Merging vibrational spectroscopic data for wine classification according to the geographic origin

Fusing 1H NMR and Raman experimental data for the improvement of wine recognition models

The present study proposes the development of new wine recognition models based on Artificial Intelligence (AI) applied to the mid-level data fusion of 1H NMR and Raman data. In this regard, a supervised machine learning method, namely Support Vector Machines (SVMs), was applied for classifying wine samples with respect to the cultivar, vintage, and geographical origin. Because the association between the two data sources generated an input space with a high dimensionality, a feature selection algorithm was employed to identify the most relevant discriminant markers for each wine classification criterion, before SVM modeling. The proposed data processing strategy allowed the classification of the wine sample set with accuracies up to 100% in both cross-validation and on an independent test set and highlighted the efficiency of 1H NMR and Raman data fusion as opposed to the use of a single-source data for differentiating wine concerning the cultivar and vintage.

A review of the emerging technologies and systems to mitigate food fraud in supply chains

Food fraud has serious consequences including reputational damage to businesses, health and safety risks and lack of consumer confidence. New technologies targeted at ensuring food authenticity has emerged and however, the penetration and diffusion of sophisticated analytical technologies are faced with challenges in the industry. This review is focused on investigating the emerging technologies and strategies for mitigating food fraud and exploring the key barriers to their application. The review discusses three key areas of focus for food fraud mitigation that include systematic approaches, analytical techniques and package-level anti-counterfeiting technologies. A notable gap exists in converting laboratory based sophisticated technologies and tools in high-paced, live industrial applications. New frontiers such as handheld laser-induced breakdown spectroscopy (LIBS) and smart-phone spectroscopy have emerged for rapid food authentication. Multifunctional devices with hyphenating sensing mechanisms together with deep learning strategies to compare food fingerprints can be a great leap forward in the industry. Combination of different technologies such as spectroscopy and separation techniques will also be superior where quantification of adulterants are preferred. With the advancement of automation these technologies will be able to be deployed as in-line scanning devices in industrial settings to detect food fraud across multiple points in food supply chains.

Vis-NIR spectroscopic discriminant analysis of aflatoxin B1 excessive standard in peanut meal as feedstuff materials

A fast, simple and reagent-free detection method for aflatoxin B1 (AFB1) is of great significance to food safety and human health. Visible and near-infrared (Vis-NIR) spectroscopy was applied to the discriminant analysis of AFB1 excessive standard of peanut meal as feedstuff materials. Two types of excessive standard discriminant models based on spectral quantitative analysis with partial least squares (PLS) and direct pattern recognition with partial least squares-discrimination analysis (PLS-DA) were established, respectively. Multi-parameter optimization of Norris derivative filtering (NDF) was used for spectral preprocessing; the two-stage wavelength screening method based on equidistant combination-wavelength step-by-step phase-out (EC-WSP) was used for wavelength optimization. A rigorous sample experimental design of calibration-prediction-validation was utilized. The calibration and prediction samples were used for modeling and parameter optimization, and the selected model was validated using the independent validation samples. For quantitative analysis-based, the positive, negative and total recognition-accuracy rates in validation (RARV+, RARV-, and RARV) were 84.8 %, 74.6 % and 79.8 %, respectively; but, the relative root mean square error of prediction was as high as 51.0 %. For pattern recognition-based, the RARV+, RARV-, and RARV were 93.3 %, 90.5 % and 91.9 %, respectively. Moreover, the number of wavelengths N was drastically reduced to 17, and the discrete wavelength combination was in NIR overtone frequency region. The results indicated that, the EC-WSP-PLS-DA model achieved significantly better discrimination effect. Thus demonstrated that Vis-NIR spectroscopy has feasibility for the excessive standard discrimination of aflatoxin B1 in feedstuff materials.

Rapid analysis technologies with chemometrics for food authenticity field: A review

In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.

