Vibrational spectroscopy techniques, such as Fourier-transform infrared (FTIR) and Raman spectroscopy, have been successful methods for studying the interaction of light with biological materials and facilitating novel cell biology analysis. Spectrochemical analysis is very attractive in disease screening and diagnosis, microbiological studies and forensic and environmental investigations because of its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, there is now an urgent need for multivariate classification protocols allowing one to analyze biologically derived spectrochemical data to obtain accurate and reliable results. Multivariate classification comprises discriminant analysis and class-modeling techniques where multiple spectral variables are analyzed in conjunction to distinguish and assign unknown samples to pre-defined groups. The requirement for such protocols is demonstrated by the fact that applications of deep-learning algorithms of complex datasets are being increasingly recognized as critical for extracting important information and visualizing it in a readily interpretable form. Hereby, we have provided a tutorial for multivariate classification analysis of vibrational spectroscopy data (FTIR, Raman and near-IR) highlighting a series of critical steps, such as preprocessing, data selection, feature extraction, classification and model validation. This is an essential aspect toward the construction of a practical spectrochemical analysis model for biological analysis in real-world applications, where fast, accurate and reliable classification models are fundamental.

振动光谱技术，例如傅立叶变换红外（FTIR）和拉曼光谱，已经成为研究光与生物材料相互作用并促进新型细胞生物学分析的成功方法。光谱化学分析因其低成本，最少的样品制备，无损性质和基本准确的结果而在疾病筛查和诊断，微生物学研究以及法医和环境研究中非常有吸引力。然而，现在迫切需要允许人们分析生物学衍生的光谱化学数据以获得准确和可靠结果的多元分类方案。多变量分类包括判别分析和类建模技术，其中结合分析多个光谱变量以区分未知样本并将其分配给预定义组。事实证明，对于复杂数据集的深度学习算法的应用对于提取重要信息并以易于解释的形式对其进行可视化至关重要，这一事实证明了对此类协议的要求。因此，我们为振动光谱数据（FTIR，拉曼和近红外）的多变量分类分析提供了指南，重点介绍了一系列关键步骤，例如预处理，数据选择，特征提取，分类和模型验证。