Analytical and Chemometric Investigation of Phenolic Content of South African Red Wines

The main objective of the work was to develop a simple, robust and selective analytical tool that allows predicting the authenticity of Tempranillo wines from DOCa Rioja. The techniques of voltammetry and absorbance-transmission and fluorescence excitation emission matrix (A-TEEM) spectroscopy have been applied in combination with machine learning (ML) algorithms to classify red wines from DOCa Rioja according to region (Alavesa, Alta or Oriental) and category (young, crianza or reserva).

Many high quality Open Source scientific applications have been available for a long time. Some of them have proved to be particularly useful for carrying out the usual activities involved in viticultural research projects, such as statistical analyses (including spatial analyses), GIS work, database management (possibly integrated with statistical and spatial analysis) and even “low-level” often highly time-consuming activities (e.g. repetitive task on text files).

Consumers’ demand for non-alcoholic wine has notably increased in the last years: this trend is a consequence of a growing interest in more healthy habits, and as a response to higher alcohol levels in wine due to climate change. In addition, drinking limitations due to physiological/pathological conditions (e.g., pregnancy, diabetes, hepatic disorders), driving regulations, ethical/religious considerations, and high import taxes on alcoholic beverages have positively influenced this marked (us$ 1.6 billion in 2021). International organisation of vine and wine (OIV) established that alcohol content defining wines must not be less than 8.5% vol, (OIV, 2017).

Development of the indigenous microflora is not insignificant on the wine quality. S. cerevisiae indigenous strains are low tolerant to ethanol.

Grape harvest time is one of the most fundamental aspects that affect grape quality and thus wine quality. Many factors influence the decision of harvest; among them technological and phenolic maturity of grape. Technological ripeness is mainly related to sugar concentration, titratable acidity and pH. Conventional methods for chemical analysis of grapes are normally sample-destructive, time-consuming, include laborious sample preparation steps, and generate chemical waste, thereby limiting their utility in online/in-line quality monitoring. Moreover, destructive analyses can be performed only on a limited number of fruit pieces and, thus, their statistical relevance could be limited. This study evaluated the ability of a lab-scale hyperspectral imaging (HYP-IM) technique to predict titratable acidity, organic acid and sugar content of grapes. Samples of Cabernet franc and Chenin blanc grapes were consecutively collected six times at weekly intervals after veraison. The images were recorded thanks to the hyperspectral imaging camera Pica L (Resonon) in a spectral range from 400 to 1000 nm. Statistics were performed using Microsoft Xlstat software. Successively, the berries were analyzed for their sugar (glucose and fructose) and organic acid (malic and tartaric acid) content and titratable acidity according to usual methods.