Rapid Measurement of Phenolics Compounds in Red Wine Using Raman Spectroscopy

Gallego, Álvaro L; Guesalaga, Andrés; Bordeu, Edmundo; González, A. Sánchez

doi:10.1109/tim.2010.2051611

Cited by 34 publications

(11 citation statements)

References 15 publications

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“…The RPD values (> 2.5) indicated that the calibration models were adequate to measure phenolic compounds as low, medium, and high, qualitatively. Results from this study agree with those reported in Cabernet Sauvignon by other authors using Raman spectroscopy [23,24]. SECV: standard error of cross-validation, SD: standard deviation, RPD: residual predictive deviation (SD/SECV) value, R 2 : coefficient of determination in cross-validation.…”

Section: Resultssupporting

confidence: 91%

“…It has been reported that the strong fluorescence background associated with the absorption of hydroxycinnamic acids (phenolics) and related phenolic compounds determine a Raman shift depending on the excitation [18][19][20][21][22], where, the observed emission in the wines is attributed to the excited state proton transfer [23,24].…”

Section: Resultsmentioning

confidence: 99%

“…Overall, the performance of the calibration models developed clearly showed that phenolic compounds could be measured/predicted in wines using the combination of Raman spectroscopy and chemometrics. However, some other authors reported that Raman spectroscopy might be considered as a less sensitive technique comparing with other vibrational spectroscopic methods, although it might offers the advantage of being less sensitive to the presence of water in samples such as wine [18][19][20][21][22][23][24]. Overall, Raman spectroscopy might be considered as a good alternative to avoid the interference of these major components in the wine matrix to measure or predict different minor compounds in wine (e.g., phenolic compounds) that are of importance in determining provenance and the quality.…”

Section: Resultsmentioning

confidence: 99%

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Using Raman Spectroscopy as a Fast Tool to Classify and Analyze Bulgarian Wines—A Feasibility Study

et al. 2019

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Raman spectroscopy, being able to provide rich information about the chemical composition of the sample, is gaining an increasing interest in the applications of food. Raman spectroscopy was used to analyze a set of wine samples (red and white) sourced from rarely studied traditional Bulgarian wines. One of the objectives of this study was to attempt the fast classification of Bulgarian wines according to variety and geographic origin. In addition, calibration models between phenolic compounds and Raman spectroscopy were developed using partial least squares (PLS) regression using cross-validation. Good calibration statistics were obtained for total phenolic compounds (by the Folin–Ciocalteu method) and total phenolic compounds and phenolic acids (spectrophotometrically at 280 nm) where the coefficient of determination (R2) and the standard error in the cross-validation (SECV) were 0.81 (474.2 mg/dm3 gallic acid), 0.87 (526.6 mg/dm3 catechin equivalents), and 0.81 (44.8 mg/dm3 caffeic equivalents), respectively. This study has demonstrated that Raman spectroscopy can be suitable for measuring phenolic compounds in both red and white wines.

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Section: Resultssupporting

confidence: 91%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Using Raman Spectroscopy as a Fast Tool to Classify and Analyze Bulgarian Wines—A Feasibility Study

et al. 2019

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“…Vibrational spectroscopic techniques such as FTIR and Raman can be used to analyze food products [31,32]. Raman spectroscopy is more suitable for the analysis of aqueous systems because water has none or minimal interference with Raman scattering [33].…”

Section: Resultsmentioning

confidence: 99%

Discrimination of Apple Liqueurs (Nalewka) Using a Voltammetric Electronic Tongue, UV-Vis and Raman Spectroscopy

Śliwińska

García-Hernández

Kościński

et al. 2016

Sensors

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The capability of a phthalocyanine-based voltammetric electronic tongue to analyze strong alcoholic beverages has been evaluated and compared with the performance of spectroscopic techniques coupled to chemometrics. Nalewka Polish liqueurs prepared from five apple varieties have been used as a model of strong liqueurs. Principal Component Analysis has demonstrated that the best discrimination between liqueurs prepared from different apple varieties is achieved using the e-tongue and UV-Vis spectroscopy. Raman spectra coupled to chemometrics have not been efficient in discriminating liqueurs. The calculated Euclidean distances and the k-Nearest Neighbors algorithm (kNN) confirmed these results. The main advantage of the e-tongue is that, using PLS-1, good correlations have been found simultaneously with the phenolic content measured by the Folin–Ciocalteu method (R2 of 0.97 in calibration and R2 of 0.93 in validation) and also with the density, a marker of the alcoholic content method (R2 of 0.93 in calibration and R2 of 0.88 in validation). UV-Vis coupled with chemometrics has shown good correlations only with the phenolic content (R2 of 0.99 in calibration and R2 of 0.99 in validation) but correlations with the alcoholic content were low. Raman coupled with chemometrics has shown good correlations only with density (R2 of 0.96 in calibration and R2 of 0.85 in validation). In summary, from the three holistic methods evaluated to analyze strong alcoholic liqueurs, the voltammetric electronic tongue using phthalocyanines as sensing elements is superior to Raman or UV-Vis techniques because it shows an excellent discrimination capability and remarkable correlations with both antioxidant capacity and alcoholic content—the most important parameters to be measured in this type of liqueurs.

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“…1), which limit the development of spectroscopy in industrial and commercial applications, such as rapid identification of chemicals [1,2], and physical properties of small molecules [3,4], feature extraction [5,6], material recognition [7,8]. The spectra overlap are usually modeled as a linear degradation system, b=l k+n, (1) where b is the degraded spectrum, l is the latent spectrum, n is the additive noise usually assumed as Gaussian distribution, is the convolution operator.…”

Section: Introductionmentioning

confidence: 99%

Blind infrared spectroscopic data restoration with the similarity of multi-scales regularization

Liu

Shu

et al. 2015

2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

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Band overlap and random noise exist widely when the spectra are captured using an infrared spectrometer, especially when the problems of instrument aging has become more and more serious recently. In this paper, via introducing the similarity of multiscales, a blind spectral deconvolution method is proposed. Considering similarity of the latent spectrum between different scales, it is used as a prior to constrain the estimated latent spectrum similar to pre-scale to reduce artifacts which is produced from deconvolution. Experiments indicate that the proposed method is able to obtain better performance than the state-of-the-art methods, and obtain satisfying deconvolution results with fewer artifacts. The recovered infrared spectra can easily extract the spectral features and recognize the unknown objects.

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Rapid Measurement of Phenolics Compounds in Red Wine Using Raman Spectroscopy

Cited by 34 publications

References 15 publications

Using Raman Spectroscopy as a Fast Tool to Classify and Analyze Bulgarian Wines—A Feasibility Study

Using Raman Spectroscopy as a Fast Tool to Classify and Analyze Bulgarian Wines—A Feasibility Study

Discrimination of Apple Liqueurs (Nalewka) Using a Voltammetric Electronic Tongue, UV-Vis and Raman Spectroscopy

Blind infrared spectroscopic data restoration with the similarity of multi-scales regularization

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