During a short chemometrics course in the seventh semester of the chemistry undergraduate program, students receive a brief theoretical introduction to multivariate calibration, focused on partial least-squares regression as the most commonly employed data processing tool. The theory is complemented with the use of MVC1_R, an easy-to-use software developed in-house as an R Shiny application. The present report describes student activities with the latter software in the development of mathematical models to predict quality parameters of corn seeds from near-infrared spectra. Subsequently, an experimental project is carried out involving near-infrared spectral measurements, which are widely used in several industrial fields for quality control. To process the obtained data, students apply the knowledge acquired during the theoretical/software sessions.

中文翻译：

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开发和实施R Shiny应用程序，向高年级本科生引入多元校准

在化学本科课程的第七学期的短期化学计量学课程中，学生将获得有关多元校准的简要理论介绍，重点是偏最小二乘回归作为最常用的数据处理工具。通过使用MVC1_R（一种作为R Shiny应用程序在内部开发的易于使用的软件）对该理论进行了补充。本报告介绍了使用后者的软件在数学模型的开发中进行的学生活动，以从近红外光谱预测玉米种子的质量参数。随后，进行了一项涉及近红外光谱测量的实验项目，该测量已广泛用于几个工业领域中以进行质量控制。要处理获得的数据，