Different Types Of Rice Research Articles

Chewing characteristics of rice and reasons for differences between three rice types with different amylose contents

This study investigated the physicochemical structural changes in different types of rice (japonica rice [JR], indica rice [IR], and waxy rice [WR]) during oral digestion and explored the reasons for differences in oral digestion between the three different types. The results showed that, compared with JR (42.41 ± 3.06 mg/g) and WR (26.82 ± 0.67 mg/g), IR had the highest amylose content (49.95 ± 3.33 mg/g) and, related to this, hydrolysis rate. A correlation analysis showed that, the higher the amylose content, the harder the texture of rice, leading to longer chewing times and, as a result, a greater degree of hydrolysis. In addition, the higher the amylose content, the lower the exudate content and viscosity of rice, which affects chewing time and frequency, thereby affecting the degree of hydrolysis. Both X-ray computed tomography and scanning electron microscopy indicated that cooked IR had the loosest structure and the most pores, that were conducive to chewing and crushing and therefore contributed to the high hydrolysis rate. Analysis of the exudate structure showed that the amount of exudate affected rice pores. More exudates lead to pore coverage and a tight structure.

Rice sample preparation method for ICP-MS and CV-AFS analysis: Elemental levels and estimated intakes

Eight sample digestion procedures were compared to determine 41 elements in rice samples by ICP-MS and CV-AFS. Analytical methods were evaluated using certified rice flour reference material (NIST 1568b) and recovery experiments. The microwave-assisted digestion of 0.5 g rice sample and reagent mixture of 2 mL HNO3, 0.5 mL H2O2, and 0.5 mL deionized water yielded the best recovery for all elements ranging from 90 to 120% at three different levels, bias% within 10%, and precision (coefficient of variation percent, CV% intra- and inter-day) below 15%. The best analytical method was applied to the elemental determination in nine types of rice available on the Italian market. Daily or weekly rice consumption meets the nutritional and safety requirements of EFSA and WHO. The present study allows extensive and detailed knowledge of the content of essential and non-essential/toxic elements in different types of rice produced or packaged in Italy.

Determination of Selected Metals and Metalloids in Different Types of Rice by Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES).

Pantanal is a unique biome located in Brazil, with diverse fauna and flora, being home to native species such as the rice types Oryza latifolia and Oryza rufipogon. Rice is a staple food for two-thirds of the population, with increasing consumption, especially in Asia, regions of Sub-Saharan Africa, countries in the Caribbean, and Latin America. The per capita rice consumption had an average consumption of 160g/day. However, rice consumption may lead to the intake of other harmful substances to health, such as toxic metals/metalloids. The determination of potential hazards in native species is crucial in maintaining the local population in good health. In this study, we determined the concentration of essential elements and potentially toxic elements in seven different types of Brazilian rice grains, including the two Pantanal native species O. latifolia and O. rufipogon, using ICP-OES to identify their nutritional richness or potential toxicity. The contaminant with the highest HQ levels was chromium, with an HQ above one only in the native species. All species (commercial and native) showed carcinogenic risk considering inorganic arsenic. Rice exhibits duality in its classification, providing nutritional content and leading people to potential risks of overexposure to toxic elements. While rice can be part of a healthy and nutritious diet, more studies should be conducted on avoiding or remedying contamination with toxic elements.

Using satellite image data to identify rice varieties through linear spectral unmixing method (case study: Karangjati Sub District, Ngawi Regency)

<p>Remote sensing technology has increasingly emerged as a potent tool for precision agriculture, particularly in facilitating the mapping and monitoring of crops on a large scale. An application of this technology is the identification of different types of rice by analyzing the pixels acquired in satellite images. Regrettably, the pixels in the image have been mixed from different recorded items. Therefore, they have the potential to influence the outcome of the identification. An effective approach to addressing this problem is to employ the linear spectral unmixing (LSU) technique. The LSU approach quantifies the ratio of pure objects in every pixel of an image by utilizing the spectral value associated with the endmember of the rice variety. The investigation was carried out in the Karangjati District during the generative stage (70 ± DAP) of the rice planting season. The data indicates that the dominant variety is Inpari 32 HDB. The data validation tests, which involved the use of a confusion matrix and Kappa analysis, resulted in an overall accuracy rate of 85.48% and a Kappa analysis score of 70.6%.<strong></strong></p>

