{"paper_id":"3f6e5d83-e271-4dd0-9e8e-e381024baeee","body_text":"A study for potential rapid discrimination of smokeless powders by near-infrared spectroscopy and chemometric modeling methods for forensic purposes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A study for potential rapid discrimination of smokeless powders by near-infrared spectroscopy and chemometric modeling methods for forensic purposes Hongling Guo, Yinghua Feng, Yiting Guo, Xiuli Zhang, Ping Wang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8317775/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Smokeless powder is the primary propellant in civilian and military ammunition. And in China, the use of propellant for making homemade ammunition and bombs is an incipient criminal practice. The identification and discrimination of the propellant used can provide forensic information for its sources. Depending upon the ammunition fabricant and ammunition type, the recipe of propellant changes. The characterization of smokeless powders in terms of its spectral components is useful for differentiating the propellants. In this work, near-infrared spectroscopy (NIR) and chemometric modeling were used to explore a possibility of differentiation and prediction of smokeless powders from different sources. By comparison, the proposed neural network model showed an average accuracy of over 80%. Also, the potential for discrimination of smokeless powders was well demonstrated by using easy and fast near-infrared spectroscopic analyses. The use of chemical agents and time-consuming chromatography and mass spectrometry could, thereby, be avoided. Physical sciences/Chemistry Physical sciences/Engineering Physical sciences/Materials science Smokeless powder Near-infrared spectroscopy Chemometrics neural network model Propellant Forensics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Smokeless powder is the primary propellant in civilian and military ammunition and is the most common low explosive used to fabricate improvised explosive devices (IEDs) such as pipe bombs and home-made ammunition. Propellant can be used as initiator in explosive mixtures along with other components such as coal, ammonium nitrate, sulfur, etc [ ]. Also, in China, there are cases involving home-made guns with an unknown source of propellants. When the suspects have been arrested, it is necessary to make a rapid screening of the smokeless powders that might be available in their houses, or in manufacturing places which has not been used in the IEDs or ammunitions. The major classes of compounds in smokeless propellants include energetics, stabilizers, plasticizers, flash suppressants, deterrents, opacifiers, and dyes [ ]. Also, the chemical compounds are both complex and different. According to the main chemical substances in the energetic class of compounds, smokeless powders can be classified into three different types: single-base, double-base, and triple-base. A single-base powder mainly contains nitrocellulose, nitrocellulose and nitroglycerin are main component for double-base propellants, and a triple-base powder mainly contains nitrocellulose, nitroglycerine, and nitroguanidine. Propellants can be characterized according to existing organic components. Many studies have been performed where the intact smokeless powders and residues have been analyzed for some specific target analyte. Infrared spectroscopy, chromatography, and mass spectroscopy have been commonly used for the determination of different ingredients in smokeless powders [ , , , ]. Infrared spectroscopy has normally been used for the analysis of nitrocellulose, but it showed a limited application for other substances with low amounts. Chromatography and mass spectroscopy have been widely used for the analyses of target substances (such as diphenylamine and N,N′-Dimethyl carbanilide stabilizers). The disadvantage of this targeted approach is that when compounds outside of the target group are present, it becomes impossible to identify them. The targeted approach requires tedious sample preparation procedures, uses toxic reagents, and forms harmful waste. It also consumes or destroys the original samples. This point becomes critical when forensic results are urgently required and the evidence must not be destroyed, which is a common situation in the forensic analysis. Nondestructive techniques with improved discrimination possibilities (such as Raman spectroscopy) have, therefore, been introduced for the analyses of intact smokeless powders and residue particles. Raman spectroscopy has reported to have been used for the identification of the unburnt smokeless powder particles [ , , ]. With the development of instrumental technology and chemometrics, near-infrared (NIR) spectroscopy has been found capable of recording spectra of complicated compounds, with minor (or no) sample pretreatments. And it is not necessary to specify the target compounds. NIR has been widely used as a quality control method in the agriculture, food, petrochemical engineering, and pharmacy industries [ , , ]. An it has also been reported as a fast quality control tool for the determination of ingredient concentrations in rock propellant fuel liquid pre-mixes [ ]. Furthermore, NIR has been used for an in-process determination of hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) in a solid propellant intermediate[ ]. Reports about the use of NIR in propellants have mainly focused on a fast quantification of some specific compounds in the propellants and the accuracy of the used algorithm [ , ]. The possibility to use NIR as a non-target approach for the discrimination and specification of smokeless powders collected from different sources has been investigated in the present study. Chemometric approaches were applied to evaluate the spectra data obtained. 2. Materials and methods 2.1. Collection and preparation of samples Smokeless powders from 79 cartridges collected from different manufacturers were used as known source samples to verify the method used. Detailed information was presented in Table 1 . The propellants were taken out from the cartridges by using a cartridge puller. Prior to NIR analysis, they were equilibrated at room temperature (of about 25℃) for 24 h. Table 1 Ammunition propellant sample information for measurement Series Number Manufacturer Cartridge category Production Year Caliber (mm) Manufacturer ID Series Number Manufacturer Cartridge category Production Year Caliber (mm) Manufacturer ID s1 121 51 pistol 1958 7.62 Manufacturer A s41 81 59 rifle 1966 9 Manufacturer I s2 121 51 pistol 1963 7.62 Manufacturer A s42 911 56 rifle 1968 5.6 Manufacturer J s3 121 51 pistol 1964 7.62 Manufacturer A s43 911 56 rifle 1979 5.6 Manufacturer J s4 121 51 pistol 1965 7.62 Manufacturer A s44 C Sporting rifle unknown 5.6 Manufacturer K s5 121 51 pistol 1970 7.62 Manufacturer A s45 CJ SS109 rifle 2000 5.6 Manufacturer L s6 121 51 pistol 1972 7.62 Manufacturer A s46 Czech Republic pistol unknown 7.65 Manufacturer M s7 121 51 pistol 1979 7.62 Manufacturer A s47 KKJ Nail unknown 6.8ⅹ18 Manufacturer N s8 121 51 pistol 1981 7.62 Manufacturer A s48 KKJ Nail unknown 6.3ⅹ16 Manufacturer N s9 121 51 pistol 1987 7.62 Manufacturer A s49 KKJ Nail unknown 6.3ⅹ16 Manufacturer N s10 121 51 pistol 1991 7.62 Manufacturer A s50 LY Parabellum pistol 1994 9 Manufacturer O s11 121 51 pistol 2018 7.62 Manufacturer A s51 NS Nail unknown 6.8ⅹ11 Manufacturer P s12 121 Revolverpistol 2005 9 Manufacturer A s52 NS Nail unknown 6.8ⅹ11 Manufacturer P s13 121 DAP92 pistol 2019 9 Manufacturer A s53 NS Nail unknown 6.8ⅹ18 Manufacturer P s14 121 DAP92 pistol 2018 9 Manufacturer A s54 NS Nail unknown 6.8ⅹ18 Manufacturer P s15 121 64 pistol 2007 7.62 Manufacturer A s55 NS Nail unknown 5.6ⅹ16 Manufacturer P s16 121 64 pistol 1990 7.62 Manufacturer A s56 NS Nail unknown 5.6ⅹ16 Manufacturer P s17 121 64 pistol 1990 7.62 Manufacturer A s57 NS Nail unknown 6.3ⅹ16 Manufacturer P s18 121 64 pistol 1992 7.62 Manufacturer A s58 NS Nail unknown 5.6ⅹ16 Manufacturer P s19 121 64 pistol 1995 7.62 Manufacturer A s59 NS Nail unknown 6.8ⅹ11 Manufacturer P s20 121 64 pistol 1996 7.62 Manufacturer A s60 NS Nail unknown 5.6ⅹ16 Manufacturer P s21 121 64 pistol 1990 7.62 Manufacturer A s61 NS Nail unknown 6.8ⅹ11 Manufacturer P s22 121 92 rifle 2002 7.62 Manufacturer A s62 NS Nail unknown 6.8ⅹ11 Manufacturer P s23 301 64 pistol 1980 7.62 Manufacturer B s63 NS Nail unknown 6.8ⅹ11 Manufacturer P s24 301 64 pistol 1987 7.62 Manufacturer B s64 Ω Sporting rifle unknown 5.6 Manufacturer Q s25 311 64 pistol 1989 7.62 Manufacturer C s65 Double Ring Sporting rifle unknown 5.6 Manufacturer R s26 311 64 pistol 1992 7.62 Manufacturer C s66 △ Sporting rifle Unknown 5.6 Manufacturer S s27 311 64 pistol 1994 7.62 Manufacturer C s67 △ Sporting rifle unknown 5.6 Manufacturer S s28 311 64 pistol 2004 7.62 Manufacturer C s68 YRD Nail unknown 6.8ⅹ11 Manufacturer T s29 611 56 rifle 1967 7.62 Manufacturer D s69 YRD Nail unknown 6.8ⅹ11 Manufacturer T s30 671 53 pistol 2013 7.62 Manufacturer E s70 YRD Nail unknown 6.8ⅹ11 Manufacturer T s31 71 56 rifle 1956 7.62 Manufacturer F s71 YRD Nail unknown 6.8ⅹ18 Manufacturer T s32 724 DAP92 pistol 2007 5.8 Manufacturer G s72 YRD Nail unknown 6.8ⅹ18 Manufacturer T s33 791 9mm pistol 2016 9 Manufacturer H s73 YRD Nail unknown 5.6ⅹ16 Manufacturer T s34 791 56 pistol 1988 7.62 Manufacturer H s74 YRD Nail unknown 5.6ⅹ16 Manufacturer T s35 791 SS109 rifle 2001 5.56 Manufacturer H s75 YRD Nail unknown 5.6ⅹ16 Manufacturer T s36 791 DCV05 pistol 2008 5.8 Manufacturer H s76 YRD Nail unknown 6.3ⅹ16 Manufacturer T s37 791 95 pistol 2016 5.8 Manufacturer H s77 YRD Nail unknown 6.3ⅹ16 Manufacturer T s38 791 pistol 2014 5.8 Manufacturer H s78 YRD Nail unknown 5.6ⅹ16 Manufacturer T s39 791 DVP88A pistol 2017 5.8 Manufacturer H s79 YRD Nail unknown 5.6ⅹ16 Manufacturer T s40 81 56 rifle 1964 7.62 Manufacturer I 2.2. Acquisition of NIR spectra The NIR analyses of the smokeless powders were carried out with a Fourier transform NIR spectrometer (Nicolet Antaris-II, ThermoFisher Scientific, U.S.A.), which was equipped with an integrating sphere module and an InGaAs detector. The sample cup was loaded with about 20 mg of propellant sample and rotated continuously at a constant speed. The NIR spectra were then collected in a diffuse reflectance mode at room temperature (25℃±2℃), and each sample was analyzed three times. All spectra were recorded as an absorbance with a resolution of 4 cm − 1 and at a wavenumber range from 4000 cm − 1 to 10,000 cm − 1 . Also, each spectrum had an average of 32 scans. 