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Jul.  2020
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Rapid identification of the black marker ink based on infrared fingerprint spectroscopy

  • Corresponding author: WANG Jifen, wangjifen58@126.com
  • Received Date: 2019-09-18
    Accepted Date: 2019-11-07
  • In order to propagate reactionary thinking and disrupt social security management, reactionary forces often write and post various reactionary slogans with a marker. Undoubtedly, it's significant to identify the marker ink in forensic science. The paper collected and analyzed the infrared fingerprint data of 40 black marker pens from 5 brands including Guangbo and so on. Pre-processing used multi-scatter correction, peak area normalization, automatic baseline correction, and Savitzky-Golay smoothing to create a black marker ink identification model based on multilayer perceptron (MLP). The result showed that the infrared fingerprint can reflect the subtle changes of the molecular structure, which can effectively distinguish the water-based and oil-based markers. For 4 oily marker samples, it was found that the MLP model has the best feature extraction on the 30-dimensional matrix, whose accuracy rate reached 100%. Besides, feature 12, feature 26, and feature 17 were of the highest importance in model construction, with 0.0355, 0.0347, and 0.0346, respectively. Among them, the Letu brand samples had a high degree of convergence, concentrated distribution, and the difference in ink composition and content were small, while the Baoke brand samples were the opposite. In the confirmatory analysis, 8 samples to be determined achieved 100% accuracy, which was ideal. In summary, infrared fingerprints combined with multilayer perceptron can achieve accurate identification between black marker ink brands. The method improved the efficiency of inspection and identification, reduced the cost of identification and fulfilled the rapid and accurate inspection goal for frontline law enforcement personnel, which has certain universality and reference significance.
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Rapid identification of the black marker ink based on infrared fingerprint spectroscopy

    Corresponding author: WANG Jifen, wangjifen58@126.com
  • Institute of Forensic Science and Technology, People's Public Security University of China, Beijing 102623, China

Abstract: In order to propagate reactionary thinking and disrupt social security management, reactionary forces often write and post various reactionary slogans with a marker. Undoubtedly, it's significant to identify the marker ink in forensic science. The paper collected and analyzed the infrared fingerprint data of 40 black marker pens from 5 brands including Guangbo and so on. Pre-processing used multi-scatter correction, peak area normalization, automatic baseline correction, and Savitzky-Golay smoothing to create a black marker ink identification model based on multilayer perceptron (MLP). The result showed that the infrared fingerprint can reflect the subtle changes of the molecular structure, which can effectively distinguish the water-based and oil-based markers. For 4 oily marker samples, it was found that the MLP model has the best feature extraction on the 30-dimensional matrix, whose accuracy rate reached 100%. Besides, feature 12, feature 26, and feature 17 were of the highest importance in model construction, with 0.0355, 0.0347, and 0.0346, respectively. Among them, the Letu brand samples had a high degree of convergence, concentrated distribution, and the difference in ink composition and content were small, while the Baoke brand samples were the opposite. In the confirmatory analysis, 8 samples to be determined achieved 100% accuracy, which was ideal. In summary, infrared fingerprints combined with multilayer perceptron can achieve accurate identification between black marker ink brands. The method improved the efficiency of inspection and identification, reduced the cost of identification and fulfilled the rapid and accurate inspection goal for frontline law enforcement personnel, which has certain universality and reference significance.