Effect of weak alkaline electrolyzed water on rice cooking: Promoting rapid water absorption and enhancing the quality and digestibility

Promoting the rapid water absorption of rice is beneficial for increasing the cooking quality and digestibility of rice. Under the action of an electric field, weak alkaline electrolyzed water (WAEW) had a smaller cluster system compared to tap water, with a 17.30% decrease in DDAA-OH (double donor-double acceptor) and a 22.89% increase in Free-OH. Therefore, there were fewer hydrogen bonds formed between water molecules, resulting in more free proton donors and proton acceptors, which increased the possibility of forming more hydrogen bonds with other substances. During the soaking process, WAEW could effectively increase the water absorption rate, maximum water content, and water diffusion rate in different types of rice. As WAEW formed more hydrogen bonds with substances in the rice, it was easier to combine with substances in the rice, forming more bound water. Under the combined effect of the alkaline environment of WAEW and the characteristics of its own water molecules, the surface and internal structure of the rice were eroded, the surface layer was peeled off, and a large number of pore structures were formed. Along with the increased water absorption, more substances such as starch and protein dissolved from the rice. The cooked rice soaked in WAEW had greater sensory quality, higher digestibility and glycemic index. Therefore, WAEW could be used as a new method for cooking rice, promoting the rapid absorption of water and increasing the sensory quality and digestibility.

Glycemic Index and glycemic load for different types of cooked rice for healthy volunteers

The effect of eating different types of rice was studied, including basmati rice, local amber rice, brown rice and diet rice, in addition to bulgur and mung bean on the glycemic response and glycemic Index values. The effect of eating rice cooked by pressing and Boiled methods on the blood sugar response of healthy volunteers was studied, where the results of the study included ten volunteers. The highest increase in blood sugar level response occurred after consuming amber rice cooked by pressing method, which reached 160.3 (mg / 100 ml). After thirty minutes of eating it, while bulgur rice cooked by Boiled method has raised the level of blood sugar response less than rice of all kinds and at a rate of 121.7 (mg / 100 ml) after eating it by thirty, the study determined the glycemic index and blood sugar load for each sample of raw, mixed and cooked rice (pressing and boiled) in addition to bulgur. The results showed a variation in the values of the glycemic index at the level of (p≥0.05) for different types of rice. The results showed that the highest glycemic index was for amber rice cooked by pressing method with a percentage of (88.8) and the lowest glycemic index was for bulgur cooked by Boiled method by (37). While the glycemic index of white basmati rice in the ways of pressing and Boiled was 66.6 and 53 respectively, and the additions that were added to basmati rice, which included mash, cinnamon and turmeric, had a clear role in reducing the blood sugar index, as the values of the glycemic Index for basmati rice pressed and mixed with mash, cinnamon and turmeric are 60, 56 and 57 respectively, while the values of the glycemic Index for basmati rice mixed with mash, cinnamon and turmeric are 49.2. 48 and 46, respectively.

High-resistant starch and low-glutelin content 1 rice benefits gut function in obese patients

The intestinal microbiota plays a crucial role in human health, yet the impact of high-resistant starch and low-glutelin rice in the gut of obese individuals remains unexplored. In this study, different types of rice, namely japonica rice (control), low-glutelin content 1 rice (Lgc1), high-resistant starch and low-glutelin content 1 rice (HR + Lgc1), and commercially available low-glycemic index rice (LowGI), were utilized as samples to examine their in vitro digestion and fermentation, as well as the impact of HR + Lgc1 on the intestinal microbiota in obese individuals. The findings revealed that HR + Lgc1 rice exhibited lower in vitro digestion rates for starch and protein compared to the other three types. Following 24 h of in vitro fermentation, the total short-chain fatty acid content in HR + Lgc1 was 108.67 mmol/L, which was 60.33, 51.26, and 17.29% that in control, Lgc1, and LowGI, respectively. Moreover, HR + Lgc1 rice displayed an inhibitory effect on the production of harmful branched-chain fatty acid metabolites. Analysis through 16S rDNA sequencing indicated that, after fermentation, HR + Lgc1 significantly enhanced the abundance of beneficial bacteria such as Bifidobacterium, Parabacteroides, and Faecalibacterium in the gut, whereas it reduced the abundance of harmful bacteria such as Escherichia-shigella. Our findings may contribute to the development of new foods rich in dietary fiber to improve the digestive function of patients with multiple comorbidities.

