Browsing by Subject "Chemometrics"

Now showing 1 - 3 of 3
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    Estimation of human bloodstains time since deposition using ATR-FTIR spectroscopy and chemometrics in simulated crime conditions
    (Elsevier, 2024-06-26) Mengual-Pujante, Miguel; Peran, Antonio J.; Ortiz, Antonio; Pérez Cárceles, María Dolores; Ciencias Sociosanitarias
    Blood in the form of stains is one of the most frequently encountered fluid in crime scene. Estimation of the time since deposition (TSD) is of great importance to guide the police investigation and the clarification of criminal offences. The time elapsed since deposition is usually estimated by modelling the physicochemical degradation of blood biomolecules over time. This work shows an ATR-FTIR spectroscopy and chemometrics study to estimate TSD of bloodstains on various surfaces and under different ambient conditions (indoor and outdoor). For a period from 0 to 212 days, a total of 960 stains were analyzed. Most of the eleven partial least squares regression (PLSR) models obtained showed a good prediction capacity, with a Residual Predictive Deviation (RPD) value higher than 3, and R2 higher than 0.90. Models for non-rigid supports showed better predictive capacity than those for rigid ones. A non-rigid surface model including the various non-rigid surfaces and ambient conditions was elaborated, which might be the most useful one from the criminalistic point of view. These results show that this technique can be a rapid, robust, and trustable tool for in situ determination of the TSD of bloodstains at crime scenes.
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    Open Access
    Forensic examination of textile fibres using Raman imaging and multivariate analysis
    (Elsevier B.V., 2021-12-03) Zapata, Félix; Ortega-Ojeda, Fernando E.; García-Ruiz, Carmen; Química Analítica; Facultades,Servicios y Escuelas::Facultades de la UMU::Facultad de Química
    Vibrational spectroscopic techniques have shown to be highly suitable for the identification and comparison of textile fibres and clothing fabrics. On the other hand, new chemical imaging modes based on these spectroscopic techniques are becoming useful in multiple fields. This is particularly important to, for instance, chemically visualize and screen different samples including forensic evidence (crime scene investigation), chemical and food products (quality control), biological tissues and living beings (medical imaging), among others. This study explores the forensic examination and selective chemical visualization of textile fibres and clothing fabrics using Raman imaging. Four experiments were performed, which were focused on the screening of (i) white different materials made of 100 % cotton (gauze, cotton wool, t-shirt, and swab), (ii) polyester and cotton fabrics evidence of the same colour, (iii) five different coloured cotton fabrics, and (iv) textile fibres of different materials (acrylic, cotton, nylon, polyester, and silk). Several methods of multivariate chemometric analysis including principal component analysis (PCA), multivariate analysis of variance (MANOVA), and multivariate curve resolution (MCR) were applied to enhance the limited visual comparison of the spectra accomplished with the unaided eye. The results evidenced the suitability of Raman imaging to statistically discriminate textile fibres and fabrics due to the chemical composition of both the clothing material and the dyestuff.
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    Open Access
    Head-space gas chromatography coupled to mass spectrometry for the assessment of the contamination of mayonnaise by yeasts
    (Elsevier B.V., 2019-08-15) Arroyo-Manzanares, N.; Markiv, B.; Hernández, J.D.; López-García, I.; Guillén, I.; Vizcaíno, P.; Hernández Córdoba, Manuel; Viñas López-Pelegrín, Pilar; Química Analítica
    Head-space (HS) gas chromatography (GC) coupled to mass spectrometry (MS) is proposed for the assessment of the contamination of mayonnaise as an alternative to plate counting, which is the technique commonly used for evaluating microbial contamination. More specifically, this method was applied in the detection of Candida metapsilosis and Zygosaccharomyces bailii, both of great importance in term of food spoilage since they are resistant to many of the common methods of food preservation. Different chemometric models were investigated using the data obtained by GC-MS (m/z profile, area of the chromatographic peaks and entire chromatographic profile), in order to obtain the highest classification success. The best results were obtained using the chromatographic profile (success rate of 92%). Contaminated samples could also be classified according to the concentration of yeast, obtaining a success rate of 87.5%. Finally, a chemometric model was constructed in an attempt to differentiate between strains.