Fluorescence spectroscopy and multivariate methods for the determination of brandy adulteration with mixed wine spirit

Synchronous fluorescence spectroscopy and multivariate classification for the discrimination of cachaças and rums

Although cachaça and rum are distilled beverages obtained from the same raw material, they present differences in their chemical compositions. In this study, synchronous fluorescence spectroscopy was used combined with supervised classification models based on the partial least squares discriminant analysis to develop a rapid and low-cost model for discriminating between 50 cachaça and 40 rum samples. Partial least squares discriminant analysis models were constructed using synchronous fluorescence spectra recorded at wavelength differences of 10-100 nm. Initially, spectra were preprocessed by the first derivative with the Savitzky-Golay smoothing, and filter width and polynomial order were selected through face-centered central composite designs. For the construction and validation models, the spectra data were split into two datasets: the training and the test sets containing 60 (C, n = 33; R, n = 27) and 30 (C, n = 17; R, n = 13) samples, respectively. The best discrimination was achieved using fluorescence spectra recorded at wavelength difference 10 nm, allowing the discrimination of cachaça and rum with a classification efficiency of 98%. These results indicate that synchronous fluorescence spectroscopy offers a promising approach for the authentication of cachaças and rums.

Non-Invasive Monitoring of Ethanol and Methanol Levels in Grape-Derived Pisco Distillate by Vibrational Spectroscopy

Handheld Raman and portable FT-IR spectroscopy devices were evaluated for fast and non-invasive determination of methanol and ethanol levels in Peruvian Pisco. Commercial Peruvian Pisco (n = 171) samples were kindly provided by the UNALM Alliance for Research in Alcohol and its Derivatives (Lima, Peru) and supplemented by purchases at grocery and online stores. Pisco spectra were collected on handheld Raman spectrometers equipped with either a 1064 nm or a 785 nm excitation laser and a portable infrared unit operating in transmission mode. The alcohol levels were determined by GC-MS. Calibration models used partial least-squares regression (PLSR) to develop prediction algorithms. GC-MS data revealed that 10% of Pisco samples had ethanol levels lower than 38%, indicating possible water dilution. Methanol levels ranged from 10 to 130 mg/100 mL, well below the maximum levels allowed for fruit brandies. Handheld Raman equipped with a 1064 nm excitation laser gave the best results for determining ethanol (SEP = 1.2%; RPre = 0.95) and methanol (SEP = 1.8 mg/100 mL; RPre = 0.93). Randomly selected Pisco samples were spiked with methanol (75 to 2800 mg/100 mL), and their Raman spectra were collected through their genuine commercial bottles. The prediction models gave an excellent performance (SEP = 98 mg/100 mL; RPre = 0.97), allowing for the non-destructive and non-contact determination of methanol and ethanol concentrations without opening the bottles.

Age Discrimination of Chinese Baijiu Based on Midinfrared Spectroscopy and Chemometrics

Baijiu is a traditional and popular Chinese liquor which is affected by the storage time. The longer the storage time of Baijiu is, the better its quality is. In this paper, the raw and mellow Baijiu samples from different storage time are discriminated accurately throughout midinfrared (MIR) spectroscopy and chemometrics. Firstly, changing regularities of the substances in Chinese Baijiu are discussed by gas chromatography-mass spectrometry (GC-MS) during the aging process. Then, infrared spectrums of Baijiu samples are processed by smoothing, multivariate baseline correction, and the first and second derivative processing, but no significant variation can be observed. Next, the spectral date pretreatment methods are constructively introduced, and principal component analysis (PCA) and discriminant analysis (DA) are developed for data analyses. The results show that the accuracy rates of samples by the DA method in calibration and validation sets are 91.7% and 100%, respectively. Consequently, an identification model based on support vector machine (SVM) and PCA is established combined with the grid search strategy and cross-validation methods to discriminate the age of Chinese Baijiu validly, where 100% classification accuracy rate is obtained in both training and test sets.

