Identification of various food residuals on denim based on hyperspectral imaging system and combination optimal strategy

As the science and technology develop, crime methods and scenes have become increasingly complex and diverse. Trace evidence analysis has become a more and more important criminal investigation technology and liquid is the main form of trace evidence. Food can provide not only energy, but clues to solve crimes. In this study, we build a hyperspectral imaging system to detect liquid residue traces, including apple juice, coffee, cola, milk and tea, on denims with light, middle and dark colors. The obtained hyperspectral images are first subjected to spectral calibration and hyperspectral data pretreatment. Subsequently, Partial Least Squares (PLS) is applied to select the informative wavelengths from the preprocessed spectra. For modeling phase, the combination optimal strategy, support vector machine (SVM) combined with random forest (RF), is developed to establish classification models. The experimental results demonstrate that the combination optimal model can achieve TPR, FPR, Precision, Recall, F₁, and AUC of 83.5%, 2.30%, 79.7%, 83.5%, 81.6%, and 94.7% for classifying fabrics contaminated by various food residuals. With respect to the classification of liquid and fabric types, the combination optimal model also yields satisfactory classification performance. In future work, we will expand the types of liquid, and make appropriate adjustment to algorithms for improving the robustness of classification models. This research may play a positive role in the construction of a harmonious society.