Classification of instant coffees based on caffeine content and roasting degree using NIR spectrometry and multivariate analysis

Abstract

Instant coffee is a beverage obtained from the dehydration of roasted coffee extract. Its quality depends on several factors, such as the type of bean and grinding, drying decaffeination, and roasting degree processes. In this work, NIR spectrometry and multivariate classification tools were combined to develop, for the first time, methods for authenticating and checking the compliance of decaffeinated instant coffees in order to ensure consumer food safety, as well as discriminating between regular instant coffees according to the roasting degree (traditional and extra-strong) for monitoring the production chain of regular instant coffees. First, one-class methods were built and validated to authenticate decaffeinated instant coffees and to in order to alert caffeine-sensitive consumers to potential risks. For this purpose, two near-infrared (NIR) instruments (benchtop and portable) and Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) and One-Class Partial Least Squares (OC-PLS) were used. After Partial Least Squares – Discriminant Analysis (PLS-DA), Successive Projections Algorithm for Interval Selection in Partial Least Squares – Discriminant Analysis (iSPA-PLS-DA), and the same NIR instruments were used to build and validate multiclass methods to discriminate regular instant coffees according to the roasting degree (traditional and extra-strong). The result for the classification of decaffeinated and roasting degree instant coffees was 100% and 98% correct classification rate using DD-SIMCA and iSPA-PLS-DA, respectively. Therefore, the combination of NIR spectroscopy and multivariate classification techniques proved valuable and fast tools for checking the quality of instant coffees.