Quantifying the Relationship between Rice Starch Gelatinization and Moisture‐Electrical Conductivity of Paddy during Soaking

J Food Process Engineering

Brenner

2016

Self Cite

The effects of parboiling indicators (soaking temperature and steaming time) on the degree of starch gelatinization (DSG) and several rice quality properties [head rice yield (HRY), color value, lightness, and hardness] of parboiled rice were investigated. A mathematical model was used to predict DSG and rice quality from the parboiling conditions, which were varied as follows: 60, 65, and 70 °C for the soaking temperature and 2, 6, and 10 min for the steaming time (at 96 °C). A central composite design was applied and a total of 13 experimental runs were generated for each step. An ANOVA test showed the all models were significant (p < 0.01) and there was no significant lack of fit in any of the response variables. The most appropriate model for prediction the amount of DSG, HRY, color value, lightness, and hardness of samples was linear (R2 = 0.87), quadratic (R2 = 0.92), quadratic (R2 = 0.97), quadratic (R2 = 0.86), and linear (R2 = 0.94) model, respectively. The DSG and hardness increased with increasing soaking temperature and steaming time, while the HRY and lightness showed a maximum at a steaming time of 6 min. The statistical model was used to predict the optimal parboiling conditions for global optimization of the quality properties. Practical applications Parboiled rice enjoys high popularity because of its high nutritional content. The most important effect of parboiling is rice gelatinization. The degree of starch gelatinization (DSG) and quality properties (head rice yield, color value, lightness, and hardness) of parboiled rice are determined by the parboiling indicators (PI): soaking temperature and steaming time. These properties can be predicted based on PI using mathematical models. Increasing the parboiling indicators increases the cost of parboiling process. For the parboiling industry, it will help to determine the soaking temperature and steaming time of rice to yield the desired product quality. For this reason, optimizing the parboiling process is economically important for the rice industry.

Section: Methodsmentioning

confidence: 99%

“…DSG (%) of parboiled rice was measured during steaming according to the following equation (Kraus et al, ):

normalD normalS normalG = true[1 - \frac{Δ H}{{Δ H}^{*}} true] \times 100

where

Δ

H * is the enthalpy change of raw rice and

Δ

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mathematical modeling of starch gelatinization and some quality properties of parboiled rice based on parboiling indicators using RSM

J Food Process Engineering

Brenner

2016

Self Cite

“…A device made by Taghinezhad et al [31] was used for the immersion of hulls. This device includes a tank and a temperature control system.…”

Section: Parboiling Hullsmentioning

confidence: 99%

Application of Artificial Neural Networks, Support Vector, Adaptive Neuro-Fuzzy Inference Systems for the Moisture Ratio of Parboiled Hulls

Sharabiani

Kaveh²,

et al. 2022

Applied Sciences

Self Cite

Drying as an effective method for preservation of crop products is affected by various conditions and to obtain optimum drying conditions it is needed to be evaluated using modeling techniques. In this study, an adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and support vector regression (SVR) was used for modeling the infrared-hot air (IR-HA) drying kinetics of parboiled hull. The ANFIS, ANN, and SVR were fed with 3 inputs of drying time (0–80 min), drying temperature (40, 50, and 60 °C), and two levels of IR power (0.32 and 0.49 W/cm2) for the prediction of moisture ratio (MR). After applying different models, several performance prediction indices, i.e., correlation coefficient (R2), mean square error index (MSE), and mean absolute error (MAE) were examined to select the best prediction and evaluation model. The results disclosed that higher inlet air temperature and IR power reduced the drying time. MSE values for the ANN, ANFIS tests, and SVR training were 0.0059, 0.0036, and 0.0004, respectively. These results indicate the high-performance capacity of machine learning methods and artificial intelligence to predict the MR in the drying process. According to the results obtained from the comparison of the three models, the SVR method showed better performance than the ANN and ANFIS methods due to its higher R2 and lower MSE.

“…After drying, the husking of the parboiled paddy was performed by a hulling instrument (Satake Ltd, Tokyo, Japan). Then, the samples were polished using a polisher (Satake Ltd, Tokyo, Japan) during 90 s [48]. Broken and whole grains were divided by a laboratory rice grader (FQS-13X20, Sensewealth, China).…”

Section: Head Rice Yield (Hry)mentioning

confidence: 99%

Quantifying of the relationship between novel intermittent drying variables and some quality properties of parboiled rice using response surface methodology

Rasooli

2019

CI&CEQ

Self Cite

In this research, the effects of intermittent drying variables on some quality properties of parboiled rice were investigated, then a mathematical model was applied to predict the value of quality features non-destructively using response surface methodology (RSM). The intermittent drying variables consisted of hot air temperature (40, 50 and 60 °C), radiation intensity (0.21, 0.31 and 0.41 W/cm 2) and microwave power (100, 200 and 300 W). The intermittent drying was performed at two stages with a tempering time between drying steps using a hybrid drying of hot air-infrared radiation and microwave drying at the first stage and second stage, respectively. According to RSM results, the effect of drying variables on the quality properties of parboiled rice were significant (p < 0.01). Also, the best mathematical model for prediction of quality properties of samples was the quadratic equation (R 2 = 0.96-0.98). The HRY (61.8-73.2%), hardness (118.63-215.27 N) and color value (17.28-19.22) increased, while the lightness (64.17-59.51) decreased during drying. RSM can be able to predict the optimization parameters for the best quality properties (i.e., HRY = 72.42%, lightness = 59.47, color value = 19.25 and hardness = 213.91 N) based on temperature of 60 °C, radiation of 0.41 W/cm 2 and power of 300 W.