【摘要】 为建立一种快速、无损的粉底液分类方法，将市面上随机收集的50个不同品牌、不同色号、不同产地、不同价位的粉底液在高光谱模式下选取波长范围在400～600nm进行无损检测，每组样本选取随机点位各测3次，共测得150组数据。将光谱数据采用Savitzky-Golay平滑处理，主成分分析对数据进行特征值提取，选取前7个主成分作为后续实验数据集；借助K-Means聚类法对150组数据分类，50个粉底液被分成5类，由Fisher判别式验证分类效果；最后以聚类结果为依据建立BP神经网络模型（BPNN），分类准确率达到88.89%，效果良好。研究表明BP神经网络结合高光谱技术自动识别、分类粉底液具有可行性和科学性，在公安机关物证检验及法庭科学上可发挥有效作用。更多还原

【Abstract】 In order to establish a non-destructive and rapid method of foundation fluid testing and classification, 50randomly collected foundation fluids with different colors, different origins and different prices on the market were selected in a wavelength range of 400～600nm for NDT in hyperspectral mode. Random points of each sample were selected for 3 times, and a total of 150 sets of data were measured. The spectral data were smoothed by Savitzky-Golay,and the feature values were extracted by principal component analysis, and the top 7 principal components were selected as subsequent experimental data sets; 150 groups of data were classified by K-Means clustering method, 50 foundation were divided into 5 categories, and the classification effect was verified by Fisher discriminant; finally, BP neural network model(BPNN) was established based on the clustering results, and the classification accuracy reached 88.89%with good results. The research shows that BP neural network combined with hyperspectral technology can be feasible and scientific, and can play an effective role in the inspection of material evidence in public security organs and court science.更多还原