Application of compression sensing method in the safety detection of harmful substances in textiles

Abstract:

In order to improve the detection efficiency of harmful substances in the textile production process, this paper applies the compression sensing method to the detection process of harmful substances. The observation matrix is used as the sample mixing scheme, and the detection times that are far less than that of the samples to be tested are obtained through the mixed detection. After the mixed detection is completed, the content of harmful substances in the original samples is reconstructed from the mixed detection values according to the corresponding reconstruction algorithm, and then the number and detection rate of unqualified samples are obtained. Finally, the influence of the mixed detection matrices generated by different parameters on the reconstruction effect is explored by simulation experiments, and it is verified by the real detection project of detecting harmful substances bisphenol A, aromatic amines and formaldehyde in fiber textiles. The verification results show that the mixed sample detection scheme based on compression perception proposed in this paper can not only ensure the accuracy of the detection, but also can reduce the detection cost and improve the detection efficiency.