Feature extraction for identification of drug and explosive concealed by body packing based on positive matrix factorization

In the analysis towards the energy dispersive X-ray diffraction (EDXRD) spectra of drug and explosive concealed by body packing, positive matrix factorization (PMF) was introduced to extract features from EDXRD spectra of samples in a set of drugs and explosive concealed in the anthropomorphic phantom, because PMF prevents the negative factors from occurring, avoids contradicting physical reality, and makes factors more easily interpretable. In order to compare with the features extracted by PMF, Principal Component Analysis (PCA) and robust PCA were investigated. Then, K-nearest neighbour (KNN) and support vector machine (SVM) were introduced to classify the samples according to the features extracted by PMF, PCA and robust PCA. It is shown that the recognition rates obtained by PMF are highest (above 99.5%) and insensitive to classifiers. This work demonstrates that PMF is effective in feature extraction for identification of drug and explosive concealed by body packing.