ANN modeling for optimum storage condition based on viscoelastic characteristics and sensory evaluation of frozen cooked rice

Kono, S.; Kawamura, Izumi; Araki, Toshimitsu; Sagara, Yasuyuki

doi:10.1016/j.ijrefrig.2015.10.009

Cited by 11 publications

(3 citation statements)

References 25 publications

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“…Using regression analysis, a correlation was created between moisture ratio, drying time, drying temperature, and air velocity using Minitab version.17 [33,34]. (Fig.…”

Section: Moisture Ratio Processing By Ann Predictive Modelingmentioning

confidence: 99%

Artificial neural network modeling for drying kinetics of paddy using a cabinet tray dryer

Subramanyam

Narayanan

2023

CI&CEQ

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The study of drying kinetics and characteristics of agricultural products is essential for drying time estimation, designing dryers, and optimizing the drying process. Moisture diffusivity under different drying conditions is crucial to process and equipment design. The drying kinetics of paddy using a cabinet tray dryer was modeled using an Artificial Neural Network (ANN) technique. For predicting moisture ratio and drying rate, the Levenberg-Marquardt (L.M.) training algorithm with TANSIGMOID and TANSIGMOID hidden layer activation function provided superior results. A comparative evaluation of the predicting abilities of ANN and 12 different mathematical drying models was also carried out. The Midilli model was found to be adequate for fitting the experimental data with an R2 comparable to that of the ANN. However, the RMSE observed for ANN (0.0360) was significantly lower than that of the midilli model (0.1673 to 0.712). Effective moisture diffusivity increased with an increase in temperature from 15.05 to 28.5 x 10-9 m2/s. The activation energy for drying paddy grains varied between 6.8 to 7.3 kJ/mol, which showed a moderate energy requirement for moisture diffusion.

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“…Using regression analysis, a correlation was created between moisture ratio, drying time, drying temperature, and air velocity using Minitab version.17 [33,34]. (Fig.…”

Section: Moisture Ratio Processing By Ann Predictive Modelingmentioning

confidence: 99%

Artificial neural network modeling for drying kinetics of paddy using a cabinet tray dryer

Subramanyam

Narayanan

2023

CI&CEQ

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“…Moreover, they also demonstrated that low-temperature storage could extend the period of acceptable quality of baked goods without chemical preservatives [ 23 ]. To date, several studies have focused on the physicochemical changes in cooked rice that underwent low-temperature storage [ 24 , 25 , 26 ]. However, relatively few studies have addressed the starch digestibility and the microstructure (including after storage and throughout the starch digestion process) of low-temperature storage modified cooked rice (especially frozen-stored cooked rice), not to mention the related structure–digestibility interactions.…”

Section: Introductionmentioning

confidence: 99%

Impact of Low-Temperature Storage on the Microstructure, Digestibility, and Absorption Capacity of Cooked Rice

Liu

Bess

et al. 2022

Foods

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This study examined the effects of low-temperature storage on the microstructural, absorptive, and digestive properties of cooked rice. Cooked rice was refrigerated and stored at 4 °C for 0.5, 1, 3, 5, and 7 days, as well as frozen and preserved at −20, −40, and −80 °C for 0.5, 1, 3, 5, 7, 14, 21, and 28 days. The results indicated that the stored rice samples generally exhibited a higher absorption capacity for oil, cholesterol, and glucose than the freshly cooked rice. In addition, after storage, the digestibility of the cooked rice declined, namely, the rapidly digestible starch (RDS) content and estimated glycemic index (eGI) decreased, whereas the slowly digestible starch (SDS) and resistant starch (RS) content increased. Moreover, the increment of the storage temperatures or the extension of storage periods led to a lower amylolysis efficiency. Scanning electron microscopy (SEM) analysis indicated that storage temperature and duration could effectively modify the micromorphology of the stored rice samples and their digestion. Moreover, microstructural differences after storage and during simulated intestinal digestion could be correlated to the variations in the absorption capacity and digestibility. The findings from this study will be useful in providing alternative storage procedures to prepare rice products with improved nutritional qualities and functional properties.

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“…The results indicated that the model could predict the properties with high correlations and low mean square errors. Furthermore, with ANN the optimum frozen condition of cooked rice has been predicted by Kono et al (2016). It was shown that the ANN models predicted the sensory evaluation scores with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

A neural network based model to analyze rice parboiling process with small dataset

Behroozi‐Khazaei

Nasirahmadi

2017

J Food Sci Technol

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In this study, milling recovery, head rice yield, degree of milling and whiteness were utilized to characterize the milling quality of Tarom parboiled rice variety. The parboiled rice was prepared with three soaking temperatures and steaming times. Then the samples were dried to three levels of final moisture contents [8, 10 and 12% (w.b)]. Modeling of process and validating of the results with small dataset are always challenging. So, the aim of this study was to develop models based on the milling quality data in parboiling process by means of multivariate regression and artificial neural network. In order to validate the neural network model with a little dataset, K-fold cross validation method was applied. The ANN structure with one hidden layer and Tansig transfer function by 18 neurons in the hidden layer was selected as the best model in this study. The results indicated that the neural network could model the parboiling process with higher degree of accuracy. This method was a promising procedure to create accuracy and can be used as a reliable model to select the best parameters for the parboiling process with little experiment dataset.

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ANN modeling for optimum storage condition based on viscoelastic characteristics and sensory evaluation of frozen cooked rice

Cited by 11 publications

References 25 publications

Artificial neural network modeling for drying kinetics of paddy using a cabinet tray dryer

Artificial neural network modeling for drying kinetics of paddy using a cabinet tray dryer

Impact of Low-Temperature Storage on the Microstructure, Digestibility, and Absorption Capacity of Cooked Rice

A neural network based model to analyze rice parboiling process with small dataset

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