Fine classification and phenological analysis of rice paddy based on multi-temporal general compact polarimetric SAR data.

Fine classification and phenological information of rice paddy are of great significance for precision agricultural management. General compact polarimetric (CP) synthetic aperture radar (SAR) offers the advantage of providing rich polarimetric information, making it an important means of monitoring rice growth. Therefore, in response to the current challenges of difficulty in rice type classification and the small differences in phenological polarimetric characteristics, a novel strategy for fine classification and phenological analysis of rice paddy is proposed. This strategy thoroughly explores the polarimetric information of general CP SAR data and the target scattering characterization capabilities under different imaging modes. Firstly, the general CP SAR data is formalized using the standard CP descriptors, followed by the extraction of general CP features through the ΔαB /αB target decomposition method. optimal CP features are generated to achieve fine classification of rice paddy. Finally, 6 phenological stages of rice are analyzed based on the general CP features. The experiment results of rice classification show that the classification accuracy based on this strategy exceeds 90%, with a Kappa coefficient above 0.88. The highest classification accuracies were observed for transplanting hybrid rice paddy (T-H) and direct-sown japonica rice paddy (D-J), at 80.9% and 89.9%, respectively. The phenological evolution rule of the two rice types indicate that from early June (seedling stage) to late July (elongation stage), the CP feature variation trends of T-H and D-J are generally consistent. However, from October (mature stage) to November (harvest stage), the variation trends of the CP features for T-H and D-J are significantly different. The study found that from the booting-heading stage to the harvest stage, the linear π/4 mode outperforms circular and elliptical polarimetric modes in distinguishing different types of rice. Throughout the entire phenological period of rice growth, CP SAR of linear π/4 mode is surpasses that of other linear modes in discriminating different type of rice. The proposed strategy enables high-precision fine classification rice paddy, and the extracted general CP αB parameter effectively reflects the phenological change trends in rice growth.

Statistical Farm Records: Drivers and Challenges Among Smallholder Rice Farmers in Ndop Plain, Cameroon

The study seeks to analyze statistical farm records: drivers and challenges among smallholder rice farmers in Ndop Plain, Cameroon. Specifically, to determine the factors influencing smallholder rice farmers in Ndop Plain to keep statistical farm records and to analyze the challenges in the exercise. Methodologically, the study employed primary data collected from some 470 rice farmers using a well-structured questionnaire. Purposive sampling technique was applied to select respondents and a conventional Probit model used to estimate the result. It was observed that: male farmer, level of education, used of records, used of technology to manage statistical farm records, production of different types of rice, use of farm record for input and output analysis, had farm record training and Movement from manual to electronic record keeping are significantly influencing farmers’ statistical farm records practices in Ndop Plain. The challenges observed with record keeping are: fundamental error keeping, bulky records, inadequate finance, records misplacement and burdensome to keep farm records. Considering that only 34% of farmers are involved in records keeping and willing to move from manual way of keeping records to electronic, the study suggests that an online App is created for easy accessibility and inputting of these farm records. This will be a wise-step for the government to scale the process of farm output.

