基于拉曼光谱和DCGAN数据增强的珍珠粉掺伪检测研究

珍珠粉和珍珠层粉化学成分相似,但是珍珠层粉的药用价值远低于珍珠粉,并且珍珠层粉制备容易,成本底,常被不法商家用于冒充或掺入珍珠粉中流入市场,谋取利益.因此,对珍珠粉掺伪鉴别和纯度检测具有重要的意义.采用激光拉曼光谱结合深度学习研究珍珠粉掺伪快速鉴别和纯度分析.将纯珍珠粉和珍珠层粉按一定比例混合,制成珍珠粉质量百分数分别...

Detection of Pearl Powder Adulteration Based on Raman Spectroscopy and DCGAN Data Enhancement

The chemical composition of pearl powder and nacre powder is similar, However, the medicinal value of nacre powder is far lower than that of nacre powder, and nacre powder is easier to prepare at low cost, which is often used by illegal businesses to fake or mix into nacre powder to enter the market and seek illegal interests. Therefore, identifying pearl powder adulteration and purity analysis is of great significance. This paper used Raman spectroscopy combined with deep learning technology to study the rapid identification and purity analysis of pearl powder adulteration. The pure pearl powder and nacre powder were mixed according to a certain proportion to make 270 samples with 9 kinds of purity of 0%, 25%, 50%, 75%, 80%, 85%, 90%, 95% and 100% respectively. Then the Raman spectrum of the samples was collected, and the parameters were set as follows: the resolution is 4.5 cm-1, the integration time is 3 000 ms, and the laser power is 20 mW. A Deep Convolutional Generative Adversarial Network (DCGAN) model was built to enhance the Raman spectra of the samples. Furthermore, K-nearest neighbor, Random forest, Decision tree, and one-dimensional convolution neural network (1DCNN) classifiers were used to identify the authenticity of a small proportion of adulterated samples with the purity of 85%, 90%, 95% and 100%. At the same time, a quantitative model for the purity prediction of 9 kinds of adulterated pearl powder samples was established by using a one-dimensional convolutional neural network. The results showed as follows: compared with the original spectral data, the Raman spectral data generated by the DCGAN data enhancement method was significantly better than the traditional data enhancement methods in the two evaluation indexes of peak signal-to-noise ratio and structural similarity. For the identification of pearl powder adulteration, the accuracies of the qualitative models established by DCGAN data enhancement method combing with four different classifiers have all reached 100%. For the quantitative detection of the purity of the pearl powders, the model established by DCGAN-1DCNN method has achieved the best performance. For the test set, the determination coefficient (R2) was 0.988 4 and the prediction root mean square error (RMSEP) was 0.034 8 as the loss value of 1DCNN network was 0.001 2. Raman spectroscopy combined with the DCGAN method provides a rapid and simple method for identifying and purity analysis of pearl powder adulteration. The data enhancement method of deep convolution generation countermeasure network has important research significance and application value in spectral analysis technology.