Non-invasive identification of commercial green tea blends using NIR spectroscopy and support vector machine

Abstract

In this study, we aimed to discriminate four commercial blends of green tea in bagged (inside its sachet) and non-bagged conditions using near-infrared (NIR) spectroscopy and support vector machines (SVM) for data modelling. To choose optimal parameters for the models, we applied Bayesian optimization, which provided accurate models. Two spectrometers were evaluated: a benchtop and a handheld, both presenting reliable results for non-bagged tea (accuracies of 90% and 93%, respectively). However, for bagged tea models, the classification performance of benchtop was superior to handheld equipment, yielding accuracies of 93% and 82%, respectively. Classification accuracies using SVM outperformed partial least squares discriminant analysis (PLS–DA) for handheld and tea inside teabag models. The results indicated that the proposed methodology has the potential to be applied in automatic quality control coupling NIR sensors and machine learning for data processing.