Spectralprint techniques for wine and vinegar characterization, authentication and quality control: Advances and projections

A Review of Wine Authentication Using Spectroscopic Approaches in Combination with Chemometrics

“…NMR spectroscopy can be applied for qualitative analysis to determine molecular structures and for compositional profiling of a sample [51], as well as for quantitative analysis of analytes such as amino acids, alcohols, sugars, carboxylic acids and their derivatives, and phenolic compounds [50]. NMR can be based on acquisition of 1 H, 2 H, or 13 C spectra; for wine authentication, 1 H NMR spectroscopy is most advantageous as data acquisition is fast and highly reproducible compared to other techniques [33]. Moreover, NMR with advancements such as automation of analysis has been introduced commercially and adapted to wine authentication (e.g., Bruker's WineScreener TM ) [50].…”

A Methodology Based on FT-IR Data Combined with Random Forest Model to Generate Spectralprints for the Characterization of High-Quality Vinegars

“…For our study, support vector machine and random forest were selected as nonparametric techniques that would allow a predictive model to be generated. According to the most recent studies, RF has never been applied to wine or vinegar samples, while SVM has rarely been used for this purpose [ 13 ]. However, both models have exhibited considerable potential when applied to other foodstuffs [ 22 , 23 ].…”

“…These attributes make spectroscopic techniques a more appropriate option for the development of vanguard/rearguard analytical strategies for the control of production processes, so that the need for specific corrective actions can be determined in the shortest possible time [ 12 ]. Numerous methods can be found in the literature where the individual identification of the compounds of interest in wine, as well as in related drinks, is conducted by Fourier-transform infrared spectroscopy (FT-IR) [ 13 , 14 ]. It must be noted that the IR spectra from wines, vinegar, and other beverages are complex mixtures of overlapped peaks, i.e., unresolved peaks.…”

“…For this reason, nontargeted chemical analyses based on spectroscopic techniques combined with chemometrics are becoming more frequently used for food characterization [ 15 , 16 ]. In this sense, the use of the whole spectral range in combination with chemometrics to identify a unique fingerprint, which has been recently given the name of spectralprint, allows a rapid characterization of each sample with minimum or no preparation at all [ 13 ]. In this sense, Ríos-Reina et al applied fluorescence excitation–emission spectroscopy coupled to parallel factor analysis (PARAFAC) and support vector machine (SVM) to characterize and classify the three abovementioned Spanish wine vinegars with PDO and concluded that SVM classification models provide higher predictive accuracy (over 92%) [ 17 ].…”

“…According to the recently published review by Ríos-Reina et al, there are not many studies where the suitability of the different spectroscopic techniques that have been used in this field were compared for effectiveness [ 13 ]. Thus, the development of an analytical method that is suitable to be implemented as a routine for the characterization of vinegar remains a challenge.…”