Feasibility of near-infrared spectroscopy as a tool to estimate carotenoid content in 'IAC Rurik' specialty potato cultivar

Author(s)

Izidoro, Maiqui ; Pedrosa, Vanessa Maria Dantas ; Spricigo, Poliana Cristina ; Factor, Thiago Leandro ; Lossolli, Nathalia Aparecida Barbosa ; Cunha Junior, Luis Carlos ; Guariglia, Bruna Ariel Dias ; de Lima, Kassio Michell Gomes ; de Morais, Camilo de Lelis Medeiros ; Teixeira, Gustavo Henrique de Almeida

• Document type: Journal article
• Source: Journal of Food Composition and Analysis; v. 139, p. 11-pg., 2024-12-20.

Abstract

The study explores the use of NIR spectroscopy with chemometric techniques as a non-destructive method to determine carotenoids in fresh 'IAC Rurik,' a new yellow potato rich in these compounds. Tubers were harvested in 2022 and in 2023. Reflectance FT-NIR spectra were acquired on the periderm of 200 tubers, in two positions. Spectra were pre-processed and regression models were developed using partial least square (PLS), support vector (SVR), ridge regression, k-nearest neighbors (KNN), interval-partial least squares (iPLS), and kernel-partial least squares (kPLS) regressions. Multivariate classification was carried out by applying principal component analysis with linear discriminant analysis (PCA-LDA) and partial least squares discriminant analysis (PLS-DA). Carotenoid content prediction was better obtained using mean-centered spectra and ridge regression (RMSEP = 0.0028 g kg- 1, R2P = 0.90, RPD = 2.57, RER = 4.21 %). The classification of three groups (low = 0.031-0.045 g kg- 1; average = 0.045-0.065 g kg- 1; and high = 0.065-0.078 g kg- 1) was possible by applying PLS-DA with a correct classification of 93 %, 70 %, and 86 %, respectively for low, average, and high carotenoid content. Thus, NIR spectroscopy can be used as a non-destructive method to predict carotenoids and classify 'IAC Rurik' tubers based on their carotenoid content. (AU)