Near-infrared spectroscopy for rapid classification of fruit spirits

Jakubíková, Michaela; Sádecká, Jana; Kleinová, Angela; Májek, Pavel

doi:10.1007/s13197-016-2254-4

Cited by 14 publications

(8 citation statements)

References 34 publications

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“…Figure 4 exhibits a representative NIR spectrum of the AWS, similar to those obtained by other authors for wine spirit, grape marc spirit, fruit spirits, whisky and vodka [ 28 , 29 , 37 , 40 , 41 , 42 , 43 ].…”

Section: Resultssupporting

confidence: 68%

“…Regarding Figure 3 , it is possible to establish that the NIR spectra followed the trend of sample differentiation, which was also observed in Figure 2 . However, NIR spectra showed that other compounds present in AWS could affect their relative position along the PCA axes [ 8 , 39 , 40 ]. In Figure 3 , the AWS samples aged with chestnut wood and Limousin oak wood are presented separately to understand better.…”

Section: Resultsmentioning

confidence: 99%

“…According to Jakubíková et al [ 40 ], which used NIR spectroscopy to distinguish fruit spirits, the spectral region of 6050–5500 cm −1 is the more accurate to discriminate the different beverages analysed using PCA with linear discriminant analysis and general discriminant analysis models that giving 100% classification of spirits.…”

Section: Resultsmentioning

confidence: 99%

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

Anjos

Caldeira

Fernandes

et al. 2021

Sensors

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Near-infrared spectroscopic (NIR) technique was used, for the first time, to predict volatile phenols content, namely guaiacol, 4-methyl-guaiacol, eugenol, syringol, 4-methyl-syringol and 4-allyl-syringol, of aged wine spirits (AWS). This study aimed to develop calibration models for the volatile phenol’s quantification in AWS, by NIR, faster and without sample preparation. Partial least square regression (PLS-R) models were developed with NIR spectra in the near-IR region (12,500–4000 cm−1) and those obtained from GC-FID quantification after liquid-liquid extraction. In the PLS-R developed method, cross-validation with 50% of the samples along a validation test set with 50% of the remaining samples. The final calibration was performed with 100% of the data. PLS-R models with a good accuracy were obtained for guaiacol (r2 = 96.34; RPD = 5.23), 4-methyl-guaiacol (r2 = 96.1; RPD = 5.07), eugenol (r2 = 96.06; RPD = 5.04), syringol (r2 = 97.32; RPD = 6.11), 4-methyl-syringol (r2 = 95.79; RPD = 4.88) and 4-allyl-syringol (r2 = 95.97; RPD = 4.98). These results reveal that NIR is a valuable technique for the quality control of wine spirits and to predict the volatile phenols content, which contributes to the sensory quality of the spirit beverages.

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Section: Resultssupporting

confidence: 68%

Section: Resultsmentioning

confidence: 99%

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

Anjos

Caldeira

Fernandes

et al. 2021

Sensors

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“…The absorbance data from the preprocessed NIR spectrum were then analyzed using XLSTAT version 2019. 4…”

Section: Chemometrics Analysismentioning

confidence: 99%

Discrimination of cassava, taro, and wheat flour using near-infrared spectroscopy and chemometrics

et al. 2020

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There is a difference in the selling price for cassava, taro, and wheat flour, with taro flour having a higher price. It could be a reason for adulterating the taro flour from the other two flours and reducing quality. This study aims to distinguish the three types of flour using the near-infrared (NIR) spectra combined with chemometrics. The NIR spectra of all samples were measured at a wavelength of 1000-2500 nm. The multivariate analysis used was principal component analysis (PCA), and PCA followed with discriminant analysis (DA). The preliminary process of the signal using area normalization was carried out before the multivariate analysis. The PCA results showed that most of the samples were grouped in their respective groups except for two samples, namely 1 sample of taro flour and 1 sample of cassava flour. Meanwhile, the PCA-DA results using seven main components showed that the three samples were grouped well. DA validation was carried out using the cross-validation method, showing that the samples could be identified into their respective groups. Therefore, a combination of NIR spectrum and chemometric analysis can be used to differentiate cassava, taro, and wheat flour

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“…12 Jakubikova et al classified 67 fruit spirits (apple, apricot, pear and plum spirits) by type using NIR combined with multivariate analysis. 13 Although there are numerous reports on the use of NIR spectroscopy in food and beverage analysis, 9,10,[12][13][14][15] the potential of NIR spectroscopy in the wine spirit/brandy ageing degree estimation have not been studied.…”

Section: Introductionmentioning

confidence: 99%

Identification of phenolic and alcoholic compounds in wine spirits and their classification by use of multivariate analysis

Čiča

Pezer

Mrvčić

et al. 2019

JSCS

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During the ageing period wine spirits are changing their color, chemical composition and sensory characteristics. These changes should be simply monitored. The aim of this study was to develop partial least squares regression (PLS) models for higher alcohols and phenols in wine spirits as well as to show the feasibility of the NIR spectroscopy combined with chemometric tools to distinguish wine spirits and brandies with different ageing degree. To get the reference values, the usual methods for the analysis of spirits drinks were used. Ethanol, esters, acids, methanol and higher alcohols were studied. Wine spirits and brandies phenol composition was determined by liquid chromatography. Principal component analysis (PCA) was used to classify the wine spirits and brandies according to their phenolic and higher alcohols composition. Moreover, the Partial least squares regression (PLS regression) was used to calibrate and predict expected contents of higher alcohols and phenols in the wine spirits. Success of the classification of samples by ageing based on individual alcohols was 93.8 %, while success of the classification based on individual phenols raised to 100 %. This efficiency of the prediction was evaluated by use of linear discriminator analysis (LDA).

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Near-infrared spectroscopy for rapid classification of fruit spirits

Cited by 14 publications

References 34 publications

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

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

Discrimination of cassava, taro, and wheat flour using near-infrared spectroscopy and chemometrics

Identification of phenolic and alcoholic compounds in wine spirits and their classification by use of multivariate analysis

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