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

John-Lewis Zinia Zaukuu; Zeenatu Suglo Adams; Nana Ama Donkor-Boateng; Eric Tetteh Mensah; Donald Bimpong; Lois Adofowaa Amponsah

doi:10.1038/s41598-024-61220-1

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

Sci Rep. 2024 May 7;14(1):10426. doi: 10.1038/s41598-024-61220-1.

Authors

John-Lewis Zinia Zaukuu¹, Zeenatu Suglo Adams^{2

3}, Nana Ama Donkor-Boateng^{2

4}, Eric Tetteh Mensah², Donald Bimpong², Lois Adofowaa Amponsah²

Affiliations

¹ Department of Food Science and Technology, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana. zaukuu.jz@knust.edu.gh.
² Department of Food Science and Technology, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
³ Department of Food Science and Technology, Ho Technical University, Ho, Volta Region, Ghana.
⁴ Department of Hospitality Management, Takoradi Technical University, Takoradi, Western Region, Ghana.

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, RMSE_CV 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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Flour / analysis
Food Contamination / analysis
Glycine max / chemistry
Macau
Machine Learning*
Powders*
Spectrophotometry / methods
Spectroscopy, Near-Infrared* / methods
Zea mays* / chemistry

Substances

Powders