Quantitative analysis of moisture content and particle size in a fluidized bed granulation process using near infrared spectroscopy and acoustic emission combined with data fusion strategies

Monitoring granule property is essential for fluidization maintenance and product quality control in fluidized bed granulation (FBG). In this study, two non-invasive techniques, near-infrared (NIR) spectroscopy and acoustic emission (AE), were applied for quantitative analysis of moisture content (MC) and median particle size (D50) in a FBG process, combined with chemometrics and data fusion strategies. Partial least squares (PLS) and support vector machine (SVM) regression models were established based on NIR and AE spectral data. The optimal quantitative models were identified considering the effect of spectra preprocessing and variable selection. In the comparison study, the best separate models for MC and D50 quantification were based on NIR and AE, respectively. The NIR model exhibited the better prediction ability with the determination coefficient of validation set (R2v) of 0.9815, root mean square error of validation set (RMSEv) of 0.2226 %, and residual predictive deviation (RPD) of 7.4674 for MC. Meanwhile, the AE model presented the better prediction performance with R2v of 0.9710, RMSEv of 18.2643 μm, and RPD of 5.9740 for D50. Furthermore, among three data fusion strategies, the high-level fusion model achieved the best overall performance on D50 quantification with R2v of 0.9863, RMSEv of 12.5707 μm, and RPD of 8.6798. The results indicated that both NIR and AE are effective monitoring tools for MC and D50 analysis in fluidized bed granulation process. In addition, a more accurate and reliable analysis of particle size can be achieved by combining NIR and AE technology with high-level data fusion.

Improving the Accuracy of Saffron Adulteration Classification and Quantification through Data Fusion of Thin-Layer Chromatography Imaging and Raman Spectral Analysis

Agricultural crops of high value are frequently targeted by economic adulteration across the world. Saffron powder, being one of the most expensive spices and colorants on the market, is particularly vulnerable to adulteration with extraneous plant materials or synthetic colorants. However, the current international standard method has several drawbacks, such as being vulnerable to yellow artificial colorant adulteration and requiring tedious laboratory measuring procedures. To address these challenges, we previously developed a portable and versatile method for determining saffron quality using a thin-layer chromatography technique coupled with Raman spectroscopy (TLC-Raman). In this study, our aim was to improve the accuracy of the classification and quantification of adulterants in saffron by utilizing mid-level data fusion of TLC imaging and Raman spectral data. In summary, the featured imaging data and featured Raman data were concatenated into one data matrix. The classification and quantification results of saffron adulterants were compared between the fused data and the analysis based on each individual dataset. The best classification result was obtained from the partial least squares-discriminant analysis (PLS-DA) model developed using the mid-level fusion dataset, which accurately determined saffron with artificial adulterants (red 40 or yellow 5 at 2-10%, w/w) and natural plant adulterants (safflower and turmeric at 20-100%, w/w) with an overall accuracy of 99.52% and 99.20% in the training and validation group, respectively. Regarding quantification analysis, the PLS models built with the fused data block demonstrated improved quantification performance in terms of R² and root-mean-square errors for most of the PLS models. In conclusion, the present study highlighted the significant potential of fusing TLC imaging data and Raman spectral data to improve saffron classification and quantification accuracy via the mid-level data fusion, which will facilitate rapid and accurate decision-making on site.

Spectral separation degree method for Vis-NIR spectroscopic discriminant analysis of milk powder adulteration