Integrated genome-wide differentiation and association analyses identify causal genes underlying breeding-selected grain quality traits in japonica rice

Improving grain quality is a primary objective in contemporary rice breeding. Japanese modern rice breeding has developed two different types of rice, eating and sake-brewing rice, with different grain characteristics, indicating the selection of variant gene alleles during the breeding process. Given the critical importance of promptly and efficiently identifying genes selected in past breeding for future molecular breeding, we conducted genome scans for divergence, genome-wide association studies, and map-based cloning. Consequently, we successfully identified two genes, OsMnS and OsWOX9D, both contributing to rice grain traits. OsMnS encodes a mannan synthase that increases the white core frequency in the endosperm, a desirable trait for sake brewing but decreases the grain appearance quality. OsWOX9D encodes a grass-specific homeobox-containing transcription factor, which enhances grain width for better sake brewing. Furthermore, haplotype analysis revealed that their defective alleles were selected in East Asia, but not Europe, during modern improvement. In addition, our analyses indicate that a reduction in grain mannan content during African rice domestication may also be caused a defective OsMnS allele due to breeding selection. This study not only reveals the delicate balance between grain appearance quality and nutrition in rice but also provides a new strategy for isolating causal genes underlying complex traits, based on the concept of “breeding-assisted genomics” in plants.

Effects of milling degree on nutritional, sensory, gelatinization and taste quality of different rice varieties

The impact of variety and processing accuracy on nutritional, sensory and taste qualities of rice is studied. Husked rice yield and whole brown rice rate in japonica rice (Nanjing-No.5055) were higher than indica rice, they were 82.10% and 80.90%, respectively. Milling treatment resulting in low protein, ash, fat, dietary fiber, total phenols and oryzanol content and high starch content in rice. Four types of saturated fatty acids (C14:00, C16:00, C18:00, C20:00) and six types of unsaturated fatty acids (C14:1, C16:1, C18:1, C18:2, C18:3, C20:4, C22:1) were detected from rice fat, which content and compositional proportion varied with the increase of milling degree. The changes in composition content and bran integrity leads to increased whiteness, gel consistency and palatability and reduced yellowness and gelatinization temperature. Principal component analysis showed that the effect of milling on the quality characteristics of rice are different and finite. Yuzhenxiang in component index score was decreased most significantly, it drops from 10.03 to −5.24, while the improvement of sensory and taste quality in Nanjing-No.5055 is the most apparent. The research results will be benefit to provide more useful information and ideas in appropriate milling processing for different types of rice.

The Impact of Different Types of Rice and Cooking on Postprandial Glycemic Trends in Children with Type 1 Diabetes with or without Celiac Disease

The aims of this study were to evaluate: (i) the chemical and nutritional composition of rice before and after cooking and (ii) postprandial glycemic impacts in children and adolescents with type 1 diabetes (T1D) after eating two different types of rice ("Gigante Vercelli" white rice and "Artemide" black rice) or white rice cooked "risotto" style or boiled using an advanced hybrid closed loop (AHCL) system (Tandem Control-IQTM). General composition and spectrophotometric analyses of raw and cooked rice were performed. Eight T1D subjects (four males and four females, aged 11 ± 1.4 years), two with celiac disease (CD), using an AHCL system were enrolled. "Gigante Vercelli" white rice cooked as risotto or boiled and boiled "Artemide" rice were prepared by the same cook on two evenings. Continuous glucose monitoring metrics were evaluated for 12 h after meal consumption. Total dietary fiber was higher for both rice types after cooking compared with raw rice. Cooking as risotto increased polyphenols and antioxidants (p < 0.05) in both rice varieties, and total starch decreased after boiling (p < 0.05) in white rice. There was a significant peak in glycemia after consuming risotto and boiled white rice (p < 0.05), while the mean glycemic peak remained <180 mg/dL in individuals eating boiled Artemide rice. There were no significant differences in automatic basal or auto-bolus insulin deliveries by the AHCL according to different types of rice or cooking method. Our findings suggest that glycemic trends are impacted by the different chemical and nutritional profiles of rice but are nevertheless well controlled by AHCL systems.

Nitrogen Use Traits of Different Rice for Three Planting Modes in a Rice-Wheat Rotation System

