Description
In recent years, growing attention has been focused on the utilization of plants for the extraction of bioactive compounds for health purposes. Black chokeberry (Aronia melanocarpa) repre-sents a less known fruit species that are rich in phytonutrients and is becoming popular for the dietary supplement ingredients in America and some European countries. The consumption of Chokeberry as fresh fruit is, however, limited also to its strong astringency due to the high con-tent of condensed tannins. This study investigated the potential of using spectra obtained from a hyperspectral imaging system for the prediction of the internal composition of Aronia berries with the aim of selecting fruit for nutritional content and astringency level. Four different maturi-ty stages of berries from green to black were used, in order to increase the concentration range of internal constituents. The prediction performances of models obtained in the Visible-Near In-frared (VIS-NIR) (400–1000 nm) and in the Near Infrared (NIR) (900–1700 nm) regions were compared. Analyzed constituents included Vitamin C, total antioxidant, phenols, anthocyanin, soluble solids content (SSC), and condensed tannins, responsible for the astringency. For vita-min C, partial least square regression (PLSR) combined with different data pretreatments result-ed in a satisfactory prediction in the NIR region obtaining the R2pred value of 0.83 while total phenol could be predicted in the same region but with a lower performance with R2pred value of 0.64. As for total antioxidant, anthocyanin, SSC, and condensed tannin, Vis-NIR spectra contained higher information allowing R2 values in the prediction of 0.82, 0.74, 0.67, and 0.82 respectively. Obtained results are encouraging for the development of an online optical system for the selection and classification of chokeberry fruit.
