Core Viewpoint - The article discusses the evolution, technical framework, applications, and future prospects of modern intelligent spectral analysis technology, emphasizing its integration of advanced optical sensing, chemometrics, and artificial intelligence for rapid, non-destructive, and precise detection of complex samples [2][10]. Group 1: Evolution of Spectral Analysis Technology - Spectroscopy originated from studies of light-matter interactions and has evolved to include various electromagnetic radiation spectra for analysis [3]. - The 1960s saw the application of spectroscopy in chemical quantitative analysis, but complex samples required extensive pre-treatment, limiting efficiency [4]. - The advent of chemometrics in the 1980s revitalized near-infrared spectroscopy, enabling non-destructive, rapid multi-component analysis without sample pre-treatment [5][6]. Group 2: Technical Framework - Modern intelligent spectral analysis technology is characterized by three main features: non-destructive rapid multi-dimensional chemical information acquisition, adaptive intelligent modeling, and comprehensive application coverage [22]. - The technology integrates optical instruments, chemometrics, and machine learning methods to establish calibration models for quantitative or qualitative analysis [32][34]. Group 3: Applications and Impact - Intelligent spectral analysis has found applications in high-throughput laboratory analysis, rapid field detection, and industrial online monitoring across various sectors, including agriculture, pharmaceuticals, and environmental monitoring [9][21]. - The technology's ability to meet the demands for real-time, non-destructive, and high-throughput analysis has driven its adoption in industrial quality control, agricultural monitoring, and environmental assessments [9][21]. Group 4: Future Prospects and Challenges - Future developments in intelligent spectral analysis are expected to focus on knowledge creation, multi-technology integration, cloud-native platforms, and standardization, transitioning from tools to intelligent decision-making systems [2][10]. - Challenges remain in theoretical modeling, interpretability, and data standardization, which need to be addressed for broader application and effectiveness [2][10].