引言
  • 记号笔墨水的检验与认定是司法鉴定中一项重要的工作。在宗教极端活动、渗透颠覆破坏活动和民族分裂活动中,反动分子常会用记号笔书写并张贴各种反动标语,宣扬反动思想,借此扰乱社会治安和公共秩序,破坏安定团结的政治局面。侦查部门通过对收缴标语、信件等上边记号笔墨水进行鉴定研究,可以确定记号笔的品牌和生产厂家,进而追溯其来源,从而为认定(排除)嫌疑人提供线索和有力证据[1-2]

    目前在司法鉴定中,针对记号笔墨水的鉴别研究较少,相关研究则集中于圆珠笔油墨的检验区分[3-5]。TREJOS等人[6]借助扫描电镜能谱、激光剥蚀(探针)电感耦合等离子体质谱、实时直接分析质谱和裂解-气相色谱-质谱法对来自全球的319个样本油墨的种类和品牌展开了区分研究。实验发现,激光剥蚀(探针)电感耦合等离子体质谱区分效果最好,其与实时直接分析质谱互补检验可有效改善和提高同类型油墨的分类能力。AKHMEROVA[7]建立了流动注射分析结合液相色谱-电喷雾电离-质谱法对圆珠笔笔划中油墨进行区分的方法。实验得出液相色谱流动相为甲酸水溶液(质量分数为0.001)和乙腈时分离效果最佳。由于染料峰强度低,基线噪音高,流动注射有效弥补了仅使用色谱方法的缺陷。该方法简单快速,可用于油墨快速检验和区分。

    在油墨检验鉴定领域,检验人员常用的方法有色谱法[8-11]和质谱分析法[12-15]等。这类方法相对较为繁琐,成本较高,而且会破坏样本,不利于后续检测。因此,如何研究新的方法对其进行快速无损检验是油墨检验鉴定领域的热点问题之一。红外光谱法作为一种无损检验方法,与传统的色谱质谱法相比具有成本低、操作简便、不破坏样本等优点,一直以来备受检验人员青睐而广泛应用于诸多领域[16-20]。其中指纹区的吸收峰具有较强的特征性,可用于区别不同化合物结构上的微小差异[21]。但由于记号笔墨水是混合物,当样本数量较多时,借助光谱图直接分析会产生较大的主观误差且耗时耗力,此外,成分的混杂使得谱图之间交叉混淆现象较多,无法直接实现对样品合理地区分。

    基于此,本研究中采集并分析40个黑色记号笔红外指纹图谱数据,同时借助化学计量学分析方法,建立基于多层感知器(multilayer perceptron, MLP)——中红外指纹图谱的记号笔墨水分类模型,同时对相关结果展开分析与讨论,实现对其品牌间的准确区分和归类,以期为黑色记号笔墨水快速准确地检验鉴别提供一定的参考和借鉴。

1.   实验
  • 样本:从市场上收集了40支记号笔,其中水性记号笔油有广博共计8支水性记号笔,油性记号笔有三木、乐途、宝克和金万年共计32支油性记号笔(见表 1)。

    type samples
    water-based markers Guangbo-1, Guangbo-2, Guangbo-3, Guangbo-4, Guangbo-5, Guangbo-6, Guangbo-7, Guangbo-8
    oily-based markers Sanmu-1, Sanmu-2, Sanmu-3, Sanmu-4, Sanmu-5, Sanmu-6 Letu-1, Letu-2, Letu-3, Letu-4, Letu-5, Letu-6, Letu-7, Letu-8, Letu-9, Letu-10, Letu-11, Letu-12, Letu-13, Letu-14 Jinwannian-1, Jinwannian-2, Jinwannian-3, Jinwannian-4, Jinwannian-5, Jinwannian-6 Baoke-1, Baoke-2, Baoke-3, Baoke-4, Baoke-5, Baoke-6

    Table 1.  The details of 40 samples

    仪器:Nicolet 5700型傅里叶变换红外光谱仪(Thermo Fisher Scientific公司),衰减全反射附件(Thermo Fisher Scientific公司),氘化三甘氨酸硫酸酯探测器(Thermo Fisher Scientific公司),KBr分束器(Thermo Fisher Scientific公司),OPUS光谱数据处理软件(德国Bruker公司)。扫描次数为32次,分辨率为4cm-1,光谱采集范围为1300cm-1~400cm-1,每个样本采集3次光谱曲线,取均值作为实验样本光谱数据,实验温度为(27±2)℃,相对湿度为47%。

