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

In this study, we aimed to discriminate four commercial blends of green tea in bagged (inside its sachet) and nonbagged 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 nonbagged 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.

Automated summary

This study aimed to differentiate four commercial blends of green tea using NIR spectroscopy and SVM, with Bayesian optimization providing accurate models. The results indicated that the proposed methodology has the potential to be applied in automatic quality control.