用光谱+AI预测葡萄汁属性和葡萄酒产地，准确率超91%。
Wine Characterisation with Spectral Information and Predictive Artificial Intelligence
- 结合光谱数据与机器学习，分两阶段预测酒质与产地。
- 支持向量机表现最佳，产地分类准确率和F1超91%。
- 低波段250-420nm波长影响最大，指导未来传感器设计。
本文利用人工品鉴与紫外-可见（UV-Vis）扫描光谱仪获取的吸光度数据，分别预测葡萄汁属性与葡萄酒产地。该方法将机器学习（ML）与光谱技术结合，在酿酒两个阶段实现较简单的应用，改进传统感官分析与产地鉴定方式。新方法克服了复杂传感器的不足，借助光谱指纹技术，系统探讨了人工智能在葡萄酒分析领域的应用。结果显示，支持向量机（SVM）在属性与产地预测任务中均表现最优，产地预测的准确率和F1分数均超过91%。特征重要性分析表明，影响较大的波长主要集中在扫描范围较低端（250–420纳米），为未来研究中选择验证方法与传感器提供了重要参考。本研究为葡萄酒及其他饮料行业未来融合大数据与物联网提供新思路，显著推动‘智慧酒庄’发展。
原文摘要 · Abstract (English)
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'.
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