Physical,Chemical, and Sensory Properties of Antioxidant‐Enriched Raw and Cooked Rice by Vacuum‐Drying Impregnation in a Semidry State

Vacuum drying was employed with a vacuum impregnation technique in a semidry state to enrich rice with antioxidants of beetroot juice. The properties of the vacuum‐dried raw and cooked rice grains were characterized. The various raw rice grains (three varieties and two storage time periods) exhibited a significant absorption of beetroot juice, which was evident from the red‐violet beetroot color of the rice, as distinguished from the white color of the control. The color increase (ΔE= 20−40) was linear with the juice content (R² = 0.96−0.99). Their total phenolic (TP) contents and 2,2‐diphenyl‐2‐picrylhydrazyl radical (DPPH) scavenging activities were enhanced (ΔTP = 21−260 mg of gallic acid equivalents/100 g of rice db and ΔDPPH = 22−64 mg of vitamin C equivalents/100 g of rice db). Their grain integrity was reduced (Δforce = −1 to −63), which was potentially associated with the formation of grain surface cracks (linear relationship of %crack and %juice with R² = 0.94−0.98). After cooking, the enriched rice grains were linearly elongated with added juice (R² = 0.88−0.97, up to 1.6‐, 2.0‐, and 2.0‐fold for Sanpatong 1, Khao Dawk Mali 105, and Chainart 1 rice samples, respectively), and the overall volume of the cooked rice was increased (likely not linear, up to 3.2‐, 4.3‐, and 4.8‐fold for Sanpatong 1, Khao Dawk Mali 105, and Chainart 1 rice samples, respectively). Such improvements in cooking qualities were obtained by this simple vacuum‐drying technique, in comparison to existing rice‐aging processes that are more time consuming. The sensorial scores of the resultant rice products were excellent. Vacuum drying is an effective tool to improve the antioxidant value of rice as well as its cooking quality, and the raw quality remains appreciable. It is a simple and rapid process that could be practical for manufacturing healthy rice products.     

Starch Pasting and Textural Attributes of Elbow Macaroni as Impacted by Dough Moisture,Dough Mixing Time,and Macaroni Cooking Time

The effects of dough moisture, mixing time, and cooking time on uncooked and cooked elbow macaroni by means of starch pasting and macaroni textural characteristics were investigated. In conventional elbow macaroni production, cooking time was found to have significant contributions to cooked macaroni starch pasting properties, indicating that degree of starch cook dependent on cooking time was the main influence on cooked macaroni starch pasting phenomena. Dough moisture also showed some significant (P < 0.05) relationships with cooked macaroni starch pasting properties; however, mixing time did not show significant effect. Cooked macaroni starch pasting properties showed significantly (P < 0.05) high correlations with cooked macaroni firmness and stickiness. Cooking time was the only major variable contributing to variations in cooked elbow macaroni starch and consequently in pasting and texture characteristics. Cooking time was highly related to firmness and stickiness of cooked elbow macaroni (P < 0.0001, R² = 0.8148; P < 0.0001, R² = 0.6215, respectively). In addition, dough moisture had a slight significant (P < 0.05) effect on cooked elbow macaroni firmness and stickiness. Cooked elbow macaroni firmness and stickiness were found to be highly correlated (P = 0.0001, R² = 0.8459). Increases in firmness increased cooked elbow macaroni stickiness. As a result, when elbow macaroni was cooked for shorter times, firmer and stickier macaroni was obtained.     

Protein and Apparent Amylose Contents of Milled Rice by NIR‐FT/Raman Spectroscopy

The chemometric calibration of near‐infrared Fourier‐transform Raman (NIR‐FT/Raman) spectroscopy was investigated for the purpose of providing a rigorous spectroscopic technique to analyze rice flour for protein and apparent amylose content. Ninety rice samples from a 1996 collection of short, medium, and long grain rice grown in four states of the United States, as well as Taiwan, Korea, and Australia were investigated. Milled rice flour samples were scanned in rotating cups with a 1,064 nm (NIR) excitation laser using 500 mW of power. Raman scatter was collected using a liquid N₂ cooled Ge detector over the Raman shift range of 175–3,600 cm^‐1. The spectral data was preprocessed using baseline correction with and without derivatives or with derivatives alone and normalization. Nearly equivalent results were obtained using all of the preprocessing methods with partial least squares (PLS) models. However, models using baseline correction and normalization of the entire spectrum, without derivatives, showed slightly better performance based on the criteria of highest r² and the lowest SEP with low bias. Calibration samples (n = 57) and validation samples (n = 33) were chosen to have similar respective distributions for protein and apparent amylose. The best model for protein was obtained using six factors giving r² = 0.992, SEP = 0.138%, and bias = ‐0.009%. The best model for apparent amylose was obtained using eight factors giving r² = 0.985, SEP = 1.05%, and bias = ‐0.006%.     

