Prediction of phenolic compounds in red wine fermentations by visible and near infrared spectroscopy

Estimation of the age of human bloodstains under the simulated indoor and outdoor crime scene conditions by ATR-FTIR spectroscopy

Estimation of the age of human bloodstains is of great importance in forensic practices, but it is a challenging task because of the lack of a well-accepted, reliable, and established method. Here, the attenuated total reflection (ATR)-Fourier transform infrared (FTIR) technique combined with advanced chemometric methods was utilized to determine the age of indoor and outdoor bloodstains up to 107 days. The bloodstain storage conditions mimicked crime scene scenarios as closely as possible. Two partial least squares regression models—indoor and outdoor models with 7–85 days—exhibited good performance for external validation, with low values of predictive root mean squared error (5.83 and 4.77) and high R² values (0.94 and 0.96) and residual predictive deviation (4.08 and 5.14), respectively. Two partial least squares–discriminant analysis classification models were built and demonstrated excellent distinction between fresh (age ≤1 d) and older (age >1 d) bloodstains, which is highly valuable for forensic investigations. These findings demonstrate that ATR-FTIR spectroscopy coupled with advanced chemometric methods can be employed as a rapid and non-destructive tool for age estimation of bloodstains in real-world forensic investigation.

Influence of Sampling Component on Determination of Soluble Solids Content of Fuji Apple Using Near-Infrared Spectroscopy

Fuji apples from two production areas were separated into six batches by different experimenters. After applying light (500-1010 nm) on the surface of intact ones for their visible and near-infrared (NIR) spectra, destructive samples of three apple components were taken to determine the soluble solids content (SSC). Correlation and regression coefficients between the second Savitzky-Golay derivative of the spectra and SSC were analyzed to reveal that SSC values derived from the different apple components showed significantly different responses in the visible region. However, similar responses, particularly in the NIR section (730-932 nm), remained, including two sugar bands at 890 and 906 nm. On the basis of applying above characteristic bands to remove the interference signals, partial least square (PLS) and multiple linear regression (MLR) showed similar effective performances. According to the analysis of variance (ANOVA) method, sampling methods had significant effect on quantitative accuracy, and the model, using SSC values detected from the outer flesh cuboid (2.5 × 2.5 × 1.5 cm³), provided the best performance with lower root mean square error of prediction and higher correlation coefficient.

Near-Infrared Spectroscopy Assay of Key Quality-Indicative Ingredients of Tongkang Tablets

The objective of this paper is to develop an easy and fast near-infrared spectroscopy (NIRS) assay for the four key quality-indicative active ingredients of Tongkang tablets by comparing the true content of the active ingredients measured by high performance liquid chromatography (HPLC) and the NIRS data. The HPLC values for the active ingredients content of Cimicifuga glycoside, calycosin glucoside, 5-O-methylvisamminol and hesperidin in Tongkang tablets were set as reference values. The NIRS raw spectra of Tongkang tablets were processed using first-order convolution method. The iterative optimization method was chosen to optimize the band for Cimicifuga glycoside and 5-O-methylvisamminol, and correlation coefficient method was used to determine the optimal band of calycosin glucoside and hesperidin. A near-infrared quantitative calibration model was established for each quality-indicative ingredient by partial least-squares method on the basis of the contents detected by HPLC and the obtained NIRS spectra. The correlation coefficient R 2 values of the four models of Cimicifuga glycoside, calycosin glucoside, 5-O-methylvisamminol and hesperidin were 0.9025, 0.8582, 0.9250, and 0.9325, respectively. It was demonstrated that the accuracy of the validation values was approximately 90% by comparison of the predicted results from NIRS models and the HPLC true values, which suggested that NIRS assay was successfully established and validated. It was expected that the quantitative analysis models of the four indicative ingredients could be used to rapidly perform quality control in industrial production of Tongkang tablets.

