Xianqiao Hu

Quantification of multiple volatile compounds in cooked rice was performed using HS-SPME-GC-MS. Standard addition was used to eliminate the matrix effect, and a reference compound was introduced to improve the linearity of the calibration curve.

Fragrant rice has various flavor types, mainly the popcorn flavor, corn flavor and lotus root flavor. Chinese fragrant rice from China and Thai fragrant rice from Thailand were analyzed. GC-MS was used to determine the volatile compounds of fragrant rice. It was found that there were 28 identical volatile compounds between Chinese and Thai fragrant rice. The key compounds of different flavor types of fragrant rice were obtained by comparing the common volatile compounds. The key compounds of the popcorn flavor were 2-butyl-2-octenal, 4-methylbenzaldehyde, ethyl 4-(ethyloxy)-2-oxobut-3-enoate and methoxy-phenyl-oxime. The key compounds of the corn flavor were 2,2′,5,5′-tetramethyl-1,1′-biphenyl, 1-hexadecanol, 5-ethylcyclopent-1-enecarboxaldehyde and cis-muurola-4(14), 5-diene. By using a combination of GC-MS and GC-O, the flavor spectrogram of fragrant rice was constructed, and the characteristic flavor compounds of each flavor type were identified. It was found that the characteristic flavor compounds of the popcorn flavor were 2-butyl-2-octenal, 2-pentadecanone, 2-acetyl-1-pyrroline, 4-methylbenzaldehyde, 6,10,14-trimethyl-2-pentadecanone, phenol and methoxy-phenyl-oxime. The characteristic flavor compounds of the corn flavor were 1-octen-3-ol, 2-acetyl-1-pyrroline, 3-methylbutyl 2-ethylhexanoate, methylcarbamate, phenol, nonanal and cis-muurola-4(14), 5-diene. The characteristic flavor compounds of the lotus root flavor were 2-acetyl-1-pyrroline, 10-undecenal, 1-nonanol, 1-undecanol, phytol and 6,10,14-trimethyl-2-pentadecanone. The resistant starch content of lotus root flavor rice was relatively high (0.8%). The correlation between flavor volatiles and functional components was analyzed. It was found that the fat acidity of fragrant rice was highly correlated (R = 0.86) with the characteristic flavor compounds, such as 1-octen-3-ol, 2-butyl-2-octenal and 3-methylbutyl-2-ethylhexanoate. The characteristic flavor compounds had an interactive contribution to the production of the different flavor types of fragrant rice.

Cooking is an important process before rice is consumed and constitutes the key process for rice flavor formation. In this paper, dynamic changes in aroma- and sweetness-related compounds were tracked during the entire cooking process (including washing with water, presoaking, and hydrothermal cooking). The volatiles, fatty acids, and soluble sugars in raw rice, washed rice, presoaked rice, and cooked rice were compared. After being washed with water, the total volatiles decreased while aldehydes and unsaturated fatty acids increased. Meanwhile, oligosaccharides decreased and monosaccharides increased. The changes in fatty acids and soluble sugars caused by the presoaking process were similar to those in the water-washing process. However, different changes were observed for volatiles, especially aldehydes and ketone. After hydrothermal cooking, furans, aldehydes, alcohols, and esters increased while hydrocarbons and aromatics decreased. Moreover, all fatty acids increased; among these, oleic acids and linoleic acid increased most. Unlike with washing and presoaking, all soluble sugars except fructose increased after hydrothermal cooking. Principal component analysis showed that cooked rice possessed a volatile profile that was quite different from that of uncooked rice, while washed rice and presoaked rice possessed similar volatile profiles. These results indicated that hydrothermal cooking is the pivotal process for rice flavor formation.

