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Fan Z, Zhang J, Ma C, Cong B, Huang P. The application of vibrational spectroscopy in forensic analysis of biological evidence. Forensic Sci Med Pathol 2024:10.1007/s12024-024-00866-9. [PMID: 39180652 DOI: 10.1007/s12024-024-00866-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/30/2024] [Indexed: 08/26/2024]
Abstract
Vibrational spectroscopy is a powerful analytical domain, within which Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy stand as exemplars, offering high chemical specificity and sensitivity. These methodologies have been instrumental in the characterization of chemical compounds for an extensive period. They are particularly adept at the identification and analysis of minute sample quantities. Both FTIR and Raman spectroscopy are proficient in elucidating small liquid samples and detecting nuanced molecular alterations. The application of chemometrics further augments their analytical prowess. Currently, these techniques are in the research phase within forensic medicine and have yet to be broadly implemented in examination and identification processes. Nonetheless, studies have indicated that a combined classification model utilizing FTIR and Raman spectroscopy yields exceptional results for the identification of biological fluid-related information and the determination of causes of death. The objective of this review is to delineate the current research trajectory and potential applications of these two vibrational spectroscopic techniques in the detection of body fluids and the ascertainment of causes of death within the context of forensic medicine.
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Affiliation(s)
- Zehua Fan
- Department of Forensic Pathology, Institute of Forensic Science, Shanghai Key Laboratory of Forensic Medicine, Academy of Forensic Science, Shanghai, 200063, People's Republic of China
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Hebei Medical University, Shijiazhuang, 050000, People's Republic of China
| | - Ji Zhang
- Department of Forensic Pathology, Institute of Forensic Science, Shanghai Key Laboratory of Forensic Medicine, Academy of Forensic Science, Shanghai, 200063, People's Republic of China
| | - Chunling Ma
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Hebei Medical University, Shijiazhuang, 050000, People's Republic of China
| | - Bin Cong
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Hebei Medical University, Shijiazhuang, 050000, People's Republic of China.
| | - Ping Huang
- Institute of Forensic Science, Fudan University, Shanghai, 200032, People's Republic of China.
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Wontor K, Clisham C, Hummel J, Cizdziel JV. Analysis of automotive paint layers on plastic substrates using chemical imaging μ-FTIR and O-PTIR microspectroscopy. J Forensic Sci 2024. [PMID: 38943352 DOI: 10.1111/1556-4029.15575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 05/24/2024] [Accepted: 06/21/2024] [Indexed: 07/01/2024]
Abstract
Automobile paint chips are a crucial piece of trace evidence for forensic investigators. This is because automotive paints are composed of multiple layers, including the primer, basecoat, and clearcoat, each of which has its own chemical composition that can vary by vehicle make, model, year, and manufacturing plant. Thus, Fourier-transform infrared (FTIR) spectral databases for automobile paint systems have been established to aid law enforcement in, for example, narrowing search parameters for a suspect's vehicle. Recently, car manufacturers have implemented primers on plastic substrates that are much thinner (~5 μm) than those on metal substrates, making it more difficult to manually separate for analyses. Here, we evaluated FTIR microspectroscopy (μ-FTIR) and optical photothermal infrared spectroscopy (O-PTIR) to chemically image cross sections of paint chips without manually separating the layers. For μ-FTIR, transmission and transflection modes provided the highest quality spectra compared to reflection and μ-ATR analyses. Point analysis was preferable to chemical imaging, as peaks were identified in the point (MCT) detector's lower spectral range that was below the imaging (FPA) detector's cutoff, such as those associated with titanium dioxide. Reduced spectral range can lead to a similar issue in O-PTIR analyses depending on instrument configuration. However, its complementary Raman spectra showed strong titanium dioxide peaks, providing an alternate means of identification. Both techniques are likely to become more relevant as they are non-destructive and avoid manual separation of the layers. O-PTIR is particularly well-suited for analysis of the thin primer layer due to its superior spatial resolution.