Adulteration detection of adding ordinary milk powder to high-end dedicated milk powder is challenging due to the high similarity. Using visible and near-infrared (Vis-NIR) spectroscopy combined with k-nearest neighbor (kNN), the discriminant analysis models of pure brand milk powder and its adulterated milk powder (including unary and binary adulteration) were established. Standard normal variate transformation and Norris derivative filter (D = 2, S = 11, G = 5) were jointly used for spectral preprocessing. The separation degree and separation degree spectrum between two spectral populations were proposed and used to describe the differences between the two spectral populations, based on which, a novel wavelength selection method, named separation degree priority combination-kNN (SDPC-kNN), was proposed for wavelength optimization. SDPC-wavelength step-by-step phase-out-kNN (SDPC-WSP-kNN) models were established to further eliminate interference wavelengths and improve the model effect. The nineteen wavelengths in long-NIR region (1100-2498 nm) with a separation degree greater than 0 were used to establish single-wavelength kNN models, the total recognition-accuracy rates in prediction (RAR_P) all reached 100%, and the total recognition-accuracy rate in validation (RAR_V) of the optimal model (1174 nm) reached 97.4%. In the visible (400-780 nm) and short-NIR (780-1100 nm) regions with the separation degree all less than 0, the SDPC-WSP-kNN models were established. The two optimal models (N = 7, 22) were determined, the RAR_P values reached 100% and 97.4% respectively, and the RAR_V values reached 96.1% and 94.3% respectively. The results indicated that Vis-NIR spectroscopy combined with few-wavelength kNN has feasibility of high-precision milk powder adulteration discriminant. The few-wavelength schemes provided a valuable reference for designing dedicated miniaturized spectrometer of different spectral regions. The separation degree spectrum and SDPC can be used to improve the performance of spectral discriminant analysis. The SDPC method based on the separation degree priority proposed is a novel and effective wavelength selection method. It only needs to calculate the distance between two types of spectral sets at each wavelength with low computational complexity and good performance. In addition to combining with kNN, SDPC can also be combined with other classifier algorithms (e.g. PLS-DA, PCA-LDA) to expand the application scope of the method.

Identification of Chinese red wine origins based on Raman spectroscopy and deep learning

In this study, we combined Raman spectroscopy with deep learning for the first time to establish an accurate, simple, and fast method to identify the origin of red wines. We collected Raman spectra from 200 red wine samples of the Cabernet Sauvignon variety from four different origins with a portable Raman spectrometer. The red wine samples, made in 2021, were from the same producer in China. Differences were found by analyzing the Raman spectra of red wine samples. These differences are mainly caused by ethanol, carboxylic acids, and polyphenols. After further analysis, for different origins, the different performances of these substances on the Raman spectrum are related to the climate and geographical conditions of the origin. The Raman spectra were analyzed by principal component analysis (PCA). The data with PCA dimensionality reduction were imported into an artificial neural network (ANN), multifeature fusion convolutional neural network (MCNN), GoogLeNet, and residual neural network (ResNet) to establish red wine origin identification models. The classification results of the model prove that climate, geography, and other conditions can provide support for the classification of red wine origin. The experiments showed that all four models performed well, among which MCNN performed the best with 93.2% classification accuracy, and the area under the curve (AUC) was 0.987. This study provides a new means to classify the origin of red wine and opens up new ideas for identifying origins in the food field.

Modeling method and miniaturized wavelength strategy for near-infrared spectroscopic discriminant analysis of soy sauce brand identification

The identification of soy sauce brands can avoid adulteration and fraud, which is meaningful for food safety screening. Using visible and near-infrared (Vis-NIR) spectroscopy combined with k-nearest neighbor (kNN), the four-category discriminant models of soy sauce brands were established. The soy sauce of three brands (identification) and the other ten brands (interference) were collected, and a total of four categories of samples were obtained. The spectral datasets of two measurement modals (1 mm, 10 mm) were obtained. Based on moving-window (MW) waveband screening and wavelength step-by-step phase-out (WSP), the MW-WSP-kNN algorithm was proposed and applied to the wavelength optimization for the four-category discriminant analysis. Using calibration-prediction-validation experiment design, various high accuracy models with a small number of wavelengths located in NIR region were determined. In the independent validation, for the 1 mm measurement modal, the selected thirty-five dual-wavelength models and one three-wavelength model were located in NIR combined and overtone frequency regions respectively, all achieved 100% total recognition accuracy rate (RAR_Total); for the 10 mm measurement modal, the selected seven three-wavelength models located in NIR overtone frequency region all reached more than 96.8% RAR_Total, and the optimal RAR_Total was 97.8%. The results showed the feasibility of small number of wavelengths' NIR spectroscopy applied to multi-category discriminant of soy sauce brands, with the advantages of rapid, simple and miniaturized. The proposed various small number of wavelengths' models provided a valuable reference for the design of small dedicated spectrometer with different measurement modals. The integrated optimization method and wavelength selection strategy here are also expected to be applied to other fields.

