Dietary Fibre Impacts the Texture of Cooked Whole Grain Rice

Development of Whole-Grain Rice Lines Exhibiting Low and Intermediate Glycemic Index with Decreased Amylose Content

The demand for rice varieties with lower amylose content (AC) is increasing in Southeast Asia, primarily due to their desirable texture and cooking qualities. This study presents the development of whole-grain rice lines with low to intermediate glycemic index (GI) and reduced AC. We selected six rice lines for in vivo GI assessment based on their starch properties. We successfully identified two lines with low AC that exhibited low and intermediate GI values, respectively. Our findings indicate that dietary fiber (DF) content may significantly influence rice GI. The selected whole-grain low-GI line showed a higher ratio of soluble dietary fiber (SDF) to insoluble dietary fiber (IDF) compared to control varieties, highlighting SDF's potential positive role in lowering whole-grain rice's GI. This study underscores the feasibility of developing rice varieties with desirable agronomic traits, nutritional traits, and culinary attributes, particularly for individuals managing their blood sugar levels. Additionally, we proposed the positive role of starch composition, especially DF content, in modulating the GI of rice. This study reinforces the importance of incorporating starch properties and DF content into rice breeding programs to produce more health-oriented and marketable rice varieties.

Path coefficient analysis unraveled nutrient factors directly impacted the textural characteristics of cooked whole-grain purple rice

Introduction

Whole-grain pigmented rice (WCP) provides many nutritional benefits compared to non-pigmented varieties. The textural quality of cooked whole-grain rice, particularly its hardness, is crucial for consumers' preferences.

Materials and Methods

We investigated the impact of multiple-grain nutrient components on textural attributes through Pearson Correlation and Path Coefficient Analyses (PCA).

Results and Discussion

From correlation analysis, we found that the dietary fibre index (DFI), soluble and insoluble fibre (SDF and IDF), and amylose/amylopectin contents (AC/AP) influenced hardness (HRD) significantly. Nonetheless, the binary correlation failed to detect the impact of protein on hardness; instead, it strongly affected adhesiveness (ADH). The PCA revealed protein, AC/AP, and DFI significantly impacted HRD and ADH. Furthermore, DFI antagonised protein and AC/AP to define HRD, while AC/AP and DFI opposed the direct effects of protein on ADH. DFI's effects on HRD were more appealing among low AC than high or waxy rice groups. Instead, the effect of protein was more appealing among waxy rice varieties. Based on PCA, rice breeders can now rely on three nutrient factors, protein, DFI, and AC/AP, to redesign whole-grain pigmented rice to achieve consumer acceptance and well-being.

Terahertz spectra reconstructed using convolutional denoising autoencoder for identification of rice grains infested with Sitophilus oryzae at different growth stages

Rice grains are often infected by Sitophilus oryzae due to improper storage, resulting in quality and quantity losses. The efficacy of terahertz time-domain spectroscopy (THz-TDS) technology in detecting Sitophilus oryzae at different stages of infestation in stored rice was employed in the current research. Terahertz (THz) spectra for rice grains infested by Sitophilus oryzae at different growth stages were acquired. Then, the convolutional denoising autoencoder (CDAE) was used to reconstruct THz spectra to reduce the noise-to-signal ratio. Finally, a random forest classification (RFC) model was developed to identify the infestation levels. Results showed that the RFC model based on the reconstructed second-order derivative spectrum with an accuracy of 84.78%, a specificity of 86.75%, a sensitivity of 86.36% and an F1-score of 85.87% performed better than the original first-order derivative THz spectrum with an accuracy of 89.13%, a specificity of 91.38%, a sensitivity of 88.18% and an F1-score of 89.16%. In addition, the convolutional layers inside the CDAE were visualized using feature maps to explain the improvement in results, illustrating that the CDAE can eliminate noise in the spectral data. Overall, THz spectra reconstructed with the CDAE provided a novel method for effective THz detection of infected grains.

A Rapid and Accurate Quantitative Analysis of Cellulose in the Rice Bran Layer Based on Near-Infrared Spectroscopy

Cultivating rice varieties with lower cellulose content in the bran layer has the potential to enhance both the nutritional value and texture of brown rice. This study aims to establish a rapid and accurate method to quantify cellulose content in the bran layer utilizing near-infrared spectroscopy (NIRS), thereby providing a technical foundation for the selection, screening, and breeding of rice germplasm cultivars characterized by a low cellulose content in the bran layer. To ensure the accuracy of the NIR spectroscopic analysis, the potassium dichromate oxidation (PDO) method was improved and then used as a reference method. Using 141 samples of rice bran layer (rice bran without germ), near-infrared diffuse reflectance (NIRdr) spectra, near-infrared diffuse transmittance (NIRdt) spectra, and fusion spectra of NIRdr and NIRdt were used to establish cellulose quantitative analysis models, followed by a comparative evaluation of these models' predictive performance. Results indicate that the optimized PDO method demonstrates superior precision compared to the original PDO method. Upon examining the established models, their predictive capabilities were ranked in the following order: the fusion model outperforms the NIRdt model, which in turn surpasses the NIRdr model. Of all the fusion models developed, the model exhibiting the highest predictive accuracy utilized fusion spectra (NIRdr-NIRdt (1st der)) derived from preprocessed (first derivative) diffuse reflectance and transmittance spectra. This model achieved an external predictive R2p of 0.903 and an RMSEP of 0.213%. Using this specific model, the rice mutant O2 was successfully identified, which displayed a cellulose content in the bran layer of 3.28%, representing a 0.86% decrease compared to the wild type (W7). The utilization of NIRS enables quantitative analysis of the cellulose content within the rice bran layer, thereby providing essential technical support for the selection of rice varieties characterized by lower cellulose content in the bran layer.