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Affiliation(s)
- Kendall Wontor
- Department of Chemistry and Biochemistry, University of Mississippi, University, Mississippi, USA
| | - Carly Clisham
- Department of Chemistry and Biochemistry, University of Mississippi, University, Mississippi, USA
| | - Jessica Hummel
- Department of Chemistry and Biochemistry, University of Mississippi, University, Mississippi, USA
| | - James V Cizdziel
- Department of Chemistry and Biochemistry, University of Mississippi, University, Mississippi, USA
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Kaur N, Sharma A, Sharma V. Advancing automobile identification and brand discrimination from tyre rubber through Machine learning algorithms for forensic investigations. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 309:123821. [PMID: 38183735 DOI: 10.1016/j.saa.2023.123821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 12/24/2023] [Accepted: 12/26/2023] [Indexed: 01/08/2024]
Abstract
Criminal instances involving collision accidents, hit-and-run incidents, abduction, hostage-taking, and the unauthorised transit of forbidden items generally include evidence involving rubber traces from automobile tyres. These traces can be located on the road surface, in clothing, on the victim(s) themselves, or on items as skid marks following sudden stopping and spinning around. These traces serve as crucial evidence by reducing the range of suspects by revealing linkages between the getaway vehicle, the site of the crime, and the perpetrator through the tyre's brand, producer, or origin. This study offered a way for classifying 220 tyre rubber samples from different brands using various machine learning algorithms in PyCaret in conjunction with rapid and non-destructive ATR-FTIR spectroscopy equipped with diamond crystal. On spectral information from ATR-FTIR, pre-processing tools such as baseline correction, smoothing, derivatization, and normalisation were also implemented prior to machine learning. This approach has the potential to be advantageous for efficiently and non-destructively identifying rubber traces as forensic evidence and for facilitating brand recognition of automobile tyres.
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Affiliation(s)
- Navreet Kaur
- Institute of Forensic Science & Criminology, Panjab University, Chandigarh 160014, India
| | - Akanksha Sharma
- Institute of Forensic Science & Criminology, Panjab University, Chandigarh 160014, India
| | - Vishal Sharma
- Institute of Forensic Science & Criminology, Panjab University, Chandigarh 160014, India.
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Wei CT, You JL, Weng SK, Jian SY, Lee JCL, Chiang TL. Enhancing forensic investigations: Identifying bloodstains on various substrates through ATR-FTIR spectroscopy combined with machine learning algorithms. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 308:123755. [PMID: 38101254 DOI: 10.1016/j.saa.2023.123755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 10/16/2023] [Accepted: 12/08/2023] [Indexed: 12/17/2023]
Abstract
The forensic analysis of bloodstains on various substrates plays a crucial role in criminal investigations. This study presents a novel approach for analyzing bloodstains using Attenuated Total Reflectance Fourier Transform Infrared spectroscopy (ATR-FTIR) in combination with machine learning. ATR-FTIR offers non-destructive and non-invasive advantages, requiring minimal sample preparation. By detecting specific chemical bonds in blood components, it enables the differentiation of various body fluids. However, the subjective interpretation of the spectra poses challenges in distinguishing different fluids. To address this, we employ machine learning techniques. Machine learning is extensively used in chemometrics to analyze chemical data, build models, and extract useful information. This includes both unsupervised learning and supervised learning methods, which provide objective characterization and differentiation. The focus of this study was to identify human and porcine blood on substrates using ATR-FTIR spectroscopy. The substrates included paper, plastic, cloth, and wood. Data preprocessing was performed using Principal Component Analysis (PCA) to reduce dimensionality and analyze latent variables. Subsequently, six machine learning algorithms were used to build classification models and compare their performance. These algorithms comprise Partial Least Squares Discriminant Analysis (PLS-DA), Decision Trees (DT), Logistic Regression (LR), Naive Bayes Classifier (NBC), Support Vector Machine (SVM), and Neural Network (NN). The results indicate that the PCA-NN model provides the optimal solution on most substrates. Although ATR-FTIR spectroscopy combined with machine learning effectively identifies bloodstains on substrates, the performance of different identification models still varies based on the type of substrate. The integration of these disciplines enables researchers to harness the power of data-driven approaches for solving complex forensic problems. The objective differentiation of bloodstains using machine learning holds significant implications for criminal investigations. This technique offers a non-destructive, simple, selective, and rapid approach for forensic analysis, thereby assisting forensic scientists and investigators in determining crucial evidence related to bloodstains.