2.3 Algorithmic statistics 79 smokeless powder samples were received from 20 different manufacturers. Basic descriptive statistics were performed to evaluate the distribution of NIR data for all samples. The calculation of SSSVs (specific significant statistical values) followed standard protocols. UMAP (Uniform Manifold Approximation and Projection) was performed using the UMAP-learn Python package, version 0.5.1. A deep learning neural network model (NN) model was also developed for a more comprehensive and accurate classification prediction. Furthermore, comparisons between the NN model and four classical traditional machine learning models were made, including the Stochastic Gradient Descent Classifier (SGDC), Support Vector Machine (SVM), K-Nearest Neighbors Classifier (KN), and Random Forest Classifier (RF). We constructed a neural network model using the keras module in TensorFlow, consisting of six main components: a feature extraction network that takes data with dimensions identical to the input and outputs a one-dimensional vector of length 784; a feature map transformation module that converts the one-dimensional vector into a three-dimensional tensor of size 28 x 28 x 1; a feature extraction framework based on VGG19 for deep feature extraction; an embedding network that maps the extracted feature maps to the classification layer for classification discrimination. Throughout the training process, we utilized cross-entropy minimization as the loss function and employed the SGD optimization method with a momentum parameter of 0.9, lasting for 1000 epochs. All network layers used ReLU as the activation function. All machine learning methods are executed according to functions provided in the python package Scikit-Learn, with all parameters at default values. We have trained our models using a split of 90% training data and 10% validation data. For each model, we conducted a grid search to tune the hyperparameters, selecting the combination maximizing the validation accuracy. Accuracy was used to evaluate the performance of different models. Accuracy measured the proportion of correctly predicted samples out of the total samples. 3. Results and Discussion 3.1 NIR analyses The major peaks of the NIR spectra for smokeless powder samples were broad and similar. The NIR spectra were mainly formed by the absorption of hydrogen-containing groups of organic molecules. Also, the absorption bands corresponded to overtones and combinations of fundamental vibrations (stretching or bending). Nitrocellulose was the main component in these bands, irrespective of the type of smokeless powder. Figure 1 presents the NIR spectra of five randomly selected samples (s1, s8, s14, s31, and s62), which are dominated by three major peaks 5240 cm − 1 , 5800 cm − 1 , and 7000 cm − 1 between 4500 cm − 1 and 7500 cm − 1 . The three absorption peaks, along with a series of peaks between 4150 cm − 1 and 5000 cm − 1 , mainly corresponded to overtones and combinations of C-H and O-H vibrations of nitrocellulose. It was, however, difficult to make further discrimination and identification only by these NIR spectra. Figure 1 NIR spectra of s1, s8, s14, s31and s62 samples The majority of propellants from different sources in this study showed absorbance values that were predominantly distributed in the range of 0.9–1.1 (Fig. 2 a). However, some samples from the manufacturers B, F, K, Q, R, and T showed some spectral absorbance values exceeding 1.2. It was interesting to find that the absorbance values of rifle-type cartridges were higher than the absorbance values for other types of cartridges. Correlations between smokeless powders produced by different manufacturers were also calculated. Notably, the similarity of most of the smokeless powders from different manufacturers was remarkably high, with correlation coefficients larger than 0.8 (Fig. 2 b). However, smokeless powders from manufacturers D, E, and F showed a lower NIR spectral similarity with other samples. 3.2 Descriptive statistics Descriptive statistical analyses of all collected sample data were also conducted. The results revealed that the samples involved 20 different manufacturers, with a highly uneven distribution of sample quantities. Specifically, the samples produced by manufacturers A, P, T, and H accounted for over 60% (162/237) (Fig. 3 A). Among these samples, there were 14 different cartridge types. 60% (156/237) of the total data were comprised by nails and pistols with 7.62mm caliber from 64 pistol and 51 pistol model of cartridges (Fig. 3 B). The manufacturers produced one or more types of cartridges. Manufacturers A and H produced the most diverse range of cartridge types, each producing four different types. Manufacturer A produced cartridges of the following types: cartridges with 7.62 mm caliber for Chinese 51 model pistol, 64 model pistol, 92 model rifle and pistols with 9mm caliber. Manufacturer H, on the other hand, produced cartridges of the following types: cartridges with 5.8 mm caliber for Chinese pistols, 56 model pistol, and SS109 model rifle and pistols with 9mm calibers (Fig. 4 A). The spectral absorbance of cartridges from different manufacturers have also been analyzed in the present study. As a result, the cartridges from 16 of the 20 manufacturers showed near-infrared characteristic distributions in the wavenumber range of 2750 cm − 1 to 3500 cm − 1 . The manufacturer K and Q produced primarily cartridges of the 5.6 mm rifle type, with the strongest characteristic peaks at 3972 cm − 1 and 4007 cm − 1 , respectively. However, the manufacturer R and S produced 5.6 mm rifle-type cartridges, which exhibited the strongest near-infrared characteristic peaks at 3646 cm − 1 and 3072 cm − 1 , respectively (Fig. 4 B). The extreme imbalance of all NIR data between samples possess a significant challenge when trying to accurately identify the cartridge sources. 3.3 Model analysis of data The Uniform Manifold Approximation and Projection (UMAP) algorithm has here been used for the dimension reduction and clustering analyses. The results showed that cartridges from manufacturers C, P, and N, which showed the largest variations in averaged absorbance value, were closely clustered in the low-dimensional projection space. However, the cartridges from other manufacturers were clustered together (Fig. 5 ), which made them difficult to distinguish. Furthermore, the NIR spectra of cartridges from different manufacturers showed some differences, but the distinguishing features were not clear. It was, therefore, a challenge to establish clear classification boundaries for an accurate and systematic categorization of samples for statistical and dimensionality reduction clustering analyses. With the large similarity and imbalance of NIR data for all cartridges, it was impossible to make an accurate differentiation based only on a simple comparison of NIR spectra and descriptive statistics. It was, instead, necessary to use more complex algorithmic methods for the NIR data analyses. 3.4 Algorithmic modeling With the aim of developing a comprehensive and accurate classification model, a deep-learning neural network method (NN) has in the present study been used to analyze the spectra of different cartridges from various manufacturers. For evaluation purposes, the performance of this model was then compared with four classical traditional machine learning models: Stochastic Gradient Descent Classifier (SGDC), Support Vector Machine (SVM), K-Nearest Neighbors Classifier (KN), and Random Forest Classifier (RF). The training and test data were divided with a ratio of 9:1, and 10-fold cross-validation was used to mitigate the evaluation error. The results showed that the average accuracy in the discrimination of sources of different cartridges from 20 different manufacturers was all below 60%. This result was based on their simulated near infrared spectra and by using the four traditional machine learning models. In contrary, the neural network modeling approach resulted in a corresponding average accuracy of over 80% (Fig. 6 ). 