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.

nalytical data of 260 sensory evaluated wine samples have been statistically examined. All samples had been classified by at least one taster of the five member jury as having "Brett character". Wines have been finally judged as "Good", having "Other defects" or "Brett character". 4-etylphenol (4-EP) and 4-ethylguaiacol (4-EG) concentrations showed different distributions for the "Brett character" group while the other two groups could not be distinguished from each-other. Threshold concentrations for 4-EP, 4-EG and their sum (4-EP + 4-EG) were calculated to classify wine samples as "non-Brett" and "Brett character". 4-EP concentrations were found to be the most reliable markers, with a 245 µg/l lower and 968 µg/l upper threshold. Below or above this range a sample can reliably be classified as "non-Brett" and "Brett character" respectively, while within this range only sensory evaluation can distinguish the two characteristics. Other tested classical analytical parameters did not show significant differences between these groups except for SO2 which was found to be lower in the "Brett character" group, stressing the importance of sulpihiting as a tool in the fight against Brettanomyces.

Palinka production has a long tradition in Hungary and the neighboring countries. Previously, the fruit distillate was produced exclusively using the traditional Pot-Still Double Distillation (PSDD) technology. This distillation method means, in practice, a simple fractional distillation repeated twice. However, in other industries, such as the petroleum industry or the pharmaceutical industry, a continuous, so-called repeated distillation procedure is used (RCDS – Rectification Column Distillation Systems). In the production of palinka, the latter procedure has gained more and more ground in recent years, thus displacing the traditional technology. In the territory of today’s Hungary, there are more than 16,000 registered private palinka distillers. However, based on public databases, it is not possible to know the proportion of the two different palinka making processes used in palinka production. The two processes differ to a large degree. The amount of hearts obtained using the continuous operation plate rectification column (RCDS) is lower, while its alcohol content is very high: 75 – 90 vol%, depending on the fruit. On the other hand, when using the traditional pot-still double distillation (PSDD) method, the amount of hearts is higher, but its alcohol content is lower (60 – 70%). The continuous procedure, also called single-stage, is faster. This is one of the reasons for its popularity because it makes production more economical. The objective of our research was to find out whether a significant difference could be detected between the two plum palinkas produced using the two different distillation technologies, based on current legal requirements. Our research also included sensory testing to determine whether consumers could distinguish between the products manufactured in different ways. Our analyses were carried out in 2019 in the accredited laboratory of the National Food Chain Safety Office and among the students and staff of the Gödöllő campus of Szent István University.