Vis/NIR Absorbance and Multivariate Analysis for Identifying Infusions of Herbal Teas Cultivated Organically

Ready-to-drink herbal teas are increasingly popular due to their pleasant aroma and taste, with plants cultivated organically showing improved quality properties. Vis/NIR absorbance and multivariate analysis were used for classifying infused herbal teas cultivated under organic systems, in addition to testing various spectral pretreatments to assess the identification accuracy improvement. A total of 150 herbal tea infusions (boldo, carqueja, chamomile, fennel, and lemon grass) were evaluated, and six spectral pretreatments (centering, standard normal variation, object-wise standardization, first derivative, second derivative, and detrending) were applied to the spectra. Principal component analysis (PCA) and the partial least squares discriminant analysis (PLS-DA) were used to distinguish the infused herbal teas. Clustering patterns were affected by the pretreatments, and the PCA was capable of separating the infused herbal teas. The PLS-DA was efficient in identifying the infusions, reaching kappa values from 0.97 to 1.00 with optimal latent variable numbers from two to five. Detrending and object-wise standardization pretreatments led to better results and required fewer latent variables. The proposed methodology presents the potential to be used in a fast, safe, environmentally friendly (without chemical reagents), and nondestructive way, appearing as essential for meeting the technological development of the agrifood industry.