4. Conclusion The aim of the study has been to classify various smokeless powders from different cartridges, which have been produced by different manufacturers. Near-infrared spectroscopy and a classification method were used for this purpose. The similarity and imbalance of NIR data for all collected cartridges posed a significant challenge for identification. Both traditional machine learning and deep learning neural network models were used to identify the optimal classification model. Compared with the traditional machine learning models SGDC, SVM, KN, and RF, the neural network(NN) modeling approach provided an average accuracy exceeding 80%. The results indicated that the combination of near-infrared spectroscopy and deep learning neural networks can be used for the identification of smokeless powder sources. This combination provides a quick and practical way to screen and analyze suspected smokeless powders found at ammunition and bomb making crime scenes. Declarations Data availability statement The data that supports the findings of this study are available in the supplementary material of this paper. CRediT authorship contribution statement: Hongling Guo: Conceptualization, Methodology, Formal analysis, Investigation, Writing - original draft. Yinghua Feng: Methodology, Formal analysis, Investigation, Writing - original draft. Yiting Guo: investigation Xiuli Zhang: Formal analysis, Investigation Ping Wang: investigation Can Hu: investigation Hongcheng Mei: investigation Yajun Li: investigation Jun Zhu: Writing -review & editing. Funding declaration The work was funded by the grant of Ministry of Public Security under Grant Basic Work Plan for Strengthening Police Force of Ministry of Public Security(2022JC12),Grant Central Public-Interest Scientific Institution Basal Research Fund(2024JBGS002),Grant Double Ten Project of Ministry of Public Security, China (2021SSGG028) and Grant Technology Research Project of the Ministry of Public Security, China (2021JSYJC08). References Álvarez, Á., Yáñez, J., Contreras, D., Saavedra, R., Sáez, P. & Amarasiriwardena, D. Propellant’s differentiation using FTIR-photoacoustic detection for forensic studies of improvised explosive devices. Forensic Sci. Int. 280 , 169-175 (2017). . Heramb, R. M. & McCord, B. R. The manufacture of smokeless powders and their forensic analysis: A brief review. Forensic Sci. Commun . 4 , 1-7 (2002). ASTM E2999-17: Standard Test method for analysis of organic compounds in smokeless powder by gas chromatography-mass spectrometry and Fourier transform infrared spectroscopy (2022). López-López M, Ferrando, JL, García-Ruiz C. Comparative analysis of smokeless gun powders by Fourier transform infrared and Raman spectroscopy. Anal. Chim. Acta. 2012; 717: 92-99. https://doi.org/10.1016/j.aca.2011.12.022. Thomas, J. L., Lincoln, D. & McCord, B. R. Separation and detection of smokeless powder additives by ultra performance liquid chromatography with tandem mass spectrometry (UPLC/MS/MS). J. Forensic Sci. 58(3) , 609-615 (2013). Gassner, A. L. & Weyermann, C. LC-MS method development and comparison of sampling materials for the analysis of organic gunshot residues. Forensic Sci. Int. 264 , 47-55 (2016). Khandasammy, S. R., Rzhevskii, A. & Lednev, I. K. A novel two-step method for the detection of organic gunshot residue for forensic purposes: Fast fluorescence imaging followed by Raman microspectroscopic identification. Anal. Chem. 91, 11731-11737 (2019). López-López, M., Ángeles Fernández de la Ossa, M. & García-Ruiz, C. Fast analysis of complete macroscopic gunshot residues on substrates using Raman imaging. Appl. Spectrosc . 69 , 889-893 (2015). Khandasammy, S. R., Bartlett, N. R., Halámková, L. & Lednev, I. K. Hierarchical modelling of Raman spectroscopic data demonstrates the potential for manufacturer and caliber differentiation of smokeless powders. Chemosensors . 11(1), 11-24 (2023). Dou, Y., Qu, N., Wang, B., Chi, Y. Z. & Ren, Y. L. Simultaneous determination of two active components in compound aspirin tablets using principal component artificial neural networks (PC-ANNs) on NIR spectroscopy. Eur. J. Pharm. Sci. 32, 193-199 (2007). Ouyang, Q., Zhao, J. & Chen, Q. Measurement of non-sugar solids content in Chinese rice wine using near infrared spectroscopy combined with an efficient characteristic variables selection algorithm. Spectrochim. Acta Part A . 151, 280-285 (2015). Reboucas, M. V., Santos, J. B. D., Domingos, D. & Massa, A. R. C. G. Near-infrared spectroscopic prediction of chemical composition of a series of petrochemical process streams for aromatics production. Vib. Spectrosc. 52 , 97-102 (2010). Judge, M. D. The application of near-infrared spectroscopy for the quality control analysis of rocket propellant fuel pre-mixes. Talanta. 62 , 675-679, (2004). Zou, Q., Deng, G., Guo, X., Jiang, W. & Li, F. A green analytical tool for in-process determination of RDX content of propellant using the NIR system. ACS Sustain. Chem. Eng. 1 , 1506-1510 (2013). Zhou, S., Wang, Z. Q., Lu, L. M., Yin, Q. S., Yu, L. H. & Deng, G. D. Rapid quantification of stabilizing agents in single-base propellants using near infrared spectroscopy. Infrared Phys. Technol. 77 , 1-7 (2016). Guo, Z. Q., Ren, Q., Huang, Y. Z. & Dong, S. L. Application of near infrared spectroscopy in determination of components of detonator. Chin. J. Spectrosc. Lab. 23(2) , 187-190 (2006). Additional Declarations No competing interests reported. Supplementary Files NIRdataforallsamples.zip Cite Share Download PDF Status: Published Journal Publication published 03 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 02 Jan, 2026 Reviews received at journal 31 Dec, 2025 Reviews received at journal 28 Dec, 2025 Reviewers agreed at journal 17 Dec, 2025 Reviewers agreed at journal 15 Dec, 2025 Reviewers agreed at journal 15 Dec, 2025 Reviewers agreed at journal 15 Dec, 2025 Reviewers invited by journal 15 Dec, 2025 Editor invited by journal 11 Dec, 2025 Editor assigned by journal 10 Dec, 2025 Submission checks completed at journal 10 Dec, 2025 First submitted to journal 09 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":48667,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDistribution of (A) manufacturers and (B) cartridge types of the collected samples\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8317775/v1/1d0d24d305913e392281f31a.png\"},{\"id\":98516323,\"identity\":\"3709a6a0-c6bb-496f-ae08-da3e0283129b\",\"added_by\":\"auto\",\"created_at\":\"2025-12-18 12:45:11\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":183055,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDistributions of (A) cartridge types and (B) characteristic peaks for different cartridge manufacturers\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8317775/v1/73380f5f94cb19b0f74d81c1.png\"},{\"id\":98625160,\"identity\":\"cd2197bf-0d7f-4457-affc-ee1d04062bf1\",\"added_by\":\"auto\",\"created_at\":\"2025-12-19 17:08:58\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":88598,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eClustering results for all cartridge samples, as calculated by the UMAP algorithm\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8317775/v1/714d7588f7b75e3fd559001c.png\"},{\"id\":98624564,\"identity\":\"ec6e6ddd-c2d2-41f2-9d03-fb98a26b07be\",\"added_by\":\"auto\",\"created_at\":\"2025-12-19 17:08:30\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":41328,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eComparison of averaged accuracies for the NN model and four traditional methods\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8317775/v1/d071f49dd918523bf4217ff4.png\"},{\"id\":106344533,\"identity\":\"07968aa9-ad6e-48af-9306-8360fa6596bc\",\"added_by\":\"auto\",\"created_at\":\"2026-04-07 16:15:27\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1609548,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8317775/v1/00d04f02-d662-4564-b70b-47905f269847.pdf\"},{\"id\":98516343,\"identity\":\"e7ab311b-4849-4561-a8b8-d50c130d14b1\",\"added_by\":\"auto\",\"created_at\":\"2025-12-18 12:45:11\",\"extension\":\"zip\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":5541101,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"NIRdataforallsamples.zip\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8317775/v1/8bba19f6af322364395e98a0.zip\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"A study for potential rapid discrimination of smokeless powders by near-infrared spectroscopy and chemometric modeling methods for forensic purposes\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eSmokeless powder is the primary propellant in civilian and military ammunition and is the most common low explosive used to fabricate improvised explosive devices (IEDs) such as pipe bombs and home-made ammunition. Propellant can be used as initiator in explosive mixtures along with other components such as coal, ammonium nitrate, sulfur, etc [\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn1\\\" id=\\\"#FNLinkFn1\\\"\\u003e\\u003c/a\\u003e]. Also, in China, there are cases involving home-made guns with an unknown source of propellants. When the suspects have been arrested, it is necessary to make a rapid screening of the smokeless powders that might be available in their houses, or in manufacturing places which has not been used in the IEDs or ammunitions.