X-Ray Detection of Fissures in Rough Rice Kernels

2017 ◽  
Vol 33 (5) ◽  
pp. 721-728 ◽  
Author(s):  
Zephania R. Odek ◽  
Bhagwati Prakash ◽  
Terry J. Siebenmorgen
Keyword(s):  
Rice Yield ◽  
X Rays ◽  
Milled Rice ◽  
Substantial Impact ◽  
X Ray ◽  
X Ray Imaging ◽  
Head Rice ◽  
Rough Rice ◽  
Head Rice Yield ◽  
Rice Drying

Abstract. X-ray imaging is a viable method of fissure detection in rough rice kernels owing to the ability of X-rays to penetrate hulls, thus allowing visualization of internal rice kernel structure. Traditional methods of fissure detection are only applicable for brown and milled rice, and therefore cannot be used to study fissures developed during rough rice drying. In this study, the fissure detection capability of an X-ray system was evaluated and the relationship between head rice yield (HRY), as measured through laboratory milling, and the percentage of fissured rough rice kernels was determined. Long-grain rice lots of various cultivars were dried using heated air at 60°C, 10% relative humidity (RH) for five drying durations to produce different degrees of fissuring, and then milled to determine HRY. A strong linear correlation (R2 = 0.95) between HRY and the percentage of fissured rough rice kernels after drying was determined. This correlation confirms the substantial impact that kernel fissures have on milling yields. Overall, these findings show the effectiveness of X-ray imaging in rough rice fissure detection, which could allow for drying research that may provide a better understanding of kernel fissuring kinetics. Keywords: Fissures, Grainscope, Head rice yield, Rice drying, X-ray imaging.

Effect of Post-Drying Tempering of Rice on Minimizing Kernel Fissuring and Maximizing Moisture Removal

2021 ◽  
Vol 65 (1) ◽  
pp. 1-9
Author(s):  
Zephania Odek ◽  
Terry J. Siebenmorgen ◽  
Andronikos Mauromoustakos ◽  
Griffiths G. Atungulu
Keyword(s):  
Rice Yield ◽  
Yield Reduction ◽  
List Type ◽  
Humidity Ratio ◽  
Percentage Points ◽  
X Ray Imaging ◽  
Head Rice ◽  
Rough Rice ◽  
Head Rice Yield ◽  
Rice Drying

HighlightsMore moisture can be removed in a single drying pass without severely fissuring kernels when samples are tempered than when immediately cooled without tempering.Tempering rice kernels immediately after drying can reduce the percentage of fissured kernels by up to half of that when kernels are immediately cooled without tempering.Abstract. Improper rice drying results in kernel fissuring, leading to head rice yield reduction due to breakage during milling. The objective of this study was to determine the percentage points (pp) of moisture content (MC) reduction that can be achieved in a single drying pass without significantly fissuring kernels. Long-grain rough rice of cultivars CL XL745 and Diamond at initial MCs of 18%, 17%, 16%, 15%, and 14% were dried using air at 45°C/20% relative humidity (RH), 50°C/15% RH, 55°C/12% RH, 60°C/10% RH, and 65°C/8% RH to MCs of 17%, 16%, 15%, 14%, 13%, or 12% with and without post-drying tempering. All temperature/RH combinations resulted in a humidity ratio of 0.012 kg water kg-1 dry air. Tempering was conducted at the drying air temperature for 4 h. The resulting samples achieved between 1 and 7 pp of MC reduction in a single drying pass. The pp of MC reduction that can be attained in a single drying pass without causing significant fissuring varied across the cultivars tested. Generally, ~2 pp of MC reduction was achieved in a single drying pass for CL XL745 and ~4 pp for Diamond without causing adverse fissuring when samples were not tempered after drying. However, with tempering, ~3.5 pp of MC reduction was achieved in a single drying pass for CL XL745 and ~5.5 pp for Diamond without causing significant fissuring. However, these amounts varied depending on the drying air conditions and initial MC. For both cultivars, tempering immediately after drying reduced the fissured kernel percentage by up to half of that when the kernels were not tempered. These findings quantify the importance of rice tempering and provide information on how much moisture can be safely removed in a single drying pass. Such findings may be applied to different dryer types to reduce fissuring due to drying, thereby minimizing head rice yield reductions. Keywords: Drying, Glass transition, Rice quality, Single-pass drying, X-ray imaging.

