Comparison of adaptive neuro-fuzzy inference system and artificial neural networks (MLP and RBF) for estimation of oxidation parameters of soybean oil added with curcumin

Asnaashari, Elahe; Asnaashari, Maryam; Ehtiati, Ahmad; Farahmandfar, Reza

doi:10.1007/s11694-015-9226-7

Cited by 26 publications

(17 citation statements)

References 28 publications

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“…According to the results shown, the accuracy of ANFIS model in this study was higher than ANN and mathematical models. Similar results were reported by various researchers for comparison between ANN and ANFIS model (Asnaashari, Asnaashari, Ehtiati, & Farahmandfar, ; Simha, Pushpadass, Franklin, Kumar, & Manimala, ). Simulation and its control in drying process are very important, because drying is nonlinearity phenomena.…”

Section: Resultssupporting

confidence: 88%

The effect of ultrasound pre‐treatment on quality, drying, and thermodynamic attributes of almond kernel under convective dryer using ANNs and ANFIS network

Kaveh

Jahanbakhshi

Abbaspour‐Gilandeh

et al. 2018

J Food Process Engineering

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In this study, different drying conditions were investigated on quality and thermodynamic properties of almond kernel. Experiments were performed using a convection dryer with ultrasound pretreatment in 40, 50, 60, and 70 °C air temperature, 1 m/s air velocity, and duration of ultrasonic pre‐treatment of 0 min (for control sample), 10, 20, and 40 min. The drying kinetic of the almond kernel was estimated by 15 mathematical models. Furthermore, Artificial Neural Networks (ANNs) and Adaptive Neuro‐Fuzzy Inference Systems (ANFIS) were applied to fit the experimental data on the thin layer drying. The lowest and highest values of the effective moisture diffusivity (Deff) was 1.81 × 10−9 and 9.70 × 10−9 m2/s, respectively. Activation energy (Ea) of the samples was obtained between 26.35 and 36.44 kJ/mol. The highest and lowest values of specific energy consumption (SEC) were calculated 561.72 and 169.88 kW hr/kg, respectively. Maximum (13.14%) and the minimum (7.1%) values of shrinkage were achieved at air temperatures of 70 and 40 °C, respectively. The color changing of dried samples was obtained between 9.14 and 17.96. Furthermore, results revealed that the ANFIS model had the high ability to predict the moisture ratio (R2 = 0.9998 and MSE = 0.0003) during drying. As a result, ANFIS model has the highest ability to evaluate all output as compared with other models and ANNs method. Practical applications Algorithms are modern methods that have been successfully applied to solve the various problems and modeling in engineering and science. Drying is one of the oldest procedures to preserve the food quality. Reduction of moisture content to a certain value can be caused to decay and minimize the microbiological activity and deteriorating chemical reactions in agricultural products, respectively. Determination of almond drying process under convective with ultrasound pre‐treatment dryer in terms of desirable thermal properties (effective moisture diffusivity and energy consumption) provides the high‐quality products. Furthermore, this research can be able to provide a technical basis for almond drying and the related equipment designing.

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Section: Resultssupporting

confidence: 88%

The effect of ultrasound pre‐treatment on quality, drying, and thermodynamic attributes of almond kernel under convective dryer using ANNs and ANFIS network

Kaveh

Jahanbakhshi

Abbaspour‐Gilandeh

et al. 2018

J Food Process Engineering

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“…One important measure of rancidity of foods might be recognized as formation of free fatty acids (Asnaashari, Asnaashari, Ehtiati, & Farahmandfar, ). FFA are formed due to hydrolysis of triglycerides and may get promoted by reaction of oil with moisture.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of antioxidant properties of lemon verbena (Lippia citriodora) essential oil and its capacity in sunflower oil stabilization during storage time

Farahmandfar

Asnaashari

pourshayegan

et al. 2018

Food Science & Nutrition

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In this study, lemon verbena essential oil as a natural antioxidant was used to increase the stability of sunflower oil, and stabilization effects in terms of storage conditions were compared with synthetic antioxidant (BHT). For this purpose, the antioxidant activity of the essential oil was determined by DPPH assay and β‐carotene bleaching method. Then, lemon verbena essential oil (0, 400, 800, and 1,600 ppm) was added to sunflower oil without synthetic antioxidant and stored at 60°C for 60 days. Results from different parameters (peroxide value, free fatty acid, iodine value, total polar compound, carbonyl value, conjugated dienes, and oxidative stability index) were in agreement with each other, suggesting that lemon verbena essential oil (1,600 ppm) could act better than BHT in inhibition of lipid oxidation in sunflower oil and can be used as predominant alternative of synthetic antioxidants.

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“…In QSAR modeling, ANNs must be built with care, to avoid over‐fitting due to noise in the data. Several studies were published on the application of MLP ANNs for quantitative prediction of antioxidant activity …”

Section: Introductionmentioning

confidence: 99%

Interpretation of ANN‐based QSAR models for prediction of antioxidant activity of flavonoids

2018

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Quantitative structure-activity relationships (QSARs) built using machine learning methods, such as artificial neural networks (ANNs) are powerful in prediction of (antioxidant) activity from quantum mechanical (QM) parameters describing the molecular structure, but are usually not interpretable. This obvious difficulty is one of the most common obstacles in application of ANN-based QSAR models for design of potent antioxidants or elucidating the underlying mechanism. Interpreting the resulting models is often omitted or performed erroneously altogether. In this work, a comprehensive comparative study of six methods (PaD, PaD , weights, stepwise, perturbation and profile) for exploration and interpretation of ANN models built for prediction of Trolox-equivalent antioxidant capacity (TEAC) QM descriptors, is presented. Sum of ranking differences (SRD) was used for ranking of the six methods with respect to the contributions of the calculated QM molecular descriptors toward TEAC. The results show that the PaD, PaD and profile methods are the most stable and give rise to realistic interpretation of the observed correlations. Therefore, they are safely applicable for future interpretations without the opinion of an experienced chemist or bio-analyst. © 2018 Wiley Periodicals, Inc.

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Comparison of adaptive neuro-fuzzy inference system and artificial neural networks (MLP and RBF) for estimation of oxidation parameters of soybean oil added with curcumin

Cited by 26 publications

References 28 publications

The effect of ultrasound pre‐treatment on quality, drying, and thermodynamic attributes of almond kernel under convective dryer using ANNs and ANFIS network

The effect of ultrasound pre‐treatment on quality, drying, and thermodynamic attributes of almond kernel under convective dryer using ANNs and ANFIS network

Evaluation of antioxidant properties of lemon verbena (Lippia citriodora) essential oil and its capacity in sunflower oil stabilization during storage time

Interpretation of ANN‐based QSAR models for prediction of antioxidant activity of flavonoids

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