At present, there is a limited understanding of nitrogen (N) accumulation, translocation, and utilization in different types of rice grown using different planting methods in a rice–wheat rotation system. Systematic experiments were conducted with six rice cultivars, including two japonica-indica hybrids (JIHR), two japonica conventional rice (JCR) cultivars, and two indica hybrid rice (IHR) cultivars, to study the effects on N use of plants in three transplanting modes: (1) the pothole seedling machine transplanting mode (PM), (2) the carpet seedling machine transplanting mode (CM), and (3) the mechanical direct seeding mode (DM). Results showed that at stem elongation stage, for N content and uptake, the planting methods were ranked in the order PM &lt; CM &lt; DM, and at heading and maturity the order was PM &gt; CM &gt; DM. After stem elongation the rankings for N accumulation, ratio of N accumulation to total N, and N uptake rate were PM &gt; CM &gt;DM. Thus, on the basis of a certain amount of N accumulation in the early growth phase, increasing the N uptake rate and N accumulation in the middle and late growth phases are ways to increase total N uptake for the PM and CM modes compared to DM. In addition, the PM/JIHR treatment had the highest N uptake at maturity. The N contents of leaves, stem-sheaths, and panicles at heading and maturity for the three planting modes were ranked PM &gt; CM &gt; DM. Moreover, the N translocation amount, apparent N translocation rate, and translocation conversion rate of leaves under PM were significantly higher than for CM and DM, which would increase N accumulation in the grain. The N uptake per 100 kg grain and the partial factor productivity of applied N under PM were larger than for CM and DM, but the N use efficiency of grain yield and biomass were smaller for PM than for CM and DM. In conclusion, rice grown using PM, especially JIHR, had higher total N uptake and N utilization compared to the CM and DM modes, and cultivation measures to improve the N use efficiency of grain yield and biomass could be appropriately applied to further improve N use in a rice–wheat rotation system.

Determination of Glyphosate in White and Brown Rice with HPLC-ICP-MS/MS.

In 2017, the European Commission renewed the approval of glyphosate (GLY) but only for five years. GLY remains one of the most controversial and studied molecules. A simplified method was tested for the determination of GLY in white rice (WR) and brown rice (BR), after extraction only with a methanol solution, by liquid chromatography coupled with inductively coupled mass triple quadrupole (HPLC-ICP-MS/MS) with a PRP-X100 anionic column. After performing a test on groundwater, the quantification of GLY in WR and BR was validated in terms of the LOD, LOQ, accuracy, precision, linearity, and the matrix effect. The LOD was 0.0027 mg kg-1 for WR and 0.0136 mg kg-1 for BR. The LOQ was 0.0092 mg kg-1 for WR and 0.0456 mg kg-1 for BR. The mean recoveries were within 76-105% at three fortification levels. The relative standard deviation for the analysis (five replicates for three spike levels) was &lt; 11% for both matrices. A linear response was confirmed in all cases in the entire concentration range (R2WR = 1.000 and R2BR = 0.9818). The proposed method could be considered useful for the determination of GLY in different types of rice and designed and adapted for other cereals. The matrix effect, quantified in BR matrix extraction, could be avoided by using a matrix-matched calibration line.

The Effectiveness of Natural Plant Powder in Controlling Rice Weevils (Sitophilus oryzae)

The rice industry today, very depends on synthetic pesticides to protect against losses. However, synthetic pesticides pose toxicity and give a negative effect on humans, animals, and the environment. Thus, this study aims to observe the effectiveness of natural powders such as neem (Azadirachta indica) and black pepper (Piper nigrum) in controlling rice weevil (Sitopylus oryzae) during rice storage. Treatments included in this experiment comprised control where no plant powder was applied (T0), black pepper powder (T1), neem leaves powder (T2), and a combination of neem with black pepper (T3). All treatments were conducted on different types of rice, Malaysia (local) and Thailand rice. 30 g of dosage for all treatments were applied to 100 g of rice with 20 weevils introduced. The experiment was laid out in a completely randomized design (CRD) with 32 samples. The finding shows natural plant powder was significant (P= 0.000) in controlling rice weevil. Meanwhile, neem leaves powder (T2) was more effective in controlling rice weevils during storage due to the lowest survival rate (Local: 78.75%, Thailand: 55%) and the highest mortality rate (Local: 21.25%, Thailand: 45%) of rice weevils for both types of rice. Besides, neem leaves powder also showed the lowest rice damage data (Local: 20.75%, Thailand: 20.35%) compared to other treatments. Study findings can help to meet current health, environment, and safety standards.

Rapid Testing System for Rice Quality Control through Comprehensive Feature and Kernel-Type Detection.

