Detecting vegetable oils in black soldier fly larvae oil using infrared spectroscopy coupled with one-class and hierarchical modelling

The Black Soldier Fly Larvae (BSFL) oil is a by-product from the animal feed industry. BSFL oil is rich in medium-chain fatty acids, which has led to an increase in demand for pharmaceutical or animal feed industry. This study evaluated the authenticity of BSFL oil and its adulteration with vegetable oils using two portable near infrared (NIR) spectrometers ( spectrometer 1 ( 900 – 1700 nm ) and spectrometer 2 (1350 – 2500 nm)) and Fourier-transform middle infrared (FTIR) spectroscopy (4000 – 650 cm -1 ) combined with chemometric methods. Principal Component Analysis (PCA) showed partial separation between pure and adulterated samples, but some mixtures, like BSFL + sunflower and BSFL + corn, overlapped due to similar composition. Data Driven Soft Independent Modelling Class Analogy (DD-SIMCA) was used to authenticate pure BSFL oil, reaching up to 100% sensitivity and specificity in test set, especially with spectrometer 2 and FTIR . Multi-class PLS-DA models were able to classify BSFL oil and its mixtures, although they showed low classification rates (True Positive Rate (TPR) < 30%) for BSFL oil adulterated with canola or corn oil, especially using spectrometer 1 . Hierarchical PLS-DA models were developed to improve discrimination, reaching better classification rates for all adulterated classes. Overall, DD-SIMCA, PLS-DA and hierarchical models combined with NIR or FTIR spectroscopy are promising tools to detect BSFL oil adulteration in a fast and non-destructive way, with potential applications in quality control and fraud prevention. • NIR and FTIR with chemometrics effectively authenticate BSFL oil. • DD-SIMCA achieved 100% sensitivity for pure BSFL oil across all devices. • Hierarchical modelling outperformed multi-class PLS-DA for discrimination task. • NIR and FTIR could help preserve BSFL oil authenticity in the insect supply chain.