FTIR Spectroscopy for Grape and Wine Analysis

Bauer, Rolene; Nieuwoudt, Hélène; Bauer, Florian; Kossman, Jens; Koch, Klaus; H, Esbensen Kim

doi:10.1021/ac086051c

Cited by 115 publications

(96 citation statements)

References 41 publications

(70 reference statements)

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“…The calibration and validation sets were created with randomly selected observations and the results of the regression analysis were also visualised with prediction plots showing the regression correlation coefficient (R 2 ) of the created models. The regression coefficient expresses the connection between predictions and the actual results of the chemical parameters and gives an idea about the predictive efficiency of the model (Bauer et al 2008). The evaluation of the calibration models were performed by computing root mean square error of calibration and prediction (RMSEC, RMSEP) and residual predictive deviation (RPD).…”

Section: Multivariate Analysismentioning

confidence: 99%

Application of Mid-infrared Spectroscopy for the Measurement of Several Quality Parameters of Alcoholic Beverages, Wine and Raki

2012

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Mid-infrared (IR) spectroscopy, which is a rapid and relatively small amount of waste producing technique, was used to predict several quality parameters of two types of alcoholic beverages, wine and raki. Mid-infrared spectra of red, rose and white wines and a traditional aniseed alcoholic beverage, raki were collected and relations were established between measured chemical parameters (pH, brix, total phenol content, anthocyanin content, titratable acidity, sugar content, electrical conductivity and some colour parameters) of these beverages and their infrared spectra using chemometric techniques. Partial least square regression provided excellent prediction of total phenol (R 2 00.97) and anthocyanin contents (R 2 00.98) of wine samples and a good prediction of pH (R 2 00.9), brix (R 2 00.92) and colour intensity (R 2 00.93) values were obtained. Brix, total phenol and sugar content of raki samples were also estimated very successfully (R 2 00.99) for raki and good prediction was obtained with pH value. Mid-IR spectroscopy in combination with chemometrics could be a promising technique for determination of several quality parameters of alcoholic beverages simultaneously and rapidly.

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Section: Multivariate Analysismentioning

confidence: 99%

Application of Mid-infrared Spectroscopy for the Measurement of Several Quality Parameters of Alcoholic Beverages, Wine and Raki

2012

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“…Vibrational molecular spectroscopy techniques are very useful for food and beverages' analysis due to their flexibility, efficiency and low cost [6]. Particularly, the use of FT-Raman is advantageous due to the small volume of sample required, the high data reproducibility and speed of analysis.…”

Section: Introductionmentioning

confidence: 99%

Physicochemical characterization of Lavandula spp. honey with FT-Raman spectroscopy

Anjos

Santos

Paixão³

et al. 2018

Talanta

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A B S T R A C TThis study aimed to evaluate the potential of FT-Raman spectroscopy in the prediction of the chemical composition of Lavandula spp. monofloral honey. Partial Least Squares (PLS) regression models were performed for the quantitative estimation and the results were correlated with those obtained using reference methods.Good calibration models were obtained for electrical conductivity, ash, total acidity, pH, reducing sugars, hydroxymethylfurfural (HMF), proline, diastase index, apparent sucrose, total flavonoids content and total phenol content. On the other hand, the model was less accurate for pH determination. The calibration models had high r 2 (ranging between 92.8% and 99.9%), high residual prediction deviation -RPD (ranging between 4.2 and 26.8) and low root mean square errors. These results confirm the hypothesis that FT-Raman is a useful technique for the quality control and chemical properties' evaluation of Lavandula spp honey. Its application may allow improving the efficiency, speed and cost of the current laboratory analysis.

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“…The types and amounts of different phenolic compounds present have been used as broad indicators of wine quality and good correlation has been obtained between several aspects of the phenolic content and assessed quality of red wine [16]. However, it would be useful to be able to perform a single measurement that is representative of a wine's composition universally correlates with perceived wine quality [17].…”

Section: Introductionmentioning

confidence: 99%

The Use of Fourier Transform Infrared (FTIR) Spectroscopy and Artificial Neural Networks (ANNs) to Assess Wine Quality

Agatonović-Kuštrin¹,

Morton²,

Yusof³

2013

Mod Chem Appl

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The aim of this study was to develop a simple method to assess wine quality from its Fourier Transform Infrared Spectroscopy (FTIR) spectrum with minimal or no sample preparation. FTIR spectral data of selected wine samples, grape variety, wine barrel type, wine type and production year were correlated with total phenolic content, total and volatile acidity and alcohol content using Artificial Neural Networks (ANNs). A total of 20 (2 whites and 18 reds) different wines used in this study came from three different states across Australia; New South Wales, Victoria and South Australia.FTIR spectroscopy proved to be a promising technique that provides a rapid and accurate method in the quality assessment of wine. A plot of the values predicted by the validated ANN models showed excellent correlation with the experimentally measured values for acetic acid concentration, alcohol content, total phenols, and total acidity (r=0.898-0.942).

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FTIR Spectroscopy for Grape and Wine Analysis

Cited by 115 publications

References 41 publications

Application of Mid-infrared Spectroscopy for the Measurement of Several Quality Parameters of Alcoholic Beverages, Wine and Raki

Application of Mid-infrared Spectroscopy for the Measurement of Several Quality Parameters of Alcoholic Beverages, Wine and Raki

Physicochemical characterization of Lavandula spp. honey with FT-Raman spectroscopy

The Use of Fourier Transform Infrared (FTIR) Spectroscopy and Artificial Neural Networks (ANNs) to Assess Wine Quality

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