\\u003c/p\\u003e \\u003cp\\u003eThe major classes of compounds in smokeless propellants include energetics, stabilizers, plasticizers, flash suppressants, deterrents, opacifiers, and dyes [\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn2\\\" id=\\\"#FNLinkFn2\\\"\\u003e\\u003c/a\\u003e]. Also, the chemical compounds are both complex and different. According to the main chemical substances in the energetic class of compounds, smokeless powders can be classified into three different types: single-base, double-base, and triple-base. A single-base powder mainly contains nitrocellulose, nitrocellulose and nitroglycerin are main component for double-base propellants, and a triple-base powder mainly contains nitrocellulose, nitroglycerine, and nitroguanidine. Propellants can be characterized according to existing organic components. Many studies have been performed where the intact smokeless powders and residues have been analyzed for some specific target analyte. Infrared spectroscopy, chromatography, and mass spectroscopy have been commonly used for the determination of different ingredients in smokeless powders [\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn3\\\" id=\\\"#FNLinkFn3\\\"\\u003e\\u003c/a\\u003e,\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn4\\\" id=\\\"#FNLinkFn4\\\"\\u003e\\u003c/a\\u003e,\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn5\\\" id=\\\"#FNLinkFn5\\\"\\u003e\\u003c/a\\u003e,\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn6\\\" id=\\\"#FNLinkFn6\\\"\\u003e\\u003c/a\\u003e]. Infrared spectroscopy has normally been used for the analysis of nitrocellulose, but it showed a limited application for other substances with low amounts. Chromatography and mass spectroscopy have been widely used for the analyses of target substances (such as diphenylamine and N,N\\u0026prime;-Dimethyl carbanilide stabilizers). The disadvantage of this targeted approach is that when compounds outside of the target group are present, it becomes impossible to identify them. The targeted approach requires tedious sample preparation procedures, uses toxic reagents, and forms harmful waste. It also consumes or destroys the original samples. This point becomes critical when forensic results are urgently required and the evidence must not be destroyed, which is a common situation in the forensic analysis. Nondestructive techniques with improved discrimination possibilities (such as Raman spectroscopy) have, therefore, been introduced for the analyses of intact smokeless powders and residue particles. Raman spectroscopy has reported to have been used for the identification of the unburnt smokeless powder particles [\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn7\\\" id=\\\"#FNLinkFn7\\\"\\u003e\\u003c/a\\u003e,\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn8\\\" id=\\\"#FNLinkFn8\\\"\\u003e\\u003c/a\\u003e,\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn9\\\" id=\\\"#FNLinkFn9\\\"\\u003e\\u003c/a\\u003e].\\u003c/p\\u003e \\u003cp\\u003eWith the development of instrumental technology and chemometrics, near-infrared (NIR) spectroscopy has been found capable of recording spectra of complicated compounds, with minor (or no) sample pretreatments. And it is not necessary to specify the target compounds. NIR has been widely used as a quality control method in the agriculture, food, petrochemical engineering, and pharmacy industries [\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn10\\\" id=\\\"#FNLinkFn10\\\"\\u003e\\u003c/a\\u003e,\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn11\\\" id=\\\"#FNLinkFn11\\\"\\u003e\\u003c/a\\u003e,\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn12\\\" id=\\\"#FNLinkFn12\\\"\\u003e\\u003c/a\\u003e]. An it has also been reported as a fast quality control tool for the determination of ingredient concentrations in rock propellant fuel liquid pre-mixes [\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn13\\\" id=\\\"#FNLinkFn13\\\"\\u003e\\u003c/a\\u003e]. Furthermore, NIR has been used for an in-process determination of hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) in a solid propellant intermediate[\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn14\\\" id=\\\"#FNLinkFn14\\\"\\u003e\\u003c/a\\u003e]. Reports about the use of NIR in propellants have mainly focused on a fast quantification of some specific compounds in the propellants and the accuracy of the used algorithm [\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn15\\\" id=\\\"#FNLinkFn15\\\"\\u003e\\u003c/a\\u003e,\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn16\\\" id=\\\"#FNLinkFn16\\\"\\u003e\\u003c/a\\u003e]. The possibility to use NIR as a non-target approach for the discrimination and specification of smokeless powders collected from different sources has been investigated in the present study. Chemometric approaches were applied to evaluate the spectra data obtained.\\u003c/p\\u003e\"},{\"header\":\"2. Materials and methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1. Collection and preparation of samples\\u003c/h2\\u003e \\u003cp\\u003eSmokeless powders from 79 cartridges collected from different manufacturers were used as known source samples to verify the method used. Detailed information was presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. The propellants were taken out from the cartridges by using a cartridge puller. Prior to NIR analysis, they were equilibrated at room temperature (of about 25℃) for 24 h.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eAmmunition propellant sample information for measurement\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"12\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c11\\\" colnum=\\\"11\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c12\\\" colnum=\\\"12\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSeries\\u003c/p\\u003e \\u003cp\\u003eNumber\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eManufacturer\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCartridge\\u003c/p\\u003e \\u003cp\\u003ecategory\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eProduction Year\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eCaliber\\u003c/p\\u003e \\u003cp\\u003e(mm)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer\\u003c/p\\u003e \\u003cp\\u003eID\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eSeries\\u003c/p\\u003e \\u003cp\\u003eNumber\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eManufacturer\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eCartridge category\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eProduction\\u003c/p\\u003e \\u003cp\\u003eYear\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003eCaliber\\u003c/p\\u003e \\u003cp\\u003e(mm)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer ID\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e51 pistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1958\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es41\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e81\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e59 rifle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e1966\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer I\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e51 pistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1963\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es42\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e911\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e56 rifle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e1968\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e5.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer J\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e51 pistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1964\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es43\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e911\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e56 rifle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e1979\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e5.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer J\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e51 pistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1965\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es44\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eSporting rifle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e5.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer K\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e51 pistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1970\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es45\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eCJ\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eSS109 rifle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e2000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e5.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer L\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e51 pistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1972\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es46\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eCzech Republic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003epistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e7.65\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer M\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e51 pistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1979\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es47\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eKKJ\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eNail\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e6.8ⅹ18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer N\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e51 pistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1981\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es48\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eKKJ\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eNail\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e6.3ⅹ16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer N\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e51 pistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1987\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es49\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eKKJ\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eNail\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e6.3ⅹ16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer N\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e51 pistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1991\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eLY\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eParabellum pistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e1994\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer O\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e51 pistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2018\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es51\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eNS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eNail\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e6.8ⅹ11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer P\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRevolverpistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2005\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es52\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eNS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eNail\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e6.8ⅹ11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer P\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDAP92\\u003c/p\\u003e \\u003cp\\u003epistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2019\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es53\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eNS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eNail\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e6.8ⅹ18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer P\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDAP92\\u003c/p\\u003e \\u003cp\\u003epistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2018\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es54\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eNS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eNail\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e6.8ⅹ18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer P\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e64 pistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2007\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es55\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eNS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eNail\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e5.6ⅹ16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer P\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e64 pistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1990\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es56\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eNS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eNail\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e5.6ⅹ16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer P\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e64 pistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1990\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es57\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eNS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eNail\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e6.3ⅹ16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer P\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e64 pistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1992\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es58\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eNS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eNail\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e5.6ⅹ16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer P\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e64 pistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1995\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es59\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eNS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eNail\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e6.8ⅹ11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer P\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e64 pistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1996\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eNS\\u003c/p\\u003e 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\\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es61\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eNS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eNail\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e6.8ⅹ11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer P\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es22\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e92 rifle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2002\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eNS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eNail\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e6.8ⅹ11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer P\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es23\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e301\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e64 pistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1980\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer B\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e 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align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1987\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer B\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es64\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eΩ\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eSporting rifle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e5.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer Q\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e311\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e64 pistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1989\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer C\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es65\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eDouble Ring\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eSporting rifle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e5.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer R\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es26\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e311\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e64 pistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1992\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer C\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es66\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e△\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eSporting rifle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eUnknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e5.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer S\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es27\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e311\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e64 pistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1994\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer C\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es67\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e△\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eSporting rifle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e5.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer S\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es28\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e311\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e64 pistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2004\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer C\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es68\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eYRD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eNail\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e6.8ⅹ11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer T\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es29\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e611\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e56 rifle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1967\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer D\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es69\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eYRD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eNail\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e6.8ⅹ11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer T\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e671\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e53 pistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2013\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer E\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es70\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eYRD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eNail\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e6.8ⅹ11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer