Rapid and noncontact identification of soybean flour in edible insect using NIR spectral imager: A case study in Protaetia brevitarsis seulensis powder

Abstract

Edible insects are notably considered novel foods with high amounts of protein, making them valuable. There are still no reported cases of edible insect adulteration, but there is a potential issue as valuable products, particularly during supply chains. This work demonstrated the feasibility of near-infrared hyperspectral imaging (NIR-HSI), ranging from 1000 nm to 2100 nm, for rapid and nondestructive identification of soybean flour in Protaetia brevitarsis seulensis (PBS) powder. Three different approaches to soybean flour detection were realized by using an extended principal component analysis (PCA), data-driven-soft independent modelling of class analogy (DD-SIMCA), and regression algorithms, namely partial least squares regression (PLSR) and one-dimensional convolutional neural networks (1D-CNN). Our study demonstrated that extended PCA for soybean flour pixel identification showed a poor linear correlation (R2 = 0.835) and the error (RMSE = 12.39%) between the identified soybean flour pixel and its actual concentrations. By employing DD-SIMCA, 100% accuracy was achieved, allowing the superior performance of one-class classification method. In conjunction with regression methods, 1D-CNN with the Savitzky-Golay first derivative (SG1) spectra generated the optimum prediction accuracy, indicated by an R2P of 0.99, an RMSEP of 1.15%, and an RPD of 12.92. Furthermore, a chemical image derived from the 1D-CNN showed a clear visualization of adulterated PBS. Finally, NIR-HSI optimized with a 1D-CNN model could be a promising technique for the identification of soybean flour in PBS powder in a nondestructive manner.