Non-invasive prediction of maca powder adulteration using a pocket-sized spectrophotometer and machine learning techniques

Abstract

Abstract Discriminating different cultivars of maca powder (MP) and detecting their authenticity after adulteration with potent adulterants such as maize and soy flour is a challenge that has not been studied with non-invasive techniques such as near infrared spectroscopy (NIRS). This study developed models to rapidly classify and predict 0, 10, 20, 30, 40, and 50% w/w of soybean and maize flour in red, black and yellow maca cultivars using a handheld spectrophotometer and chemometrics. Soy and maize adulteration of yellow MP was classified with better accuracy than in red MP, suggesting that red MP may be a more susceptible target for adulteration. Soy flour was discovered to be a more potent adulterant compared to maize flour. Using 18 different pretreatments, MP could be authenticated with R2 CV in the range 0.91–0.95, RMSECV 6.81–9.16 g/,100 g and RPD 3.45–4.60. The results show the potential of NIRS for monitoring Maca quality.

Keywords

• Adulteration
• Chemometrics
• Maca
• Near infrared
• Spectra-preprocessing