Use of Visible and Short-Wave Near-Infrared Hyperspectral Imaging To Fingerprint Anthocyanins in Intact Grape Berries

In red grape berries, anthocyanins account for about 50% of the skin phenols and are responsible for the final wine color. Individual anthocyanin levels and compositional profiles vary with cultivar, maturity, season, region, and yield and have been proposed as chemical markers to differentiate wines and to provide valuable information regarding the adulteration of musts and wines. A fast, easy, solvent-free, nondestructive method based on visible, short-wave, and near-infrared hyperspectral imaging (HSI) in intact grape berries to fingerprint the color pigments in eight different grape varieties was developed and tested against HPLC. Predictive models based on modified partial least-squares (MPLS) were built for 14 individual anthocyanins with coefficients of determination of cross-validation (R²_CV) ranging from 0.70 to 0.93. For the grouping of total and nonacylated anthocyanins, external validation was conducted with coefficient of determination of prediction (R²_P) of 0.86. HSI could potentially become an alternative to HPLC with reduced analysis time and labor costs while providing reliable and robust information on the anthocyanin composition of grape berries.

A novel chemometric classification for FTIR spectra of mycotoxin-contaminated maize and peanuts at regulatory limits

The rapid identification of mycotoxins such as deoxynivalenol and aflatoxin B₁ in agricultural commodities is an ongoing concern for food importers and processors. While sophisticated chromatography-based methods are well established for regulatory testing by food safety authorities, few techniques exist to provide a rapid assessment for traders. This study advances the development of a mid-infrared spectroscopic method, recording spectra with little sample preparation. Spectral data were classified using a bootstrap-aggregated (bagged) decision tree method, evaluating the protein and carbohydrate absorption regions of the spectrum. The method was able to classify 79% of 110 maize samples at the European Union regulatory limit for deoxynivalenol of 1750 µg kg^-1 and, for the first time, 77% of 92 peanut samples at 8 µg kg^-1 of aflatoxin B₁. A subset model revealed a dependency on variety and type of fungal infection. The employed CRC and SBL maize varieties could be pooled in the model with a reduction of classification accuracy from 90% to 79%. Samples infected with Fusarium verticillioides were removed, leaving samples infected with F. graminearum and F. culmorum in the dataset improving classification accuracy from 73% to 79%. A 500 µg kg^-1 classification threshold for deoxynivalenol in maize performed even better with 85% accuracy. This is assumed to be due to a larger number of samples around the threshold increasing representativity. Comparison with established principal component analysis classification, which consistently showed overlapping clusters, confirmed the superior performance of bagged decision tree classification.

Near-infrared spectroscopy for rapid classification of fruit spirits

Multivariate analysis combined with near-infrared (NIR) spectral analysis was evaluated to classify fruit spirits. A total of 67 fruit spirits (12 apple, 18 apricot, 19 pear and 18 plum spirits) were analyzed. NIR spectra were collected in the wavenumber range of 4000-10,000 cm(-1). Linear discriminant analysis based on principal component analysis (PCA-LDA) and general discriminant analysis (GDA) based directly on NIR spectral data were used to classify the samples. The prediction performance of models in different wavenumber ranges was also investigated. The best PCA-LDA and GDA models gave a 100 % classification of spirits of the four fruit kinds in the wavenumber range from 5500 to 6050 cm(-1) corresponding to either the C-H stretch of the first overtones of CH3 and CH2 groups, or to compounds containing O-H aromatic groups. The results demonstrated that NIR spectroscopy could be used as a rapid method for classification of fruit spirits.

Exploiting Phenylpropanoid Derivatives to Enhance the Nutraceutical Values of Cereals and Legumes

Phenylpropanoids are a diverse chemical class with immense health benefits that are biosynthesized from the aromatic amino acid L-phenylalanine. This article reviews the progress for accessing variation in phenylpropanoids in germplasm collections, the genetic and molecular basis of phenylpropanoid biosynthesis, and the development of cultivars dense in seed-phenylpropanoids. Progress is also reviewed on high-throughput assays, factors that influence phenylpropanoids, the site of phenylpropanoids accumulation in seed, Genotype × Environment interactions, and on consumer attitudes for the acceptance of staple foods rich in phenylpropanoids. A paradigm shift was noted in barley, maize, rice, sorghum, soybean, and wheat, wherein cultivars rich in phenylpropanoids are grown in Europe and North and Central America. Studies have highlighted some biological constraints that need to be addressed for development of high-yielding cultivars that are rich in phenylpropanoids. Genomics-assisted breeding is expected to facilitate rapid introgression into improved genetic backgrounds by minimizing linkage drag. More research is needed to systematically characterize germplasm pools for assessing variation to support crop genetic enhancement, and assess consumer attitudes to foods rich in phenylpropanoids.

