Relationship between accumulated temperature and quality of paddy

Jin, Yi; Wong, Kok Wai; Wu, Zhimin; Qi, Debo; Wang, Rui; Han, Feng; Wu, Wenfu

doi:10.1080/10942912.2019.1566241

Cited by 29 publications

(15 citation statements)

References 37 publications

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“…First, the accumulated temperature was calculated according to the drying process parameters given in Table 1, and the calculation method followed formulas 1-3. The formulas are from reference, [21] and this theory was also confirmed in reference. [22] Then, the drying time was estimated by the definition of accumulated temperature, that is, the product of drying temperature and drying time.…”

Section: Predictive Test Of Total Drying Timesupporting

confidence: 64%

“…The relative humidity control accuracy of this test device is ±2%. [21] The machine is 1700 mm high and the transverse section is a square with side length of 550 mm. The regulating function are: 1. wind shunt; 2. axial flow fan; 3. material bin door; 4. material tray; 5. sensor bin; 6. electric sealing valve; 7. heating pipe; 8. inner tank; 9. wet exhaust fan; 10. test-bed shell; 11. sensor group; and 12. humidifier.…”

Section: Drying Equipment and Principlementioning

confidence: 99%

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^Optimization of hot air drying process of corn using genetic algorithm and response surface methodology

Chen

Cheng

et al. 2020

International Journal of Food Properties

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To study the effect of temperature and humidity on the drying indexes and quality indexes of corn and to optimize the corn drying process, an orthogonal experiment of hot air drying with variable temperature and humidity was designed. The drying process of the actual dryer was simulated by taking the gradient of hot air temperature (x) and the gradient of absolute humidity (y) as the experimental factors. The results showed that the drying indexes (drying time, average drying rate, accumulated temperature) reached optimal values under the drying conditions of cooling and dehumidifying; among them, the optimal value of x was 10°C (50°C, 40°C, and 30°C), and the optimal value of y was 8 g/m 3 (25 g/m 3 , 17 g/m 3 , and 9 g/ m 3 ). The quality indexes (percentage of damage, fatty acid value, germination rate) reached the optimal values under the drying conditions of heating and humidifying; among them, the optimal value of y for the three quality indicators was 8 g/m 3 (9 g/m 3 , 17 g/m 3 , and 25 g/m 3 ), but the optimal value of x were different, the optimal value of x were 2.65°C (39.35°C , 42°C, and 44.65°C), 2.24°C (39.76°C, 42°C, and 44.24°C) and 6.33°C (35.67°C, 42°C, and 48.33°C). A genetic algorithm was used to transform the multi-objective function into one index, and the results show that: when x was 2.26°C (39.74°C, 42.00°C, and 44.26°C), y was 4.84 g/m 3 (12.16 g/m 3 , 17.00 g/m 3 , and 21.84 g/m 3 ), the comprehensive characteristics of the drying process can be optimized.

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Section: Predictive Test Of Total Drying Timesupporting

confidence: 64%

Section: Drying Equipment and Principlementioning

confidence: 99%

^Optimization of hot air drying process of corn using genetic algorithm and response surface methodology

Chen

Cheng

et al. 2020

International Journal of Food Properties

Self Cite

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“…Many studies have proven that the concept of an accumulated temperature in agronomy can be used to describe the grain drying process [22]. In our previous studies [23], we found that under certain drying conditions, the accumulated temperature required for grain moisture to reach the safe level is a relatively stable quantity. Therefore, based on the concept of the effective drying accumulated temperature, a mathematical model of grain drying was constructed in order to describe the grain drying process from a new perspective and to improve the grain drying model accuracy.…”

Section: Introductionmentioning

confidence: 86%

Reconstruction of Rice Drying Model and Analysis of Tempering Characteristics Based on Drying Accumulated Temperature

et al. 2021

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Previous research has shown that the accumulated temperature can describe drying processes as well as crop growth. To describe the mass and heat transfer processes in the rice drying process more accurately, a mathematical model of rice drying was proposed based on the drying accumulated temperature, and the optimal tempering ratio for conventional hot air drying was obtained through data comparison and analysis. First, it was proven that there was an exponential relationship between the moisture ratio and the drying accumulated temperature of rice. Second, by comparing and analyzing the fitting results of seven different drying mathematical models, the model with the highest fitting degree was selected and reconstructed to obtain the drying accumulated temperature–moisture ratio model. Finally, the new model was used to fit the results of two drying experiments without and with tempering, and the tempering characteristics of rice drying were proved by comparing and analyzing the coefficient difference between the two models. The results showed that the optimal tempering ratio was 3. This study thus provides a reference for rice drying process parameters.

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“…The drying treatments were implemented in a laboratory thin layer dryer. [14] During the drying process, the samples were weighed every 10 min to estimate the moisture loss, as in Eq. (1), until the samples reached the desired moisture content; they were then returned to cold storage (4°C) until further analysis.…”

Section: Sample Preparationmentioning

confidence: 99%

Correlation between moisture content and machine vision image characteristics of corn kernels

Wang

Han

Jin

et al. 2020

International Journal of Food Properties

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The moisture content of grain is one of the most important factors determining quality, grade, and price and affecting the physical appearance such as the morphology and color of grain kernels. The objective of this study was to evaluate the correlation between moisture content and image characteristics of corn kernels. A simple and inexpensive machine vision system was set up to obtain images and weights of individual kernels of four varieties of corn. The corn kernels were dried from 32% to 12% (wet basis) moisture at low temperature. A total of 23 morphological and 24 color features were extracted from the individual kernel images. Four optimized feature sets (morphological, color, morphological-color, and weight-morphological-color) were used to fit moisture content using a stepwise linear regression. The coefficient of determination (R 2 ) of the linear regression improved considerably with the combination of morphology, color, and weight. The best results, with an R 2 of greater than 0.96, were achieved using the optimized weight-morphological-color set for all corn varieties. The results indicate that the machine vision technique proposed in this study has the potential for rapid measurement of grain moisture content in the industry.

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Relationship between accumulated temperature and quality of paddy

Cited by 29 publications

References 37 publications

^Optimization of hot air drying process of corn using genetic algorithm and response surface methodology

^Optimization of hot air drying process of corn using genetic algorithm and response surface methodology

Reconstruction of Rice Drying Model and Analysis of Tempering Characteristics Based on Drying Accumulated Temperature

Correlation between moisture content and machine vision image characteristics of corn kernels

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