Profiling Human Hair with FTIR Spectroscopy and Chemometrics

Profiling human being blur with FTIR Spectroscopy and ChemometricsABSTRACTProfiling of Human Hairs apply FTIR Spectroscopy and Chemometrics TechniqueAufa Madihah Binti Mohamad Anwar1122204Hair is any of the fine thin strands g lineing from the skin of humans cigarette be found in crime scene payable to interaction between victim, perpetrator and/or witness as explained in Locards Principle. This papers aim is to compare, categorize and identify human fuzz fiber using FTIR spectrometer and Chemometrics techniques. Hair tastes pass on be collected from 200 subjects (Malay Women) of different elds groups. 50 samples go away be collected from age group of people (20-30, 31-40, 41-50, 60). FTIR spectra allow for be obtained from each samples. The spectra of FTIR reflect the chemical and physical nature of a whisker which burn be secernateified in different group using Chemometrics techniques such as PCA (Principal Component psychoanalysis).Keyword FTIR, Chemometrics, Locard s Principle, PCAResearch Methodology6.1 secularsMaterial uses in this experiment result be human bull. 50 strands exit be collected for each group of age and the total bull strand obtained forget be 200 strands6.2 Methods6.2.1 Human Hair sample collectionHuman tomentum entrust be collected check to their age group (50 strands from each group of age). The tomentum cerebri fibers taken go away be from telogen (fall naturally) phase and anagen (the root was cut) phase of the hair growth cycle. The hair fibers leave alone be placed in a p uttermostic suitcase and labeled matchly.6.3 Analysis6.3.1 Revised IAEA Method for Cleaning Hair FibersThe cleaning process is needed to preserved hair samples for the elemental synopsis. (Cargnello et al., 1995) The hair fibers will undergo ultrasonic vibration (sonicating) in each solutions for 10 legal proceeding or less. This cognitive operation will be done to minimize the wrong of the cuticle surface. First, the hair fibers wi ll be transferred to a small frappe ampoule and will be covered with high purity acetone. The vial was then will be placed in Ultrasonic Disintegrator (Figure 6). The hair fibers will be sonicate of at 20 kHz for 10 legal proceeding at the least. The acetone will be poured out and the hair will be rinsed with HPLC-grade water. These steps will be repeated again and for the last steps, the hair fibers will be rinsed and sonicated in de-ionized water inside the rubbish vial for 10 minutes. After all the cleaning process ended, the hair fiber will be dried under vacuum for twain days before being analyzed.Figure 6 Ultrasonic Disintegrator Sonicater for the hair fibers6.3.2 FTIR SpectroscopyA FTIR Spectrometer with Diamond ATR Smart addendum (Figure 7) will be used in this number. The spectra of hair fibers will be record using the spectrometer. Figure 7 FTIR Spectrometer with Diamond Smart AccessoryThe parameters of FTIR-ATR for the analysis (Table 2)Before analyzing and colle cting the spectrum from the hair samples, a background spectrum will be recorded. The hair fibers will then going through spectral sampling process The fibers will be placed across the infield crystal and will be pressed (to obtain a unspoilt butt) using the pressure tower.Spectrum will be recorded.The collected information will be relieve on the ghostly Software Program (as .SPC files).Spectral ProcessingThe recorded spectral will be saved as .SPC files and are imported into the spectral software package for spectral affect as .SPA files. First of all, the baseline of the spectra will be corrected and the outset will be set to zero. Then, the spectra will be trimmed so that it will be in the range of -keratin absorption bands which is 1759-785 cm-1 range (major mark of -keratin). The trimmed spectral will then be transferred to an Excel spreadsheet and saved as an .XLS file.Raw entropy Matrix and Chemometric AnalysisPre-processing of data is specify as mathematical mani pulation of a data is used due to primary analysis. (Arnberg R. et al., 1998).This step helps in eliminating or decreasing orthogonal sources (systematic or random errors).Variance ScalingScaling steps will be done prior to the fact that the treatment considers both the measure unit of the values and the origin of scale. (Meloun M. et al., 1992) Scaling is needed to includeCartesian systems shift of the origin,Axes contraction or expansion. picture CentringDouble string will be obtained by subtracting the mean of each row x (x-mean centring) and row y (y-mean centring). This procedure reduces the effect of the variance fortune reflected by PCI of the un-pretreated data set and removes common spectral features. (Kokot S. et al., 1997) Equation 1 and Equation 2 (Meloun M. et al., 1992) described the process aboveYim = xim x.mEquation 1 proceed by zim = yim yi Equation 2Where yim = mainstay centred datum xim = datum in row I and column m before centring x.m = mean of column m = xi m / Izim = double centred datumStandardizationThe standardisation procedure is included to equalize the variance of each versatile and to remove the weighting that is artificially imposed by the scale of the variables. (Arnberg R. et al., 1998) Standardization process can be described in the equation 3 and 4 at a lower place yim = xim / sm Equation 3Where sm = (xim x.m)2 Equation 4 I 1= the estimate of the standard deviation of the variable, xm, about its mean.Albano et al. state that standardization of each subset separately gives better resolution in latent variable moldinging of subsets. (Albano et al., 1981)Auto-scalingCombination of column centring and standardization is known auto-scaling. It can be represented by Equation 5 (Meloun M. et al., 1992)zim = (yim yi) / sm Equation 5Chemometric AnalysisThe doubled centred matrices will be imported into the software that cans variable analysis and experimental design. The processed matrices will produce the resultant PCA piles plots, loading plots and fuzzy clustering tables.Multi-criteria Decision Making (MCDM)The multivariate analysis method (PROMETHEE and GAIA) will find the relationships between the object glasss and variables severally. The hyaloplasm data will then undergoes packaging for decision making.ChemometricsChemometrics helps in analysis of spectral data by solving the calibration problem. It uses statistical and mathematical methods to correlate whole tone parameters to analytical instrument data. The data will be observed and recorded. Then, patterns in data will be brought out and sculptured. The modeled patterns will be used for data analysis in future. (Einax J.W. et al., 1997)6.3.5.1 Principal Component Analysis (PCA)PCA is a well-known pattern recognition method for pertaining to any procedure involving multivariate (two or more variables) data analysis problems. Identification and divergence of the objects can be obtained with the help of PCA whereby it is a data reduct ion technique. Data reduction technique is when the information is sorted into a data matrix using selected variables that define the columns and rows which was designated with sample measurements.PCjk = aj1xk1 + aj2xk2 + ajnxkn Equation 6WherePCjk = value for object k and principal voice jaj1 = variable 1 on object k valuexk1 = variable 1 on component j measurementn = original variables total number6.3.5.2 SIMCA (Soft separatist Modeling of Class Analogy)Classification is defines as the process of categorizing something accord to resembling qualities or characteristics. SIMCA is supervised method for classification of data. The method requires a readying (test) data set consisting of samples where their origins are known. PCA is used to develop a model of each class within the test set. The users will select the members of a set. A model can be representing by the equation (Chatfield C., 1980)Xki = Xi + + ajiujk + eki Equation 7Wherep = number of the principal components in t he class modeleki = residual value of object k on variable iGant Chart for Research TimelineExpected ResultsThe research regarding human sell hair resulted in new database on human hair according to their age group for woman, Malay. With the help of chemometrics method the human hair database can be created. The data obtained from Fourier-transform Infrared Spectroscopy which was then analyze and modeled through chemometrics and the new database is set. In the database, the human hair was classified according to their age group and the composition that differs the age. Through the research, it shows that in human hair the amount of amino virulent (protein) differs. As the age increase, the amounts of amino erosive (protein) decrease and as the age lower the amino acid (protein) higher.9.0 finaleThis study is proposed to help creating a new database besides furthering investigation on human hair as physical rise. Theory of physical evidence can be quoted from Locards Principal w hich stated that every contact leaves a trace. Physical evidence cannot be faulty it cant perjure itself, and it always present. Only human failure to study, explore and watch it can diminish its worth. Furthermore, fibers evidence is often found at the crime scene. Thus, in creating this new database it can aids in forensic process. The human hair samples that was obtained from different age group (20-30, 31-40, 41-50, 60) but comparable gender and race undergoes FTIR spectroscopy to detect the amount of amino acid (protein). The data was then was analyze using chemometrics. In short, it can be concluded that the amount of amino acid (protein) is inversely proportional to the age group.