Fissure Detection and Measurement in Rough Rice Using X-Ray Imaging

2019 ◽  
Vol 62 (4) ◽  
pp. 859-866
Author(s):  
Hu Shi ◽  
Terry J. Siebenmorgen ◽  
Hengliang Luo ◽  
Zephania Odek
Keyword(s):  
Rice Yield ◽  
Economic Value ◽  
Milling Process ◽  
X Ray ◽  
X Ray Imaging ◽  
Head Rice ◽  
Rough Rice ◽  
Relative Standard ◽  
Head Rice Yield ◽  
Rice Samples

Abstract. Fissures in rice kernels that develop prior to harvest and post-harvest processing significantly reduce head rice yield, a crucial parameter for evaluating rice quality and economic value in the rice industry. In this study, fissures in rough rice were revealed by scanning approximately 50 rough rice kernels at a time using an x-ray system. An algorithm was developed to detect and measure fissures in rough rice kernels in the x-ray images using the Python programming language coupled with the OpenCV library. This algorithm successfully segmented individual rice kernels in the x-ray images using the gap-filling method. The algorithm detected fissures by adaptive thresholding of each rice kernel and applying a series of filters. Data on kernel parameters (number, area, length, and width) and fissure parameters (percentage of kernels fissured and fissure number, area, and length per kernel) were produced for the images to characterize kernel size and fissuring levels of the rice sample. This algorithm demonstrated good repeatability in measuring kernel and fissure parameters, with relative standard deviations of less than 4% and 9%, respectively. The accuracy of the developed algorithm in measuring fissures was validated by visual inspection of rough rice, with a deviation of less than 2% in percentage of kernels fissured. The fissure detection and measurement algorithm provides a useful tool for quantifying fissures in rough rice samples using x-ray imaging. This information could be used to quantify fissuring levels and predict head rice yield for rough rice samples without a cumbersome milling process. Keywords: Cracks, Fissure, Imaging, Rice, X-ray.

Effect of Storage Moisture Content on Milling Characteristics of Rough Rice

2019 ◽  
Vol 62 (4) ◽  
pp. 1011-1019
Author(s):  
Bhagwati Prakash ◽  
Terry J. Siebenmorgen ◽  
Kristen E. Gibson ◽  
Shweta Kumari
Keyword(s):  
Moisture Content ◽  
Rice Yield ◽  
Surface Lipid ◽  
Milled Rice ◽  
Financial Return ◽  
Head Rice ◽  
Rough Rice ◽  
Head Rice Yield ◽  
Milling Characteristics ◽  
The Mean

Abstract. Rough rice in the Mid-South U.S. is typically stored and milled at a moisture content (MC) between 12% and 13% on a wet basis. Drying harvested rice to lesser MCs requires increasingly greater energy and reduces the overall mass of rice, both of which translate into lesser financial return for the crop. Considering these disadvantages of drying and storing rice at lesser MCs, farmers and grain handlers have been interested in exploring storing rice at slightly greater MCs. The current study was undertaken to evaluate the effect of storing rice at five MCs (11%, 12%, 13%, 14%, and 15%) on milling characteristics, particularly surface lipid content (SLC), milled rice yield (MRY), and head rice yield (HRY); additionally, the effects of storing rice at two storage temperatures (25°C and 35°C) and several storage durations (up to one year) on milling characteristics were investigated. Five long-grain rice lots were harvested in 2016 and 2017 from several locations in Arkansas; rice from each lot was gently dried to the target MCs and then stored in sealed glass jars at selected temperatures. With an increase in storage MC, shorter milling durations were needed to achieve a given SLC, which could potentially reduce the cost of the milling operation. However, rice samples stored at greater MCs were observed to have lesser HRYs, which could reduce the economic value of rice. The mean HRYs of the 15% MC samples were 4.8 to 9.1 percentage points less than the mean HRYs of the 12% MC samples. This study quantifies the milling characteristics of rice when stored for various durations at different MCs and temperatures. Overall, these data will allow the rice industry to make informed decisions related to storage conditions of rice, specifically storage MC. Keywords: Head rice yield, Milling, Moisture content, Rice, Storage.

