Journal of Engineering Research

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.

Chemometrics; Complex samples; Data analysis; Optimization strategies; Machine learning