Real wine or not? Protecting wine with traceability and authenticity for consumers: chemical and technical basis, technique applications, challenge, and perspectives

Wine is a high-value alcoholic beverage welcomed by consumers because of its flavor and nutritional value. The key information on wine bottle label is the basis of consumers' choice, which also becomes a target for manufacturers to adulterate, including geographical origin, grape variety and vintage. With the improvement of wine adulteration technology, modern technological means are needed to solve the above mentioned problems. The chemical basis of wine determines the type of technique used. Detection technology can be subdivided into four groups: mass spectrometry techniques, spectroscopic techniques, chromatography techniques, and other techniques. Multivariate statistical analysis of the data was performed by means of chemometrics methods. This paper outlines a series of procedures for wine classification and identification, and classified the analytical techniques and data processing methods used in recent years with listing their principles, advantages and disadvantages to help wine researchers choose appropriate methods to meet the challenge and ensure wine traceability and authenticity.

Comparison of different fluorescence techniques in brandy classification by region of production

Fluorescence spectrometry coupled with chemometrics was used to discriminate between 44 brandies originating from different countries. The kind of spectrum (emission, total luminescence and synchronous fluorescence), the geometry of sample illumination (front-face and right angle), and the sample type (bulk and diluted) were considered to compare the brandy classification. Firstly, the emission and synchronous fluorescence spectra (SFS) were processed by the principal component analysis (PCA) and the excitation-emission matrix (EEM) fluorescence spectra were modeled by unfolded PCA and parallel factor analysis (PARAFAC). Secondly, the scores of PCA/PARAFAC components were used in the linear discriminant analysis (LDA). Finally, the quality of the PCA-LDA and PARAFAC-LDA models was compared. Total correct classification using emission spectra was poor, regardless of the experimental conditions. The highest total correct classification (95.5%) was achieved by processing the SFS recorded at wavelength difference of 20 and 60nm on the diluted samples. However, 90.9% observed for bulk samples and their SFS at wavelength difference of 20nm in the right angle geometry as well as EEM fluorescence spectra in both geometries is still an acceptable result.

Exploiting combined absorption and front face fluorescence spectroscopy to chase classification: A proof of concept in the case of Sardinian red wines

We present the analysis of optical spectroscopy fingerprints of three different varieties of Sardinian red wine, Carignano, Cannonau and Monica, belonging to Italian DOC (Controlled Origin Designation) red wines family. We selected 15 commercial wines (five for each variety), produced in the middle and south regions of Sardinia form monovarietal grapes. Despite different grapes, farming and tasting, all the samples share common absorption and fluorescence features. A two steps analysis is applied to achieve good classification of the wines. We recorded typical absorbance spectra with a large absorption below 300 nm and a visible absorption band at about 520 nm. Colour attributes according to chromaticity coordinates were evaluated by means of transmittance data and coupled to wine aging data to achieve classification. Despite similar values of lightness (L*), chroma (C*_ab), and hue angle (h°_ab) for the three varieties, good clustering was gathered for Carignano and Cannonau wines, whilst Monica variety has wide spread chromatic coordinates. UV excited fluorescence spectra display large and composite emission bands from the near UV down to the whole visible range. We performed multivariate analysis in the framework of the principal component analysis of fluorescence spectra coupled with absorption ones being able to achieve good clustering between the three varieties. One anomaly among Cannonau wines is discussed.

Quality evaluation and traceability of Bletilla striata by fluorescence fingerprint coupled with multiway chemometrics analysis

BACKGROUND

Traditional methods of evaluating herbs were mainly based on chromatographic techniques. They usually included tedious sample preparation procedures, taking tens of minutes to hours, and consume solvents as well as standards for external calibration. In this paper, the feasibility of employing a fluorescence fingerprint coupled with multi-way chemometrics analysis for quality evaluation and traceability of Bletilla striata were investigated.

