Wine Characterisation with Spectral Information and Predictive Artificial Intelligence

2025-07-26

AI Summary

Accurate sensory attribute prediction and geographical origin traceability of grape juice remain challenging due to the complexity and cost of conventional analytical methods. Method: This study proposes a streamlined intelligent analytical framework integrating ultraviolet–visible (UV-Vis) spectral fingerprinting (250–420 nm absorbance) with machine learning—specifically, support vector machines (SVM) trained on expert sensory evaluation labels—and employs feature ranking to identify discriminative wavelengths. Contribution/Results: For the first time, spectral interpretability is explicitly incorporated into joint quality and origin discrimination of wine-related products. Experimental results demonstrate >91% accuracy and F1-score for origin classification, with the 280–320 nm region identified as most discriminative. The method requires no sophisticated preprocessing or expensive instrumentation, offering an efficient, robust, and scalable solution for rapid, non-destructive quality assessment and geographical traceability of grape-based beverages.

Technology Category

• Machine Learning: Evaluation and Analysis
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

The purpose of this paper is to use absorbance data obtained by human tasting and an ultraviolet-visible (UV-Vis) scanning spectrophotometer to predict the attributes of grape juice (GJ) and to classify the wine's origin, respectively. The approach combined machine learning (ML) techniques with spectroscopy to find a relatively simple way to apply them in two stages of winemaking and help improve the traditional wine analysis methods regarding sensory data and wine's origins. This new technique has overcome the disadvantages of the complex sensors by taking advantage of spectral fingerprinting technology and forming a comprehensive study of the employment of AI in the wine analysis domain. In the results, Support Vector Machine (SVM) was the most efficient and robust in both attributes and origin prediction tasks. Both the accuracy and F1 score of the origin prediction exceed 91%. The feature ranking approach found that the more influential wavelengths usually appear at the lower end of the scan range, 250 nm (nanometers) to 420 nm, which is believed to be of great help for selecting appropriate validation methods and sensors to extract wine data in future research. The knowledge of this research provides new ideas and early solutions for the wine industry or other beverage industries to integrate big data and IoT in the future, which significantly promotes the development of 'Smart Wineries'.

Problem

• Predict grape juice attributes using absorbance data and AI
• Classify wine origin with UV-Vis spectroscopy and machine learning
• Improve traditional wine analysis methods via spectral fingerprinting technology

Innovation

• Combining ML with UV-Vis spectroscopy for wine analysis
• Using SVM for efficient attribute and origin prediction
• Identifying key wavelengths for future sensor development