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Affiliation(s)
- Chun-Ta Wei
- School of Defense Science, Chung Cheng Institute of Technology, National Defense University, Taoyuan 335009, Taiwan
| | - Jhu-Lin You
- Department of Chemical and Materials Engineering, Chung Cheng Institute of Technology, National Defense University, Taoyuan 335009, Taiwan; System Engineering and Technology Program, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
| | - Shiuh-Ku Weng
- Department of Electronic Engineering, Chien Hsin University of Science and Technology, Taoyuan 320678, Taiwan.
| | - Shun-Yi Jian
- Department of Material Engineering, Ming Chi University of Technology, New Taipei 243303, Taiwan; Center for Plasma and Thin Film Technologies, Ming Chi University of Technology, New Taipei 243303, Taiwan.
| | - Jeff Cheng-Lung Lee
- Department of Criminal Investigation, Taiwan Police College, Taipei 116078, Taiwan
| | - Tang-Lun Chiang
- School of Defense Science, Chung Cheng Institute of Technology, National Defense University, Taoyuan 335009, Taiwan
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Zhang X, Yang F, Xiao J, Qu H, Jocelin NF, Ren L, Guo Y. Analysis and comparison of machine learning methods for species identification utilizing ATR-FTIR spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 308:123713. [PMID: 38056185 DOI: 10.1016/j.saa.2023.123713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/26/2023] [Accepted: 11/28/2023] [Indexed: 12/08/2023]
Abstract
Accurate identification of insect species holds paramount significance in diverse fields as it facilitates a comprehensive understanding of their ecological habits, distribution range, and impact on both the environment and humans. While morphological characteristics have traditionally been employed for species identification, the utilization of empty pupariums for this purpose remains relatively limited. In this study, ATR-FTIR was employed to acquire spectral information from empty pupariums of five fly species, subjecting the data to spectral pre-processing to obtain average spectra for preliminary analysis. Subsequently, PCA and OPLS-DA were utilized for clustering and classification. Notably, two wavebands (3000-2800 cm-1 and 1800-1300 cm-1) were found to be significant in distinguishing A. grahami. Further, we established three machine learning models, including SVM, KNN, and RF, to analyze spectra from different waveband groups. The biological fingerprint region (1800-1300 cm-1) demonstrated a substantial advantage in identifying empty puparium species. Remarkably, the SVM model exhibited an impressive accuracy of 100 % in identifying all five fly species. This study represents the first instance of employing infrared spectroscopy and machine learning methods for identifying insect species using empty pupariums, providing a robust research foundation for future investigations in this area.
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Affiliation(s)
- Xiangyan Zhang
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha 410013, Hunan, China
| | - Fengqin Yang
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha 410013, Hunan, China
| | - Jiao Xiao
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha 410013, Hunan, China
| | - Hongke Qu
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute and School of Basic Medicine Sciences, Central South University, Changsha, Hunan, China
| | - Ngando Fernand Jocelin
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha 410013, Hunan, China
| | - Lipin Ren
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha 410013, Hunan, China.
| | - Yadong Guo
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha 410013, Hunan, China.