On-line near-infrared spectroscopy optimizing and monitoring biotransformation process of γ-aminobutyric acid

Near-infrared spectroscopy (NIRS) with its fast and nondestructive advantages can be qualified for the real-time quantitative analysis. This paper demonstrates that NIRS combined with partial least squares (PLS) regression can be used as a rapid analytical method to simultaneously quantify l-glutamic acid (l-Glu) and γ-aminobutyric acid (GABA) in a biotransformation process and to guide the optimization of production conditions when the merits of NIRS are combined with response surface methodology. The high performance liquid chromatography (HPLC) reference analysis was performed by the o-phthaldialdehyde pre-column derivatization. NIRS measurements of two batches of 141 samples were firstly analyzed by PLS with several spectral pre-processing methods. Compared with those of the HPLC reference analysis, the resulting determination coefficients (R2), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) of the external validation for the l-Glu concentration were 99.5%, 1.62 g/L, and 11.3, respectively. For the GABA concentration, R2, RMSEP, and RPD were 99.8%, 4.00 g/L, and 16.4, respectively. This NIRS model was then used to optimize the biotransformation process through a Box-Behnken experimental design. Under the optimal conditions without pH adjustment, 200 g/L l-Glu could be catalyzed by 7148 U/L glutamate decarboxylase (GAD) to GABA, reaching 99% conversion at the fifth hour. NIRS analysis provided timely information on the conversion from l-Glu to GABA. The results suggest that the NIRS model can not only be used for the routine profiling of enzymatic conversion, providing a simple and effective method of monitoring the biotransformation process of GABA, but also be considered to be an optimal tool to guide the optimization of production conditions.

On-line monitoring the extract process of Fu-fang Shuanghua oral solution using near infrared spectroscopy and different PLS algorithms

An on-line near infrared (NIR) spectroscopy monitoring method with an appropriate multivariate calibration method was developed for the extraction process of Fu-fang Shuanghua oral solution (FSOS). On-line NIR spectra were collected through two fiber optic probes, which were designed to transmit NIR radiation by a 2mm flange. Partial least squares (PLS), interval PLS (iPLS) and synergy interval PLS (siPLS) algorithms were used comparatively for building the calibration regression models. During the extraction process, the feasibility of NIR spectroscopy was employed to determine the concentrations of chlorogenic acid (CA) content, total phenolic acids contents (TPC), total flavonoids contents (TFC) and soluble solid contents (SSC). High performance liquid chromatography (HPLC), ultraviolet spectrophotometric method (UV) and loss on drying methods were employed as reference methods. Experiment results showed that the performance of siPLS model is the best compared with PLS and iPLS. The calibration models for AC, TPC, TFC and SSC had high values of determination coefficients of (R(2)) (0.9948, 0.9992, 0.9950 and 0.9832) and low root mean square error of cross validation (RMSECV) (0.0113, 0.0341, 0.1787 and 1.2158), which indicate a good correlation between reference values and NIR predicted values. The overall results show that the on line detection method could be feasible in real application and would be of great value for monitoring the mixed decoction process of FSOS and other Chinese patent medicines.

Size-dependent CdSe quantum dot–lysozyme interaction and effect on enzymatic activity

Size-dependent (2.5 and 6.3 nm) interaction of MPA modified hydrophilic CdSe quantum dots with lysozyme are reported.

Near-Infrared Spectroscopy as an Analytical Process Technology for the On-Line Quantification of Water Precipitation Processes during Danhong Injection

This paper used near-infrared (NIR) spectroscopy for the on-line quantitative monitoring of water precipitation during Danhong injection. For these NIR measurements, two fiber optic probes designed to transmit NIR radiation through a 2 mm flow cell were used to collect spectra in real-time. Partial least squares regression (PLSR) was developed as the preferred chemometrics quantitative analysis of the critical intermediate qualities: the danshensu (DSS, (R)-3, 4-dihydroxyphenyllactic acid), protocatechuic aldehyde (PA), rosmarinic acid (RA), and salvianolic acid B (SAB) concentrations. Optimized PLSR models were successfully built and used for on-line detecting of the concentrations of DSS, PA, RA, and SAB of water precipitation during Danhong injection. Besides, the information of DSS, PA, RA, and SAB concentrations would be instantly fed back to site technical personnel for control and adjustment timely. The verification experiments determined that the predicted values agreed with the actual homologic value.