Recent Developments in Surface-Enhanced Raman Spectroscopy and Its Application in Food Analysis: Alcoholic Beverages as an Example

Surface-enhanced Raman spectroscopy (SERS) is an emerging technology that combines Raman spectroscopy and nanotechnology with great potential. This technology can accurately characterize molecular adsorption behavior and molecular structure. Moreover, it can provide rapid and sensitive detection of molecules and trace substances. In practical application, SERS has the advantages of portability, no need for sample pretreatment, rapid analysis, high sensitivity, and ‘fingerprint’ recognition. Thus, it has great potential in food safety detection. Alcoholic beverages have a long history of production in the world. Currently, a variety of popular products have been developed. With the continuous development of the alcoholic beverage industry, simple, on-site, and sensitive detection methods are necessary. In this paper, the basic principle, development history, and research progress of SERS are summarized. In view of the chemical composition, the beneficial and toxic components of alcoholic beverages and the practical application of SERS in alcoholic beverage analysis are reviewed. The feasibility and future development of SERS are also summarized and prospected. This review provides data and reference for the future development of SERS technology and its application in food analysis.

Performance Improvement of NIR Spectral Pattern Recognition from Three Compensation Models’ Voting and Multi-Modal Fusion

Inspired by aquaphotomics, the optical path length of measurement was regarded as a perturbation factor. Near-infrared (NIR) spectroscopy with multi-measurement modals was applied to the discriminant analysis of three categories of drinking water. Moving window-k nearest neighbor (MW-kNN) and Norris derivative filter were used for modeling and optimization. Drawing on the idea of game theory, the strategy for two-category priority compensation and three-model voting with multi-modal fusion was proposed. Moving window correlation coefficient (MWCC), inter-category and intra-category MWCC spectra, and k-shortest distances plotting with MW-kNN were proposed to evaluate weak differences between two spectral populations. For three measurement modals (1 mm, 4 mm, and 10 mm), the optimal MW-kNN models, and two-category priority compensation models were determined. The joint models for three compensation models’ voting were established. Comprehensive discrimination effects of joint models were better than their sub-models; multi-modal fusion was better than single-modal fusion. The best joint model was the dual-modal fusion of compensation models of one- and two-category priority (1 mm), one- and three-category priority (10 mm), and two- and three-category priority (1 mm), validation’s total recognition accuracy rate reached 95.5%. It fused long-wave models (1 mm, containing 1450 nm) and short-wave models (10 mm, containing 974 nm). The results showed that compensation models’ voting and multi-modal fusion can effectively improve the performance of NIR spectral pattern recognition.

Analysis of Selected Minerals in Homemade Grape Vinegars Obtained by Spontaneous Fermentation

Fruit vinegars are widely used as a spice and food preservative. They are considered as functional food, containing many bioactive compounds with pro-health benefits. Grape vinegars can be also a source of mineral compounds. Their quantity and diversity can be determined by environmental factors and growing conditions, such as temperature, mineral composition of the soil, heavy metal contamination, sunlight availability as well as grape variety and fruit ripeness stage. The aim of the study was to determine the content of minerals in homemade grape vinegars, obtained by spontaneous fermentation. Five different grape (Vitis vinifera L.) varieties were used in the study (Cabernet Cortis, Johanniter, Solaris, Souvignier gris and Prior). Moreover, the effect of sugar addition in the fermentation process on the mineral content was examined. The mineral content was determined using the ICP-OES method. Among the analysed samples, potassium was the most abundant element (936.07-1472.3 mg/L of vinegar). Comparative analysis showed that the content of Ca, Fe and Cr was significantly higher in vinegars prepared from red varieties than in white-coloured ones. In turn, vinegars prepared from white grape varieties contained statistically significantly higher content of potassium. Vinegar colour did not have a significant influence on the content of the remaining elements included in the analysis. Furthermore, statistical analysis did not reveal any significant differences in the content of the analysed minerals in any of the grape varieties used between the samples with and without sugar addition.