Milled Rice Quality Assessment

2010 ◽  
Vol 6 (2) ◽  
Author(s):  
Reza Farahmandfar ◽  
Esfandiyar Farahmandfar ◽  
Mahdi Ghasemi Varnamkhasti ◽  
Mahdi Zarei
Keyword(s):  
Rice Yield ◽  
Milling Process ◽  
Milled Rice ◽  
Rice Varieties ◽  
Breeding Lines ◽  
Head Rice ◽  
Rough Rice ◽  
Degree Of Milling ◽  
Head Rice Yield ◽  
Important Processing

Milling, an important processing step of rough rice, is usually done to produce white, polished grains. In this paper the quality of 22 milled rice varieties, common in Mazandaran, Iran, are investigated. These rice varieties included local varieties and breeding lines. Parameters assessed were head rice yield, degree of milling, husk removed percent, and total milling recovery. Results obtained revealed that the Tarom Mahali and Champa varieties have the highest head rice yield as 60.58 and 66.39 % and total milling recovery as 69.96 and 71.38 %, respectively. The greatest degree of milling value was found for the Haraz variety with a mean of 16.06 %. Also, it was found that the husk removed percent values were not statistically different among the varieties studied. Finally, considering all results obtained, the varieties of Tarom Mahali, Champa, and Neda showed to be more economical in the milling process.

Broadleaf Signalgrass (Brachiaria platyphylla) Duration of Interference in Rice (Oryza sativa)

Weed Science ◽  
1988 ◽  
Vol 36 (6) ◽  
pp. 747-750 ◽  
Author(s):  
John T. McGregor ◽  
Roy J. Smith ◽  
Ronald E. Talbert
Keyword(s):  
Rice Yield ◽  
Dry Weight ◽  
Kernel Weight ◽  
Rough Kernel ◽  
Weed Interference ◽  
Head Rice ◽  
Rough Rice ◽  
Head Rice Yield ◽  
Rice Yields ◽  
Brachiaria Platyphylla

Interference from broadleaf signalgrass at a density of 180 plants/m2reduced rough rice yields of ‘Bond’ a maximum of 48% at 95 days after rice emergence and reduced yields of ‘Mars' a maximum of 21% from season-long interference. Interference durations of 40 days or longer reduced the panicles/m2, culms/m2, and plant height of rice. Straw dry weight of Bond and Mars was reduced 41 and 26%, respectively, from season-long interference. Increased durations of weed interference did not affect the number of spikelets/panicle, percent filled spikelets, rough kernel weight, or head rice yield of either cultivar. Broadleaf signalgrass produced less dry weight and fewer panicles/m2when grown with Mars than with Bond.

Prediction of Rice Milling Yield and Quality Attributes during Storage Using Regression Analyses

2019 ◽  
Vol 62 (5) ◽  
pp. 1259-1268
Author(s):  
Soraya Shafiekhani ◽  
Jung Ae Lee ◽  
Griffiths G. Atungulu
Keyword(s):  
Hybrid Rice ◽  
Storage Temperature ◽  
Rice Yield ◽  
Storage Conditions ◽  
Milled Rice ◽  
Yield And Quality ◽  
Head Rice ◽  
Rough Rice ◽  
Milling Yield

