Characterization of Chinese rice wine taste attributes using liquid chromatographic analysis, sensory evaluation, and an electronic tongue

Yu, Haiyan; Zhao, Jie; Li, Fenghua; Tian, Huaixiang; Ma, Xinbin

doi:10.1016/j.jchromb.2015.05.037

Cited by 51 publications

(30 citation statements)

References 40 publications

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“…Traditionally, CRW is made from glutinous rice, wheat Qu and yeast using a simultaneous saccharification and fermentation process. Glutinous rice has relatively higher amylopectin content . Wheat Qu, made from wheat, is used as the source of both microbes and enzymes in a semi‐solid or solid state .…”

Section: Introductionmentioning

confidence: 99%

Characterization of the volatile profile of Chinese rice wine by comprehensive two‐dimensional gas chromatography coupled to quadrupole mass spectrometry

Xie

Qian

et al. 2019

J Sci Food Agric

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BACKGROUND: Chinese rice wine (CRW) is a kind of traditional fermentation wine in China. Aged CRW is more popular among consumers owing to its harmonious and pleasant flavor. The volatile profile of CRW has been extensively studied using gas chromatography/mass spectrometry (GC/MS). However, flavor components in CRW are far richer than those detected by GC/MS. To obtain more information about the volatile profile of fresh (5-year) and aged (10-year) CRW, a method based on comprehensive two-dimensional gas chromatography coupled to quadrupole mass spectrometry (GC×GC/qMS) was developed. The major volatile compounds contributing to the characteristic aroma of fresh and aged CRW were identified by surrogate odor activity value (OAV). RESULTS: Ninety-eight volatile compounds were detected in the 5-year CRW samples and 107 in the 10-year samples by GC×GC/qMS. The numbers of compounds detected by GC×GC/qMS for the 5-year and 10-year samples were 71.4 and 65.4% higher than those detected by GC/MS. The aged wine had a more complex volatile profile than the fresh wine, with an increase in esters and aldehydes and a decrease in alcohols and organic acids. There were 22 volatile compounds with surrogate OAV > 1. Nine were the potent key aroma compounds in CRW: ethyl isovalerate (OAV 500-33 500), ethyl butyrate (OAV 84-334), ethyl isobutyrate (OAV 49-170), 2-nonenal (OAV 20-100), ethyl heptanoate (OAV 1-74), ethyl hexanoate (OAV 60-77), phenylethyl alcohol (OAV 2-18), benzaldehyde (OAV 28-30) and hexanal (OAV 4-11).CONCLUSION: GC×GC/qMS showed better separation than GC/MS. The presented GC×GC/qMS method was suitable for characterization of the volatile profile of CRW.

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Section: Introductionmentioning

confidence: 99%

Characterization of the volatile profile of Chinese rice wine by comprehensive two‐dimensional gas chromatography coupled to quadrupole mass spectrometry

Xie

Qian

et al. 2019

J Sci Food Agric

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“…Wine quality is affected by many physicochemical indicators, such as phenolic compounds (Weingerl, Strlič, & Kočar, ), amines (Manetta et al, ), anthocyanin (Avizcuri, Saenz‐Navajas, Echavarri, Ferreira, & Fernandez‐Zurbano, ), bitterness (Ferrer‐Gallego, Hernández‐Hierro, Rivas‐Gonzalo, & Escribano‐Bailón, ), lees (Dimou et al, ), and food additive content (Amidzic Klaric, Klaric, Mornar, & Nigovic, 2015). These physicochemical wine indicators can be tested with the help of machinery such as an electronic tongue (Yu et al, ), an electronic nose (Prieto et al, ), and a spectroanalysis instrument (Castro Grijalba, Fiorentini, Martinez, & Wuilloud, 2016; Wu et al, ). Physicochemical tests cannot directly reflect wine quality or sensorium, nor can it fully explain the interaction between various physicochemical indicators.…”

Section: Resultsmentioning

confidence: 99%

“…Frequently used evaluation methods include cluster analysis (Kuo, Su, Zulvia, & Lin, ; Płotka‐Wasylka, Simeonov, & Namieśnik, ; Srivastava, Mishra, & Mishra, ), principal component analysis (Carvalho, Pereira, Pereira, Pinto, & Marques, ; Giovenzana, Civelli, Beghi, Oberti, & Guidetti, ; Lv et al, ), neural network (Huang, Liu, & Ren, ; Jeswal & Chakraverty, ; King & Shellie, ), and sensory evaluation (Diako, Vixie, Weller, Dycus, & Ross, ; Tomadoni, Moreira, Pereda, & Ponce, ; Yu, Zhao, Li, Tian, & Ma, ). Płotka‐Wasylka et al () characterized the fruits used for wine‐making using cluster analysis, which is a traditional technique.…”

