Global calibration for non-targeted fraud detection in quinoa flour using portable hyperspectral imaging and chemometrics

Qianyi Wu; Magdi A A Mousa; Adel D Al-Qurashi; Omer H M Ibrahim; Kamal A M Abo-Elyousr; Kent Rausch; Ahmed M K Abdel Aal; Mohammed Kamruzzaman

doi:10.1016/j.crfs.2023.100483

Global calibration for non-targeted fraud detection in quinoa flour using portable hyperspectral imaging and chemometrics

Curr Res Food Sci. 2023 Mar 17:6:100483. doi: 10.1016/j.crfs.2023.100483. eCollection 2023.

Authors

Qianyi Wu¹, Magdi A A Mousa², Adel D Al-Qurashi², Omer H M Ibrahim², Kamal A M Abo-Elyousr², Kent Rausch¹, Ahmed M K Abdel Aal², Mohammed Kamruzzaman¹

Affiliations

¹ Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
² Department of Arid Land Agriculture, Faculty of Meteorology, Environment and Arid Land Agriculture, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.

Abstract

Quinoa is one of the highest nutritious grains, and global consumption of quinoa flour has increased as people pay more attention to health. Due to its high value, quinoa flour is susceptible to adulteration. Cross-contamination between quinoa flour and other flour can be easily neglected due to their highly similar appearance. Therefore, detecting adulteration in quinoa flour is important to consumers, industries, and regulatory agencies. In this study, portable hyperspectral imaging in the visible near-infrared (VNIR) spectral range (400-1000 nm) was applied as a rapid tool to detect adulteration in quinoa flour. Quinoa flour was adulterated with wheat, rice, soybean, and corn in the range of 0-98% with 2% increments. Partial least squares regression (PLSR) models were developed, and the best model for detecting the % authentic flour (quinoa) was obtained by the raw spectral data with R²p of 0.99, RMSEP of 3.08%, RPD of 8.77, and RER of 25.32. The model was improved, by selecting only 13 wavelengths using bootstrapping soft shrinkage (BOSS), to R²p of 0.99, RMSEP of 2.93%, RPD of 9.18, and RER of 26.60. A visualization map was also generated to predict the level of quinoa in the adulterated samples. The results of this study demonstrate the ability of VNIR hyperspectral imaging for adulteration detection in quinoa flour as an alternative to the complicated traditional method.

Keywords: Adulteration; BOSS; PLSR; Quinoa flour; VNIR hyperspectral imaging; Visualization.