Jacek Namieśnik—Analytical Chemist and Dedicated Biker: From Wine Analysis to Toxic Compounds

Jacek Namieśnik, who died at the age of 69, was one of the most influential analytical chemists in Poland at the second half of the 20th century and the first two decades of the 21st century [...]

Determination of sweeteners in wine by liquid chromatography coupled with mass spectrometry (LC/MS)

Sweeteners are food additive substances that give a sweet taste to foods but their use in oenological practices is forbidden. Making use of the capabilities of liquid chromatography coupled with mass spectrometry, a method for wine analysis was developed and validated for the detection and quantitation of some of the most widely used sweeteners: aspartame, potassium acesulfame, sodium cyclamate, saccharin, sucralose and stevioside. A matrix-matched calibration was used for all compounds obtaining a linear concentration range from 50 μg/L to 1000 μg/L. The limit of detection ranged from 0.002 mg/L to 0.014 mg/L, and the limit of quantification varied between 0.005 mg/L and 0.048 mg/L. Precision and recovery were assessed for 50 μg/L, 250 μg/L and 1000 μg/L with repeatability and intermediate precision values from 0.6% to 21.6% and 2.7% to 26.4% respectively, and recoveries ranging from 60% to 126%. These results were achieved using minimal sample preparation with a fast and high throughput method that is applicable to a wide range of wine matrices.

Comprehensive Classification and Regression Modeling of Wine Samples Using 1H NMR Spectra

Recently, 1H NMR (nuclear magnetic resonance) spectroscopy was presented as a viable option for the quality assurance of foods and beverages, such as wine products. Here, a complex chemometric analysis of red and white wine samples was carried out based on their 1H NMR spectra. Extreme gradient boosting (XGBoost) machine learning algorithm was applied for the wine variety classification with an iterative double cross-validation loop, developed during the present work. In the case of red wines, Cabernet Franc, Merlot and Blue Frankish samples were successfully classified. Three very common white wine varieties were selected and classified: Chardonnay, Sauvignon Blanc and Riesling. The models were robust and were validated against overfitting with iterative randomization tests. Moreover, four novel partial least-squares (PLS) regression models were constructed to predict the major quantitative parameters of the wines: density, total alcohol, total sugar and total SO2 concentrations. All the models performed successfully, with R2 values above 0.80 in almost every case, providing additional information about the wine samples for the quality control of the products. 1H NMR spectra combined with chemometric modeling can be a good and reliable candidate for the replacement of the time-consuming traditional standards, not just in wine analysis, but also in other aspects of food science.

Application of Ultraviolet-Visible Absorption Spectroscopy with Machine Learning Techniques for the Classification of Cretan Wines

The present study was aimed at the identification, differentiation and characterization of red and white Cretan wines, which are described with Protected Geographical Indication (PGI), using ultraviolet–visible absorption spectroscopy. Specifically, the grape variety, the wine aging process and the role of barrel/container type were investigated. The combination of spectroscopic results with machine learning-based modelling demonstrated the use of absorption spectroscopy as a facile and low-cost technique in wine analysis. In this study, a clear discrimination among grape varieties was revealed. Moreover, a grouping of samples according to aging period and container type of maturation was accomplished, for the first time.