Abstract
The interest in the quality control of the raw materials, intermediates, and final products, as well as production methods, of beer has increased significantly in recent decades due to the needs and expectations of consumers. Increasing in the industrialization and globalization of beer supply chains led to a need for novel analytical tools suitable for the rapid and reliable characterization of the materials involved. In this study, an ultracompact instrument operating in the NIR region of the spectrum, microNIR, was tested for the chemical investigation of barley malts. The essential raw materials for brewing require careful control since they deeply affect the characteristic flavor and taste of the final products. Therefore, a robust prediction model able to classify base and specialty barley malts was developed starting from NIR measurements. Soft Independent Class Analogy (SIMCA) was selected as the chemometric technique for the optimization of two prediction models, and ground and sieved materials were investigated using spectroscopy. The microNIR/chemometric approach proposed in this study permitted the correct prediction of the malt samples included in the external validation set, providing false positive and false negative rates no higher than 3.41% and 0.25%, respectively, and confirming the feasibility of the novel analytical platform.