Abstract. Regression analyses were performed to determine the storage conditions that exhibited the best outcomes for long-grain, hybrid milled rice yield and quality. This study evaluated mold population on rough rice, milled rice discoloration, and head rice yield (HRY) after storage of rough rice in airtight conditions at moisture contents (MCs) of 12.5%, 16%, 19%, and 21% wet basis and temperatures of 10°C, 15°C, 20°C, 27°C, and 40°C at two-week intervals for 12 weeks. The experiment used a popular long-grain hybrid rice cultivar (XL745). Rice lots were procured from fields with and without conventional treatment of the field with fungicide for plant disease management. Field treatment and no field treatment were considered as a block, and a Mann-Whitney test was conducted to determine effect. The response surface method, an extension of second-order polynomial regression, was used to examine optimal treatment conditions. Mold population and milled rice discoloration from a combination of storage conditions were predicted using regression models. The first-order and second-order terms of temperature indicated a nonlinear relationship between temperature and ln(discoloration). The MC was positively associated with ln(discoloration), but the degree of impact may change with temperature because the interaction term was significant. From the model evaluation (R2 and lack-of-fit test), the discoloration level is expected to be 57% (49% to 66% confidence interval) under conditions of 20% MC, 40°C, and nine weeks of storage for samples procured from fungicide-treated rice fields. This discoloration change is substantial compared to the initial discoloration of 9%. At high temperature (40°C) and MC (21%), discoloration started immediately after two weeks of storage. Anaerobic storage conditions impeded mold growth, especially at high storage temperature (40°C). Low mold populations were observed in rice stored at low MC (16%). According to the regression model, the critical storage temperature that may lead to discoloration is between 27°C and 40°C. Pre-harvest fungicide treatment of rice in the field for disease control significantly improved the HRY but had no significant influence on mold population or discoloration. This study suggests a range of storage conditions to prevent losses in milling yield and quality of rice. In addition, the studied storage conditions mimicked the typical conditions for on-farm, in-bin drying and storage in the U.S. Mid-South, especially for the top layers of rice inside the bin, and therefore provide an important reference for growers and rice processors using in-bin structures to manage the quality of long-grain hybrid rice. Keywords: Discoloration, Head rice yield, Mold population, Regression analysis, Rice quality, Rice storage.

Establishment of Rice Quality Prediction Model for Intermittent Drying

2021 ◽  
Vol 64 (4) ◽  
pp. 1355-1363
Author(s):  
Qi Song ◽  
Xinhua Wei
Keyword(s):  
Moisture Content ◽  
Prediction Model ◽  
Intelligent Control ◽  
Evaluation Method ◽  
Rice Yield ◽  
Drying Process ◽  
Rice Quality ◽  
Head Rice ◽  
Head Rice Yield ◽  
Rice Drying

HighlightsThis study explored the feasibility of developing an evaluation method for rice quality.A unified quality scale for different drying cycles facilitates evaluation of rice quality after drying.A head rice yield (HRY) prediction model was established that fit well with the actual HRY.The established HRY prediction model can be used as a performance index for optimization of rice drying.Abstract. Intelligent control of the drying process is important to achieve better rice quality. An effective quality evaluation method is the basis for intelligent control of rice drying. To study the effects of intermittent drying on the quality of paddy rice and explore the feasibility of establishing a quality evaluation method, intermittent drying experiments were conducted with variety Nanjing 9108 (Oryza sativa L.). The paddy samples were dried from an initial moisture content of 23.10% to 14% wet basis (w.b.). The paddy samples were initially dried at 60°C to various moisture contents without tempering. These pre-dried samples were then dried using different drying temperatures to obtain specific moisture content reductions, tempered, and then dried again at 60°C to the final moisture content of 14% w.b. without tempering. After drying, the quality parameters of the paddy samples were measured and analyzed. The R2 values of the head rice yield (HRY) prediction model, chalkiness prediction model, and protein prediction model established in this study were 0.75, 0.44, and 0.26, respectively. The HRY prediction model was shown to accurately predict HRY in the intermittent drying experiments. Within the range of the model parameters, the effectiveness of the HRY prediction model was explored by constant-temperature intermittent drying and variable-temperature intermittent drying. The results showed that if the summation of the predicted changes in HRY is large, then the measured HRY will be large. Therefore, the HRY prediction model can be used as a performance index for rolling optimization of the paddy drying process. Keywords: Head rice yield, Intermittent drying, Prediction model, Rice quality.

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