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Di Santo R, Niccolini B, Romanò S, Vaccaro M, Di Giacinto F, De Spirito M, Ciasca G. Advancements in Mid-Infrared spectroscopy of extracellular vesicles. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 305:123346. [PMID: 37774583 DOI: 10.1016/j.saa.2023.123346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 08/07/2023] [Accepted: 09/02/2023] [Indexed: 10/01/2023]
Abstract
Extracellular vesicles (EVs) are lipid vesicles secreted by all cells into the extracellular space and act as nanosized biological messengers among cells. They carry a specific molecular cargo, composed of lipids, proteins, nucleic acids, and carbohydrates, which reflects the state of their parent cells. Due to their remarkable structural and compositional heterogeneity, characterizing EVs, particularly from a biochemical perspective, presents complex challenges. In this context, mid-infrared (IR) spectroscopy is emerging as a valuable tool, providing researchers with a comprehensive and label-free spectral fingerprint of EVs in terms of their specific molecular content. This review aims to provide an up-to-date critical overview of the major advancements in mid-IR spectroscopy of extracellular vesicles, encompassing both fundamental and applied research achievements. We also systematically emphasize the new possibilities offered by the integration of emerging cutting-edge IR technologies, such as tip-enhanced and surface-enhanced spectroscopy approaches, along with the growing use of machine learning for data analysis and spectral interpretation. Additionally, to assist researchers in navigating this intricate subject, our manuscript includes a wide and detailed collection of the spectral peaks that have been assigned to EV molecular constituents up to now in the literature.
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Affiliation(s)
- Riccardo Di Santo
- Dipartimento di Neuroscienze, Sezione di Fisica, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; Fondazione Policlinico Universitario "A. Gemelli" IRCCS, 00168 Rome, Italy.
| | - Benedetta Niccolini
- Dipartimento di Neuroscienze, Sezione di Fisica, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Sabrina Romanò
- Dipartimento di Neuroscienze, Sezione di Fisica, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Maria Vaccaro
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, 00168 Rome, Italy
| | - Flavio Di Giacinto
- Dipartimento di Neuroscienze, Sezione di Fisica, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; Fondazione Policlinico Universitario "A. Gemelli" IRCCS, 00168 Rome, Italy
| | - Marco De Spirito
- Dipartimento di Neuroscienze, Sezione di Fisica, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; Fondazione Policlinico Universitario "A. Gemelli" IRCCS, 00168 Rome, Italy
| | - Gabriele Ciasca
- Dipartimento di Neuroscienze, Sezione di Fisica, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; Fondazione Policlinico Universitario "A. Gemelli" IRCCS, 00168 Rome, Italy
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Affadu-Danful GP, Kalkan AK, Zhang L, Lavine BK. Analysis of Automotive Paint Smears Using Attenuated Total Reflection Infrared Microscopy. APPLIED SPECTROSCOPY 2023; 77:281-291. [PMID: 36241610 DOI: 10.1177/00037028221136122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Paint smears represent a type of automotive paint sample found at a crime scene that is problematic for forensic automotive paint examiners to analyze as there are no reference materials present in automotive paint databases to generate hit-lists of potential suspect vehicles. Realistic paint smears are difficult to create in a laboratory and have also proven challenging to analyze because of the mixing of the various automotive paint layers. A procedure based on an impact tester has been developed to create smears to simulate paint transfer between vehicles during a collision. Data collected from 24 original equipment manufacturer (OEM) paints in simulated collisions using an impact tester with a steel (inert) substrate to simulate vehicle to vehicle collisions shows that attenuated total reflection infrared microscopy can isolate individual paint layers. For each OEM paint sample, the corresponding smear obtained was dependent upon the conditions used. By varying these conditions, the number of distinct layers obtained could be tuned for each of the OEM paints investigated. Furthermore, the IR spectrum of each layer extracted from the paint smear using alternating least squares was found to compare favorably to an in-house OEM paint infrared spectral library for each layer as the correct match (make and model of the vehicle from which the smear originated) was always found as a top five hit in the hit-list. The results of this study indicate that paint smears developed using an impactor can serve as the basis of realistic proficiency tests for forensic laboratories.