T\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es31\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e71\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e56 rifle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1956\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer F\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es71\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eYRD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eNail\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e6.8ⅹ18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer T\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es32\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e724\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDAP92\\u003c/p\\u003e \\u003cp\\u003epistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2007\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer G\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es72\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eYRD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eNail\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e6.8ⅹ18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer T\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es33\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e791\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9mm pistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2016\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer H\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es73\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eYRD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eNail\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e5.6ⅹ16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer T\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e791\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e56 pistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1988\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer H\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es74\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eYRD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eNail\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e5.6ⅹ16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer T\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e791\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSS109 rifle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5.56\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer H\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es75\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eYRD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eNail\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e5.6ⅹ16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer T\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es36\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e791\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDCV05\\u003c/p\\u003e \\u003cp\\u003epistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2008\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer H\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es76\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eYRD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eNail\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e6.3ⅹ16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer T\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es37\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e791\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e95 pistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2016\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer H\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es77\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eYRD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eNail\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e6.3ⅹ16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer T\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es38\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e791\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003epistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2014\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer H\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es78\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eYRD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eNail\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e5.6ⅹ16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer T\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es39\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e791\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDVP88A pistol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2017\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer H\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003es79\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eYRD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eNail\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e5.6ⅹ16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eManufacturer T\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003es40\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e81\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e56 rifle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1964\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eManufacturer I\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c12\\\" namest=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2. Acquisition of NIR spectra\\u003c/h2\\u003e \\u003cp\\u003eThe NIR analyses of the smokeless powders were carried out with a Fourier transform NIR spectrometer (Nicolet Antaris-II, ThermoFisher Scientific, U.S.A.), which was equipped with an integrating sphere module and an InGaAs detector. The sample cup was loaded with about 20 mg of propellant sample and rotated continuously at a constant speed. The NIR spectra were then collected in a diffuse reflectance mode at room temperature (25℃\\u0026plusmn;2℃), and each sample was analyzed three times. All spectra were recorded as an absorbance with a resolution of 4\\u003c/p\\u003e \\u003cp\\u003ecm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e and at a wavenumber range from 4000 cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e to 10,000 cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e. Also, each spectrum had an average of 32 scans.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3 Algorithmic statistics\\u003c/h2\\u003e \\u003cp\\u003e79 smokeless powder samples were received from 20 different manufacturers. Basic descriptive statistics were performed to evaluate the distribution of NIR data for all samples. The calculation of SSSVs (specific significant statistical values) followed standard protocols. UMAP (Uniform Manifold Approximation and Projection) was performed using the UMAP-learn Python package, version 0.5.1.\\u003c/p\\u003e \\u003cp\\u003eA deep learning neural network model (NN) model was also developed for a more comprehensive and accurate classification prediction. Furthermore, comparisons between the NN model and four classical traditional machine learning models were made, including the Stochastic Gradient Descent Classifier (SGDC), Support Vector Machine (SVM), K-Nearest Neighbors Classifier (KN), and Random Forest Classifier (RF).\\u003c/p\\u003e \\u003cp\\u003eWe constructed a neural network model using the keras module in TensorFlow, consisting of six main components: a feature extraction network that takes data with dimensions identical to the input and outputs a one-dimensional vector of length 784; a feature map transformation module that converts the one-dimensional vector into a three-dimensional tensor of size 28 x 28 x 1; a feature extraction framework based on VGG19 for deep feature extraction; an embedding network that maps the extracted feature maps to the classification layer for classification discrimination. Throughout the training process, we utilized cross-entropy minimization as the loss function and employed the SGD optimization method with a momentum parameter of 0.9, lasting for 1000 epochs. All network layers used ReLU as the activation function. All machine learning methods are executed according to functions provided in the python package Scikit-Learn, with all parameters at default values. We have trained our models using a split of 90% training data and 10% validation data. For each model, we conducted a grid search to tune the hyperparameters, selecting the combination maximizing the validation accuracy. Accuracy was used to evaluate the performance of different models. Accuracy measured the proportion of correctly predicted samples out of the total samples.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"3. Results and Discussion\",\"content\":\"\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1 NIR analyses\\u003c/h2\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe major peaks of the NIR spectra for smokeless powder samples were broad and similar. The NIR spectra were mainly formed by the absorption of hydrogen-containing groups of organic molecules. Also, the absorption bands corresponded to overtones and combinations of fundamental vibrations (stretching or bending). Nitrocellulose was the main component in these bands, irrespective of the type of smokeless powder. Figure\\u0026nbsp;1 presents the NIR spectra of five randomly selected samples (s1, s8, s14, s31, and s62), which are dominated by three major peaks 5240 cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, 5800 cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, and 7000 cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e between 4500 cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e and 7500 cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e. The three absorption peaks, along with a series of peaks between 4150 cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e and 5000 cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, mainly corresponded to overtones and combinations of C-H and O-H vibrations of nitrocellulose. It was, however, difficult to make further discrimination and identification only by these NIR spectra.