Application of Innovative TGA/Chemometric Approach for Forensic Purposes: The Estimation of the Time since Death in Contaminated Specimens

Chronothanatology has always been a challenge in forensic sciences. Therefore, the importance of a multidisciplinary approach for the characterization of matrices (organs, tissues, or fluids) that respond linearly to the postmortem interval (PMI) is emerging increasingly. The vitreous humor is particularly suitable for studies aimed at assessing time-related modifications because it is topographically isolated and well-protected. In this work, a novel approach based on thermogravimetry and chemometrics was used to estimate the time since death in the vitreous humor and to collect a databank of samples derived from postmortem examinations after medico–legal evaluation. In this study, contaminated and uncontaminated specimens with tissue fragments were included in order to develop a classification model to predict time of death based on partial least squares discriminant analysis (PLS-DA) that was as robust as possible. Results demonstrate the possibility to correctly predict the PMI even in contaminated samples, with an accuracy not lower than 70%. In addition, the correlation coefficient of the measured versus predicted outcomes was found to be 0.9978, confirming the ability of the model to extend its feasibility even to such situations involving contaminated vitreous humor.

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Time of death dependent criteria in vitreous humor—Accuracy of estimating the time since death

Forensic Science International ◽

10.1016/j.forsciint.2005.12.002 ◽

2006 ◽

Vol 164 (2-3) ◽

pp. 87-92 ◽

Cited By ~ 60

Author(s):

Burkhard Madea ◽

Alexander Rödig

Keyword(s):

Vitreous Humor ◽

Time Since Death ◽

Time Of Death

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A Novel Approach to Characterization of Composite Polymer Matrix Materials for Integrated Computational Materials Engineering Approaches

AIAA Scitech 2021 Forum ◽

10.2514/6.2021-1889 ◽

2021 ◽

Author(s):

Michael N. Olaya ◽

Gregory M. Odegard ◽

Marianna Maiaru

Keyword(s):

Polymer Matrix ◽

Composite Polymer ◽

Materials Engineering ◽

Novel Approach ◽

Integrated Computational Materials Engineering ◽

Computational Materials

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A Novel Approach for Missing Value Replacement in MLP-RMSProp Based Classification Model

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i11.619 ◽

2019 ◽

Vol 7 (11) ◽

pp. 6-19

Author(s):

G. Suresh ◽

S. Saraswathi

Keyword(s):

Classification Model ◽

Missing Value ◽

Novel Approach

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Potential use of molecular and structural characterization of the gut bacterial community for postmortem interval estimation in Sprague Dawley rats

Scientific Reports ◽

10.1038/s41598-020-80633-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Huan Li ◽

Siruo Zhang ◽

Ruina Liu ◽

Lu Yuan ◽

Di Wu ◽

...

Keyword(s):

High Throughput Sequencing ◽

Interval Estimation ◽

The Body ◽

Taxon Richness ◽

Time Since Death ◽

Sprague Dawley ◽

Sprague Dawley Rats ◽

Level Data ◽

Species Taxon

AbstractOnce the body dies, the indigenous microbes of the host begin to break down the body from the inside and play a key role thereafter. This study aimed to investigate the probable shift in the composition of the rectal microbiota at different time intervals up to 15 days after death and to explore bacterial taxa important for estimating the time since death. At the phylum level, Proteobacteria and Firmicutes showed major shifts when checked at 11 different intervals and emerged at most of the postmortem intervals. At the species level, Enterococcus faecalis and Proteus mirabilis showed a downward and upward trend, respectively, after day 5 postmortem. The phylum-, family-, genus-, and species-taxon richness decreased initially and then increased considerably. The turning point occurred on day 9, when the genus, rather than the phylum, family, or species, provided the most information for estimating the time since death. We constructed a prediction model using genus-level data from high-throughput sequencing, and seven bacterial taxa, namely, Enterococcus, Proteus, Lactobacillus, unidentified Clostridiales, Vagococcus, unidentified Corynebacteriaceae, and unidentified Enterobacteriaceae, were included in this model. The abovementioned bacteria showed potential for estimating the shortest time since death.

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Multidisciplinary Approach for Detailed Characterization of Shale Gas Reservoirs: A Netherlands Showcase

10.4043/24383-ms ◽

2013 ◽

Author(s):

Yvonne Anja Schavemaker ◽

Mart Zijp ◽

Jan ter Heege ◽

Susanne Nelskamp ◽

Johan Ten Veen

Keyword(s):

Shale Gas ◽

Multidisciplinary Approach ◽

Gas Reservoirs ◽

Detailed Characterization ◽

Shale Gas Reservoirs

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A Novel Approach to Oil Layer Recognition Model Using Whale Optimization Algorithm and Semi-Supervised SVM

Symmetry ◽

10.3390/sym13050757 ◽

2021 ◽

Vol 13 (5) ◽

pp. 757

Author(s):

Yongke Pan ◽

Kewen Xia ◽

Li Wang ◽

Ziping He

Keyword(s):

Optimization Algorithm ◽

Oil And Gas ◽

The Other ◽

Whale Optimization Algorithm ◽

Classification Model ◽

Support Vector ◽

Recognition Model ◽

Novel Approach ◽

Whale Optimization ◽

Oil And Gas Reservoir

The dataset distribution of actual logging is asymmetric, as most logging data are unlabeled. With the traditional classification model, it is hard to predict the oil and gas reservoir accurately. Therefore, a novel approach to the oil layer recognition model using the improved whale swarm algorithm (WOA) and semi-supervised support vector machine (S3VM) is proposed in this paper. At first, in order to overcome the shortcomings of the Whale Optimization Algorithm applied in the parameter-optimization of the S3VM model, such as falling into a local optimization and low convergence precision, an improved WOA was proposed according to the adaptive cloud strategy and the catfish effect. Then, the improved WOA was used to optimize the kernel parameters of S3VM for oil layer recognition. In this paper, the improved WOA is used to test 15 benchmark functions of CEC2005 compared with five other algorithms. The IWOA–S3VM model is used to classify the five kinds of UCI datasets compared with the other two algorithms. Finally, the IWOA–S3VM model is used for oil layer recognition. The result shows that (1) the improved WOA has better convergence speed and optimization ability than the other five algorithms, and (2) the IWOA–S3VM model has better recognition precision when the dataset contains a labeled and unlabeled dataset in oil layer recognition.

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