The assessment of food quality is of significant importance as it allows control over important features, such as ensuring adherence to food standards, longer shelf life, and consistency and quality of taste. Rice is the predominant dietary source of half the world’s population, and Pakistan contributes around 80% of the rice trade worldwide and is among the top three of the largest exporters. Hitherto, the rice industry has depended on antiquated methods of rice quality assessment through manual inspection, which is time consuming and prone to errors. In this study, an efficient desktop-application-based rice quality evaluation system, ‘National Grain Tech’, based on computer vision and machine learning, is presented. The analysis is based on seven main features, including grain length, width, weight, yellowness, broken, chalky, and/or damaged kernels for six different types of rice: IRRI-6, PK386, 1121 white and Selah, Super kernel basmati brown, and white rice. The system was tested in rice factories for 3 months and demonstrated 99% accuracy in determining the size, weight, color, and chalkiness of rice kernels. An accuracy of 98.8% was achieved for the classification of damaged and undamaged kernels, 98% for determining broken kernels, and 100% for paddy kernels. The results are significant because the developed system improves the local rice quality testing capacity through a faster, more accurate, and less expensive mechanism in comparison to previous research studies, which only evaluated four features of the singular rice type, rather than the seven features achieved in this study for six rice types.

Integrated Metabolomics and Transcriptomics Analyses Reveal the Metabolic Differences and Molecular Basis of Nutritional Quality in Landraces and Cultivated Rice.

Rice (Oryza sativa L.) is one of the most globally important crops, nutritionally and economically. Therefore, analyzing the genetic basis of its nutritional quality is a paramount prerequisite for cultivating new varieties with increased nutritional health. To systematically compare the nutritional quality differences between landraces and cultivated rice, and to mine key genes that determine the specific nutritional traits of landraces, a seed metabolome database of 985 nutritional metabolites covering amino acids, flavonoids, anthocyanins, and vitamins by a widely targeted metabolomic approach with 114 rice varieties (35 landraces and 79 cultivars) was established. To further reveal the molecular mechanism of the metabolic differences in landrace and cultivated rice seeds, four cultivars and six landrace seeds were selected for transcriptome and metabolome analysis during germination, respectively. The integrated analysis compared the metabolic profiles and transcriptomes of different types of rice, identifying 358 differentially accumulated metabolites (DAMs) and 1982 differentially expressed genes (DEGs), establishing a metabolite–gene correlation network. A PCA revealed anthocyanins, flavonoids, and lipids as the central differential nutritional metabolites between landraces and cultivated rice. The metabolite–gene correlation network was used to screen out 20 candidate genes postulated to be involved in the structural modification of anthocyanins. Five glycosyltransferases were verified to catalyze the glycosylation of anthocyanins by in vitro enzyme activity experiments. At the same time, the different mechanisms of the anthocyanin synthesis pathway and structural diversity in landrace and cultivated rice were systematically analyzed, providing new insights for the improvement and utilization of the nutritional quality of rice landrace varieties.

Deep CNN-based damage classification of milled rice grains using a high-magnification image dataset