\\u003c/p\\u003e \\u003cp\\u003eFigure\\u0026nbsp;1 NIR spectra of s1, s8, s14, s31and s62 samples\\u003c/p\\u003e \\u003cp\\u003eThe majority of propellants from different sources in this study showed absorbance values that were predominantly distributed in the range of 0.9\\u0026ndash;1.1 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ea). However, some samples from the manufacturers B, F, K, Q, R, and T showed some spectral absorbance values exceeding 1.2. It was interesting to find that the absorbance values of rifle-type cartridges were higher than the absorbance values for other types of cartridges. Correlations between smokeless powders produced by different manufacturers were also calculated. Notably, the similarity of most of the smokeless powders from different manufacturers was remarkably high, with correlation coefficients larger than 0.8 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eb). However, smokeless powders from manufacturers D, E, and F showed a lower NIR spectral similarity with other samples.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2 Descriptive statistics\\u003c/h2\\u003e \\u003cp\\u003eDescriptive statistical analyses of all collected sample data were also conducted. The results revealed that the samples involved 20 different manufacturers, with a highly uneven distribution of sample quantities. Specifically, the samples produced by manufacturers A, P, T, and H accounted for over 60% (162/237) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA). Among these samples, there were 14 different cartridge types. 60% (156/237) of the total data were comprised by nails and pistols with 7.62mm caliber from 64 pistol and 51 pistol model of cartridges (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe manufacturers produced one or more types of cartridges. Manufacturers A and H produced the most diverse range of cartridge types, each producing four different types. Manufacturer A produced cartridges of the following types: cartridges with 7.62 mm caliber for Chinese 51 model pistol, 64 model pistol, 92 model rifle and pistols with 9mm caliber. Manufacturer H, on the other hand, produced cartridges of the following types: cartridges with 5.8 mm caliber for Chinese pistols, 56 model pistol, and SS109 model rifle and pistols with 9mm calibers (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA). The spectral absorbance of cartridges from different manufacturers have also been analyzed in the present study. As a result, the cartridges from 16 of the 20 manufacturers showed near-infrared characteristic distributions in the wavenumber range of 2750 cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e to 3500 cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e. The manufacturer K and Q produced primarily cartridges of the 5.6 mm rifle type, with the strongest characteristic peaks at 3972 cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e and 4007 cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, respectively. However, the manufacturer R and S produced 5.6 mm rifle-type cartridges, which exhibited the strongest near-infrared characteristic peaks at 3646 cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e and 3072 cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, respectively (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eB). The extreme imbalance of all NIR data between samples possess a significant challenge when trying to accurately identify the cartridge sources.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3 Model analysis of data\\u003c/h2\\u003e \\u003cp\\u003eThe Uniform Manifold Approximation and Projection (UMAP) algorithm has here been used for the dimension reduction and clustering analyses. The results showed that cartridges from manufacturers C, P, and N, which showed the largest variations in averaged absorbance value, were closely clustered in the low-dimensional projection space. However, the cartridges from other manufacturers were clustered together (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e), which made them difficult to distinguish.\\u003c/p\\u003e \\u003cp\\u003eFurthermore, the NIR spectra of cartridges from different manufacturers showed some differences, but the distinguishing features were not clear. It was, therefore, a challenge to establish clear classification boundaries for an accurate and systematic categorization of samples for statistical and dimensionality reduction clustering analyses. With the large similarity and imbalance of NIR data for all cartridges, it was impossible to make an accurate differentiation based only on a simple comparison of NIR spectra and descriptive statistics. It was, instead, necessary to use more complex algorithmic methods for the NIR data analyses.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.4 Algorithmic modeling\\u003c/h2\\u003e \\u003cp\\u003eWith the aim of developing a comprehensive and accurate classification model, a deep-learning neural network method (NN) has in the present study been used to analyze the spectra of different cartridges from various manufacturers. For evaluation purposes, the performance of this model was then compared with four classical traditional machine learning models: Stochastic Gradient Descent Classifier (SGDC), Support Vector Machine (SVM), K-Nearest Neighbors Classifier (KN), and Random Forest Classifier (RF). The training and test data were divided with a ratio of 9:1, and 10-fold cross-validation was used to mitigate the evaluation error.\\u003c/p\\u003e \\u003cp\\u003eThe results showed that the average accuracy in the discrimination of sources of different cartridges from 20 different manufacturers was all below 60%. This result was based on their simulated near infrared spectra and by using the four traditional machine learning models. In contrary, the neural network modeling approach resulted in a corresponding average accuracy of over 80% (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"4. Conclusion\",\"content\":\"\\u003cp\\u003eThe aim of the study has been to classify various smokeless powders from different cartridges, which have been produced by different manufacturers. Near-infrared spectroscopy and a classification method were used for this purpose. The similarity and imbalance of NIR data for all collected cartridges posed a significant challenge for identification. Both traditional machine learning and deep learning neural network models were used to identify the optimal classification model. Compared with the traditional machine learning models SGDC, SVM, KN, and RF, the neural network(NN) modeling approach provided an average accuracy exceeding 80%. The results indicated that the combination of near-infrared spectroscopy and deep learning neural networks can be used for the identification of smokeless powder sources. This combination provides a quick and practical way to screen and analyze suspected smokeless powders found at ammunition and bomb making crime scenes.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eData availability statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe data that supports the findings of this study are available in the supplementary material of this paper.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCRediT authorship contribution statement:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eHongling Guo: Conceptualization, Methodology, Formal analysis, Investigation, Writing - original draft.\\u003c/p\\u003e\\n\\u003cp\\u003eYinghua Feng: Methodology, Formal analysis, Investigation, Writing - original draft.\\u003c/p\\u003e\\n\\u003cp\\u003eYiting Guo: investigation\\u003c/p\\u003e\\n\\u003cp\\u003eXiuli Zhang: Formal analysis, Investigation\\u003c/p\\u003e\\n\\u003cp\\u003ePing Wang: investigation\\u003c/p\\u003e\\n\\u003cp\\u003eCan Hu: investigation\\u003c/p\\u003e\\n\\u003cp\\u003eHongcheng Mei: investigation\\u003c/p\\u003e\\n\\u003cp\\u003eYajun Li: investigation\\u003c/p\\u003e\\n\\u003cp\\u003eJun Zhu: Writing -review \\u0026amp; editing.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding declaration\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe work was funded by the grant of Ministry of Public Security under Grant Basic Work Plan for Strengthening Police Force of Ministry of Public Security(2022JC12),Grant Central Public-Interest Scientific Institution Basal Research Fund(2024JBGS002),Grant Double Ten Project of Ministry of Public Security, China (2021SSGG028) and Grant Technology Research Project of the Ministry of Public Security, China (2021JSYJC08).\\u003c/p\\u003e\"},{\"header\":\" References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003e\\u0026Aacute;lvarez, \\u0026Aacute;., Y\\u0026aacute;\\u0026ntilde;ez, J., Contreras, D., Saavedra, R., S\\u0026aacute;ez, P. \\u0026amp; Amarasiriwardena, D. Propellant\\u0026rsquo;s differentiation using FTIR-photoacoustic detection for forensic studies of improvised explosive devices. \\u003cem\\u003eForensic Sci. Int.