MCR-ALS and NIRS applied on the evaluation of the Lepidium meyenii antioxidant activity

Daiane Ribeiro Soares,Paulo Henrique Março,Patricia Valderrama,Rhayanna Priscila Gonçalves

doi:10.3895/rebrapa.v7n1.3464

Abstract

The Peruvian Maca (Lepidium Meyenii Walp) is a food which is widely consumed around the Peruvian Andes and has been used in Brazil thanks to medicinal properties attributed by popular culture. Due to lack of information on this cultivar, it was proposed a study to evaluate the effect of adding Peruvian Maca in transgenic soybean oil. For this purpose, the spectra of soybean oil samples with and without addition of Maca after heating at different temperatures were measured at Visible and Near Infrared regions. The results were evaluated by chemometric methods of Principal Component Analysis (PCA) and Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS). The results suggest that Maca inhibits the appearance of oxidation products, which is evidenced when comparing the results obtained for the heated oil with and without addition of Maca.

Highlights

The Peruvian Maca (Lepidium Meyenii Walp) is a food which is widely consumed around the Peruvian Andes and has been used in Brazil thanks to medicinal properties attributed by popular culture
Due to lack of information on this cultivar, it was proposed a study to evaluate the effect of adding Peruvian Maca in transgenic soybean oil
The results were evaluated by chemometric methods of Principal Component Analysis (PCA) and Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS)

Summary

Introduction

Devido à falta de informações sobre este cultivar, foi proposto um estudo para avaliar o efeito da adição de Maca Peruana em óleo de soja transgênica. Foram medidos espectros das amostras de óleos de soja com e sem adição de Maca após aquecimento em diferentes temperaturas, nas regiões Visível e Infravermelho Próximo. Os resultados obtidos foram avaliados através de métodos quimiométricos de Análise de Componentes Principais (Principal Component Analysis – PCA) e de Resolução Multivariada de Curvas com Mínimos Quadrados Alternados (Multivariate Curve Resolution with Alternating Least Squares – MCR-ALS).

Results

Conclusion