• Damage classification of milled rice grains is demonstrated using fine-tuned deep CNN models. • High-magnification dataset of 8048 images consisting of seven types of rice grain damages is constructed. • Five state-of-the-art CNN models (EfficientNet-B0, ResNet-50, InceptionV3, MobileNetV2, MobileNetV3) used for damage classification. • EfficientNet-B0 is the best performing CNN model with an overall classification accuracy of 98.32 % • EfficientNet-B0 successfully classifies the chalky damaged rice further into three subclasses. Surface quality evaluation of pre-processed rice grains is a key factor in determining their market acceptance, storage stability, processing quality, and the overall customer approval. On one end the conventional methods of surface quality evaluation are time-intensive, subjective, and inconsistent. On the other end, the current methods are limited to either sorting of healthy rice grains from the damaged ones, without classifying the latter, or focusing on segregating the different types of rice. A detailed classification of damage in milled rice grains has been largely unexplored due to the lack of an extensive labelled image dataset and the application of advanced CNN models thereon; that enables quick, accurate, and precise classification by excelling at end-to-end tasks, minimizing pre-processing, and eliminating the need for manual feature extraction. In this study, a machine vision system is developed to first construct a dataset of 8048 high-magnification (4.5 x) images of damaged rice refractions, that are obtained through the on-field collection. The dataset spans across seven damage classes, namely, healthy, full chalky, chalky discolored, half chalky, broken, discolored, and normal damage. Subsequently, five different state-of-the-art memory efficient Deep-CNN models, namely, EfficientNet-B0, ResNet-50, InceptionV3, MobileNetV2, and MobileNetV3 are adopted and fine-tuned to enable damage classification of milled rice grains. Experimental results show that the EfficientNet-B0 is the best performing model in terms of the accuracy, average recall, precision, and F1-score. It achieves an individual class accuracy of 98.33%, 96.51%, 95.45%, 100%, 100%, 99.26%, and 98.72% for healthy, full chalky, chalky discolored, half chalky, broken, discolored, and normal damage class respectively. The EfficientNet-B0 architecture achieves an overall classification accuracy of 98.37 % with a significantly reduced model size (47 MB) and a small prediction time of 0.122 s and can sub-classify the chalky class further into 3 different classes i.e. , full chalky, half chalky, and chalky discolored. Overall, this study demonstrates the Deep CNN architectures applied to a high-magnification image dataset enables the classification of damaged rice grains with high accuracy, which could be utilized as a tool for better and more objective quality assessment of the damaged rice grains at market and trading locations.

Use of Protein Content, Amylose Content, and RVA Parameters to Evaluate the Taste Quality of Rice.

Taste quality of rice is the key to its value. However, it is greatly affected by rice types and the environment. It is a complex but necessary factor to accurately evaluate the taste quality of various types of rice in different environments. In this study, 7 different types of rice with different taste values were used as materials, and 12 nitrogen fertilizer treatments were applied to obtain 84 different rice taste values. We used protein content, amylose content, and RVA to evaluate changes in the taste value of rice. Rice with high taste value tended to have higher amylose content, peak viscosity, hold viscosity, final viscosity, and breakdown, as well as lower protein content, pasting temperature, and peak time. Protein and amylose contents affected the taste value of rice by affecting the RVA profiles except for setback. For high and low taste-value rice types, protein content could explain 66.8 and 42.9% of the variation in taste value, respectively. In the case of medium taste-value type, protein content was not enough to evaluate the taste quality of rice. Stickiness could explain 59.6% of the variation in taste value. When the protein content of rice was less than 6.61% or greater than 9.34%, it could be used to reflect the taste quality of rice. When the protein content was in between the two, protein content was not enough to reflect the taste quality of rice. Our results suggested that protein content could better reflect the taste quality change for rice, which provided a theoretical and technical basis for the accurate evaluation of the taste value of various types of rice.

Normal rice flours perform better in gluten-free bread than glutinous rice flours.

This study aims to determine gluten-free bread-making potential of different types of rice, particularly comparing normal rice versus glutinous rice flours. Proximate and chemical compositions, hydration, and dough mixing and pasting properties of ten rice cultivars (i.e., seven types of normal rice and three types of glutinous rice), and quality parameters (specific volume, texture profile, and crumb structure) of gluten-free bread from these flours were assessed. Significant differences were observed in flour properties among different types of rice. Significant correlations were observed between bread specific volume and rice amylose content (r=0.91, p<0.01), as well as pasting peak time (r=0.86, p<0.01) and final viscosity (r=0.77, p<0.01). Further, strong correlations were observed between bread resilience and properties of rice flour, such as amylose content (r=0.91, p<0.01), pasting peak viscosity (r=0.83, p<0.01), and final viscosity (r=0.93, p<0.01). In conclusion, the normal rice types exhibited much better gluten-free bread-making performances than glutinous flour. Important parameters of rice flour determining its gluten-free bread-making properties include amylose content, water retention capacity, and pasting properties. PRACTICAL APPLICATION: Compared with glutenous rice flour, normal rice flour leads to more viscous paste and gluten-free breads with larger volume, evener texture, and better resilience. This study provides guidance for practical uses of rice flours in improving gluten-free dough and bread quality.