach for the prediction of glutinous rice dehydration

Jimoh,

Hashim,

Shamsudin

et al. 2024

J Sci Food Agric

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BackgroundFive computational intelligence approaches, namely: gaussian process regression (GPR), artificial neural network (ANN), decision tree (DT), ensemble of trees (EoT), and support vector machine (SVM) were used to describe the evolution of moisture during the dehydration process of glutinous rice. The hyperparameters of the models were optimized with three strategies i.e., Bayesian optimization, grid search and random search. To understand the parameters that facilitate intelligence model adaptation to the dehydration process, global sensitivity analysis (GSA) was used to compute the impact of the input variables on the model output.ResultThe result shows that the optimum computational intelligence techniques include the 3–9‐1 topology trained with Bayesian regulation function for ANN, gaussian kernel function for SVM, matern covariance function combined with zero mean function for the GPR, boosting method for EoT, and 4 minimum leaf size for DT models. The GPR has the highest performance with R2 of 100% and 99.71% during calibration and testing of the model respectively. The GSA reveals that all the models significantly rely on the variation in time as the main factor that affects the model outputs.ConclusionTherefore, the computational intelligence models especially the GPR can be applied for an effective description of moisture evolution during small‐scale and industrial dehydration of glutinous rice.This article is protected by copyright. All rights reserved.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Optimization of computational intelligence approach for the prediction of glutinous rice dehydration

Jimoh,

Hashim,

Shamsudin

et al. 2024

J Sci Food Agric

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“…In SVR, the input variables are nonlinearly mapped in a feature space using kernel transformation function. To implement the process, the inputs and outputs values relationship is linearized in the feature space (Luka et al, 2022). Equation (14) illustrates the general state of SVR equation:

y = w Φ (x) + b (Φ : R_{normaln} \to R_{normalN})

x ∈ R n refers to the input value, y ∈ R N refers to the output, b refers to the bias term, w ∈ R N refers to the coefficient factor, and Φ refers to the mapping function whose input is transformed into a high‐dimensional vector.…”

Section: Methodsmentioning

confidence: 99%

Section: Support Vector Regressionmentioning

confidence: 99%

Machine learning approaches for estimating apricot drying characteristics in various advanced and conventional dryers

Kaveh,

Çetin,

Khalife

et al. 2023

J Food Process Engineering

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Drying plays a crucial role in preserving the quality of agricultural products. Nevertheless, suboptimal conditions in drying systems have an adverse effect on drying characteristics and energy efficiency. Machine learning approaches are innovative and reliable that have been successfully used to solve such challenges and achieve optimization in drying processes. In this study, five machine learning approaches (multilayer perceptron [MLP], gaussian processes [GP], support vector regression [SVR], k‐nearest neighbors [kN], and random forest [RF]) were used to estimate moisture content and moisture ratio of apricot in five various dryers (convective [CV], microwave [MW], infrared [IR], microwave‐convective [MW‐CV], and infrared‐convective [IR‐CV]). Also, the values of specific energy consumption (SEC) and effective moisture diffusivity (Deff) were calculated in these dryers. Accordingly, the best result of the Deff (3.14 × 10−10 m2/s) and the minimum value of the drying time (130 min) and SEC (18.67 MJ/kg) were obtained using MW‐CV hybrid dryer. While the lowest values of Deff (2.09 × 10−11 m2/s) and highest drying time (18.5 h) and SEC (209.34 MJ/kg) were detected in CV dryer at 50°C. The best correlation coefficients (R) for the estimation of moisture content were gained using RF technique for k‐fold cross validation and train‐test split with the values of 0.9908 and 0.9912, respectively. Moreover, moisture ratio results showed that the MLP achieved the highest R value over 0.9985 for both validation methodologies. In the discrimination of the drying methods, the MLP had the greatest accuracy as 82.00% and 86.00% for k‐fold cross validation and train‐test split, respectively. The results showed that the RF and ML models could potentially be used for estimation and discrimination for drying applications.Practical ApplicationsRecently, there has been an increased interest in healthy food choices such as foodstuffs, snacks, and dried products. This trend has captured the attention of both dietitians and conscious consumers. Apricots are a prime example of a valuable dried product that can be dry in various conditions. Machine learning techniques can be used for rapid and non‐destructive determination of drying characteristics and such techniques yield objective and accurate results. Present findings revealed that texture machine learning models could be used as an effective and reliable discrimination tool for dried products.

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Optimizing Parameters of the Pack Carburizing Process with Natural Energizers to Improve the Impact and Hardness Properties of Low-Carbon Steel Using NSGA-II-Based Artificial Intelligence

Wiangkham,

Aengchuan,

Sudtachat

et al. 2023

J. of Materi Eng and Perform

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Drying temperature-dependent profile of bioactive compounds and prediction of antioxidant capacity of cashew apple pomace using coupled Gaussian Process Regression and Support Vector Regression (GPR–SVR) model

Cited by 12 publications

References 25 publications

Optimization of computational intelligence approach for the prediction of glutinous rice dehydration

Optimization of computational intelligence approach for the prediction of glutinous rice dehydration

Machine learning approaches for estimating apricot drying characteristics in various advanced and conventional dryers

Optimizing Parameters of the Pack Carburizing Process with Natural Energizers to Improve the Impact and Hardness Properties of Low-Carbon Steel Using NSGA-II-Based Artificial Intelligence

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