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.

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

In a global context where trading of wines involves considerable economic value, the requirement to guarantee wine authenticity can never be underestimated. With the ever-increasing advancements in analytical platforms, research into spectroscopic methods is thriving as they offer a powerful tool for rapid wine authentication. In particular, spectroscopic techniques have been identified as a user-friendly and economical alternative to traditional analyses involving more complex instrumentation that may not readily be deployable in an industry setting. Chemometrics plays an indispensable role in the interpretation and modelling of spectral data and is frequently used in conjunction with spectroscopy for sample classification. Considering the variety of available techniques under the banner of spectroscopy, this review aims to provide an update on the most popular spectroscopic approaches and chemometric data analysis procedures that are applicable to wine authentication.

Chemometric Strategies for Spectroscopy-Based Food Authentication

In the last decades, spectroscopic techniques have played an increasingly crucial role in analytical chemistry, due to the numerous advantages they offer. Several of these techniques (e.g., Near-InfraRed—NIR—or Fourier Transform InfraRed—FT-IR—spectroscopy) are considered particularly valuable because, by means of suitable equipment, they enable a fast and non-destructive sample characterization. This aspect, together with the possibility of easily developing devices for on- and in-line applications, has recently favored the diffusion of such approaches especially in the context of foodstuff quality control. Nevertheless, the complex nature of the signal yielded by spectroscopy instrumentation (regardless of the spectral range investigated) inevitably calls for the use of multivariate chemometric strategies for its accurate assessment and interpretation. This review aims at providing a comprehensive overview of some of the chemometric tools most commonly exploited for spectroscopy-based foodstuff analysis and authentication. More in detail, three different scenarios will be surveyed here: data exploration, calibration and classification. The main methodologies suited to addressing each one of these different tasks will be outlined and examples illustrating their use will be provided alongside their description.

ATR-MIR spectroscopy as a tool to assist 'Tempranillo' clonal selection process: Geographical origin and year of harvest discrimination and oenological parameters prediction

The present study evaluated the ability of Attenuated Total Reflectance - Mid-Infrared (ATR-MIR) spectroscopy combined with Partial Least Squares Discriminant Analysis (PLS-DA) to discriminate the origin and harvest year of 'Tempranillo' grape clones and with Partial Least Squares (PLS) regressions to predict its contents in soluble solids (SS), pH and titratable acidity (TA). Normalized spectra of grape homogenates and normalized plus 1st Derivative spectra of grape skins allowed an overall percentage of correct classifications of 99.6% and 96.7% in validation, according to origin, and 98.3% and 90.0% in validation, according to harvest year, respectively. The normalized spectra of grape homogenates allowed a calibration and validation determination coefficients (R2) of 0.92 and 0.90 for SS, 0.90 and 0.84 for pH, 0.88 and 0.84 for TA, respectively. The ATR-MIR combined with multivariate analysis showed to be an appropriate tool to assist the clonal selection process of 'Tempranillo'.

Using Raman Spectroscopy as a Fast Tool to Classify and Analyze Bulgarian Wines-A Feasibility Study

Raman spectroscopy, being able to provide rich information about the chemical composition of the sample, is gaining an increasing interest in the applications of food. Raman spectroscopy was used to analyze a set of wine samples (red and white) sourced from rarely studied traditional Bulgarian wines. One of the objectives of this study was to attempt the fast classification of Bulgarian wines according to variety and geographic origin. In addition, calibration models between phenolic compounds and Raman spectroscopy were developed using partial least squares (PLS) regression using cross-validation. Good calibration statistics were obtained for total phenolic compounds (by the Folin–Ciocalteu method) and total phenolic compounds and phenolic acids (spectrophotometrically at 280 nm) where the coefficient of determination (R²) and the standard error in the cross-validation (SECV) were 0.81 (474.2 mg/dm³ gallic acid), 0.87 (526.6 mg/dm³ catechin equivalents), and 0.81 (44.8 mg/dm³ caffeic equivalents), respectively. This study has demonstrated that Raman spectroscopy can be suitable for measuring phenolic compounds in both red and white wines.

