Application of near-infrared spectroscopy and chemometrics for the rapid detection of insect protein adulteration from a simulated matrix

The popularity of insect protein as a food and feed supplement is growing. Protein quality, end use and prices vary considerably between different insect species, which may incentivise insect protein adulteration. Here, nearinfrared (NIR) spectroscopy and chemometrics were used to detect the presence of cricket, black soldier fly larvae (BSFL) and mealworm proteins in a simulated complex insect protein mixture. Additionally, BSFL protein powders collected from three commercial sources were investigated to determine whether the NIR-based technology can discriminate the proteins obtained from different companies based on their composition. The proximate analysis suggests compositional protein, fat and chitin differences between insect species. A partial least square (PLS) regression model obtained Q(2) values ranging from 0.991 to 0.997 for the predictions of the content of protein mixtures containing BSFL, cricket and mealworm powders mixed at various proportions. The root mean square error of cross-validation (RMSCV) values range from 1.8% to 2.9%, and residual prediction deviation (RPD) values from 10.4 to 17.1 for the adulterated insect protein powders. The accuracy of the prediction model (similar to 2%) for the adulterated percentages varied depending on the insect species. The NIR spectra could differentiate (Q(2) = 0.999) the origin of BSFL protein powders from three different companies and two types of processing (whole meal and defatted samples). Overall, this study established a rapid and low-cost insect protein adulteration monitoring pipeline for the three common insect protein powders. We envisage that NIR can be applied to assess insect adulteration, authentication, and quality control in the emerging insect food and feed industries.