Forensic analysis of red lipsticks using ATR-FTIR spectroscopy and chemometrics

Chophi, Rito; Sharma, Shveta; Singh, Rajinder

doi:10.1016/j.forc.2019.100209

Cited by 50 publications

(42 citation statements)

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“…This type of algorithm is widely used for spectral data analysis in different methods and elds of application. [56][57][58][59][60][61] This is because of the high dimensionality nature of the spectroscopic data analysed in this work and the ability of LDA to reduce these dimensions to just a few while preserving most of the information, which allows for feature extraction rather than feature selection. Using the Raman spectra of each cell obtained with different wavelengths as input, the LDA algorithm was used to classify the different cells of the combinatorial sample according to the following classi-cation targets: efficiency, V oc and [Zn/Ge] ratio.…”

Section: Machine Learning Approach For Device Performance Analysismentioning

confidence: 99%

Combinatorial and machine learning approaches for the analysis of Cu₂ZnGeSe₄: influence of the off-stoichiometry on defect formation and solar cell performance

et al. 2021

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Section: Machine Learning Approach For Device Performance Analysismentioning

confidence: 99%

Combinatorial and machine learning approaches for the analysis of Cu₂ZnGeSe₄: influence of the off-stoichiometry on defect formation and solar cell performance

et al. 2021

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“…A two dimensional plot was presented as it provided easy visualization. The same method was reported by Chophi et al [19] and Sharma et al [20]. These four PCs were tried in different set of combinations to investigate which PC combination provided better discrimination between vermilion samples.…”

Section: Resultsmentioning

confidence: 99%

“…Such drawbacks can be overlooked by utilizing non-destructive methods of analysis to generate objective results with high accuracy in quick time. For example, spectroscopic techniques like Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR) is proven to be useful in analysis of exhibits from all fields of forensic science like paints [17], inks [18], lipstick [19], kajal [20], body fluids [21][22][23], soil [24], and in the field of questioned document [25].…”

mentioning

confidence: 99%

Discrimination of vermilion (sindoor) using attenuated total reflectance fourier transform infrared spectroscopy in combination with PCA and PCA‐LDA

Chophi

Sharma

Singh

2020

Journal of Forensic Sciences

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Vermilion (sindoor) is considered sacred in the Hindu religion, and it is used routinely throughout the world by married Hindu women along the line of hair parting during marriage ceremonies, religious rituals, and festivals. Owing to its esthetic appeal, it is sometimes illegally used as a food additive; leading to potential health risks. Therefore, due to the aforementioned reasons, vermilion can likely be encountered as trace evidentiary material during crime investigations, particularly in cases of sexual and physical offenses against women. Analysis of such evidence can provide a link between the criminal, the victim, and the crime scene and thereby be utilized as associative evidence in the court of law. In the present study, ATR‐FTIR spectroscopy has been used for the examination of 37 different manufacturers of vermilion. Chemometric methods such as principal component analysis (PCA) and PCA‐LDA were performed on the obtained spectra for objective interpretation of results. PCA delivered 99.06% discrimination of samples while PCA‐LDA employed for classification purpose delivered 95.25% calibration accuracy and 88% validation accuracy. Afterward, the validity of the chemometric methods employed was tested by blind testing of samples. A preliminary study on the effect of selected substrates (cotton cloth, tissue paper, glass, and plastic) on sample analysis indicates that while sample stain on substrates could be linked to its parent source even after a month, linking an aged samples (after 8 months) could be hindered due to evaporation of components present in vermilion. Overall, the current methodology utilized has a potential prospect in future forensic‐casework.

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“…This study shows the ability to classify tire samples using their elemental signature (from LIBS) and the analysis of those signatures using principal component analysis (PCA) combined with linear discriminant analysis (LDA), a relatively simple machine learning technique. PCA has seen use in forensics for many years, [18][19][20] being a technique that uses the variance within the data to reduce the dimensionality of the data to the most relevant variables. PCA tends to be misused due to its simplicity, but it is especially useful in reducing the dimensionality of spectral data where a lot of points are simply contributions of noise.…”

Section: Introductionmentioning

confidence: 99%

Tire Classification by Elemental Signatures Using Laser-Induced Breakdown Spectroscopy

et al. 2021

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Tire evidence is a form of trace evidence that is often overlooked in today's forensics, while frequently found at crime or accident scenes, usually in the form of skid marks. The pattern of the tire skid mark has been used before to link a tire or car to a scene, but the widespread use of anti-lock braking systems makes this an almost impossible and abandoned route of analysis. With this is mind, using the chemical profile of a tire has potential to link a car or tire back to a scene in which its trace material is found. This study shows the successful use of the elemental profile of tire rubber to classify thirty-two different samples by Laser-Induced Breakdown Spectroscopy (LIBS), analyzed by Principal Component Analysis (PCA) combined with Linear Discriminant Analysis (LDA). A classification accuracy close to 99% shows the ever-growing use of LIBS as a technique of choice for forensic analysis of tire rubber, opening the path for its use as a forensic evidence.

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Forensic analysis of red lipsticks using ATR-FTIR spectroscopy and chemometrics

Cited by 50 publications

References 20 publications

Combinatorial and machine learning approaches for the analysis of Cu₂ZnGeSe₄: influence of the off-stoichiometry on defect formation and solar cell performance

Combinatorial and machine learning approaches for the analysis of Cu₂ZnGeSe₄: influence of the off-stoichiometry on defect formation and solar cell performance

Discrimination of vermilion (sindoor) using attenuated total reflectance fourier transform infrared spectroscopy in combination with PCA and PCA‐LDA

Tire Classification by Elemental Signatures Using Laser-Induced Breakdown Spectroscopy

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Forensic analysis of red lipsticks using ATR-FTIR spectroscopy and chemometrics

Cited by 50 publications

References 20 publications

Combinatorial and machine learning approaches for the analysis of Cu2ZnGeSe4: influence of the off-stoichiometry on defect formation and solar cell performance

Combinatorial and machine learning approaches for the analysis of Cu2ZnGeSe4: influence of the off-stoichiometry on defect formation and solar cell performance

Discrimination of vermilion (sindoor) using attenuated total reflectance fourier transform infrared spectroscopy in combination with PCA and PCA‐LDA

Tire Classification by Elemental Signatures Using Laser-Induced Breakdown Spectroscopy

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Combinatorial and machine learning approaches for the analysis of Cu₂ZnGeSe₄: influence of the off-stoichiometry on defect formation and solar cell performance

Combinatorial and machine learning approaches for the analysis of Cu₂ZnGeSe₄: influence of the off-stoichiometry on defect formation and solar cell performance