Sample presentation, sources of error and future perspectives on the application of vibrational spectroscopy in the wine industry

Vibrational spectroscopy encompasses a number of techniques and methods including ultra-violet, visible, Fourier transform infrared or mid infrared, near infrared and Raman spectroscopy. The use and application of spectroscopy generates spectra containing hundreds of variables (absorbances at each wavenumbers or wavelengths), resulting in the production of large data sets representing the chemical and biochemical wine fingerprint. Multivariate data analysis techniques are then required to handle the large amount of data generated in order to interpret the spectra in a meaningful way in order to develop a specific application. This paper focuses on the developments of sample presentation and main sources of error when vibrational spectroscopy methods are applied in wine analysis. Recent and novel applications will be discussed as examples of these developments.

Robust Ultraviolet-Visible (UV-Vis) Partial Least-Squares (PLS) Models for Tannin Quantification in Red Wine

The validation of ultraviolet-visible (UV-vis) spectroscopy combined with partial least-squares (PLS) regression to quantify red wine tannins is reported. The methylcellulose precipitable (MCP) tannin assay and the bovine serum albumin (BSA) tannin assay were used as reference methods. To take the high variability of wine tannins into account when the calibration models were built, a diverse data set was collected from samples of South African red wines that consisted of 18 different cultivars, from regions spanning the wine grape-growing areas of South Africa with their various sites, climates, and soils, ranging in vintage from 2000 to 2012. A total of 240 wine samples were analyzed, and these were divided into a calibration set (n = 120) and a validation set (n = 120) to evaluate the predictive ability of the models. To test the robustness of the PLS calibration models, the predictive ability of the classifying variables cultivar, vintage year, and experimental versus commercial wines was also tested. In general, the statistics obtained when BSA was used as a reference method were slightly better than those obtained with MCP. Despite this, the MCP tannin assay should also be considered as a valid reference method for developing PLS calibrations. The best calibration statistics for the prediction of new samples were coefficient of correlation (R²val) = 0.89, root mean standard error of prediction (RMSEP) = 0.16, and residual predictive deviation (RPD) = 3.49 for MCP and R²val = 0.93, RMSEP = 0.08, and RPD = 4.07 for BSA, when only the UV region (260-310 nm) was selected, which also led to a faster analysis time. In addition, a difference in the results obtained when the predictive ability of the classifying variables vintage, cultivar, or commercial versus experimental wines was studied suggests that tannin composition is highly affected by many factors. This study also discusses the correlations in tannin values between the methylcellulose and protein precipitation methods.

Study of phenolic profile and antioxidant activity in selected Moravian wines during winemaking process by FT-IR spectroscopy

Wine belongs to a family of products where the quality matters. Its quality can be in principle verified using diverse physicochemical approaches, including the determination of various chemical compounds generally accepted as chemical markers of product quality. Example of such applicable compounds is a family derived from phenols. Next to a more classical approach, infrared spectroscopy can play an important role in this game. Here we sought to develop an easy to use, ultra-fast and robust method based on FT-IR with some important advantages including lower sample and solvent consumptions. The tested and evaluated method was consequently applied in a monitoring of changes in a content of total phenolic compounds (TPC) and total antioxidant activity (TAA) during a process of wine-making. It was found out that total amount of phenolic compounds differs both for individual kind of wines, namely red, white and rose, at each processing stage of the production. The content of phenolic compounds of red and white wine increased while an opposite trend was observed in rose wine. TAA values of analysed wines showed difference between individual kind of wine and indicate the same trend like phenolic profile. Antioxidant activity values relate to changes of phenolic content during production process.

Total indices as a tool to estimate sum content of similar analytes

The estimation of sum content of similar analytes without their separation often requires the use of total indices (TIs) expressed in terms of the standard substance concentration. Theoretic and metrological features of such indices are discussed in the review. The sum content of similar analytes is estimated by any TI with a certain systematic error. It can be minimized by selecting a standard substance and/or with the justification of sensitivity coefficients for individual analytes. The publications are pointed out in which new TIs are offered or new methods to measure known TIs are described. The corresponding studies are predominantly associated with two kinds of estimates: (a) total content of organic substances in waters and (b) the total antioxidant activity of foodstuffs.