RESULTS

Relative concentrations of four markers presented in B. striata were determined by using a four-component self-weighted alternating trilinear decomposition (SWATLD) model. These markers could be applied to accurate classification and quality control of B. striata samples from different regions. Furthermore, multiway principal component analysis, multilinear partial least squares discriminant analysis (PLS-DA), unfolded PLS-DA, and SWATLD-PLS-DA models were applied to classify the B. striata samples according to their geographic origins. Consistent results were obtained showing that B. striata samples could be successfully grouped based on their geographical origins and quality.

CONCLUSION

Our results revealed that the method developed can be used for quality evaluation and traceability of B. striata. Compared with the chromatographic methods, the method employed in this study was more convenient, simpler, and more sensitive. © 2018 Society of Chemical Industry.

Discrimination of geographical origin and detection of adulteration of kudzu root by fluorescence spectroscopy coupled with multi-way pattern recognition

In this work, fluorescence spectroscopy combined with multi-way pattern recognition techniques were developed for determining the geographical origin of kudzu root and detection and quantification of adulterants in kudzu root. Excitation-emission (EEM) spectra were obtained for 150 pure kudzu root samples of different geographical origins and 150 fake kudzu roots with different adulteration proportions by recording emission from 330 to 570nm with excitation in the range of 320-480nm, respectively. Multi-way principal components analysis (M-PCA) and multilinear partial least squares discriminant analysis (N-PLS-DA) methods were used to decompose the excitation-emission matrices datasets. 150 pure kudzu root samples could be differentiated exactly from each other according to their geographical origins by M-PCA and N-PLS-DA models. For the adulteration kudzu root samples, N-PLS-DA got better and more reliable classification result comparing with the M-PCA model. The results obtained in this study indicated that EEM spectroscopy coupling with multi-way pattern recognition could be used as an easy, rapid and novel tool to distinguish the geographical origin of kudzu root and detect adulterated kudzu root. Besides, this method was also suitable for determining the geographic origin and detection the adulteration of the other foodstuffs which can produce fluorescence.

Feasibility Assessment of Synchronous Fluorescence Spectral Fusion by Application to Argan Oil for Adulteration Analysis

Synchronous fluorescence spectroscopy (SFS) is used for quantitative analysis as well as for qualitative analysis, such as with classification methods. With SFS, determination of a useful wavelength interval between the excitation and emission wavelengths (Δλ) is required. There are a multitude of Δλ intervals that can be evaluated and optimization of the best one is complex. Presented here is a fusion approach for combining Δλ intervals, thereby negating the need to perform the selection by a skilled operator. To demonstrate the feasibility of omitting selection of the best Δλ interval, adulterated argan oil samples are studied. Argan oil is made from the argan tree, endemic to southwestern Morocco, and is well-known for its cosmetic, pharmaceutical, and nutritional applications. It is considered a luxury product and exported from Morocco around the world. Consequently, detection of argan oil adulteration followed by quantitative analysis of the adulterant concentration is important. This study uses fusion of SFS spectra obtained at ten Δλ intervals to first detect adulteration of argan oil by corn oil and then determination of the corn oil content. For detection of adulteration, 15 one-class classification methods were used simultaneously over the ten Δλ sets of SFS spectra. For tuning parameter dependent classifiers such as Mahalanobis distance, non-optimized classifiers are used. Raw classification values are used, removing the need to set classifier-dependent threshold values, albeit, ultimately, a fusion decision rule is needed for classification. For quantitative analysis, two calibration approaches are evaluated with fusion of these ten Δλ SFS spectral data sets. One is multivariate calibration by partial least squares (PLS). The second approach is a univariate calibration process where the SFS spectra are summed over respective SFS spectral ranges, also known as the area under the curve (AUC). For adulteration detection and quantitation of the corn oil, prediction errors decrease with fusion compared to individually using the ten Δλ interval SFS specific data sets. For this argan oil data set, the AUC method generally provides equivalent prediction errors to PLS.

