## 1 Introduction
[2] and [PERSON] and [PERSON] [3] evaluated the physicochemical composition of wine with different levels of bentonite clays. The results obtained from the experiments indicated significant variations in turbidity, colorimetry, and pH parameters based on the concentration of bentonite added to the wine samples. Using bentonite as a fining agent improved the wines' clarity, color stability, and pH balance. [PERSON] et al. [13] studied the proteins that originated after the fining process and evaluated the efficiency of different types of bentonite. The results revealed the differential efficiency of various bentonite types in preserving specific odoriferous esters and antioxidant phenols, positively impacting the wine sensory quality. This finding is of significant interest to the wine industry as it highlights the potential benefits of using specific bentonite types in wine production. [PERSON] et al. [11] evaluated different mucilaginous plants for juice clarification ('yausabara' included). The main objective was to reduce or eliminate chemicals in the clarification process utilizing mucilaginous plants, mainly weeds, as natural clarifying agents. The study highlights the increasing demand for processed products with natural and organic characteristics, which has led to the exploration of alternative clarifying agents. The research concludes that mucilaginous plant extracts, mainly from weed species, have the potential to be used as natural clarifying agents, reducing the reliance on chemical additives. [PERSON] [10] evaluated the clarification process using natural flocculants ('yausabara' and papain) in Cabernet Sauvignon wine. AI has been increasingly applied to the wine industry, revolutionizing various aspects of wine production, consumer engagement, and fraud detection. AI has been used in wine production to optimize wine palettes and enhance product quality [14]. AI algorithms can analyze large data sets to identify patterns and create more nuanced and diverse wine profiles, catering to a broader range of palates [15; 16]. AI-powered cameras and sensors are also used to monitor every stage of production, from grape sorting to fermentation, identifying subtleties that might be missed by human observation [17; 18]. In fraud detection, AI has been trained to analyze the chemical composition of wines, enabling it to trace a bottle back to its specific vineyard and vintage with remarkable accuracy [19]. It has also been used for the prediction, modelling, and optimization of phenolic extraction [20]. However, few studies currently exist on a mathematical model to estimate turbidity in wines. [PERSON] et al. [21] present a wine quality and turbidity approach but consider white and rose wines and eight values of turbidity. The approach in the present paper focuses on the fining agents and a wide dosage range that can predict a more comprehensive turbidity range. Other study objectives can be found in the article by [PERSON], [PERSON] and [PERSON] [22], who aimed to determine the most influential variables in wine's sensory quality through artificial intelligence techniques. The study focuses on transforming a subjective method of determining the sensory quality of wine into an objective one using data mining techniques. This approach was compared with other works carried out subjectively, and it was concluded that using artificial intelligence techniques can lead to better innovation, competitiveness, and wine quality. The article also highlights the importance of the physical-chemical variables of wine and their influences on sensory quality. A decision tree was built that identified the leading influential causes of the quality of the wine, demonstrating effectiveness more significant than 95%, and the most influential variables on wine quality were alcohol, pH, sulfates, citric acid, and the alcohol and sulfate ratio. Similarly, [PERSON] et al.