Determination of omega-3 fatty acids in fish oil supplements using vibrational spectroscopy and chemometric methods

The potential of Fourier transform infrared (FT-IR), near-infrared (NIR), and Raman spectroscopic techniques combined with partial least squares (PLS) regression (PLSR) to predict concentrations of eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), and total omega-3 fatty acids (n-3 FAs) in fish oil supplements was investigated. FT-IR spectroscopy predicted EPA (coefficient of determination (R(2)) of 0.994, standard error of cross-validation (SECV) of 2.90%, and standard error of prediction (SEP) of 2.49%) and DHA (R(2) = 0.983, SECV = 2.89%, and SEP = 2.55%) with six to seven PLS factors, whereas a simpler PLS model with two factors was obtained for total n-3 FAs (R(2) = 0.985, SECV = 2.73%, and SEP = 2.75%). Selected regions in the NIR spectra gave models with good performances and predicted EPA (R(2) = 0.979, SECV = 2.43%, and SEP = 3.11%) and DHA (R(2) = 0.972, SECV = 2.34%, and SEP = 2.60%) with four to six PLS factors. Both the whole and selected NIR regions gave simple models (two PLS factors) with similar results (R(2) = 0.997, SECV = 2.18%, and SEP = 1.60%) for total n-3 FAs. The whole and selected regions of Raman spectra provided models with comparable results and predicted EPA (R(2) = 0.977, SECV = 3.18%, and SEP = 2.73%) and DHA (R(2) = 0.966, SECV = 3.31%, and SEP = 2.56%) with seven to eight PLS factors, whereas a simpler model (three PLS factors) with R(2) = 0.993, SECV = 2.82%, and SEP = 3.27% was obtained for total n-3 FAs. The results demonstrated that FT-IR, NIR, and Raman spectroscopy combined with PLSR can be used as simple, fast, and nondestructive methods for quantitative analysis of EPA, DHA, and total n-3 FAs. FT-IR and NIR spectroscopy, in particular, have the potential to be applied in process industries during production of fish oil supplements.

Analysis of phenolics in wine by high performance thin-layer chromatography with gradient elution and high resolution plate imaging

Health benefits of wine, especially with red wine, have been linked to the presence of a wide range of phenolic antioxidants. Thus, the aim of this study was to develop a simple, high performance thin layer chromatographic (HPTLC) method combined with high resolution digital plate images to visually compare multiple wine samples simultaneously on a single chromatographic plate and to quantify levels of gallic acid, caffeic acid, resveratrol and rutin, as representatives of the four different classes of phenolics found in wines. We also wanted to investigate the contribution of the investigated phenolic compounds to the total polyphenolic content (TPC) and total antioxidant capacity (TAC) of the wine samples. The average concentrations of caffeic acid, gallic acid, resveratrol, and rutin in the red wines were 2.15, 30.17, 0.59 and 2.47 mg/L respectively with their concentration below limit of quantification in the white wine samples. The highest concentration of resveratrol and rutin is found in the Cabernet and Shiraz wine samples. The amounts of gallic acid are correlated with TPC (r=0.58). Italian wines have the highest correlation between TPC and TAC (r=0.99) although they do not contain detectable amounts of resveratrol, they contain significant amount of rutin. Therefore, antioxidant properties might be associated with the presence of flavanols in these wines.

Near-infrared reflectance spectroscopy as a rapid and non-destructive analysis tool for curcuminoids in turmeric

INTRODUCTION

Turmeric has been widely used in curry powders as the main spice. Conventional chemical analysis such as high-performance liquid chromatography (HPLC) may take several hours to extract curcuminoids and prepare samples in many turmeric processing industries.

OBJECTIVE

This study was conducted to evaluate curcuminoids in turmeric powder using near-infrared reflectance spectroscopy (NIRS).

METHODS

All spectral acquisition ranged from 1100 to 2500 nm and a chemometrics analysis using partial least-squares (PLS) regression was performed to quantify the contents of individual curcuminoids. The HPLC was carried out (n = 129) to develop a PLS model based on the reference values.

RESULTS

High correlation coefficient (R(2) > 0.93) and low standard error of cross-validation (SECV < 0.20 g/100 g) and standard error of prediction (SEP < 0.13 g/100 g) values were obtained for precision and accuracy. In addition, the ratio of prediction to deviation (RPD > 2.65) values was also calculated.

