Application of electronic nose in Chinese spirits quality control and flavour assessment

Liu, Ming; XiaoMin, Han; Tu, Kang; Pan, Leiqing; Tu, Jian; Tang, Lin; Liu, Peng; Ge, Zhan; Qin, Zhong; Zheng-he, Xiong

doi:10.1016/j.foodcont.2012.02.024

Cited by 78 publications

(38 citation statements)

References 18 publications

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“…Several other studies are referenced, e.g. in Liu et al (2012), Pizzoni et al (2015), Qiu et al (2015), and Xiao et al (2014). The results of some studies concerning the authenticity of food were summarized by Dymerski et al (2011), Peris and Escuder-Gilabert (2009), and Śliwińska et al (2014 as one of the aspects of e-nose applications in food analysis.…”

Section: Introductionmentioning

confidence: 99%

Electronic Nose as a Tool for Monitoring the Authenticity of Food. A Review

2016

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Counterfeiting of food is recently one of the risks relevant for producers, distributors, retailers, consumers, and national governments from economic (price), health (allergens), and religious reasons. Flavour of several food products is one of the key attributes of their quality and authenticity. In the case of some foods, the aroma of a product is specific enough to discriminate an original product from its fraud or adulterated counterpart. Electronic nose (e-nose) is a rapid and powerful technique, which requires no special sample preparation to determine the aroma of a product. In the present review, the applications of different e-noses and chemometrics for determination of food authenticity including adulteration and confirmation of origin are discussed. E-noses of various configurations are a very promising tool for testing the authenticity of food products.

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Section: Introductionmentioning

confidence: 99%

Electronic Nose as a Tool for Monitoring the Authenticity of Food. A Review

2016

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“…PCA is an unsupervised technique that can calculate the principal components having the largest variance in order to reduce the dimensionality in data sets and allow the visualization of clusters (Alasalvar et al 2012;Kumar et al 2012). CA is an unsupervised classification technology that can cluster similar measurements from all samples Liu et al 2012). PCA was performed with the electronic nose data involving in the five replicate measurements for each sensor of each sample as the X-matrix.…”

Section: Discussionmentioning

confidence: 99%

Quality Evaluation of Beef Seasonings Using Gas Chromatography-Mass Spectrometry and Electronic Nose: Correlation with Sensory Attributes and Classification According to Grade Level

Tian

Lan

et al. 2014

Food Anal. Methods

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To evaluate the quality of beef seasonings, the aroma profiles of high-grade, medium-grade, and lowergrade beef seasonings were characterized by sensory evaluation, gas chromatography-mass spectrometry (GC-MS) and an electronic nose. Sensory evaluation indicated significant differences in the three grades of beef seasonings whose highgrade samples got the highest score of the aroma properties of beef, fat, vegetable, soy sauce, and overall flavor and the lowest score of the aroma of gamey. A total of 57 volatile compounds that included aldehydes, acids, alkenes, nitrogenand sulfur-containing compounds, phenols, alcohols, and ketones were identified by solid-phase micro-extraction gas chromatography-mass spectrometry (SPME/GC-MS). The volatile compounds showed good correlation with seven sensory attributes according to the partial least square regression (PLSR) models. Electronic nose data were processed by principal component analysis (PCA) and cluster analysis (CA). According to the obtained results, electronic nose and SPME/ GC-MS combined with multivariate statistical analysis were effective identification tools for the quality discrimination of beef seasonings.

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“…To this end, wines [12][13][14][15][16][17][18][19][20][21], tequila, whiskey, vodkas [22,23] and Chinese spirit [24] were analyzed. Until now, the application of electronic nose was based on sensors, e.g., conductometric, amperometric and piezoelectric sensors [25].…”

Section: Introductionmentioning

confidence: 99%