摘要
化学计量学在复杂样品分析中发挥着重要作用，但其数据处理仍面临诸多挑战，如高维度数据处理、信号重叠与干扰、模型泛化能力不足等。探讨了基于化学计量学的优化数据处理策略，包括降维技术、化学分解方法、机器学习与化学计量学的结合等，通过实际案例验证了这些策略在提高分析精度和模型适应性方面的显著效果。未来，化学计量学有望通过多学科交叉融合，进一步提升复杂样品分析的智能化水平，推动化学分析技术的持续发展。
关键词： 化学计量学；复杂样品；数据分析；优化策略；机器学习
Abstract
Chemometrics plays a vital role in the analysis of complex samples, yet it still faces many challenges in data processing, such as high-dimensional data handling, signal overlap and interference, and insufficient model generalization ability. This paper explores optimized data processing strategies based on chemometrics, including dimensionality reduction techniques, chemical decomposition methods, and the integration of machine learning with chemometrics. These strategies have been validated through practical cases, showing significant effects in improving analytical accuracy and model adaptability. In the future, chemometrics is expected to further enhance the intelligent level of complex sample analysis through multidisciplinary integration, promoting the continuous development of chemical analysis technologies.
Key words： Chemometrics; Complex samples; Data analysis; Optimization strategies; Machine learning
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