|
|
|
|
|
|
Identification of Different Brands Erasable Pens by Infrared Spectroscopy Combined With Chemometrics Methods |
ZHAO Yu-xuan1, ZENG Le-yang-zi2, LI Kai-kai1* |
1. College of Criminal Investigation, National Experimental Teaching Demonstrating Center, People’s Public Security University of China, Beijing 100038,China
2. Changning Branch of Shanghai Public Security Bureau, Shanghai 200336, China |
|
|
Abstract As a new writing tool, an erasable pen has the characteristics of erasable ink. Therefore, in the current public security work, common criminals use an erasable pen to tamper with documents. In order to ensure the integrity of material evidence, it is urgent to establish a non-destructive and rapid ink test of the erasable pen to provide help for finding writing tools and identifying criminal suspects. Common erasable pen inks can be divided into film-forming ink and temperature change ink. In this study, the ink of 30 erasable pens of different brands, types and colors were analyzed with Fourier transform infrared spectroscopy (FTIR). Through the analysis of infrared spectra in the range of 4 000~650 cm-1, the difference in their composition and the spectral difference of erasable pen with different fading mechanism were compared. Through the infrared spectrum information, it is found that the components of the same brand and the same model of erasable pen ink are similar, and the ink color has little influence on the infrared spectra. At the same time, comparing the infrared spectrum information of the erasable pen ink before and after erasing, it is found that there are still some special chemical components in the temperature change ink after erasing, which can be used as the identification standard. However, the residual components of film-forming ink are less after erasing, which is difficult to identify, which may be related to the particle structure of film-forming ink. In addition, the spectra in the range 650~1 500 cm-1 of erasable pen ink were analyzed by principal component analysis (PCA) and Heatmap to classify the types of erasable pen. The relationship between infrared spectrum information and principal component loadings was established. According to the loading plot, the first two principal components summarize almost all the infrared spectrum information, and the cumulative contribution rate is more than 79%. Therefore, the first two principal components are selected to a scatter plot. In order to determine the source of the erasable pen in practical work, 5 samples of the unknown pen were randomly selected from 30 kinds of ink samples, and PCA analysis was carried out with known samples at the same time, and a scatter diagram was made to realize the prediction of the types of unknown erasable pen ink samples and work well. The results indicate that FTIR combined with PCA can be used to classify the erasable pen ink rapidly. It provides a method for examining the erasable pen ink in altered documents with advantages of fast, non-destructive and high sensitivity.
|
Received: 2020-08-20
Accepted: 2020-12-17
|
|
Corresponding Authors:
LI Kai-kai
E-mail: zlkk77@163.com
|
|
[1] Amin Khatami, Shamina S Prova, Aafreen K Bagga, et al. Rapid Communications in Mass Spectrometry, 2017, 31(12): 83.
[2] Li B, Ouyang G, Yao L. Journal of Forensic Sciences, 2018, 63(5): 1545.
[3] AndréBraz, Maria López-López, CarmenGarcía-Ruiz. Forensic Science International, 2013, 232(1-3): 206.
[4] Wang Xiangfeng, Yu Jing, Zhang Ailan, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2012, 97: 986.
[5] Liu Xinlai, Li Zhen. Journal of Forensic Sciences, 2019, 64(6): 1889.
[6] Zieba-Palus Janina, Kunicki Marcin. Forensic Science International, 2006, 158(2-3): 164.
[7] Samir Senior, Ezzat Hamed, Mamdouh Masoud, et al. Journal of Forensic Sciences, 2012, 57(4): 1087.
[8] Flávia de Souza Lins Borba, Ricardo Saldanha Honorato, Anna de Juan. Forensic Science International, 2015, 249: 73.
[9] Mohamad Asri Muhammad Naeim, MatDesa Wan Nur Syuhaila, Ismail Dzulkiflee. Journal of Forensic Sciences, 2018, 63(1): 285.
[10] Lukáš Gál, Michal Oravec, Pavol Gemeiner, et al. Forensic Science International, 2015, 257: 285.
[11] GUO Yuan-yuan, LUO Yi-wen, ZHANG Qing-hua, et al(郭媛媛, 罗仪文, 张清华, 等). Chinese Journal of Forensic Sciences(中国司法鉴定), 2015, 1: 41.
[12] Vishal Sharma, Raj Kumar. Vibrational Spectroscopy, 2017, 92: 96.
[13] Trejos T, Flores A, José R Almirall. Spectrochimica Acta Part B:Atomic Spectroscopy, 2010, 65(11): 884.