\\u003c/em\\u003e\\u003cstrong\\u003e280\\u003c/strong\\u003e, 169-175 (2017). .\\u003c/li\\u003e\\n\\u003cli\\u003eHeramb, R. M. \\u0026amp; McCord, B. R. The manufacture of smokeless powders and their forensic analysis: A brief review. \\u003cem\\u003eForensic Sci. Commun\\u003c/em\\u003e. \\u003cstrong\\u003e4\\u003c/strong\\u003e, 1-7 (2002).\\u003c/li\\u003e\\n\\u003cli\\u003eASTM E2999-17: Standard Test method for analysis of organic compounds in smokeless powder by gas chromatography-mass spectrometry and Fourier transform infrared spectroscopy (2022).\\u003c/li\\u003e\\n\\u003cli\\u003eL\\u0026oacute;pez-L\\u0026oacute;pez M, Ferrando, JL, Garc\\u0026iacute;a-Ruiz C. Comparative analysis of smokeless gun powders by Fourier transform infrared and Raman spectroscopy. Anal. Chim. Acta. 2012; 717: 92-99. https://doi.org/10.1016/j.aca.2011.12.022.\\u003c/li\\u003e\\n\\u003cli\\u003eThomas, J. L., Lincoln, D. \\u0026amp; McCord, B. R. Separation and detection of smokeless powder additives by ultra performance liquid chromatography with tandem mass spectrometry (UPLC/MS/MS). \\u003cem\\u003eJ. Forensic Sci.\\u003c/em\\u003e \\u003cstrong\\u003e58(3)\\u003c/strong\\u003e, 609-615 (2013). \\u003c/li\\u003e\\n\\u003cli\\u003eGassner, A. L. \\u0026amp; Weyermann, C. LC-MS method development and comparison of sampling materials for the analysis of organic gunshot residues. \\u003cem\\u003eForensic Sci. Int. \\u003c/em\\u003e\\u003cstrong\\u003e264\\u003c/strong\\u003e, 47-55 (2016).\\u003c/li\\u003e\\n\\u003cli\\u003eKhandasammy, S. R., Rzhevskii, A. \\u0026amp; Lednev, I. K. A novel two-step method for the detection of organic gunshot residue for forensic purposes: Fast fluorescence imaging followed by Raman microspectroscopic identification. \\u003cem\\u003eAnal. Chem.\\u003c/em\\u003e \\u003cstrong\\u003e91,\\u003c/strong\\u003e 11731-11737 (2019). \\u003c/li\\u003e\\n\\u003cli\\u003eL\\u0026oacute;pez-L\\u0026oacute;pez, M., \\u0026Aacute;ngeles Fern\\u0026aacute;ndez de la Ossa, M. \\u0026amp; Garc\\u0026iacute;a-Ruiz, C. Fast analysis of complete macroscopic gunshot residues on substrates using Raman imaging. \\u003cem\\u003eAppl. Spectrosc\\u003cstrong\\u003e.\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003e \\u003cem\\u003e69\\u003c/em\\u003e,\\u003c/strong\\u003e 889-893 (2015). \\u003c/li\\u003e\\n\\u003cli\\u003eKhandasammy, S. R., Bartlett, N. R., Hal\\u0026aacute;mkov\\u0026aacute;, L. \\u0026amp; Lednev, I. K. Hierarchical modelling of Raman spectroscopic data demonstrates the potential for manufacturer and caliber differentiation of smokeless powders. \\u003cem\\u003eChemosensors\\u003c/em\\u003e. \\u003cstrong\\u003e11(1),\\u003c/strong\\u003e 11-24 (2023). \\u003c/li\\u003e\\n\\u003cli\\u003eDou, Y., Qu, N., Wang, B., Chi, Y. Z. \\u0026amp; Ren, Y. L. Simultaneous determination of two active components in compound aspirin tablets using principal component artificial neural networks (PC-ANNs) on NIR spectroscopy. Eur. \\u003cem\\u003eJ. Pharm. Sci.\\u003c/em\\u003e \\u003cstrong\\u003e32,\\u003c/strong\\u003e 193-199 (2007).\\u003c/li\\u003e\\n\\u003cli\\u003eOuyang, Q., Zhao, J. \\u0026amp; Chen, Q. Measurement of non-sugar solids content in Chinese rice wine using near infrared spectroscopy combined with an efficient characteristic variables selection algorithm. \\u003cem\\u003eSpectrochim. Acta Part A\\u003c/em\\u003e. \\u003cstrong\\u003e151,\\u003c/strong\\u003e 280-285 (2015). \\u003c/li\\u003e\\n\\u003cli\\u003eReboucas, M. V., Santos, J. B. D., Domingos, D. \\u0026amp; Massa, A. R. C. G. Near-infrared spectroscopic prediction of chemical composition of a series of petrochemical process streams for aromatics production. \\u003cem\\u003eVib. Spectrosc.\\u003c/em\\u003e \\u003cstrong\\u003e52\\u003c/strong\\u003e, 97-102 (2010). \\u003c/li\\u003e\\n\\u003cli\\u003eJudge, M. D. The application of near-infrared spectroscopy for the quality control analysis of rocket propellant fuel pre-mixes. \\u003cem\\u003eTalanta.\\u003c/em\\u003e\\u003cstrong\\u003e 62\\u003c/strong\\u003e, 675-679, (2004). \\u003c/li\\u003e\\n\\u003cli\\u003eZou, Q., Deng, G., Guo, X., Jiang, W. \\u0026amp; Li, F. A green analytical tool for in-process determination of RDX content of propellant using the NIR system. \\u003cem\\u003eACS Sustain. Chem. Eng.\\u003c/em\\u003e\\u003cstrong\\u003e1\\u003c/strong\\u003e, 1506-1510 (2013).\\u003c/li\\u003e\\n\\u003cli\\u003eZhou, S., Wang, Z. Q., Lu, L. M., Yin, Q. S., Yu, L. H. \\u0026amp; Deng, G. D. Rapid quantification of stabilizing agents in single-base propellants using near infrared spectroscopy. \\u003cem\\u003eInfrared Phys. Technol.\\u003c/em\\u003e \\u003cstrong\\u003e77\\u003c/strong\\u003e, 1-7 (2016). \\u003c/li\\u003e\\n\\u003cli\\u003eGuo, Z. Q., Ren, Q., Huang, Y. Z. \\u0026amp; Dong, S. L. Application of near infrared spectroscopy in determination of components of detonator. \\u003cem\\u003eChin. J. Spectrosc. Lab.\\u003c/em\\u003e\\u003cstrong\\u003e 23(2)\\u003c/strong\\u003e, 187-190 (2006).\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"scientific-reports\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"scirep\",\"sideBox\":\"Learn more about [Scientific Reports](http://www.nature.com/srep/)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Scientific Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Scientific Reports\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Smokeless powder, Near-infrared spectroscopy, Chemometrics, neural network model, Propellant, Forensics\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8317775/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8317775/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eSmokeless powder is the primary propellant in civilian and military ammunition. And in China, the use of propellant for making homemade ammunition and bombs is an incipient criminal practice. The identification and discrimination of the propellant used can provide forensic information for its sources. Depending upon the ammunition fabricant and ammunition type, the recipe of propellant changes. The characterization of smokeless powders in terms of its spectral components is useful for differentiating the propellants. In this work, near-infrared spectroscopy (NIR) and chemometric modeling were used to explore a possibility of differentiation and prediction of smokeless powders from different sources. By comparison, the proposed neural network model showed an average accuracy of over 80%. Also, the potential for discrimination of smokeless powders was well demonstrated by using easy and fast near-infrared spectroscopic analyses. The use of chemical agents and time-consuming chromatography and mass spectrometry could, thereby, be avoided.\\u003c/p\\u003e\",\"manuscriptTitle\":\"A study for potential rapid discrimination of smokeless powders by near-infrared spectroscopy and chemometric modeling methods for forensic purposes\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-12-18 12:45:01\",\"doi\":\"10.21203/rs.3.rs-8317775/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2026-01-02T17:57:15+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-12-31T05:17:36+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-12-28T05:38:50+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"263816666616990226245414274515853087723\",\"date\":\"2025-12-17T09:43:49+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"155647561370226855537253918948990966461\",\"date\":\"2025-12-16T04:00:51+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"276526644227020132562965303926995834847\",\"date\":\"2025-12-15T11:45:23+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"128936209952550378389130507430694503910\",\"date\":\"2025-12-15T11:42:10+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-12-15T11:05:59+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2025-12-11T12:45:33+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-12-11T03:50:21+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-12-11T03:50:05+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Scientific Reports\",\"date\":\"2025-12-09T12:35:57+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"scientific-reports\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"scirep\",\"sideBox\":\"Learn more about [Scientific Reports](http://www.nature.com/srep/)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Scientific Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Scientific Reports\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"19e0daba-86f5-4694-80a3-c4c75a35382b\",\"owner\":[],\"postedDate\":\"December 18th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[{\"id\":59826690,\"name\":\"Physical sciences/Chemistry\"},{\"id\":59826691,\"name\":\"Physical sciences/Engineering\"},{\"id\":59826692,\"name\":\"Physical sciences/Materials science\"}],\"tags\":[],\"updatedAt\":\"2026-04-07T16:11:23+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-8317775\",\"link\":\"https://doi.org/10.1038/s41598-026-45433-0\",\"journal\":{\"identity\":\"scientific-reports\",\"isVorOnly\":false,\"title\":\"Scientific Reports\"},\"publishedOn\":\"2026-04-03 15:57:43\",\"publishedOnDateReadable\":\"April 3rd, 2026\"},\"versionCreatedAt\":\"2025-12-18 12:45:01\",\"video\":\"\",\"vorDoi\":\"10.1038/s41598-026-45433-0\",\"vorDoiUrl\":\"https://doi.org/10.1038/s41598-026-45433-0\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8317775\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8317775\",\"identity\":\"rs-8317775\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}