Survey on batch-to-batch variation in spray paints: A collaborative study

Muehlethaler, Cyril; Massonnet, Geneviève; Deviterne, Marie; Bradley, Maureen J.; Herrero, Ana M.; Lezana, Itxaso Diaz de; Lauper, Sandrine; Dubois, Damien; Geyer-Lippmann, Jochen; Ketterer, Sonja; Milet, Stéphane; Bertrand, Magali; Langer, W.H.; Plage, Bernd; Gorzawski, Gabriele; Lamothe, Véronique; Marsh, Louissa; Turunen, Raija

doi:10.1016/j.forsciint.2013.02.041

Cited by 17 publications

(11 citation statements)

References 6 publications

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“…Chemometrics has been previously utilised in forensic science in order to mitigate bias and partiality in the interpretation of analytical data obtained from trace evidence . A combination of principal component analysis (PCA) and linear discriminant analysis (LDA) has been applied in the discrimination and classification of forensic evidence, including fibres, glass, inks, soil, hair, accelerants, photocopy and printer toners, paper, electrical tapes and paint …”

Section: Introductionmentioning

confidence: 99%

Characterisation and classification of automotive clear coats with Raman spectroscopy and chemometrics for forensic purposes

Marić

Bronswijk

Pitts

et al. 2016

J Raman Spectroscopy

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The clear coats from a collection of automotive paint samples of 139 vehicles, covering a range of Australian and international vehicle manufacturers and sold in Western Australia, were characterised using FT-Raman spectroscopy. Principal component analysis (PCA) revealed 19 distinct classes that were associated with the vehicles' manufacturer and model, and in the case of Australian manufacturers, the years of manufacture. Linear discriminant analysis based on the PCA groupings gave excellent discrimination between the groups with 96.9% of the calibration set and 97.6% of the validation set being correctly classified. Although the sample set comprised only vehicles available in Australia, the methodology used is universal and hence applicable in any jurisdiction that is willing and able to generate a statistically significant data set and maintain and update it as new vehicles appear on the market. A FT-Raman spectroscopy-based database would rapidly provide information regarding vehicle origin and manufacture and hence generate investigative leads for questioned paint samples found at incident sites.

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Section: Introductionmentioning

confidence: 99%

Characterisation and classification of automotive clear coats with Raman spectroscopy and chemometrics for forensic purposes

Marić

Bronswijk

Pitts

et al. 2016

J Raman Spectroscopy

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Section: Introductionmentioning

confidence: 99%

“…Some research involving the application of chemometric methods to discriminate between samples based upon their Raman spectra was also presented . Principal component analysis (PCA) was used in the differentiation of spray paints as well as tempera paint model samples . Two methods—PCA and hierarchical cluster analysis (HCA) were applied in the discrimination of red household paints, whereas linear discriminant analysis (LDA) was used for recognition and discrimination of copper phtalocyanine polymorphs …”

Section: Introductionmentioning

confidence: 99%

Application of a likelihood ratio approach in solving a comparison problem of Raman spectra recorded for blue automotive paints

Michalska

Martyna

Ziȩba‐Palus

et al. 2015

J Raman Spectroscopy

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Evidential values of Raman spectra recorded for solid and metallic blue car paints were evaluated using visual comparison as well as a statistical approach, i.e. likelihood ratio (LR) test supported by Empirical Cross Entropy (ECE) results. Raman spectra were obtained using a Renishaw inVia spectrometer equipped with a confocal Leica microscope and a near infrared semiconductor laser (785 nm) as an excitation source.When a visual comparison was performed for solid paints, in 17.7% of comparisons (53 pairs out of 300) the samples were indistinguishable, whereas for metallic paints this value was 8.5% of such comparisons (37 pairs out of 435). Comparing the spectra using an LR approach was based on variables being the areas under the most significant Raman spectra bands of pigments present in the analysed samples. Proposed LR models delivered low false positive and false negative rates (for many models lower than 10%), and the ECE plots confirmed that their performance was much better than visual comparison.

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“…Finer differences, i.e. in production batches (multiple cans from the same model), have been demonstrated to appear punctually, hence the differentiation is not always possible [4].…”

Section: Introductionmentioning

confidence: 99%

“…Beside the market studies, other more specific population surveys were conducted on graffiti traces [5,6], paint droplets found on clothing [7,8], or analysis of various spray paint production batches [4]. All these studies represent a time consuming work for the collection and analyses of the paints, mostly for the attribution of chemical groupings and the discrimination of the samples.…”

Section: Introductionmentioning

confidence: 99%

Discrimination and classification of FTIR spectra of red, blue and green spray paints using a multivariate statistical approach

Muehlethaler¹,

Massonnet²,

Esseiva³

2014

Forensic Science International

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Survey on batch-to-batch variation in spray paints: A collaborative study

Cited by 17 publications

References 6 publications

Characterisation and classification of automotive clear coats with Raman spectroscopy and chemometrics for forensic purposes

Characterisation and classification of automotive clear coats with Raman spectroscopy and chemometrics for forensic purposes

Application of a likelihood ratio approach in solving a comparison problem of Raman spectra recorded for blue automotive paints

Discrimination and classification of FTIR spectra of red, blue and green spray paints using a multivariate statistical approach

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