Malaysian Journal of Analytical Sciences Vol 19 No 4 (2015): 790 - 798

 

 

 

APPLICATION OF CHEMOMETRIC TECHNIQUES TO

COLORIMETRIC DATA IN CLASSIFYING AUTOMOBILE PAINT

 

(Aplikasi Teknik-Teknik Kimometrik untuk Data Kolorimetrik bagi Pengkelasan Cat Kereta)

 

Nur Awatif Rosli¹, Rozita Osman¹*, Norashikin Saim¹, Mohd Zuli Jaafar²

 

¹Faculty of Applied Sciences,

Universiti Teknologi MARA Shah Alam, 40450 Shah Alam, Selangor, Malaysia

²Faculty of Plantation and Agrotechnology,

Universiti Teknologi MARA, 77300 Merlimau, Melaka 

 

*Corresponding author: rozit471@salam.uitm.edu.my

 

 

Received: 23 November 2014; Accepted: 27 June 2015

 

 

Abstract

The analysis of paint chips is of great interest to forensic investigators, particularly in the examination of hit-and run cases. This study proposes a direct and rapid method in classifying automobile paint samples based on colorimetric data sets; absorption value, reflectance value, luminosity value (L), degree of redness (a) and degree of yellowness (b) obtained from video spectral comparator (VSC) technique. A total of 42 automobile paint samples from 7 manufacturers were analysed. The colorimetric datasets obtained from VSC analysis were subjected to chemometric technique namely cluster analysis (CA) and principal component analysis (PCA). Based on CA, 5 clusters were generated; Cluster 1 consisted of silver color, cluster 2 consisted of white color, cluster 3 consisted of blue and black colors, cluster 4 consisted of red color and cluster 5 consisted of light blue color. PCA resulted in two latent factors explaining 95.58 % of the total variance, enabled to group the 42 automobile paints into five groups. Chemometric application on colorimetric datasets provide meaningful classification of automobile paints based on their tone colour (L, a, b) and light intensity These approaches have the potential to ease the interpretation of complex spectral data involving a large number of comparisons.

 

Keywords: colorimetric, Cluster Analysis (CA), chemometric techniques, Principle Component Analysis (PCA), Video Spectral Comparator (VSC)

 

Abstrak

Analisis cip cat adalah penting kepada penyiasat forensik, khususnya dalam kes-kes langgar dan lari. Kajian ini mencadangkan satu kaedah yang cepat dalam mengklasifikasikan sampel cat kereta berdasarkan set data kolorimetri; nilai penyerapan, nilai pantulan, nilai kilauan (L), darjah kemerahan (a) dan darjah kekuningan (b) yang diperoleh daripada teknik pembandingan spektrum video (VSC). Sebanyak 42 sampel cat kereta daripada 7 pengeluar kereta telah dianalisis. Set data kolorimetri yang diperolehi dari VSC dianalisa menggunakan kaedah kimometrik iaitu analisis kelompok (HACA) dan analisis komponen utama (PCA). Berdasarkan HACA, 5 kelompok cat kereta telah dijana; kelompok 1 terdiri daripada warna perak, kelompok 2 terdiri daripada warna putih, kelompok 3 terdiri daripada gabungan warna biru dan hitam, kelompok 4 terdiri daripada warna merah dan kelompok 5 terdiri daripada warna biru muda. PCA menghasilkan dua faktor yang menjelaskan 95.58% daripada keseluruhan varian, membolehkan 43 sampel cat kereta yang dianalisis dikumpulkan ke dalam lima kumpulan. Penggunaan kimometrik terhadap set data kolorimetri memberikan maklumat berguna untuk pengkelasan cat kereta berdasarkan tona warna (L, a, b) dan keamatan cahaya (serapan dan kepantulan). Pendekatan-pendekatan ini mempunyai potensi yang memudahkan penaksiran data spektrum yang kompleks yang melibatkan pelbagai aspek perbandingan.

 

Kata kunci: kolorimetri, Analisis Pengumpulan Kelompok-Kelompok Berhierarki (HACA), teknik-teknik kimometrik, Analisis Komponen Utama (PCA), Pembanding Spectrum Video (VSC)

 

References

1.       Maric, M., Bronswijk, W.v., Lewis, S.W. and Pitts, K. (2014). Synchrotron FTIR characteristic of automotive primer surfacer paint coatings for forensic purposes. Talanta 118: 156-161.

2.       Zięba-Palus, J. (1999). Application of micro-Fourier transform infrared spectroscopy to the examination of paint samples. Journal of  Molecular Structure, 511-512: 327-335.

