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

Variational Mode Decomposition for Raman Spectral Denoising

As a fast and non-destructive spectroscopic analysis technique, Raman spectroscopy has been widely applied in chemistry. However, noise is usually unavoidable in Raman spectra. Hence, denoising is an important step before Raman spectral analysis. A novel spectral denoising method based on variational mode decomposition (VMD) was introduced to solve the above problem. The spectrum is decomposed into a series of modes (uk) by VMD. Then, the high-frequency noise modes are removed and the remaining modes are reconstructed to obtain the denoised spectrum. The proposed method was verified by two artificial noised signals and two Raman spectra of inorganic materials, i.e., MnCo ISAs/CN and Fe-NCNT. For comparison, empirical mode decomposition (EMD), Savitzky-Golay (SG) smoothing, and discrete wavelet transformation (DWT) are also investigated. At the same time, signal-to-noise ratio (SNR) was introduced as evaluation indicators to verify the performance of the proposed method. The results show that compared with EMD, VMD can significantly improve mode mixing and the endpoint effect. Moreover, the Raman spectrum by VMD denoising is more excellent than that of EMD, SG smoothing and DWT in terms of visualization and SNR. For the small sharp peaks, some information is lost after denoising by EMD, SG smoothing, DWT and VMD while VMD loses fewest information. Therefore, VMD may be an alternative method for Raman spectral denoising.

Sulfites detection by surface-enhanced Raman spectroscopy: A feasibility study

The exhaustive control required for the correct wine production needs of many chemical analysis throughout the process. The most extended investigations for wine production control are focused on the quantification of total and free SO₂. Most methods described in the literature have an adequate detection limit, but they usually lack reproducibility and require a previous sample treatment for the extraction of the SO₂ from the wine-matrix. In this context, Surface-Enhanced Raman Spectroscopy (SERS) can be a promising technique for free SO₂ determination without the need for any sample pre-processing. This work describes a proof of concept of a new methodology based on SERS and supported by Density Functional Theory (DFT) calculations to identify the active vibrational modes of the key molecules that contribute to the concentration of free SO₂ in solution. Theoretical predictions and experimental outcomes are brought together to chemometrics to get a simple and real-time free SO₂ monitoring. This general procedure could pave the way towards an implementation of a portable SERS detection module for in-field measurements.

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.

Effects of Extraction Methods on Phenolic Content, Antioxidant and Antiplatelet Activities of Tomato Pomace Extracts

Aqueous and ethanolic extracts of tomato pomace were examined with the aim of optimizing the extraction process of compounds with cardioprotective activity. Once the results of the ORAC response variables, total polyphenols, °Brix, and antiplatelet activity of the extracts were obtained, a multivariate statistical analysis was performed using the Statgraphics Centurion XIX software. This analysis showed that the most relevant positive effects in the inhibition of platelet aggregation were 83 ± 2% when using the agonist TRAP-6, when the working conditions were the type of tomato pomace conditioning (drum-drying process at 115 °C), phase ratio (1/8), type of solvent (ethanol 20%), and type of extraction (ultrasound-assisted solid-liquid extraction). The extracts with the best results were microencapsulated and characterized by HPLC. The presence of chlorogenic acid (0.729 mg/mg of dry sample) was found, a compound that has a potential cardioprotective effect documented in various studies, in addition to rutin (2.747 mg/mg of dry sample) and quercetin (0.255 mg/mg of dry sample). These results show that the extraction efficiency of compounds with cardioprotective activity depends largely on the polarity of the solvent, thus playing an important role in the antioxidant capacity of the extracts of tomato pomace.

A Narrative Review of Recent Advances in Rapid Assessment of Anthocyanins in Agricultural and Food Products

Anthocyanins (ACNs) are plant polyphenols that have received increased attention recently mainly due to their potential health benefits and applications as functional food ingredients. This has also created an interest in the development and validation of several non-destructive techniques of ACN assessments in several food samples. Non-destructive and conventional techniques play an important role in the assessment of ACNs in agricultural and food products. Although conventional methods appear to be more accurate and specific in their analysis, they are also associated with higher costs, the destruction of samples, time-consuming, and require specialized laboratory equipment. In this review article, we present the latest findings relating to the use of several spectroscopic techniques (fluorescence, Raman, Nuclear magnetic resonance spectroscopy, Fourier-transform infrared spectroscopy, and near-infrared spectroscopy), hyperspectral imaging, chemometric-based machine learning, and artificial intelligence applications for assessing the ACN content in agricultural and food products. Furthermore, we also propose technical and future advancements of the established techniques with the need for further developments and technique amalgamations.

Recent Developments in Plasmonic Sensors of Phenol and Its Derivatives

Many scientists are increasingly interested in on-site detection methods of phenol and its derivatives because these substances have been universally used as a significant raw material in the industrial manufacturing of various chemicals of antimicrobials, anti-inflammatory drugs, antioxidants, and so on. The contamination of phenolic compounds in the natural environment is a toxic response that induces harsh impacts on plants, animals, and human health. This mini-review updates recent developments and trends of novel plasmonic resonance nanomaterials, which are assisted by various optical sensors, including colorimetric, fluorescence, localized surface plasmon resonance (LSPR), and plasmon-enhanced Raman spectroscopy. These advanced and powerful analytical tools exhibit potential application for ultrahigh sensitivity, selectivity, and rapid detection of phenol and its derivatives. In this report, we mainly emphasize the recent progress and novel trends in the optical sensors of phenolic compounds. The applications of Raman technologies based on pure noble metals, hybrid nanomaterials, and metal–organic frameworks (MOFs) are presented, in which the remaining establishments and challenges are discussed and summarized to inspire the future improvement of scientific optical sensors into easy-to-operate effective platforms for the rapid and trace detection of phenol and its derivatives.

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.