中性笔油墨是司法鉴定中同一认定的重要物证。为提高油墨检验的准确性,本文利用拉曼光谱法对油墨样本进行无损检测。首先对预处理后的光谱数据进行降维处理,构建偏最小二乘判别分析模型;然后采用受试者工作特征曲线线下面积对预测效果进行验证,提取出36个变量投影重要性最高的特征变量;接着将特征变量作为数据输入到隐藏层神经元数目为13的多层感知器中,最终的训练正确率为87%且无过拟合现象。将变量投影重要性的特征提取与有监督的多层感知器训练相结合,可以有效压缩数据,缩短分析时间。感知器层间的连接权重可通过自主学习进行调节,提高了中性笔油墨分类结果的可信度与正确率。

Handwriting ink is an important physical evidence of the identification in judicial appraisal. In order to improve the accuracy of ink inspection, we employed Raman spectroscopy for the non-destructive inspection of ink samples. First, the pre-processed spectral data were dimensionally reduced to construct a model of partial least squares discrimination analysis. Then, after the prediction effect was verified by the area under the receiver operating characteristic curve, 36 feature variables with the highest variable importance for the projection were extracted. Furthermore, the feature variables were input as data into a multi-layer perceptron with 13 neurons in the hidden layer, and the final training accuracy rate was 87%, without overfitting. We also found that combining the feature extraction of variable importance for the projection with supervised multi-layer perceptron training could effectively compress the data and shorten the analysis time. Besides, the connection weight between perceptron layers could be adjusted through autonomous learning, which improved the credibility and accuracy of handwriting ink classification results.