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Affiliation(s)
| | - A Kaan Kalkan
- Department of Mechanical and Aerospace Engineering, 7618Oklahoma State University, Stillwater, Oklahoma, USA
| | - Linqi Zhang
- Department of Mechanical and Aerospace Engineering, 7618Oklahoma State University, Stillwater, Oklahoma, USA
| | - Barry K Lavine
- Department of Chemistry, 7618Oklahoma State University, Stillwater, Oklahoma, USA
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Zhao B, Zhai H, Shao H, Bi K, Zhu L. Potential of vibrational spectroscopy coupled with machine learning as a non-invasive diagnostic method for COVID-19. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107295. [PMID: 36706562 PMCID: PMC9711896 DOI: 10.1016/j.cmpb.2022.107295] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 11/10/2022] [Accepted: 11/29/2022] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Efforts to alleviate the ongoing coronavirus disease 2019 (COVID-19) crisis showed that rapid, sensitive, and large-scale screening is critical for controlling the current infection and that of ongoing pandemics. METHODS Here, we explored the potential of vibrational spectroscopy coupled with machine learning to screen COVID-19 patients in its initial stage. Herein presented is a hybrid classification model called grey wolf optimized support vector machine (GWO-SVM). The proposed model was tested and comprehensively compared with other machine learning models via vibrational spectroscopic fingerprinting including saliva FTIR spectra dataset and serum Raman scattering spectra dataset. RESULTS For the unknown vibrational spectra, the presented GWO-SVM model provided an accuracy, specificity and F1_score value of 0.9825, 0.9714 and 0.9778 for saliva FTIR spectra dataset, respectively, while an overall accuracy, specificity and F1_score value of 0.9085, 0.9552 and 0.9036 for serum Raman scattering spectra dataset, respectively, which showed superiority than those of state-of-the-art models, thereby suggesting the suitability of the GWO-SVM model to be adopted in a clinical setting for initial screening of COVID-19 patients. CONCLUSIONS Prospectively, the presented vibrational spectroscopy based GWO-SVM model can facilitate in screening of COVID-19 patients and alleviate the medical service burden. Therefore, herein proof-of-concept results showed the chance of vibrational spectroscopy coupled with GWO-SVM model to help COVID-19 diagnosis and have the potential be further used for early screening of other infectious diseases.
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Affiliation(s)
- Bingqiang Zhao
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China
| | - Honglin Zhai
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China.
| | - Haiping Shao
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China
| | - Kexin Bi
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China
| | - Ling Zhu
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China
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Weber A, Hoplight B, Ogilvie R, Muro C, Khandasammy SR, Pérez-Almodóvar L, Sears S, Lednev IK. Innovative Vibrational Spectroscopy Research for Forensic Application. Anal Chem 2023; 95:167-205. [PMID: 36625116 DOI: 10.1021/acs.analchem.2c05094] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Alexis Weber
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States.,SupreMEtric LLC, 7 University Pl. B210, Rensselaer, New York 12144, United States
| | - Bailey Hoplight
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States
| | - Rhilynn Ogilvie
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States
| | - Claire Muro
- New York State Police Forensic Investigation Center, Building #30, Campus Access Rd., Albany, New York 12203, United States
| | - Shelby R Khandasammy
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States
| | - Luis Pérez-Almodóvar
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States
| | - Samuel Sears
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States
| | - Igor K Lednev
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States.,SupreMEtric LLC, 7 University Pl. B210, Rensselaer, New York 12144, United States
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Affiliation(s)
- Jose Almirall
- Florida International University, Department of Chemistry and Biochemistry, Center for Advanced Research in Forensic Science, Miami, FL, USA,Corresponding author.
| | - Tatiana Trejos
- West Virginia University, Department of Forensic and Investigative Science, USA
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