Monitoring vitamin C extraction using multivariate calibration models by NIR

ABSTRACT Due to high of vitamin C content, acerola is exploited as source of this vitamin for the enrichment of industrial products. This work aimed to develop a method for monitoring vitamin C content using near infrared (NIR) during extraction procedure from acerola, thereby different processing steps were evaluated. The calibration and validation models were obtained by partial least squares regression with correlation between values by the reference method, spectrophotometry at visible 525 nm, and absorption data by near infrared spectroscopy, 800 to 2500 nm. The most robust quantification model was determined using coefficient of determination (R2), root mean square error of calibration (RMSECV) and root mean square error of prediction (RMSEP). Vitamin C content ranged from 1,188.39 to 9,959.74 mg. 100 g-1, throughout extraction procedure. The obtained RMSEP, 166.27 mg 100 g-1, indicates NIR spectroscopy as a promising tool for quantification of vitamin C during extraction from acerola, with the possibility of verifying the content in intermediate stages of production line and moreover, enabling adjustments for correction.