Rapid Variety Identification of Pure Rough Rice by Fourier‐Transform Near‐Infrared Spectroscopy

Rice variety is considered as an important factor influencing cooking and processing quality because of variations in size, shape, and constitution. Difficulty in management of rough rice with lower varietal purity becomes a significant problem in rice production and can result in the reduction of rice quality. Fourier‐transform near‐infrared (FT‐NIR) spectroscopy was used to identify the variety of rough rice through whole‐grain techniques. Moist rough rice samples (n = 259) comprising five varieties (Khao Dawk Mali 105 [KDML105], Pathum Thani 1, Suphan Buri 60, Chainat 1, and Pitsanulok 2) were gathered from different locations around Thailand and scanned in the NIR region of 9088–4000 cm^–1 in reflectance mode. Soft independent modeling of class analogies (SIMCA) and partial least squares discriminant analysis (PLSDA) methods were used for identification by utilizing preprocessed spectra. The highest identification accuracy achieved was 74.42% by the SIMCA model and 99.22% by the PLSDA model. The best PLSDA model demonstrated approximately 97% correct identification for KDML105 samples and 100% for the others. This study raises the possibility of applying FT‐NIR spectroscopy as a nondestructive technique for rapidly identifying moist rough rice varieties in routine quality assurance testing.     

Storage Stability of Flour‐Blasted Brown Rice

Brown rice was blasted with rice flour rather than sand in a sand blaster to make microperforations so that water could easily penetrate the brown rice endosperm and cook the rice in a shorter time. The flour‐blasted American Basmati brown rice, long‐grain brown rice, and parboiled long‐grain brown rice samples were stored in Ziploc storage bags under atmospheric conditions and in vacuum‐packed bags. They were periodically tested for over 10 months for changes in water absorption, free fatty acid (FFA), peroxide value (POV), viscosity changes of flour using the Rapid ViscoAnalyser (RVA), and texture of whole cooked kernel using a texture analyzer during cooking. Flour‐blasted brown rice absorbed less water but needed less cooking time than its counterpart that was not flour‐blasted. There was an increase in FFA, POV, peak viscosity (PV), final viscosity (FV), breakdown viscosity (BD), and setback viscosity (SB) during storage of flour‐blasted brown rice for 300 days, but no change was observed in texture (hardness, gumminess) and water absorption. The combined coefficient of correlation (including all types of rice) between FFA and FV is r = 0.86 and between FFA and SB is r = 0.90 at P < 0.0001.     

Quality Prediction of Rice Flour by Multiple Regression Model with Instrumental Texture Parameters of Single Cooked Milled Rice Grains

The purpose of this study was to develop highly accurate regression models with texture parameters of cooked milled rice grains for predicting pasting properties in terms of quality index of rice flour. Two methods were adopted as the texture measurement to acquire predictors for the models. In the calibration set, all the multiple regression models by a single‐grain method exhibited a higher R² than those by a three‐grain method. Each of the former models also showed a lower SEP and a higher RPD in the validation set. The prediction performance was best for consistency (RPD = 2.4). The single‐grain method was more advantageous for the pasting prediction. These results suggest that the models based on grain texture could predict rice flour quality.     

Predicted Glycemic Index and Glycemic Index of Rice Varieties Grown in Taiwan

Two cooked brown rice and six white rice varieties were selected for assessing the variations in predicted glycemic index (pGI) determined by using in vitro starch digestion and the glycemic index (GI) determined in vivo. Marked varietal differences in apparent amylose content, dietary fiber content, pGI, and GI were observed. Most of the tested rice samples were classified as medium‐GI foods. The varieties Khazar and Taikeng 9 were categorized as high‐GI foods when bread was used as the reference. But brown and white rice samples of TRGC9152 and Taichung Sen 17 fell into the low‐GI category when glucose was used as the reference. A significant correlation coefficient (r = 0.946) was found between pGI and GI of rice samples by using bread as the reference with a regression equation of GI = 28.778 + 0.717 × pGI (R² = 0.8951, P ≤ 0.001). Overall, the in vitro pGI measurement is a rapid and useful method to predict the GI of cooked rice samples.     

Rapid Prediction of Rice Quality Characteristics by Near‐Infrared Reflectance Spectroscopy for Breeding Programs

Breeding of high‐quality rice requires quick methods to evaluate the quality characteristics such as milling, grain appearance, nutritional, eating, and cooking qualities. Because routine measurements of these quality traits are time consuming and expensive, a rapid predictive method based on near‐infrared spectroscopy (NIRS) can be applied to measure these quality parameters. In this study, calibration models for measurement of grain quality were developed using a total of 570 brown and milled rice samples. The results indicated that the models developed from the spectra of brown rice for all the quality traits had the coefficient of determination for external validation (R²) larger than 0.64 except for gel consistency. The best model was developed for the protein content, with R² of 0.94 for external validation. The model for the total score of physicochemical characteristics (TSPC), a comprehensive index reflecting all other traits, had R² of 0.70 and SD/SEP of 1.70, which indicates that high or low TSPC for a given rice could be discriminated by NIRS. The models developed from brown rice were as accurate as those from milled rice. Results suggest that NIRS‐based predictions for rice quality traits may be used as indicator traits to improve rice quality in breeding programs.