Rapid and simple analysis of amphetamine-type illegal drugs using excitation-emission matrix fluorescence coupled with parallel factor analysis

Nowadays, the abuse of illegal drugs has been an increasingly grim problem in the world. Excitation–emission matrix fluorescence combined with parallel factor analysis was used to make a quantitative analysis of the simulated amphetamine-type illegal drugs. Satisfactory results were achieved for simultaneous determination of methamphetamine (MAM) and 3, 4-methylenedioxymethamphetamine (MDMA) in the presence of adulterants. The average recoveries were (99.8 ± 0.6)% and (101.6 ± 5.7)% for MAM and MDMA, respectively. Figures of merit including root-mean-square error of calibration and prediction, sensitivity and selectivity were investigated to evaluate the performance of the proposed method. The limits of detection were 0.054 and 0.002 1 μg/mL for MAM and MDMA, respectively.

On the use of the fluorescence, ultraviolet-visible and near infrared spectroscopy with chemometrics for the discrimination between plum brandies of different varietal origins

This paper investigates the use of synchronous fluorescence, UV-Vis and near infrared (NIR) spectroscopy coupled with chemometric methods to discriminate samples of high-quality plum brandies (Slivovica) of different varietal origins (Prunus domestica L.). Synchronous fluorescence spectra (SFS) for wavelength differences in the range of 70-100nm, NIR spectra in the wavenumber range of 4000-7500cm^-1 and UV-Vis spectra in the wavelength interval of 220-320nm were compared. The best discrimination models were created by linear discriminant analysis based on principal component analysis applied to SFS recorded with wavelength difference either 80nm or 100nm, allowing the classification of plum brandy according to harvest time as early (summer) and late (autumn) plum varieties; the total correct classifications were 96% and 100% for the calibration and prediction steps, respectively.

Fluorescence fingerprint as an instrumental assessment of the sensory quality of tomato juices

BACKGROUND

Sensory analysis is an important standard for evaluating food products. However, as trained panelists and time are required for the process, the potential of using fluorescence fingerprint as a rapid instrumental method to approximate sensory characteristics was explored in this study.

RESULTS

Thirty-five out of 44 descriptive sensory attributes were found to show a significant difference between samples (analysis of variance test). Principal component analysis revealed that principal component 1 could capture 73.84 and 75.28% variance for aroma category and combined flavor and taste category respectively. Fluorescence fingerprints of tomato juices consisted of two visible peaks at excitation/emission wavelengths of 290/350 and 315/425 nm and a long narrow emission peak at 680 nm. The 680 nm peak was only clearly observed in juices obtained from tomatoes cultivated to be eaten raw. The ability to predict overall sensory profiles was investigated by using principal component 1 as a regression target. Fluorescence fingerprint could predict principal component 1 of both aroma and combined flavor and taste with a coefficient of determination above 0.8.

CONCLUSION

The results obtained in this study indicate the potential of using fluorescence fingerprint as an instrumental method for assessing sensory characteristics of tomato juices.

Fluorescence fingerprinting of bottled white wines can reveal memories related to sulfur dioxide treatments of the must

For the first time, Excitation Emission Matrix (EEM) fluorescence spectroscopy was combined with parallel factor statistical analysis (PARAFAC) and applied to a set of 320 dry white wines of the Chardonnay grape variety. A four component PARAFAC model (C1, C2, C3 and C4) best explained the variability of fluorescence signatures of white wines. Subtle changes were observed in EEMs of white wines from two different vintages (2006 and 2007), where different concentrations of sulfur dioxide (0, 4, and 8 g·hL(-1)) were added to the grape must at pressing. PARAFAC results clearly indicated that sulfur dioxide added to the must subsequently influenced white wine chemistry into three distinct sulfur dioxide dose-dependent aging mechanisms. For both vintages, C1 and C2 were the dominant components affected by sulfur dioxide and likely reacting with phenolic compounds associated with some presumably proteinaceous material. Distinct component combinations revealed either SO2 dependent or vintage-dependent signatures, thus, showing the extent of the complex versatile significance underlying such fluorescence spectra, even after several years of bottle aging.