领域顶尖学者邵学广教授亲授《化学计量学与近红外光谱》，基础原理+前沿技术+核心算法全贯通！

Core Viewpoint - Near-infrared spectroscopy (NIR) is a rapidly developing analytical testing technology that offers advantages over traditional methods, such as non-destructive testing, high efficiency, low cost, and minimal sample preparation requirements. However, it requires stable instruments, comprehensive software, and suitable models, making it complex to implement [2][4]. Group 1: Overview of Near-Infrared Spectroscopy - Near-infrared spectroscopy combines spectral measurement in the near-infrared range (800-2500nm) with chemometrics, computer technology, and basic testing techniques for rapid analysis of complex samples. It can determine multiple performance indicators in just a few minutes without sample pre-treatment [2][4]. - The technology is applicable across various fields, including agriculture, pharmaceuticals, petrochemicals, and food, and is considered a modern analytical method with significant potential for agricultural analysis [2][4]. Group 2: Course Introduction - A course titled "Chemometrics and Near-Infrared Spectroscopy" is offered, taught by Professor Shao Xueguang, a recipient of the "Lu Wanzhen Near-Infrared Spectroscopy Science and Technology Award." The course covers foundational knowledge, technical aspects, and algorithms related to NIR spectroscopy [4][9]. - The course aims to alleviate the research burden by providing comprehensive and detailed instruction, addressing key issues such as effective information extraction and model evaluation in quantitative analysis [4][9]. Group 3: Target Audience - The course is designed for technical personnel in industries such as petroleum, chemicals, pharmaceuticals, tobacco, and food, as well as graduate students and researchers in chemistry-related fields [5][4]. Group 4: Course Structure - The course is divided into three main sections: - Basic Section: Covers foundational knowledge in chemometrics and NIR spectroscopy, programming basics in MATLAB and Python, and quantitative modeling methods [15][24]. - Technical Section: Focuses on modeling processes, including spectral preprocessing, variable selection, clustering, and model transfer, with practical examples [20][24]. - Algorithm Section: Discusses common methods such as principal component analysis, partial least squares regression, and neural networks, aiming to enhance understanding and practical application [24][26].