[14] Calcerrada Matías, García-Ruiz Carmen. Analytica Chimica Acta, 2015, 853: 143. |
[1] |
GUO Ya-fei1, CAO Qiang1, YE Lei-lei1, ZHANG Cheng-yuan1, KOU Ren-bo1, WANG Jun-mei1, GUO Mei1, 2*. Double Index Sequence Analysis of FTIR and Anti-Inflammatory Spectrum Effect Relationship of Rheum Tanguticum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 188-196. |
[2] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[3] |
CAO Qian, MA Xiang-cai, BAI Chun-yan, SU Na, CUI Qing-bin. Research on Multispectral Dimension Reduction Method Based on Weight Function Composed of Spectral Color Difference[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2679-2686. |
[4] |
TIAN Ze-qi1, WANG Zhi-yong1, YAO Jian-guo1, GUO Xu1, LI Hong-dou1, GUO Wen-mu1, SHI Zhi-xiang2, ZHAO Cun-liang1, LIU Bang-jun1*. Quantitative FTIR Characterization of Chemical Structures of Highly Metamorphic Coals in a Magma Contact Zone[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2747-2754. |
[5] |
ZHANG Xiao-xu1, LIN Xiao-xian3, ZHANG Dan2, ZHANG Qi1, YIN Xue-feng2, YIN Jia-lu3, 4, ZHANG Wei-yue4, LI Yi-xuan1, WANG Dong-liang3, 4*, SUN Ya-nan1*. Study on the Analysis of the Relationship Between Functional Factors and Intestinal Flora in Freshly Stewed Bird's Nest Based on Fourier Transform Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2452-2457. |
[6] |
WANG Yu-hao1, 2, LIU Jian-guo1, 2, XU Liang2*, DENG Ya-song2, SHEN Xian-chun2, SUN Yong-feng2, XU Han-yang2. Application of Principal Component Analysis in Processing of Time-Resolved Infrared Spectra of Greenhouse Gases[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2313-2318. |
[7] |
SU Ling1, 2, BU Ya-ping1, 2, LI Yuan-yuan2, WANG Qi1, 2*. Study on the Prediction Method of Pleurotus Ostreatus Protein and
Polysaccharide Content Based on Fourier Transform Infrared
Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1262-1267. |
[8] |
ZHOU Ao1, 2, YUE Zheng-bo1, 2, LIU A-zuan1, 2, GAO Yi-jun3, WANG Shao-ping3, CHUAI Xin3, DENG Rui1, WANG Jin1, 2*. Spectral Analysis of Extracellular Polymers During Iron Dissimilar
Reduction by Salt-Tolerant Shewanella Aquimarina[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1320-1328. |
[9] |
FENG Yu, ZHANG Yun-hong*. Rapid ATR-FTIR Principal Component Analysis of Commercial Milk[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 838-841. |
[10] |
YUE Kong, LU Dong, SONG Xue-song. Influence of Thermal Modification on Poplar Strength Class by Fourier Infrared Spectroscopy Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 848-853. |
[11] |
ZHANG Yan1, 2, WANG Hui-le1, LIU Zhong2, ZHAO Hui-fang1, YU Ying-ying1, LI Jing1, TONG Xin1. Spectral Analysis of Liquefaction Residue From Corn Stalk Polyhydric
Alcohols Liquefaction at Ambient Pressure[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 911-916. |
[12] |
QIAO Lu1, LIU Rui-na1, ZHANG Rui1, ZHAO Bo-yu1, HAN Pan-pan1, 2, ZHOU Chun-ya1, 3, ZHANG Yu-qing1, 4, DONG Cheng-ming1*. Analysis of Spectral Characteristics of Soil Under Different Continuous Cropping of Rehmannia Glutinosa Based on Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 541-548. |
[13] |
HAI Jing-pu1, 2, GUO Ling-hua1, 2*, QI Yu-ying1, 2, LIU Guo-dong1, 2. Research on the Spectral Prediction Model of Gravure Spot Color Scale Based on Density[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 31-36. |
[14] |
CHEN Yong1, 2, GUO Yun-zhu1, WANG Wei3*, WU Xiao-hong1, 2*, JIA Hong-wen4, WU Bin4. Clustering Analysis of FTIR Spectra Using Fuzzy K-Harmonic-Kohonen Clustering Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 268-272. |
[15] |
HU Yun-you1, 2, XU Liang1*, XU Han-yang1, SHEN Xian-chun1, SUN Yong-feng1, XU Huan-yao1, 2, DENG Ya-song1, 2, LIU Jian-guo1, LIU Wen-qing1. Adaptive Matched Filter Detection for Leakage Gas Based on Multi-Frame Background[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3307-3313. |
|
|
|
|