This manuscript presents a comprehensive approach to monitoring the whole process of craft beer production (mashing, circulation, boiling, fermentation and carbonatation), using a simple, rapid and green methodology like Near Infrared spectroscopy combined with MSPC (Multivariate Statistics Process Control). A Principal Component Analysis model is calculated with near infrared spectra (range between 800–2500 nm) collected in all the steps of the process (i.e., using a batch-to-batch approach), and a multivariate control chart is generated in order to monitor the beer development. Each batch was composed of a variable number of samples (average of 55 samples per batch) depending on the sampling time of every step. Four batches working under normal operating conditions are used to construct the model. Three external batches are used to validate the proposal (two of them with induced disturbances and another one working under normal operating conditions). The results were compared to those obtained by monitoring the solid soluble content (SSC) by using Partial Least Squares regression to ascertain the richness of the information given by NIR. The results illustrate the versatility and simplicity of the proposal and its reliability towards a global monitor and control of the beer-making procedure.

该手稿提供了一种简单的，快速的绿色方法，如近红外光谱技术与MSPC（多元统计过程控制）相结合的方法，用于监测精酿啤酒生产的全过程（糖化，循环，沸腾，发酵和碳酸化）。使用在该过程的所有步骤（即使用批间方法）中收集的近红外光谱（800-2500 nm之间）计算主成分分析模型，并生成多变量控制图以监控啤酒的发展。每个批次均由可变数量的样品组成（每批次平均55个样品），具体取决于每个步骤的采样时间。在正常操作条件下工作的四批用于构建模型。使用三个外部批次来验证建议（其中两个具有诱发的干扰，另一个在正常操作条件下工作）。将结果与通过使用偏最小二乘回归法确定NIR给出的信息的丰富度来监控固溶物含量（SSC）所获得的结果进行比较。结果说明了该提案的多功能性和简便性，以及对全球啤酒制造程序进行监控的可靠性。