Expedite SERS Fingerprinting of Portuguese White Wines Using Plasmonic Silver Nanostars

Surface-enhanced Raman Spectrosocopy (SERS) is a highly sensitive form of Raman spectroscopy, with strong selectivity for Raman-active molecules adsorbed to plasmonic nanostructured surfaces. Extremely intense Raman signals derive from "hotspots", generally created by the aggregation of a silver nanospheres colloid. An alternative and cleaner approach is the use of anisotropic silver nanoparticles, with intrinsic "hotspots", allowing a more controlled enhancement effect as it is not dependent on disordered nanoparticle aggregation. Here, a simple SERS-based test is proposed for Portuguese white wines fingerprinting. The test is done by mixing microliter volumes of a silver nanostars colloid and the white wine sample. SERS spectra obtained directly from these mixtures, with no further treatments, are analyzed by principal component analysis (PCA), using a dedicated software. Depending on the duration of the incubation period, different discrimination can be obtained for the fingerprinting. A "mix-and-read" approach, with practically no incubation, allows for a simple discrimination between the three white wines tested. An overnight incubation allows for full discrimination between varieties of wine (Verde or Maduro), as well as between wines from different Maduro wine regions. This use of SERS in a straightforward, fast and inexpensive test for wine fingerprinting, avoiding the need for prior sample treatment, paves the way for the development of a simple and inexpensive authenticity assay for wines from specific appellations.

Application of the voltammetric electronic tongue based on nanocomposite modified electrodes for identifying rice wines of different geographical origins

In the study, the voltammetric electronic tongue based on three nanocomposites modified electrodes was applied for the identification of rice wines of different geographical origins. The nanocomposites were prepared by gold and copper nanoparticles in the presence of conducting polymers (polymer sulfanilic acid, polymer glutamic acid) and carboxylic multi - walled carbon nanotubes. The modified electrodes showed high sensitivity to guanosine - 5' - monophosphate disodium salt, tyrosine and gallic acid which have good correlation with the geographical origins of rice wines. Scanning electron microscopy was performed to display the surface morphologies of the nanocomposites, and cyclic voltammetry was applied to study the electrochemical behaviors of the taste substances on the electrode surfaces. Four types of electrochemical parameters (pH, scan rates, accumulation potentials and time) were optimized for getting a low limit of the detection of each taste substance. The geographical information of rice wines was obtained by the modified electrodes based on two types of multi - frequency large amplitude pulse voltammetry, and "area method" was applied for extracting the feature data from the original information obtained. Based on the area feature data, principal component analysis, locality preserving projection (LPP), and linear discriminant analysis were applied for the classification of the rice wines of different geographical origins, and LPP presented the best results; extreme learning machine (ELM) and alibrary for support vector machines were applied for predicting the geographical origins of rice wines, and ELM performed better.

Changes in organic acids, polyphenolic and elemental composition of rosé sparkling wines treated with mannoproteins during over-lees aging

The effect of mannoproteins on the evolution of rosé sparkling wines during over-lees aging was investigated on the basis of the chemical characterization of polyphenols, organic acids, macro- and microelements using a combined analytical approach. Variations on these constituents were assessed using Raman and near-infrared spectroscopy. During the biological aging, caffeic acid, catechin, gallic acid and malvidin-3-O-glucoside were the most abundant polyphenolics in the rosé wines. The phenolic compound tyrosol, a fermentation derivative, was found at concentrations up to 98.07 mg L^-1. The addition of mannoproteins significantly affected the concentrations of organic acids and individual polyphenolic compounds, particularly trans-resveratrol, quercetin, catechin, p-coumaric and hydroxybenzoic acids that showed increased concentrations over time. The positive effects of mannoproteins were mainly observed at the end of the biological aging. The mineral composition remained stable, while potassium was the most abundant mineral in all wines. The observed changes involving these constituents may offer new insights on their behavior during wine aging and on the bioactive and nutritional quality of rosé sparkling wines.