3.       Bentley, J. (2001). Composition, manufacture and use of paint. In: B. Caddy (Ed.), Forensic Examination of Glass and Paint: Analysis and Interpretation (pp. 123-141). Taylor & Francis, London.

4.       Maric, M., Bronswijk, W.v., Lewis, S.W., Pitts, K. and Martin D.E (2013). Characterisation of chemical component migration in automotive paint by synchrotron infrared imaging. Forensic Science International 228: 165-169.

5.       Zięba-Palus, J. and Michalska, A. (2014). Photobleachig as a useful technique in reducing of fluorescence in Raman spectra of blue automobile paint samples. Vibrational Spectroscopy74: 6-12.

6.       Herendeen,  D. L., Allen, J.A. and Lera T. (2011). Philatelic shade discrimination based on measured colour. Institute for Analytical Philately, Inc. London Philatelist, 120 (1384): 105-117.

7.       Wyszecki, G. and Stiles, W.S. (2000). Colorimetry. In: Wyszecki, G., Stiles, W.S. (Eds.), Color Science. Concepts and Methods, Quantitative Data and Formulae (pp. 117-248). New York, USA: Second Ed. John Wiley & Sons.

8.       Hirschler, R., (2010). Electronic colour communication in the textile and apparel industry. Redige, 1: 43-61.

9.       Neuhauser, S. and Handler, J. (2013). Colour analysis of the equine endometrium: Comparison of spectrophotometry and computer-assisted analysis of photographs within the L*a*b* colour space system. The Veterinary Journal, 197: 753-760.

10.    Hammell, K.L., Laforest, J.P. and Dufour, J.J. (1994). Evaluation of the lean meat colour of commercial pigs produced in Quebec. Canadian Journal of Animal Science, 74: 443-449.

11.    Northcutt, J.K., Buhr, R.J. and Rowland, G.N. (2000). Relationship of broiler bruise age to appearance and tissue histological characteristics. Journal of Applied Poultry Research, 9, 13-20.

12.    Chatelain, Y., Guggisberg, D., Dufey, P.A., Vergères, G. and Hadorn, R. (2007). farbmessung an Fleisch und fleischerzeugnissen. ALP Science 507: 1-23.

13.    Mok, D.K.W. and Chau, F.T. (2006). Chemical information of Chinese medicines: a challenge to chemist, Chemometric Intelligent Laboratory System 82: 210-217.

14.    Maree, J., Kamatou, G., Gibbons, S., Viljoen A. and Vuuren, S.V. (2014). The application of GC-MS combined with chemometrics for the identification of antimicrobial compounds from selected commercial essential oils. Chemometrics and Intelligent Laboratory Systems 130: 1-10.

15.    Singh, K. P., Malik, A., Mohan, D., Sinha and Singh, V. K. (2005). Chemometric data analysis of pollutanys in wastewater-a case study. Analytica Chimica Acta 532: 15-25.

16.    Vega, M., Pardo, R., Barrado, E. and  Deban, L. (1998). Assessment of seasonal and polluting effects on the quality of river water by exploratory data analysis. Water Research 32: 3581-3592.

17.    Otto, M. (1998). Multivariate methods. In R. Kellner, J.M. Mermet, M. Otto and H.M. Widmer (Eds.), Analytical Chemistry (pp. 916). Wiley-VCH, Weinheim, Germany.

18.    Alonso-Slaces, R.M., Guyot, S., Herrero, C., Berrueta, L.A., Drilleau, J-F., Gallo, B. and Vicente, F. (2005). Chemometric classification of Basque and French ciders based on their total polyphenol contents and CIELab parameters. Food Chemistry 91: 91-98.

19.    Helena, B., Pardo, R., Vega, M., Barrado, E., Fernandez, J. M. and Fernan, L. (2000). Temporal evaluation of groundwater composition in an alluvial aquifer (Pisurga River, Spain) by Principle Component Analysis. Water Research 34(3): 807-816.

20.    Singh, K. P., Malik, A., Mohan, D. and Sinha, S. (2004). Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India)-a case study. Water Research 38: 3980-3992.

21.    Kannel, P. R., Lee, S., Kanel, S. R., and Khan, S. P. (2007). Chemometric application in classifiction and assessment of monitoring locations of an urban river system. Analytica Chimica Acta 582: 390–399.

22.    Jackson, J. E. (1991). A User’s Guide to Principal Components. Wiley, New York.

23.    Liu, C. W., Lin, K.H. and Kuo, Y. M. (2003). Application of factor analysis in the assessment of groundwater quality in a blackfoot disease area in Taiwan. Science Total Environment 313: 77-89.