Identification and quantification of industrial grade glycerol adulteration in red wine with fourier transform infrared spectroscopy using chemometrics and artificial neural networks

Synthesis and characterization of polysaccharide- and protein-based edible films and application as packaging materials for fresh fish fillets

The rising packaging industry together with global demand for sustainable production has increased the interest in developing biodegradable packaging materials. The aim of the study was to develop edible films based on pectin, gelatin, and hydroxypropyl methylcellulose and evaluate their applicability as biodegradable packaging materials for gilthead seabream fillets. Mechanical properties, water barriers, wettability of the films through contact angle measurement, optical, and UV-Vis barrier properties were evaluated for food packaging applications. The effective blend of polysaccharide and protein film-forming solutions was confirmed by the produced films with excellent optical properties, acceptable mechanical properties and adequate barriers to water vapor. The contact angle for pectin based and gelatin based films were higher than 90° indicating the hydrophobic films, while HPMC based films had contact angle lower than 90°. The produced films were tested as alternative and environmentally friendly packaging materials for gilthead seabream fillets during refrigerated storage. All tested packaging conditions resulted in similar shelf-life in packed gilthead seabream fillets (i.e. 7-8 days at 2 °C). The results showed that the developed films may reduce the use of conventional petroleum-based food packaging materials without affecting the shelf-life of fish.

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.

Upconversion fluorescent nanoparticles based-sensor array for discrimination of the same variety red grape wines

A fluorescent sensor array composed of upconversion nanomaterials to distinguish the same variety of red grape wines was constructed.

Rapid determination of baicalin and total baicalein content in Scutellariae radix by ATR-IR and NIR spectroscopy

In this study methods for the quantification of baicalin and total baicalein in Scutellariae radix with near infrared (NIR) spectroscopy and attenuated-total-reflectance mid-infrared (ATR-IR) spectroscopy in hyphenation with multivariate analysis were developed and compared. The reference analysis was performed by high performance liquid chromatography coupled to diode array detection (HPLC-DAD). Different pretreatments like standard normal variate (SNV), multiplicative scatter correction (MSC), first and second derivative Savitzky-Golay were applied on the spectra to optimize the calibrations. A principal component analysis was performed with both spectroscopic methods to distinguish wild and cultivated samples. Quality parameters obtained for test-set calibration models of ATR-IR spectroscopy (baicalin: standard error of prediction (SEP)=1.31, ratio performance to deviation (RPD)=2.91 and R(2)=0.88; total baicalein: SEP=1.02, RPD=3.24 and R(2)=0.89) and NIR spectroscopy (baicalin: SEP=1.50, RPD=2.54 and R(2)=0.88; total baicalein: SEP=1.19, RPD=2.76 and R(2)=0.84) demonstrate that both spectroscopic techniques in combination with multivariate analysis are successful tools for the quantification of baicalin and total baicalein in Scutellariae radix, but it was found that ATR-IR spectroscopy provides higher accuracy in the given application. Furthermore it was proved that wild and cultivated samples can be distinguished by ATR-IR.

Authentication of a Turkish traditional aniseed flavoured distilled spirit, raki

Consumption of traditional aniseed alcoholic beverage, raki, adulterated with methanol results in deaths, therefore, its detection is an important issue. In this study, mid-infrared spectra of pure and methanol adulterated (0.5-10% (vol/vol)) raki samples were collected with an attenuated total reflectance attachment of a Fourier-transform infrared spectrometer. Principal component analysis was used to discriminate pure and adulterated raki samples, then, a partial least square model was constructed to determine the adulterant methanol content in raki using mid-IR spectral data. A minimum threshold level of 0.5% methanol in raki samples was successfully detected. A good prediction model for determination of methanol adulteration ratio in raki samples was also constructed (R(2)=0.98 and RPD=8.35).

Technical solutions for analysis of grape juice, must, and wine: the role of infrared spectroscopy and chemometrics

Information about constituents of grape juice, must, and wine can be used for management and decision support systems in order to improve, monitor, and adapt grape and wine production to new challenges. Numerous sensors that gather this information are either currently available or in development. Nevertheless there is still a need to adapt these sensors to special requirements, for example robustness, calibration and maintenance, operating costs, duration, sensitivity, and specificity to a particular application. The sensors commonly used by the wine industry are those that are based on mid-infrared (MIR), near-infrared (NIR), visible (VIS) and ultraviolet (UV) spectroscopy. This article reviews some recent technical solutions for analysis of juice, must and wine based on the combination of infrared spectroscopy and chemometrics.

A new FT-IR method combined with multivariate analysis for the classification of vinegars from different raw materials and production processes

BACKGROUND

Due to the diversity of vinegars on the market and the increase in demand, it is considered necessary to investigate and establish criteria for classifying them in order to obtain more information concerning their real origin. New spectroscopic techniques such us mid-infrared spectroscopy with Fourier transform (FT-IR) are capable of providing information in relation to these aspects. FT-IR combined with multivariate analysis has been used to classify vinegars according to the raw materials and production processes (with or without ageing in wood). Principal component analysis (PCA), partial least-squares discriminant analysis regression (PLS-DA) and stepwise linear discriminant analysis (SLDA) were used.

RESULTS

The results obtained have been compared to those achieved using different analytical parameters (polyphenolic content, organic acids and volatile compounds). SLDA and PLS-DA results show the ability of mid-FT-IR spectra to discriminate among vinegars from different raw materials and with or without ageing in wood, with correct classification percentages similar to those obtained using different analytical parameters.

CONCLUSION

The discriminative ability combined with other advantages (e.g. rapid and non-destructive analysis, low cost) makes this new FT-IR method a promising tool for the classification and/or differentiation of vinegars.

Quantitative analysis of red wine tannins using Fourier-transform mid-infrared spectrometry

Tannin content and composition are critical quality components of red wines. No spectroscopic method assessing these phenols in wine has been described so far. We report here a new method using Fourier transform mid-infrared (FT-MIR) spectroscopy and chemometric techniques for the quantitative analysis of red wine tannins. Calibration models were developed using protein precipitation and phloroglucinolysis as analytical reference methods. After spectra preprocessing, six different predictive partial least-squares (PLS) models were evaluated, including the use of interval selection procedures such as iPLS and CSMWPLS. PLS regression with full-range (650-4000 cm(-1)), second derivative of the spectra and phloroglucinolysis as the reference method gave the most accurate determination for tannin concentration (RMSEC = 2.6%, RMSEP = 9.4%, r = 0.995). The prediction of the mean degree of polymerization (mDP) of the tannins also gave a reasonable prediction (RMSEC = 6.7%, RMSEP = 10.3%, r = 0.958). These results represent the first step in the development of a spectroscopic methodology for the quantification of several phenolic compounds that are critical for wine quality.