UV–Vis and ATR–FTIR spectroscopic investigations of postmortem interval based on the changes in rabbit plasma

Wang, Qi; He, HaiJun; Bing, Li; Lin, Hancheng; Zhang, Yinming; Ji, Zhang; Wang, Zhenyuan

doi:10.1371/journal.pone.0182161

Cited by 30 publications

(17 citation statements)

References 63 publications

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“…However, these types of physical evidence are sensitive to various environmental conditions and rely deeply on individual states and investigators' empirical judgments. To address the limitations of traditional methods, scientists have been employing various new techniques in PMI estimation, such as molecular biology (Young et al, 2013;Li et al, 2014;Feng et al, 2015;Ferreira et al, 2018), entomology (Wells et al, 2015), spectroscopy (Li et al, 2017;Zhang et al, 2017;Wang et al, 2017a;Wang et al, 2017b) and thanatochemistry (Poloz and O'Day, 2009;Kikuchi et al, 2010;Sara et al, 2014) analysis. Each method provides significant information in investigations; however, their feasibility and accuracy are limited under the conditions and timeframe (e.g.…”

Section: Introductionmentioning

confidence: 99%

Predicting postmortem interval based on microbial community sequences and machine learning algorithms

Liu

Shen

et al. 2020

Environmental Microbiology

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Microbes play an essential role in the decomposition process but were poorly understood in their succession and behaviour. Previous researches have shown that microbes show predictable behaviour that starts at death and changes during the decomposition process. Research of such behaviour enhances the understanding of decomposition and benefits estimating the postmortem interval (PMI) in forensic investigations, which is critical but faces multiple challenges. In this study, we combined microbial community characterization, microbiome sequencing from different organs (i.e. brain, heart and cecum) and machine learning algorithms [random forest (RF), support vector machine (SVM) and artificial neural network (ANN)] to investigate microbial succession pattern during corpse decomposition and estimate PMI in a mouse corpse system. Microbial communities exhibited significant differences between the death point and advanced decay stages. Enterococcus faecalis, Anaerosalibacter bizertensis, Lactobacillus reuteri, and so forth were identified as the most informative species in the decomposition process. Furthermore, the ANN model combined with the postmortem microbial data set from the cecum, which was the best combination among all candidates, yielded a mean absolute error of 1.5 AE 0.8 h within 24-h decomposition and 14.5 AE 4.4 h within 15-day decomposition. This integrated model can serve as a reliable and accurate technology in PMI estimation.

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Section: Introductionmentioning

confidence: 99%

Predicting postmortem interval based on microbial community sequences and machine learning algorithms

Liu

Shen

et al. 2020

Environmental Microbiology

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“…Fig 1A and 1B shows the raw and pre-processed spectra, where the mean spectra at the fingerprint region (1800-900 cm -1 ) are grouped by sample type. Major change trends in intensity can be highlighted by visual comparison of averaged second-derivative spectra [55]. These spectra show clear differences; for example, some samples have peaks which others lack, or horizontal shifts visible at certain peaks.…”

Section: Sample Types Can Be Differentiated Using Atr-ftir Spectroscopy and Chemometricsmentioning

confidence: 99%

Know your enemy: Application of ATR-FTIR spectroscopy to invasive species control

et al. 2022

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Extreme weather and globalisation leave our climate vulnerable to invasion by alien species, which have negative impacts on the economy, biodiversity, and ecosystem services. Rapid and accurate identification is key to the control of invasive alien species. However, visually similar species hinder conservation efforts, for example hybrids within the Japanese Knotweed complex. We applied the novel method of ATR-FTIR spectroscopy combined with chemometrics (mathematics applied to chemical data) to historic herbarium samples, taking 1580 spectra in total. Samples included five species from within the interbreeding Japanese Knotweed complex (including three varieties of Japanese Knotweed), six hybrids and five species from the wider Polygonaceae family. Spectral data from herbarium specimens were analysed with several chemometric techniques: support vector machines (SVM) for differentiation between plant types, supported by ploidy levels; principal component analysis loadings and spectral biomarkers to explore differences between the highly invasive Reynoutria japonica var. japonica and its non-invasive counterpart Reynoutria japonica var. compacta; hierarchical cluster analysis (HCA) to investigate the relationship between plants within the Polygonaceae family, of the Fallopia, Reynoutria, Rumex and Fagopyrum genera. ATR-FTIR spectroscopy coupled with SVM successfully differentiated between plant type, leaf surface and geographical location, even in herbarium samples of varying age. Differences between Reynoutria japonica var. japonica and Reynoutria japonica var. compacta included the presence of two polysaccharides, glucomannan and xyloglucan, at higher concentrations in Reynoutria japonica var. japonica than Reynoutria japonica var. compacta. HCA analysis indicated that potential genetic linkages are sometimes masked by environmental factors; an effect that can either be reduced or encouraged by altering the input parameters. Entering the absorbance values for key wavenumbers, previously highlighted by principal component analysis loadings, favours linkages in the resultant HCA dendrogram corresponding to expected genetic relationships, whilst environmental associations are encouraged using the spectral fingerprint region. The ability to distinguish between closely related interbreeding species and hybrids, based on their spectral signature, raises the possibility of using this approach for determining the origin of Japanese knotweed infestations in legal cases where the clonal nature of plants currently makes this difficult and for the targeted control of species and hybrids. These techniques also provide a new method for supporting biogeographical studies.

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“…ANNs have been applied to both X-ray spectra to predict plasma electron temperatures and densities, and to UV-vis and FTIR spectra to investigate forensics [16,17]. Lately, they have been used to estimate the diameters of nanoparticles [18,19].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Machine Learning Model to Study UV-Vis Spectra of Gold Nanospheres

Karlık

Yilmaz²,

Özdemir

et al. 2020

Plasmonics

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Here, we have employed principal component analysis (PCA) and linear discriminant analysis (LDA) to analyze the Miecalculated UV-Vis spectra of gold nanospheres (GNS). Eigen spectra of PCA perform the Fano-type resonances. PCA vector spectra determine the 3D vector fields which reveal the homoclinic orbit strange attractor. Quantum confinement effects are observed by the 3D representation of LDA. Standing wave patterns resulting from oscillations of ion-acoustic phonon and electron waves are illustrated through the eigen spectra of LDA. Such capabilities of GNPs have brought high attention to the high energy density physics applications. Furthermore, accurate prediction of gold nanoparticle (GNP) sizes using machine learning could provide rapid analysis without the need for expensive analysis. Two hybrid algorithms consist of unsupervised PCA and two different supervised ANN have been used to estimate the diameters of GNPs. PCA-based artificial neural networks(ANN) were found to estimate the diameters with a high accuracy.

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UV–Vis and ATR–FTIR spectroscopic investigations of postmortem interval based on the changes in rabbit plasma

Cited by 30 publications

References 63 publications

Predicting postmortem interval based on microbial community sequences and machine learning algorithms

Predicting postmortem interval based on microbial community sequences and machine learning algorithms

Know your enemy: Application of ATR-FTIR spectroscopy to invasive species control

A Hybrid Machine Learning Model to Study UV-Vis Spectra of Gold Nanospheres

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