A novel image-analysis based approach to evaluate some physicochemical and cooking properties of rice kernels

Jinorose, Maturada; Prachayawarakorn, Somkiat; Soponronnarit, Somchart

doi:10.1016/j.jfoodeng.2013.08.009

Cited by 22 publications

(10 citation statements)

References 19 publications

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“…Similar results to optimal cooking time of Kyrgyz varieties (with exception of Batken) measured by Deepa, Singh, and Naidu () who got results in range of 30 to 38 min. Also Jinorose, Prachayawarakorn, and Soponronnarit () measured average cooking time of rice around 30 min, which agree with results of this study. Quite similar results as imported varieties grown in Kyrgyzstan were found by Fofana et al.…”

Section: Resultssupporting

confidence: 90%

Evaluation of physical properties of rice used in traditional Kyrgyz Cuisine

Nádvorníková

Banout

Herák

et al. 2018

Food Science & Nutrition

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Eight staple rice cultivars consumed in Kyrgyzstan were evaluated for physical properties in this study. The dimensions of investigated grains correspond to 5.29–6.99 mm for length, 2.52–3.10 mm for width, and 1.88–2.13 for thickness. Equivalent diameter was in range of 3.14 – 3.47 mm, surface area took 25.35–31.90 mm². The sphericity analysis values varied from 0.480 to 0.559, aspect ratio from 0.39 to 0.55, volume of the grain was measured in range from 16.25 to 22.02 mm3, bulk density values were 0.77–0.87 g/cm3, and solid density from 1.17 to 1.41 g/cm3. The porosity of grain was equal to 28.27–39.83%, thousand kernel weight correspond to 19.67 to 27.15 g, rupture force of grain was measured in range of 63.47–155.50 N, color characteristic varied in parameters L*, a* and b*, 37.58–72.19, –0.22–10.17, and 9.65–21.12, respectively. Optimum cooking time ranged from 19.33 to 33.00 min. The water uptake ratios for 30 min of soaking were 1.21–1.28, 1.18–1.45, and 1.14–1.57 for 30, 45, and 60°C, respectively. While the water uptake ratios for 60 min of soaking were 1.22–1.42, 1.19–1.54, and 1.25–1.75 for 30, 45, and 60°C, respectively. Optimal cooking time showed that imported varieties needed lower interval for full grain cooking compared to the local Kyrgyz varieties. It was found that Kyrgyz rice varieties staying more firm after cooking as compared to imported varieties and therefore more suitable for the local traditional dish such as plov.

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Section: Resultssupporting

confidence: 90%

Evaluation of physical properties of rice used in traditional Kyrgyz Cuisine

Nádvorníková

Banout

Herák

et al. 2018

Food Science & Nutrition

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“…The novelty in this work was the incorporation of dimensionless morphological parameters, so the distance of the camera from the rice grains can vary with different setups. For fractal analysis parameters, the FD was formerly used by Jinorose et al [ 64 ] to examine the effect of the parboiling process and cooking time on the physical changes of cooked rice grains based on image analysis. The morphometric parameters could discriminate short-grain rice groups indicated by high mean values for FD, Cir, Ext, APIdx, and low AR.…”

Section: Discussionmentioning

confidence: 99%

Computer Vision and Machine Learning Analysis of Commercial Rice Grains: A Potential Digital Approach for Consumer Perception Studies

Aznan

Viejo

Pang

et al. 2021

Sensors

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Rice quality assessment is essential for meeting high-quality standards and consumer demands. However, challenges remain in developing cost-effective and rapid techniques to assess commercial rice grain quality traits. This paper presents the application of computer vision (CV) and machine learning (ML) to classify commercial rice samples based on dimensionless morphometric parameters and color parameters extracted using CV algorithms from digital images obtained from a smartphone camera. The artificial neural network (ANN) model was developed using nine morpho-colorimetric parameters to classify rice samples into 15 commercial rice types. Furthermore, the ANN models were deployed and evaluated on a different imaging system to simulate their practical applications under different conditions. Results showed that the best classification accuracy was obtained using the Bayesian Regularization (BR) algorithm of the ANN with ten hidden neurons at 91.6% (MSE = <0.01) and 88.5% (MSE = 0.01) for the training and testing stages, respectively, with an overall accuracy of 90.7% (Model 2). Deployment also showed high accuracy (93.9%) in the classification of the rice samples. The adoption by the industry of rapid, reliable, and accurate methods, such as those presented here, may allow the incorporation of different morpho-colorimetric traits in rice with consumer perception studies.

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“…Imaging technique belongs to morphological method and has been applied into a useful nondestructive measurement approach in rice industry. Studies evaluated the visual features of rice had been reported, for example, the rice classification [21][22][23] , the degree of rice milling [24] , the cracked rice detection [25] , and the cooking properties of rice kernels [26,27] . Utilizing the NIR cooled charged couple device (CCD) camera could help to capture the spectral images in the NIR band, which made the quantitative measurements possible, e.g., insects inside wheat detection [28] , wheat protein and color classification [29] , and rice seed cultivar identification [30] .…”

Section: Introduction mentioning

confidence: 99%

Prediction of protein content in rice using a near-infrared imaging system as diagnostic technique

Lin¹,

Lu²,

Chang³

2019

International Journal of Agricultural and Biological Engineering

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The aim of this research was to determine the rice protein content utilizing a NIR imaging system. The developed imaging system utilized a NIR camera which installed automatically exchanged filters with the wavelength range from 870 nm to 1014 nm. Multiple liner regression (MLR), partial least square regression (PLSR), and artificial neural network (ANN) models were employed as data analysis methods for 6.18%-9.43% rice protein detections within both the NIR imaging system and commercial NIRS. A total of 180 rice samples were used in this study, of which 120 random samples were selected as a calibration set for the MLR and PLSR models. Moreover, for establishing the back-propagation ANN model, the same 120 samples were divided into two parts, 80 samples were used for network training and the other 40 were established as the monitoring set. To compare with the results of MLR, PLSR, and ANN models, the remaining 60 of the total 180 samples were established as the validation set. Applying an MLR linear regression model composed of five wavelengths; the NIR imaging system successfully detected rice protein content. The predicting results of r val 2 and SEP were 0.769 and 0.294%, respectively. In PLSR model, utilizing the imaging system obtained the results of r val 2 = 0.782, and SEP = 0.274% within the wavelength range from 870 nm to 1014 nm. Five significant wavelengths selected by the MLR model were the same as the input data of the ANN model, and the prediction results were r val 2 = 0.806, and SEP = 0.266%. The prediction results indicated that the developed NIR imaging system has the advantages of simple, convenient operation, and high detection accuracy as well as it presents commercial potential in non-destructive detection of rice protein content.

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A novel image-analysis based approach to evaluate some physicochemical and cooking properties of rice kernels

Cited by 22 publications

References 19 publications

Evaluation of physical properties of rice used in traditional Kyrgyz Cuisine

Evaluation of physical properties of rice used in traditional Kyrgyz Cuisine

Computer Vision and Machine Learning Analysis of Commercial Rice Grains: A Potential Digital Approach for Consumer Perception Studies

Prediction of protein content in rice using a near-infrared imaging system as diagnostic technique

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