Effect of moisture content of two paddy varieties on the physical and cooked properties of produced rice

Nutritional Profiles of Yoom Noon Rice from Royal Initiative of Southern Thailand: A Comparison of White Rice, Brown Rice, and Germinated Brown Rice

For long-term food sustainability and security, it is crucial to recognize and preserve Indigenous rice varieties and their diversity. Yoom Noon is one of the non-glutinous rice (Oryza sativa L.) varieties being conserved as part of the Phanang Basin Area Development Project, which is administered by the Royal Initiative of Nakhon Si Thammarat in Southern Thailand. The goal of this research was to compare the nutritional profiles of Yoom Noon white rice, brown rice, and germinated brown rice. The results indicated that carbohydrate content was found to be the most plentiful macronutrient in all processed Yoom Noon rice types, accounting for 67.1 to 81.5% of the total. White rice had the highest carbohydrate content (p < 0.05), followed by brown rice and germinated brown rice. Brown rice had more protein and fat than white rice (p < 0.05). The maximum protein, dietary fiber, and ash content were found in germinated brown rice, followed by brown rice and white rice (p < 0.05). White rice had the highest amylose content, around 24% (p < 0.05), followed by brown rice (22%), and germinated brown rice (20%). Mg levels in all white, brown, and germinated brown rice ranged from 6.59 to 10.59 mg/100 g, which was shown to be the highest among the minerals studied (p < 0.05). Zn (4.10-6.18 mg/100 g) was the second most abundant mineral, followed by Fe (3.45-4.92 mg/100 g), K (2.61-3.81 mg/100 g), Mn (1.20-4.48 mg/100 g), Ca (1.14-1.66 mg/100 g), and Cu (0.16-0.23 mg/100 g). Se was not found in any processed Yoom Noon rice. Overall, brown rice had the highest content of macro- and micronutrients (p < 0.05). In all processed rice, thiamin was found in the highest amount (56-85 mg/100 g), followed by pyridoxine (18-44 g/100 g) and nicotinamide (4-45 g/100 g) (p < 0.05). Riboflavin was not identified in any of the three types of processed Yoom Noon rice. Individual vitamin concentrations varied among processed rice, with germinated brown rice having the highest thiamine content by around 1.5 and 1.3 folds compared to white and brown rice, respectively. The GABA level was the highest in germinated rice (585 mg/kg), which was around three times higher than in brown rice (p < 0.05), whereas GABA was not detectable in white rice. The greatest total extractable flavonoid level was found in brown rice (495 mg rutin equivalent (RE)/100 g), followed by germinated brown rice (232 mg RE/100 g), while white rice had no detectable total extractable flavonoid. Brown rice had the highest phytic acid level (11.2 mg/100 g), which was 1.2 times higher than germinated brown rice (p < 0.05). However, phytic acid was not detected in white rice. White rice (10.25 mg/100 g) and brown rice (10.04 mg/100 g) had the highest non-significant rapidly available glucose (RAG) values, while germinated brown rice had the lowest (5.33 mg/100 g). In contrast, germinated brown rice had the highest slowly available glucose (SAG) value (9.19 mg/100 g), followed by brown rice (3.58 mg/100 g) and white rice (1.61 mg/100 g) (p < 0.05).

A New Method of Rice Moisture Content Determination Using Voxel Weighting-Based from Radio Tomography Images

This manuscript presents a new method to monitor and localize the moisture distribution in a rice silo based on tomography images. Because the rice grain is naturally hygroscopic, the stored grains’ quality depends on their level of moisture content. Higher moisture content leads to fibre degradation, making the grains too frail and possibly milled. If the moisture is too low, the grains become brittle and are susceptible to higher breakage. At present, the single-point measurement method is unreliable because the moisture build-up inside the silo might be distributed unevenly. In addition, this method mostly applies gravimetric analysis, which is destructive. Thus, we proposed a radio tomographic imaging (RTI) system to address these problems. Four simulated phantom profiles at different percentages of moisture content were reconstructed using Newton’s One-Step Error Reconstruction and Tikhonov Regularization algorithms. This simulation study utilized the relationship between the maximum voxel weighting of the reconstructed RTI image and the percentage of moisture content. The outcomes demonstrated promising results, in which the weighting voxel linearly increased with the percentage of moisture content, with a correlation coefficient higher than 0.95 was obtained. Therefore, the results support the possibility of using the RTI approach for monitoring and localizing the moisture distribution inside the rice silo.