  • 借助Nicolet 5700型傅里叶变换红外光谱仪获取40个样本的红外指纹谱图,光谱预处理采用多元散射校正(multiple scatter correction, MSC)、峰面积归一化(peak area normalization, PAN)和自动基线校正(automatic baseline correction, ABC),采用Savitzky-Golay平滑谱图,光谱波数400cm-1~500cm-1处噪声较大,将以上部分剔除,选择Zscore标准化处理数据,借助化学计量分析方法,开展对样本的分析与研究。

  • 多层感知器神经网络是一种常见的人工神经网络算法,它是一种趋向结构的神经网络,映射一组输入向量到一组输出向量。最典型的MLP包含3组结构,即输入层、隐层和输出层。MLP每一层的所有神经元都与下一层相连接。其中输入层的作用是将信息输入到神经网络之中,隐层的作用即通过一系列函数将输入映射到输出,常用的函数有sigmoid函数、tanh函数和ReLU函数,输出层即输出模型分类结果,常用softmax函数,若有一个神经元j, 当输入向量为yi时,其输出向量zj的表达式为[22]

    式中, yi为神经元j上一层的第i个神经元的输出,wji表示神经元j与神经元i连接的权重,zj为神经元j的输出向量。

2.   结果及分析
  • 记号笔墨水分油性和水性两种,图 1为水性记号笔与油性记号笔的红外指纹图谱。由图 1可知,两种类型的记号笔谱图走势大致一样,但是出峰的位置,峰形等有较为明显的差异,在波数为1050cm-1处和750cm-1处,油性记号笔均有一宽峰,水性记号笔则没有峰,在波数为700cm-1处,油性记号笔有一左高右低的双峰,水性记号笔则有一尖峰。据此,借助红外指纹图谱可将8支广博品牌的记号笔样本全部区分出来。

    Figure 1.  Infrared fingerprint of water-based and oily-based markers

  • 32支油性记号笔的红外指纹图谱见图 2。由图 2可知,各样本的峰形、峰的走向和出峰的位置基本一致,在波数为1070cm-1处均有一个弱峰,在波数为1000cm-1左右均有一个左低右高的双峰,在波数830cm-1处各样本均有一宽峰,在波数为750cm-1处均有一峰,在波数为700cm-1处均有一左高右低的双峰。其中个别峰的个数以及相对峰高有所区别,在波数为1270cm-1和1160cm-1处部分样本有一宽峰,部分样本则没有,在波数为750cm-1处双峰的相对峰高有差异。依据谱图的特征开展区分工作费时费力,且将其实现准确区分难度较大。实验中借助化学计量分析方法,开展对样本品牌的分类工作。

    Figure 2.  Infrared fingerprint of oily-based markers about 4 brands

    随机在4种品牌中各选取其中一个样本,通过实验分析得到它们各自的光谱数据信息,实验结果如表 2所示。样本数据是否具备分析价值,主要通过以下3个指标进行衡量:均值、标准差和变异系数。其中均值能够反映样本数据集中趋势,标准差能够反映数据集离散程度,而变异系数即标准差与均值的比值,它是反映样本数据在单位均值上的离散程度的一项重要指标,其中变异系数小于0.15的数据可用于分析研究。由表 2可知,各样本变异系数均在0.15以下,因此这些样本数据可以满足分析研究的需要。

    brand minimum value maximum value average standard deviation coefficient of variation
    Sanmu 28.35436 74.06045 57.25220 14.386853 28.35436
    Letu 39.22037 78.16590 65.23025 12.533006 39.22037
    Jinwannian 38.93423 85.08097 71.69274 13.544799 38.93423
    Baoke 26.46985 82.26426 68.18328 12.606254 26.46985

