Multivariate Analysis in Vibrational Spectroscopy of Highly Energetic Materials and Chemical Warfare Agents Simulants

Castro-Suarez, John R.; Ortiz-Rivera, William; Galán-Freyle, Nataly Y.; Figueroa-Navedo, Amanda M.; Pacheco‐Londoño, Leonardo C.; Hernández‐Rivera, Samuel P.

doi:10.5772/54104

Cited by 2 publications

(4 citation statements)

References 39 publications

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“…A major advantage of PLS regression is its robustness; the design parameters do not change significantly when new samples are added or removed from the calibration set. [35,36] Note. FTIR = Fourier transform infrared; PLS = partial least square; RMSECV = root mean square error of cross validation.…”

Section: Discussionmentioning

confidence: 99%

“…In PLS regression, both the matrix of independent variables X (Equation 1) and the dependent variable Y (Equation 2) are represented by T and U (scores representing the projection of sample values in space) and P and Q (loadings expressing the weighing or "weight" of all answers), the LVs. [35,36]…”

Section: Multivariate Analysismentioning

confidence: 99%

“…The performance of models was assessed by RMSECV, which calculated the error of PLS regression of new concentrations based on actual concentrations using Equation 3 with the coefficient of determination, R 2 and the correlation coefficient, r, of the correlation curves between Y and X. [35,39] The RMSECV was calculated using the following equation:…”

Section: Multivariate Analysismentioning

confidence: 99%

“…where N is the number of calibration samples, y i is the reference value of the actual mixture concentration, and b y i is the concentration predicted using PLS regression. [35,40] 3 | RESULTS…”

Section: Multivariate Analysismentioning

confidence: 99%

See 3 more Smart Citations

Quantification of cocaine in ternary mixtures using partial least squares regression applied to Raman and Fourier transform infrared spectroscopy

Penido

Pacheco

Novotny

et al. 2017

J Raman Spectroscopy

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The use of chemicals is often related to suicide attempts and acute poisoning, which account for a significant number of hospital admissions. The differential diagnosis of patients exposed to poisoning is intricate and varies according to the substance used. The identification of drugs and drug abuse in cases of poisoning often requires time‐consuming and complex techniques, such as chromatography. The use of vibrational spectroscopy such as Raman spectroscopy and Fourier transform infrared spectroscopy (FTIR) enables the quick identification of toxic substances using no reagents or markers. This study aims to apply these techniques in the long term to identify and quantify substances found at the locations of suicide attempts, poisonings, and even drug consumption sites, including crack samples containing a cocaine base and adulterants commonly seized in drug busts, such as caffeine, lidocaine, and sodium carbonate. In this study, Raman and FTIR spectra of ternary mixtures of crack with caffeine and sodium carbonate and crack with lidocaine and sodium carbonate were obtained. These spectra were used for building multivariate models based on partial least squares (PLS) for determining the composition of the samples quantitatively. High correlation coefficients (r > 0.98) and small cross‐validation prediction errors (< 6%) were obtained for both Raman and FTIR spectral models. The results showed that PLS regression enables crack quantification in ternary mixtures using both Raman and FTIR spectroscopy, allowing for quick medical intervention or determination of the cause of death in cases of cocaine toxicity.

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Section: Discussionmentioning

confidence: 99%

Section: Multivariate Analysismentioning

confidence: 99%

Section: Multivariate Analysismentioning

confidence: 99%

Section: Multivariate Analysismentioning

confidence: 99%

See 2 more Smart Citations

Quantification of cocaine in ternary mixtures using partial least squares regression applied to Raman and Fourier transform infrared spectroscopy

Penido

Pacheco

Novotny

et al. 2017

J Raman Spectroscopy

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Mid-Infrared Laser Spectroscopy Applications I: Detection of Traces of High Explosives on Reflective and Matte Substrates

Pacheco‐Londoño¹,

Castro-Suarez²,

Galán-Freyle³

et al. 2019

Infrared Spectroscopy - Principles, Advances, and Applications

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Mid-infrared (MIR) lasers have revolutionized infrared vibrational spectroscopy, converting an already dominant spectroscopic analysis technique into an even more powerful, easier to use, and quicker turnaround cadre of versatile spectroscopic tools. A selection of applications, revisited under the umbrella of MIR laserbased properties, very high brightness, collimated beams, polarized sources, highly monochromatic tunable sources, and coherent sources, is included. Applications discussed concern enhanced detection, discrimination, and quantification of high explosives (HEs). From reflectance measurements of chemical residues on highly reflective metallic substrates to reflectance measurements of HEs deposited on non-reflective, matte substrates is discussed. Coupling with multivariate analyses (MVA) techniques of Chemometrics allowed near trace detection of HEs, with sharp discrimination from highly MIR absorbing substrates.

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Multivariate Analysis in Vibrational Spectroscopy of Highly Energetic Materials and Chemical Warfare Agents Simulants

Cited by 2 publications

References 39 publications

Quantification of cocaine in ternary mixtures using partial least squares regression applied to Raman and Fourier transform infrared spectroscopy

Quantification of cocaine in ternary mixtures using partial least squares regression applied to Raman and Fourier transform infrared spectroscopy

Mid-Infrared Laser Spectroscopy Applications I: Detection of Traces of High Explosives on Reflective and Matte Substrates

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