Prediction of total lipids and fatty acids in black soldier fly (Hermetia illucens L.) dried larvae by NIR-hyperspectral imaging and chemometrics.

The unique fatty acid composition of BSF larvae oil makes it suitable for various applications, including use in animal feed, aquaculture, biodiesel production, biomaterials, and the food industry. Determination of BSF larvae composition usually requires analytical methods with chemicals, thus needing emerging techniques for fast characterization of its composition. In this study, Near Infrared Hyperspectral Imaging (NIR-HSI) (928 - 2524 nm) coupled with chemometrics was applied to predict the lipid content and fatty acid composition in intact black soldier fly (BSF) larvae. Partial Least Squares Regression (PLSR) and Support Vectors Machine Regression (SVMR) models, combined with two variable selection methods, Interval Partial Least Squares (iPLS) and Bootstrapping Soft Shrinkage (BOSS), were compared. PLSR reached a good performance to predict myristic acid with Root Mean Square Error in prediction (RMSEP) = 0.45 %, while SVMR reached values of Ratio to Prediction Deviation (RPD) > 3 to predict total lipid content, lauric acid, myristic acid, palmitic acid and oleic acid. In addition, selecting wavelength by BOSS improved PLSR models (6 - 15 % increases in RPD), while iPLS improved SVMR model to predict palmitic acid (16 % increases in RPD). The study emphasizes the advantages of NIR-HSI as a non-invasive, rapid method for lipid and fatty acid quantification, which can be highly valuable for industrial applications such as monitoring BSF larvae feeding systems to ensure high-quality oil production.