Section: Introductionmentioning

confidence: 99%

Quality evaluation of Chinese red wine based on cloud model

2019

J Food Biochem

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Determining the quality of red wine is based on many qualitative and quantitative factors. Compared with other evaluation methods, the cloud model has an uncertainty transformation between a qualitative concept and its corresponding quantitative value, and the uncertainty transformation included fuzziness and randomness, which is suitable for solving the complexity of red wine evaluation. This study introduced the cloud model into quality evaluation of red wine for the first time, and a novel algorithm of comprehensive cloud model was proposed based on an addition algorithm of two cloud models. Furthermore, to validate the cloud model adopted in our red wine evaluation system, we used the gray relational analysis and fuzzy evaluation method. The evaluation result for the red wine sample was Good, and the result confirmed that our cloud model can be used to evaluate the quality of red wine. Practical applications In 2013, China surpassed France to become the largest country of red wine consumption in the world. Red wine is made by a natural fermentation process. There are several components that make up red wine, but the most abundant is grape juice. Ethyl alcohol is the second most abundant element and it is made naturally by the fermentation of the sugar in grape. There are more than 1,000 remaining components in the recipe for red wine, where 300 are comparatively important. Although the proportion of these components is not high, they are important factors in determining the quality of red wine. Sensory evaluation is the most common method used to determine the quality of red wine. This work has identified a cloud model that can be used, based on sensory evaluation, to determine the quality of red wine.

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“…E‐nose has realized the evaluation of the typical volatile compounds content in food through multiple sensors, while E‐tongue uses artificial lipid membrane technology to evaluate the sensory indexes of food digitally, which has the advantages of high accuracy and repeatability (Di Rosa, Leone, Cheli, & Chiofalo, 2017; Loutfi, Coradeschi, Mani, Shankar, & Rayappan, 2015). The combination of these two technologies is also widely used in the quality evaluation of wine (Rodriguez‐Mendez et al., 2014), beer (Nery & Kubota, 2016), and yellow rice wine (Yu, Zhao, Li, Tian, & Ma, 2015).…”

Section: Introductionmentioning

confidence: 99%

Dynamic changes in quality of jujube wine during fermentation

Tang

Cai

Shan

et al. 2020

J Food Process Preserv

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Dynamic quality changes of jujube wine during 10 days (240 hr) fermentation were evaluated by electronic nose and electronic tongue combined with multivariate statistical analysis methods. Fermentation significantly reduced the relative content of aromatic compounds, but promoted the generation of alcohol, sour compounds, umami compounds, sulfur‐containing organics, and broad range sensitivity compounds. The results of principal component analysis and cluster analysis indicate that, according to the overall quality similarity, the whole fermentation process of jujube wine can be categorized into three stages: early stage (0–24 hr), middle stage (36–132 hr), and late stage (144–240 hr). By Multivariate Analysis of Variance, significant differences (p < .001) were observed among jujube wine samples in the three fermentation stages. Further analysis using Redundancy Analysis found that the major changes of jujube wine quality occurred in the middle stage of fermentation, particularly 36–132 hr. Practical applications In the case of the wine production, employing of the appropriate fermentation time throughout the process can be problematic. Hence, in this paper, electronic nose and electronic tongue technology combined with multivariate statistical analysis methods were applied to reveal the dynamic changes in quality of jujube wine, which provided a promising instruction for quality control of jujube wine.

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Characterization of Chinese rice wine taste attributes using liquid chromatographic analysis, sensory evaluation, and an electronic tongue

Cited by 51 publications

References 40 publications

Characterization of the volatile profile of Chinese rice wine by comprehensive two‐dimensional gas chromatography coupled to quadrupole mass spectrometry

Characterization of the volatile profile of Chinese rice wine by comprehensive two‐dimensional gas chromatography coupled to quadrupole mass spectrometry

Quality evaluation of Chinese red wine based on cloud model

Dynamic changes in quality of jujube wine during fermentation

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