Non-destructive characterization and discrimination of vehicle bumpers fragments in forensic science using molecular spectral fusion analysis and chemometrics

Qiu, Weilun; Li, Wu

doi:10.1016/j.microc.2021.106299

Cited by 7 publications

(6 citation statements)

References 28 publications

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“…Mid‐level spectral data fusion is usually considered the most reliable, with a strategy that selects fewer variables to replace spectral information to reduce model computational complexity. Qiu successfully employed a mid‐level data fusion strategy using PCA in combination with two spectroscopy techniques (Micro‐laser Raman spectroscopy and Attenuated Total Reflection‐Fourier Transform Infrared spectroscopy) to accurately discriminate vehicle bumper fragments in forensic science [13]. In our study, we also adopted a mid‐level spectral data fusion strategy.…”

Section: Introductionmentioning

confidence: 99%

Comparison of feature selection and data fusion of Fourier transform infrared and Raman spectroscopy for identifying watercolor ink

Zou,

Zhang,

Wang

et al. 2024

Journal of Forensic Sciences

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The identification of different kinds of watercolor inks is an important work in the field of forensic science. Four different kinds of watercolor ink Spectroscopy data fusion strategies (Fourier Transform Infrared spectroscopy and Raman spectroscopy) combined with a non‐linear classification model (Extreme Learning Machine) were used to identify the brand of watercolor inks. The study chose Competitive Adaptive Reweighted Sampling (CARS), Random Frog (RF), Variable Combination Population Analysis‐Genetic Algorithm (VCPA‐GA), and Variable Combination Population Analysis‐Iteratively Retains Informative Variables (VCPA‐IRIV) to extract characteristic variables for mid‐level data fusion. The Cuckoo Search (CS) algorithm is used to optimize the extreme learning machine classification model. The results showed that the classification capacity of the mid‐level fusion spectra model was more satisfactory than that of single Infrared spectroscopy or Raman spectroscopy. The CS‐ELM models based on infrared spectroscopy used to recognize the watercolor ink according to brands (ZHENCAI, DELI, CHENGUANG, and STAEDTLER) obtained an accuracy of 66.67% in the test set using all spectral datasets. The accuracy of CS‐ELM models based on Raman spectroscopy was 67.39%. The characteristic wavelength selection algorithms effectively improved the accuracy of the CS‐ELM models. The classification accuracy of the mid‐level spectroscopy fusion model combined with the VCPA‐IRIV algorithm was 100%. The data fusion method increased effectively spectral information. The method could satisfactorily identify different brands of watercolor inks and support the preservation of artifacts, paintings, and forensic document examination.

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Section: Introductionmentioning

confidence: 99%

Comparison of feature selection and data fusion of Fourier transform infrared and Raman spectroscopy for identifying watercolor ink

Zou,

Zhang,

Wang

et al. 2024

Journal of Forensic Sciences

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“…LLF and MLF (feature fusion) models were found to be about 10% more accurate than single-spectrum models. One hundred and sixty vehicle bumper fragments from eight different manufacturers were subjected to Raman and attenuated total reflection FT-IR (ATR FT-IR) analysis by Qiu et al, 29 and the results showed that both LLF and MLF had stronger discriminative powers than single-spectrum models. Robert et al 15 explored Raman spectroscopy and FT-IR coupled with three data fusion strategies to predict the pH and percentage intramuscular fat content of red meat.…”

Section: Introductionmentioning

confidence: 99%

Ante- and Post-Mortem Fracture Identification Protocol Based on Low- and High-Level Fusion Using Fourier Transform Infrared Spectroscopy and Raman Spectroscopy Association

Yu,

Wu,

Xiong

et al. 2024

Appl Spectrosc

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In this study, the application of low-level fusion (LLF) and high-level fusion (HLF) strategies using a combination of Fourier transform infrared spectroscopy (FT-IR) and Raman spectroscopy in the identification of antemortem and postmortem fracture at different postmortem intervals (PMIs) was investigated. On a technical level, the same hard tissue sample can be detected using a mix of FT-IR and Raman techniques. At the method level, two cutting-edge chemometrics approaches (LLF and HLF) combining FT-IR and Raman spectroscopic data are explored. The models were ranked in accordance with their parametric quality as follows: HLF and LLF + HLF models > LLF single model > Raman single model > FT-IR single model. The LLF model performed marginally better than the Raman model, however, when compared to other models, the HLF model performed considerably better. The HLF model achieved the best performance, with both cross-validation accuracy and test data set accuracy of 0.88. The importance of the feature wavelengths in the model construction process was subsequently evaluated by intersection fusion, and it was found that the absorbance bands of amide I, PO43– ν1 ν3, and CH2 in FT-IR and phenylalanine, CO32– ν1– PO43– ν3, and amide III in Raman have outstanding contributions to the construction of antemortem and postmortem fractures identification models. Overall, the combination of FT-IR and Raman with the HLF strategy is a novel and promising approach for developing antemortem and postmortem fracture identification models at different PMIs.

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“…Qiu e al. used data fusion of Micro-laser Raman spectroscopy (MLRM) and ATR-FTIR for characterizing and differentiating 160 bumpers from 8 different manufacturers [ 50 ]. Classification algorithms included PCA, Multi-layer perceptron neural network (MLPNN), and FDA.…”

Section: Introductionmentioning

confidence: 99%

Interpol review of paint, tape, and glass evidence 2019–2022

Almirall

Trejos

2023

Forensic Science International: Synergy

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Non-destructive characterization and discrimination of vehicle bumpers fragments in forensic science using molecular spectral fusion analysis and chemometrics

Cited by 7 publications

References 28 publications

Comparison of feature selection and data fusion of Fourier transform infrared and Raman spectroscopy for identifying watercolor ink

Comparison of feature selection and data fusion of Fourier transform infrared and Raman spectroscopy for identifying watercolor ink

Ante- and Post-Mortem Fracture Identification Protocol Based on Low- and High-Level Fusion Using Fourier Transform Infrared Spectroscopy and Raman Spectroscopy Association

Interpol review of paint, tape, and glass evidence 2019–2022

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