Feasibility study on the use of near infrared spectroscopy to determine flavanols in grape seeds

• Published in: Talanta (Oxford) Vol. 82; no. 5; pp. 1778 - 1783
• Main Authors: Ferrer-Gallego, Raúl, Hernández-Hierro, José Miguel, Rivas-Gonzalo, Julián C., Escribano-Bailón, M. Teresa
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 15.10.2010; Elsevier
• Subjects: Analytical chemistry; Calibration; Chemistry; Chemometrics; Exact sciences and technology; Feasibility Studies; Flavanols; Flavonols - analysis; Grape seeds; Least-Squares Analysis; Near infrared spectroscopy; Principal Component Analysis; Reproducibility of Results; Seeds - chemistry; Spain; Spectrometric and optical methods; Spectroscopy, Near-Infrared - methods; Spectroscopy, Near-Infrared - statistics & numerical data; Vitis - chemistry; Vitis - growth & development; Spain; TT; Near infrared spectroscopy; EEC; C; DG; D; Grape seeds; TTG; M; B1; B2; B3; TE; TG; T; Flavanols; EC; Chemometrics; Sample; Use; Feasibility studies; Prediction; Algorithm; Infrared spectrometry; PLS regression; Near infrared spectrometry; Accuracy; Qualitative analysis; Feasibility; Technique; Tool
• ISSN: 0039-9140; 1873-3573
• DOI: 10.1016/j.talanta.2010.07.063

The potential of near infrared spectroscopy to determine the content of flavanols directly recording the infrared spectra of grape seeds has been evaluated. Moreover, the study shows the potential of this technique to obtain qualitative information related to the samples. In this case, the feasibility to discriminate between possible vineyards of origin has also been evaluated. Modified Partial Least Squares (MPLS) regression was used to develop the quantitative models in order to predict the content of flavanols. These models have been validated showing differences between 3.5% and 14.3% in the external validation. Moreover, Discriminant Partial Least Squares algorithm was used in the qualitative analysis to distinguish between two possible vineyards of origin and showed a high degree of accuracy. Prediction rates of samples correctly classified with a mean of 95% in internal validation and 97% in external validation were obtained. The procedure reported here seems to have an excellent potential for a fast and reasonably inexpensive analysis of these flavanols in grape seeds and could also be a tool to distinguish between possible vineyards of origin.