Apparent amylose content (AAC) is one of the most important parameters in rice quality evaluation. In this study, four rice reference materials used to test rice AAC were developed. The AAC of rice reference materials were measured by a spectrophotometric method with a defatting procedure, calibrated from potato amylose and waxy rice amylopectin at the absorption wavelengths of 620 and 720 nm. Homogeneity test (n = 20) was judged by F-test based on the mean squares of among and within bottles, and short- and long-term stability monitoring was performed by T-test to check if there was significant degradation at the delivery temperature of under 40 °C (14 days) and at 0–4 °C storage condition (18 months), respectively. After joint evaluation by ten laboratories, Dixion and Cochran statistical analyses were presented. The expanded uncertainties were calculated based on the uncertainty of homogeneity, short- and long-term stability, and inter-laboratory validation containing factor k = 2. It found that the four reference materials were homogenous and stable, and had the AAC (g/100 g, k = 2) of 2.96 ± 1.01, 10.68 ± 0.66, 17.18 ± 1.04, and 16.09 ± 1.29, respectively, at 620 nm, and 1.46 ± 0.49, 10.44 ± 0.56, 16.82 ± 0.75, and 24.33 ± 0.52, respectively, at 720 nm. It was indicated that 720 nm was more suitable for the determination of rice AAC with lower uncertainties. The determinations of the AAC of 11 rice varieties were carried out by two methods, the method without defatting and with calibration from the four rice reference materials and the method with a defatting procedure and calibrating from potato amylose and waxy rice amylopectin. It confirmed that the undefatted rice reference materials could achieve satisfactory results to test the rice samples with the AAC ranging from 1 to 25 g/100 g. It would greatly reduce the time cost and improve testing efficiency and applicability, and provide technical support for the high-quality development of the rice industry.

Background and Objectives: With rising living standards, people are more willing to pay for rice with environmental attributes (green‐labeled rice) than rice without environmental attributes (conventional rice). The aim of this study was to assess whether green‐labeled rice possesses higher quality than conventional rice. Aimed at green‐labeled rice in the market, the appearance quality, cooking quality, and protein content were systematically analyzed and compared with conventional rice.Findings: The superior quality rate (SQR) of green‐labeled rice was 92.0%, which was much higher than that of conventional rice (67.8%). Green‐labeled rice showed a lower percentage of chalky rice, chalkiness, and a higher gel consistency, alkali spreading value, and translucency. SQRs of chalkiness, transparency grade, gel consistency, alkali spreading value, and amylose content for green‐labeled rice were all higher than those of conventional rice. Rice quality was evaluated by dividing all rice into indica rice and japonica rice, resulting in a similar trend, wherein higher quality in terms of appearance and cooking quality were observed in green indica rice and green japonica rice than conventional ones. No significant difference was observed in the protein content between green‐labeled rice and conventional rice.Conclusions: Green‐labeled rice showed better quality in terms of appearance and cooking quality than conventional rice, and had similar protein content to conventional rice. It possessed higher SQRs of each quality parameter and significantly lower chalkiness and amylose content, and significantly higher translucency, gel consistency, and alkali spreading value than conventional rice.Significance and Novelty: Limited research has been reported on the overall quality of market green‐labeled rice and comparison of quality between green‐labeled rice and conventional rice. This paper systematically analyzed the overall quality of green‐labeled rice. It was important for consumers to understand the overall quality of green‐labeled rice in the market, and of guidance for rice purchase and green rice production.

Lack of effective data processing methods has limited the application of electronic tongues in rice sensory analysis. In this paper, a novel similarity analysis-artificial neural network (SA-ANN) method was developed for an electronic tongue system to determine rice sensory quality. Characteristic data were extracted from signals and arranged in data matrix. The obtained matrix data were analyzed using the similarity analysis method by comparing the data to those of a control sample, resulting in a similarity degree that was used as the input variable for the artificial neural network. The SA-ANN method was tested and compared to traditional sensory evaluation. The correlation coefficients (R) for odor, appearance, palatability, texture, and overall scores were 0.9669, 0.9711, 0.9760, 0.8654 and 0.9848, respectively. Results indicated that the developed SA-ANN technique is an efficient data processing method for use in an electronic tongue system to characterize and predict rice sensory quality.