    Table 2.  The data details of 4 kinds of samples

    光谱数据的维度过高会造成样本特征的冗杂,使计算过程变得更加复杂,增加了数据分析时长,同时也降低了模型精度,不利于数据的快速准确分析。因此必须合理控制数据维度,注意有效信息的采集。每个实验样本采集的光谱数据为波数在1304cm-1~500cm-1,分辨率为4cm-1,即实验数据为201维,其维度较高,需通过降维方式提取有效特征。

    借助主成分分析,选择降维后的1维到35维等共计35个维度下的特征变量,应用多层感知器(MLP)构建分类模型,对4种品牌的黑色记号笔墨水光谱数据展开识别工作,图 3中列举了10维、15维、20维、25维和30维等共计12个维度下分类模型的识别准确率。

    Figure 3.  The accuracy of MLP under different dimensions

    图 3可知,30维特征变量构建的MLP模型识别准确率最高,为100%,10维特征变量构建的MLP模型识别准确率最低,为59.4%。分析认为是原始数据经过降维后,30维矩阵上样本的特征信息提取较好,无用信息剔除较好,在10维、15维、25维矩阵上样本包含的信息量较少,特征信息损失较多,无法准确解释黑色记号笔墨水所包含的主要理化信息,而在31维、33维和35维矩阵上样本信息的无关特征和冗余特征较多,这增加了训练过程的时间,影响了模型的性能,降低了分类精度。综上所述,选择30维度光谱数据构建MLP分类模型,得到了各特征变量重要性分布结果(见图 4)。特征变量重要性即在MLP分类模型中,各维度变量对模型区分效果的贡献程度,由图 4可知,特征12贡献程度最高,为0.0355,其次为特征26和特征17,贡献程度分别为0.0347和0.0346,特征14贡献程度最低,为0.0316,30个特征变量的重要程度值总和为1。

    Figure 4.  The details of characteristic variable importance

    特征变量选择贡献程度最大的特征12、特征26和特征17,隐藏层数选择为一层,激活函数选择tanh函数,输出层激活函数选择softmax函数,构建MLP分类模型,得到了4种油性品牌的空间分布结果(见图 5)。

    Figure 5.  The spatial classification details of 4 brand samples

    图 5可知,4种品牌共计32样本均实现了100%的准确区分,其中x, yz分别代表特征12、特征26和特征17,乐途品牌的样本聚敛程度较高,分布集中,表明其墨水的组分和含量差异较小,宝克品牌的样本分布较为分散,其墨水的组分和含量相对差异较大。为验证模型精度,选择Sanmu-3, Sanmu-5, Letu-7,Letu-5,Jinwannian-2,Jinwannian-4,Baoke-6,Baoke-1这8个样本作为待判定样本,开展分类工作,得到了8个待判定样本的分类结果(见表 3)。由表可知各样本均实现了准确地归类,模型精度较高,可准确实现对黑色记号笔墨水品牌间的区分和认定。

    sample to be determined Sanmu Letu Jinwannian Baoke
    Sanmu-3
    Sanmu-5
    Letu-7
    Letu-5
    Jinwannian-2
    Jinwannian-4
    Baoke-6
    Baoke-1

    Table 3.  Results of 8 determined-samples

3.   结论
  • 以市面上常见的4种品牌的油性和1种品牌的水性记号笔为研究对象,提出了采用红外指纹图谱结合MLP对黑色记号笔墨水的品牌进行准确识别和分类。结果表明:借助数学模型展开模式识别,选择30纬度下的特征12、特征26和特征17,能够实现黑色记号笔墨水品牌间快速准确区分和认定的目的,准确率为100%,实验结果理想。这在一定程度上降低了检验成本,提高了检验效率,满足了基层执法人员快速准确检验的需求,具有一定程度上的实用价值。下一步研究工作中,应增加样本品牌和数量,同时对同一品牌不同生产批次的样本的差异点进行研究,力求完善黑色记号笔墨水的模式识别模型,为基层民警的检验鉴定工作提供借鉴和参考。

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