Differentiation of human blood from animal blood using Raman spectroscopy: A survey of forensically relevant species

Doty, Kyle C.; Lednev, Igor K.

doi:10.1016/j.forsciint.2017.11.033

Cited by 63 publications

(46 citation statements)

References 43 publications

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“…The same authors recently expanded their dataset to include blood samples from 6 other animal species, chimpanzee, deer, elk, ferret, fish, and macaque. 201 They also included new human donors varying in age (11-40+ years old). Results showed that most of the new animal species' blood samples were correctly predicted as non-human, with exception of samples from one chimpanzee and one macaque, which were classified as human blood.…”

Section: Raman Spectroscopymentioning

confidence: 99%

Vibrational Spectroscopy and Chemometrics in Forensic Chemistry: Critical Review, Current Trends and Challenges

Silva¹,

Braz²,

Pimentel³

2019

J. Braz. Chem. Soc.

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The present manuscript makes an extensive review of the scientific approaches developed in the last decade involving infrared and Raman spectroscopy combined with chemometrics for solving several issues in the investigation of the most relevant forensic traces, such as questioned documents and currency, explosives, gunshot residues, illicit drugs and body fluids. In addition, current trends, main challenges and the adequate use of several chemometric techniques are discussed. Principal component analysis (PCA) was found to be the most used technique. This unsupervised approach, however, has sometimes been misunderstood as a classification technique. Discriminant analysis techniques are widely employed, leaving a range of possibilities for application of class-modeling techniques, particularly in cases of problems regarding only one target class. In addition, increasingly complex dataset structures frequently require nonlinear approaches or flexible techniques such as multivariate curve resolution-alternating least squares (MCR-ALS). Results reporting, however, still lack reliable quality parameters and sample representativeness, posing a significant challenge to the solution of forensic problems. Regarding the analytical techniques, Raman has been playing an important role, especially in the area of questioned documents and of body fluids. Portable and hyperspectral imaging infrared spectrometers have also been showing significant potential in forensic applications.

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Section: Raman Spectroscopymentioning

confidence: 99%

Vibrational Spectroscopy and Chemometrics in Forensic Chemistry: Critical Review, Current Trends and Challenges

Silva¹,

Braz²,

Pimentel³

2019

J. Braz. Chem. Soc.

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“…Thus far, Raman spectroscopy has been applied to studying Hb and other blood components in addition to blood‐borne pathogens and genetic disorders, [ 18 ] as well as forensic body fluid identification and species differentiation of blood samples. [ 19–22 ] One paper has acknowledged the analysis of COHb via Raman spectroscopy, although this paper presented structural information rather than a quantitative application. [ 23 ]…”

Section: Introductionmentioning

confidence: 99%

Development of a Raman microspectrophotometric method to quantitate carboxyhemoglobin in blood

Melbourn

Brettell

Maldonado

et al. 2020

J Raman Spectroscopy

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Carboxyhemoglobin (COHb) quantitation is a routine toxicological analysis performed in cases of suspected acute carbon monoxide poisoning. Current methods available for COHb quantitation include spectrophotometric methods and gas chromatographic methods, both of which have advantages and disadvantages. The goal of the present study was to develop a Raman microspectrophotometric COHb quantitation method and assess its precision and accuracy. A strong linear correlation (average R2 = 0.98) was found to exist between COHb concentration and the ratio of the Raman intensities at approximately 1,552 and 1,580 cm−1. The limits of detection and quantitation were found to be 4.4% and 13.2% COHb, respectively. Intraday and interday studies of three calibrators indicated high precision, with the coefficient of variation ranging from 0.85% to 3.4%. The accuracy of this method was evaluated through the quantitation of blind samples, first using the proposed Raman microspectrophotometric method and then by a published UV–visible spectrophotometric method. For the proposed Raman method, 87.5% of the samples were quantitated with less than 20% error. The average variances of the two methods were found to be statistically different using a two‐sample F test, with the Raman microspectrophotometric method having a lower average variance. This study demonstrates that Raman microspectrophotometry may be used to quantitate the COHb concentration of blood samples. Furthermore, this method is comparable with established spectrophotometric methods while eliminating the need for time‐consuming sample preparation and limiting the use of consumables and the generation of waste.

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“…PLS-DA of blood spectra collected using Raman spectroscopy, diffuse reflectance spectroscopy, or spatially resolved near-infrared transmission spectroscopy have been used to discriminate human and animal blood [10][11][12][13][14][15][16]. Later, I. K. Lednev group expanded the animal species to 16 and built a binary model for discrimination of human and nonhuman blood [17]. Up to now the most frequently studied blood samples are blood droplets or bloodstains in forensic science.…”

Section: Introductionmentioning

confidence: 99%

Blood species identification based on deep learning analysis of Raman spectra

Huang¹,

Wang²,

Tian³

et al. 2019

Biomed. Opt. Express

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Blood analysis is an indispensable means of detection in criminal investigation, customs security and quarantine, anti-poaching of wildlife, and other incidents. Detecting the species of blood is one of the most important analyses. In order to classify species by analyzing Raman spectra of blood, a recognition method based on deep learning principle is proposed in this paper. This method can realize multi-identification blood species, by constructing a onedimensional convolution neural network and establishing a Raman spectra database containing 20 kinds of blood. The network model is obtained through training, and then is employed to predict the testing set data. The average accuracy of blind detection is more than 97%. In this paper, we try to increase the diversity of data to improve the robustness of the model, optimize the network and adjust the hyperparameters to improve the recognition ability of the model. The evaluation results show that the deep learning model has high recognition performance to distinguish the species of blood.

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Differentiation of human blood from animal blood using Raman spectroscopy: A survey of forensically relevant species

Cited by 63 publications

References 43 publications

Vibrational Spectroscopy and Chemometrics in Forensic Chemistry: Critical Review, Current Trends and Challenges

Vibrational Spectroscopy and Chemometrics in Forensic Chemistry: Critical Review, Current Trends and Challenges

Development of a Raman microspectrophotometric method to quantitate carboxyhemoglobin in blood

Blood species identification based on deep learning analysis of Raman spectra

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