Rapid and simultaneous analysis of multiple wine quality indicators through near-infrared spectroscopy with twice optimization for wavelength model

Chen, Jiemei; Liao, Sixia; Yao, Ling; Pan, Tao

doi:10.1007/s12200-020-1005-3

Cited by 7 publications

(2 citation statements)

References 31 publications

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“…Non-Invasive Digital Technologies such as near-infrared spectroscopy is a promising approach to assess wine quality traits. Specifically, this approach has been used to assess ripening parameters of berry such as total soluble solids, pH, and total anthocyanins [ 86 , 87 ] and to estimate wine quality traits such as alcohol content, sugar content, pH, volatile phenolic compounds [ 88 ], and sensory descriptors [ 89 ]. In addition, chemometric analysis combined with machine learning algorithms can be used to classify wine based on varietals and geographic origins [ 90 ].…”

Section: From Tradition To Innovation In the Food Supply-chainmentioning

confidence: 99%

Convergent technologies to tackle challenges of modern food authentication

Frigerio,

Campone,

Giustra

et al. 2024

Heliyon

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Section: From Tradition To Innovation In the Food Supply-chainmentioning

confidence: 99%

Convergent technologies to tackle challenges of modern food authentication

Frigerio,

Campone,

Giustra

et al. 2024

Heliyon

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“…This method usually does not require reagents, and it can measure samples directly, with the advantages of being quick and easy. Combined with the visible light region, visible-near infrared (Vis-NIR) spectroscopy has been applied in many fields, such as agriculture and food (1)(2)(3)(4)(5)(6), environment (7,8), and biomedicine (9)(10)(11)(12)(13)(14).…”

Section: Introductionmentioning

confidence: 99%

Visible and Near-Infrared Spectroscopy Combined With Bayes Classifier Based on Wavelength Model Optimization Applied to Wine Multibrand Identification

Pan

et al. 2022

Front. Nutr.

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The identification of high-quality wine brands can avoid adulteration and fraud and protect the rights and interests of producers and consumers. Since the main components of wine are roughly the same, the characteristic components that can distinguish wine brands are usually trace amounts and not unique. The conventional quantitative detection method for brand identification is complicated and difficult. The naive Bayes (NB) classifier is an algorithm based on probability distribution, which is simple and particularly suitable for multiclass discriminant analysis. However, the absorbance probability between spectral wavelengths is not necessarily strongly independent, which limits the application of Bayes method in spectral pattern recognition. This research proposed a Bayes classifier algorithm based on wavelength optimization. First, a large-scale wavelength screening for equidistant combination (EC) was performed, and then wavelength step-by-step phase-out (WSP) was carried out to reduce the correlation between wavelengths and improve the accuracy of Bayes discrimination. The proposed EC-WSP-Bayes method was applied to the 5-category discriminant analysis of wine brand identification based on visible and near-infrared (Vis-NIR) spectroscopy. Among them, four types of wine brands were collected from regular sales channels as identification brands. The fifth type of samples was composed of 21 other commercial brand wines and home-brewed wines from various sources, as the interference brand. The optimal EC-WSP-Bayes model was selected, the corresponding wavelength combination was 404, 600, 992, 2,070, 2,266, and 2,462 nm located in the visible light, shortwave NIR, and combination frequency regions. In modeling and independent validation, the total recognition accuracy rate (RARTotal) reached 98.1 and 97.6%, respectively. The technology is quick and easy, which is of great significance to regulate the alcohol market. The proposed model of less-wavelength and high-efficiency (N = 6) can provide a valuable reference for small special instruments. The proposed integrated chemometric method can reduce the correlation between wavelengths, improve the recognition accuracy, and improve the applicability of the Bayesian method.

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