This work proposes the use of near infrared (NIR) spectroscopy in diffuse reflectance mode and multivariate statistical process control (MSPC) based on principal component analysis (PCA) for real-time monitoring of the coffee roasting process. The main objective was the development of a MSPC methodology able to early detect disturbances to the roasting process resourcing to real-time acquisition of NIR spectra. A total of fifteen roasting batches were defined according to an experimental design to develop the MSPC models. This methodology was tested on a set of five batches where disturbances of different nature were imposed to simulate real faulty situations. Some of these batches were used to optimize the model while the remaining was used to test the methodology. A modelling strategy based on a time sliding window provided the best results in terms of distinguishing batches with and without disturbances, resourcing to typical MSPC charts: Hotelling's T² and squared predicted error statistics. A PCA model encompassing a time window of four minutes with three principal components was able to efficiently detect all disturbances assayed. NIR spectroscopy combined with the MSPC approach proved to be an adequate auxiliary tool for coffee roasters to detect faults in a conventional roasting process in real-time.

这项工作提出了在漫反射模式下使用近红外（NIR）光谱和基于主成分分析（PCA）的多元统计过程控制（MSPC）来实时监控咖啡烘焙过程的方法。主要目标是开发一种MSPC方法，该方法能够及早发现对焙烤过程的干扰，并将其重新配置为实时获取NIR光谱。根据实验设计，总共定义了15个焙烧批次，以开发MSPC模型。该方法论在五个批次的一组上进行了测试，其中施加了不同性质的干扰来模拟实际的故障情况。这些批次中的一些用于优化模型，而其余用于测试方法。²和平方的预测误差统计量。一个PCA模型包含三个主要成分的四分钟时间窗口，能够有效地检测出所有干扰。NIR光谱技术与MSPC方法相结合已被证明是咖啡烘焙器的一种合适的辅助工具，可以实时检测传统烘焙过程中的故障。