DD-SIMCA coupled with LOVE variable selection for enhanced detection of corn flour adulteration with black soldier fly (Hermetia illucens) using portable NIR spectroscopy

Food adulteration represents a significant challenge to ensuring authenticity, particularly in powdered matrices such as corn flour. In this study, a rapid and non-destructive methodology was developed to detect corn flour adulteration with black soldier fly (Hermetia illucens) flour using portable near-infrared (NIR) spectroscopy combined with DD-SIMCA modelling and LOVE variable selection. Authentic and adulterated samples were analysed at adulteration levels ranging from 1% to 20%, and spectra were acquired in the 900-1600 nm range. Spectral data were pre-processed using the Savitzky–Golay first derivative, standard normal variate (SNV), and mean centering. PCA revealed a compositional gradient associated with increasing adulteration level, although some overlap remained at the lowest concentrations. Application of the LOVE method reduced the number of variables from 212 to 92 while retaining the most informative spectral regions for model development. The DD-SIMCA model developed using the selected variables outperformed the full-spectrum model, achieving a sensitivity of 1.00 for the target class and a specificity of 1.00 in the independent external test set. The results demonstrate the potential of the proposed methodology for food authenticity assessment under the experimental conditions investigated.