CONCLUSION

Our results indicate that NIRS could be utilised as a control procedure or as an alternative rapid and effective quantification method.

Determination of polyphenolic compounds of red wines by UV-VIS-NIR spectroscopy and chemometrics tools

Spectral analysis is a quick and non-destructive method to analyse wine. In this work, trans-resveratrol, oenin, malvin, catechin, epicatechin, quercetin and syringic acid were determined in commercial red wines from DO Rías Baixas and DO Ribeira Sacra (Spain) by UV-VIS-NIR spectroscopy. Calibration models were developed using principal component regression (PCR) or partial least squares (PLS) regression. HPLC was used as reference method. The results showed that reliable PLS models were obtained to quantify all polyphenols for Rías Baixas wines. For Ribeira Sacra, feasible models were obtained to determine quercetin, epicatechin, oenin and syringic acid. PCR calibration models showed worst reliable of prediction than PLS models. For red wines from mencía grapes, feasible models were obtained for catechin and oenin, regardless the geographical origin. The results obtained demonstrate that UV-VIS-NIR spectroscopy can be used to determine individual polyphenolic compounds in red wines.

A novel near-infrared spectroscopy and chemometrics method for rapid analysis of several chemical components and antioxidant activity of mint (Mentha haplocalyx Briq.) samples

A novel near-infrared spectroscopy (NIRS) method has been researched and developed for the simultaneous analyses of the chemical components and associated properties of mint (Mentha haplocalyx Briq.) tea samples. The common analytes were: total polysaccharide content, total flavonoid content, total phenolic content, and total antioxidant activity. To resolve the NIRS data matrix for such analyses, least squares support vector machines was found to be the best chemometrics method for prediction, although it was closely followed by the radial basis function/partial least squares model. Interestingly, the commonly used partial least squares was unsatisfactory in this case. Additionally, principal component analysis and hierarchical cluster analysis were able to distinguish the mint samples according to their four geographical provinces of origin, and this was further facilitated with the use of the chemometrics classification methods-K-nearest neighbors, linear discriminant analysis, and partial least squares discriminant analysis. In general, given the potential savings with sampling and analysis time as well as with the costs of special analytical reagents required for the standard individual methods, NIRS offered a very attractive alternative for the simultaneous analysis of mint samples.

Effect of wine pH and bottle closure on tannins

The impact of wine pH and closure type on color, tannin concentration, and composition was investigated. A single vintage of Cabernet Sauvignon wine was divided into three batches, the pH was adjusted to 3.2, 3.5 or 3.8, and the wines were bottled under screw caps with either SaranTin (ST) or Saranex (Sx) liners. After 24 months, the tannin concentration, tannin percent yield (relating to the proportion of acid-labile interflavan bonds), and the mean degree of polymerization (mDp) had decreased significantly, all of which can contribute to the softening of wine astringency with aging. The higher pH wines contained less percent (-)-epicatechin 3-O-gallate subunits, whereas the Sx pH 3.2 wines were significantly lower in percent yield and mDp than the other wines. Overall, the tannin structure and wine color of the lower pH wines (pH 3.2) bottled under Sx screw caps changed more rapidly with aging than those of the higher pH wines (pH 3.8) bottled under ST screw caps.

Fluorescence approach for measuring anthocyanins and derived pigments in red wine

A novel fluorescence approach to monitor the evolution of anthocyanins and derivatives in red wine was developed. Some red table wines and Port wine with different vintage years were first tested with the aim to determine the ideal fluorescent conditions. The fluorescence contribution of both monomeric and polymeric anthocyanins was studied by comparing their emission spectra. By measuring the F700/F560 ratio on different wine samples and applying an inverted exponential function, it was possible to estimate the monomeric/polymeric anthocyanin absorbance ratio that is proportional to the relative content of the two classes of compounds. The methodology was further developed by using pure compounds representative of monomeric anthocyanins and anthocyanin-pyruvic acid adducts, namely, by using malvidin-3-O-glucoside and vitisin A. A fluorescence excitation ratio (FER350/550) was considered for estimating the absorbance ratio between vitisin A and malvidin-3-O-glucoside. Overall, this work aims to use fluorescence to monitor the evolution of anthocyanin derivatives and to distinguish them from their anthocyanin precursors, thereby allowing the evolution of anthocyanin pigments during wine aging to be monitored, but it also may be useful to determine age markers or even geographical markers.