Simplified tea classification based on a reduced chemical composition profile via successive projections algorithm linear discriminant analysis (SPA-LDA)

In this study, several possible approaches for simultaneous discrimination of teas based on a linear discriminant analysis with variables selected by the successive projections algorithm (SPA-LDA), featuring selection from the chemical composition according to variety (black or green tea) and geographical origin (Argentina or Sri Lanka), are explored. Chemical composition (moisture, ash, caffeine, fluoride, polyphenols, and 15 elements from both tea leaves and infusions) was used as input data for identification of the differentiating characteristics of tea samples. Thus, a strategy that allows tea discrimination using a reduced number of chemical parameters was developed. SIMCA (soft independent modeling of class analogy) and PLS-DA (partial least squares-discriminant analysis) were used along with SPA-LDA for comparison. The elemental fingerprint (chemical signature) can be used for identifying the variety and origin of the tea, and SPA-LDA provided the most successful result (100% correct classification), despite having selected just three chemical parameters (namely K, Al, and Mg). The result is extremely positive from the viewpoint of chemical analyses, because quantifications made using fewer elements naturally provide simpler, faster and less expensive methods.