墙面涂料的显微共聚焦喇曼光谱无损鉴别

为了实现对墙面涂料物证的无损鉴别，提出了显微共聚焦喇曼光谱技术结合多元建模分析的无损鉴别墙面涂料方法。采用不同Savitzky-Golay(SG)平滑多项式次数及平滑点数对分类模型准确率的影响进行预处理，同时比较了不同分类模型的区分能力。结果表明，相较于径向基函数神经网络模型，多层感知器神经网络模型对各样本的区分能力更强，且经过SG平滑1次多项式结合平滑点数27点预处理后，多层感知器神经网络模型能够实现对梅菲特等3种不同品牌墙面涂料样本，以及梅菲特3种不同类型墙面涂料样本100%的准确区分。该方法提高了检验鉴定效率，降低了检验鉴定成本，具有一定的普适性。

Non-destructive identification of wall paints by microscopic confocal Raman spectroscopy

In order to realize the non-destructive identification of wall paints, a method of fast and non-destructive identification of wall paints by microscopic confocal Raman spectroscopy and multiple modeling was proposed. The influence of Savitzky-Golay(SG) smoothing polynomial and points, and compared the identification ability of different models were investigated. The results showed that compared with radial basis function neural network model, multilayer perceptron neural network model has a stronger ability to identify samples. Different brands of wall paints have been identified exactly by multilayer perceptron neural network model after SG smoothing 1-degree polynomial and smoothing points of 27 points. At the same time, primers, surface coatings and varnishes of Meffert samples were also identified accurately. This method improved the efficiency of identification, reduced the cost, which is worth consulting.