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논문 기본 정보

자료유형
학술저널
저자정보
Rahul Joshi (Chungnam National University) Ritu Joshi (Chungnam National University) Hanim Zuhrotul Amanah (Chungnam National University) Mohammad Akbar Faqeerzada (Chungnam National University) Praveen Kumar Jayapal (Chungnam National University) Geonwoo Kim (United States Department of Agriculture) Insuck Baek (United States Department of Agriculture) Eun-Sung Park (Chungnam National University) Rudiati Evi Masithoh (Gadjah Mada University) Byoung-Kwan Cho (Chungnam National University)
저널정보
충남대학교 농업과학연구소 Korean Journal of Agricultural Science Korean Journal of Agricultural Science Vol.48 No.2
발행연도
2021.6
수록면
299 - 310 (12page)

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Glycerol is a non-volatile compound with no aromatic properties that contributes significantly to the quality of wine by providing sweetness and richness of taste. In addition, it is also the third most significant byproduct of alcoholic fermentation in terms of quantity after ethanol and carbon dioxide. In this study, Fourier transform infrared (FT-IR) spectroscopy was employed as a fast non-destructive method in conjugation with multivariate regression analysis to build a model for the quantitative analysis of glycerol concentration in wine samples. The samples were prepared by using three varieties of red wine samples (i.e., Shiraz, Merlot, and Barbaresco) that were adulterated with glycerol in concentration ranges from 0.1 to 15% (v·v<SUP>-1</SUP>), and subjected to analysis together with pure wine samples. A net analyte signal (NAS)-based methodology, called hybrid linear analysis in the literature (HLA/GO), was applied for predicting glycerol concentrations in the collected FT-IR spectral data. Calibration and validation sets were designed to evaluate the performance of the multivariate method. The obtained results exhibited a high coefficient of determination (R²) of 0.987 and a low root mean square error (RMSE) of 0.563% for the calibration set, and a R2 of 0.984 and a RMSE of 0.626% for the validation set. Further, the model was validated in terms of sensitivity, selectivity, and limits of detection and quantification, and the results confirmed that this model can be used in most applications, as well as for quality assurance.

목차

Abstract
Introduction
Materials and Methods
Results and Discussion
Conclusions
References

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