Literature DB >> 24792198

Discrimination between authentic and adulterated liquors by near-infrared spectroscopy and ensemble classification.

Hui Chen1, Chao Tan2, Tong Wu1, Li Wang1, Wanping Zhu1.   

Abstract

Chinese liquor is one of the famous distilled spirits and counterfeit liquor is becoming a serious problem in the market. Especially, age liquor is facing the crisis of confidence because it is difficult for consumer to identify the marked age, which prompts unscrupulous traders to pose off low-grade liquors as high-grade liquors. An ideal method for authenticity confirmation of liquors should be non-invasive, non-destructive and timely. The combination of near-infrared spectroscopy with chemometrics proves to be a good way to reach these premises. A new strategy is proposed for classification and verification of the adulteration of liquors by using NIR spectroscopy and chemometric classification, i.e., ensemble support vector machines (SVM). Three measures, i.e., accuracy, sensitivity and specificity were used for performance evaluation. The results confirmed that the strategy can serve as a screening tool applied to verify adulteration of the liquor, that is, a prior step used to condition the sample to a deeper analysis only when a positive result for adulteration is obtained by the proposed methodology.
Copyright © 2014 Elsevier B.V. All rights reserved.

Keywords:  Authenticity; Liquor; Near-infrared; Support vector machines

Mesh:

Year:  2014        PMID: 24792198     DOI: 10.1016/j.saa.2014.03.091

Source DB:  PubMed          Journal:  Spectrochim Acta A Mol Biomol Spectrosc        ISSN: 1386-1425            Impact factor:   4.098


  2 in total

1.  Discrimination of Chinese Baijiu grades based on colorimetric sensor arrays.

Authors:  Hao Lin; Wen-Cui Kang; Hong-Juan Jin; Zhong-Xiu Man; Quan-Sheng Chen
Journal:  Food Sci Biotechnol       Date:  2020-04-19       Impact factor: 2.391

2.  PLS-R Calibration Models for Wine Spirit Volatile Phenols Prediction by Near-Infrared Spectroscopy.

Authors:  Ofélia Anjos; Ilda Caldeira; Tiago A Fernandes; Soraia Inês Pedro; Cláudia Vitória; Sheila Oliveira-Alves; Sofia Catarino; Sara Canas
Journal:  Sensors (Basel)       Date:  2021-12-31       Impact factor: 3.576

  2 in total

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