Alan G. Ryder

Quantitative analysis of cocaine in solid mixtures using Raman spectroscopy and chemometric methods

Near-infrared (785 nm) excitation was used to obtain Raman spectra from a series of 33 solid mixtures containing cocaine, caffeine and glucose (9.8–80.6% by weight cocaine), which were then analysed using chemometric methods. Principal component analysis of the data was employed to ascertain what factors influenced the spectral variation across the concentration range. It was found that 98% of the spectral variation was accounted for by three principal components. Analysis of the score and loadings plots for these components showed that the samples can be clearly classified on the basis of cocaine concentration. Discrimination on the basis of caffeine and glucose concentrations was also possible. Quantitative calibration models were generated using partial least-squares algorithms which predicted the concentration of cocaine in the solid mixtures containing caffeine and glucose from the Raman spectrum with a root mean standard error of prediction (RMSEP) of 4.1%. Caffeine and glucose concentrations were estimated with RMSEPs of 5.2 and 6.6%, respectively. These measurements demonstrate the feasibility of using near-IR Raman spectroscopy for rapid quantitative characterization of illegal narcotics. Copyright

The effect of principal component analysis on machine learning accuracy with high-dimensional spectral data

This paper presents the results of an investigation into the use of machine learning methods for the identification of narcotics from Raman spectra. The classification of spectral data and other high-dimensional data, such as images, gene-expression data and spectral data, poses an interesting challenge to machine learning, as the presence of high numbers of redundant or highly correlated attributes can seriously degrade classification accuracy. This paper investigates the use of principal component analysis (PCA) to reduce high-dimensional spectral data and to improve the predictive performance of some well-known machine learning methods. Experiments are carried out on a high-dimensional spectral dataset. These experiments employ the NIPALS (Non-Linear Iterative Partial Least Squares) PCA method, a method that has been used in the field of chemometrics for spectral classification, and is a more efficient alternative than the widely used eigenvector decomposition approach. The experiments show that the use of this PCA method can improve the performance of machine learning in the classification of high-dimensional data.

Comparison of Derivative Preprocessing and Automated Polynomial Baseline Correction Method for Classification and Quantification of Narcotics in Solid Mixtures

This work offers a real-world comparison of derivative preprocessing and a new polynomial method described by Lieber and Mahadevan-Jansen (LMJ) for baseline correction of Raman spectra with widely varying backgrounds. This comparison is based on their outcomes in factor analysis, analyte discrimination, and quantification. Both correction methods are applied to a Raman spectra data set taken from 85 solid samples of illegal narcotics diluted with various materials. It is found that neither approach outperforms the other, as they give similar principal component analysis (PCA) models and quantification errors: cocaine and heroin show cross-validation errors of approximately 8%, while MDMA is quantified to a cross-validation error of approximately 3–4%. The LMJ method does offer several other advantages, the most significant being the retention of original peak shapes after the correction, which simplifies the interpretation of the preprocessed spectra. The LMJ method is therefore recommended for use as a baseline correction method in future research with Raman spectroscopy.

Identifications and quantitative measurements of narcotics in solid mixtures using near-IR Raman spectroscopy and multivariate analysis

Raman spectroscopy offers the potential for the identification of illegal narcotics in seconds by inelastic scattering of light from molecular vibrations. In this study cocaine, heroin, and MDMA were analyzed using near-IR (785 nm excitation) micro-Raman spectroscopy. Narcotics were dispersed in solid dilutants of different concentrations by weight. The dilutants investigated were foodstuffs (flour, baby milk formula), sugars (glucose, lactose, maltose, mannitol), and inorganic materials (Talc powder, NaHCO3, MgSO4·7H2O). In most cases it was possible to detect the presence of drugs at levels down to ∼10% by weight. The detection sensitivity of the Raman technique was found to be dependent on a number of factors such as the scattering cross-sections of drug and dilutant, fluorescence of matrix or drug, complexity of dilutant Raman spectrum, and spectrometer resolution. Raman spectra from a series of 20 mixtures of cocaine and glucose (0–100% by weight cocaine) were collected and analyzed using multivariate analysis methods. An accurate prediction model was generated using a Partial Least Squares (PLS) algorithm that can predict the concentration of cocaine in solid glucose from a single Raman spectrum with a root mean standard error of prediction of 2.3%.

Rapid characterization and quality control of complex cell culture media solutions using raman spectroscopy and chemometrics

The use of Raman spectroscopy coupled with chemometrics for the rapid identification, characterization, and quality assessment of complex cell culture media components used for industrial mammalian cell culture was investigated. Raman spectroscopy offers significant advantages for the analysis of complex, aqueous‐based materials used in biotechnology because there is no need for sample preparation and water is a weak Raman scatterer. We demonstrate the efficacy of the method for the routine analysis of dilute aqueous solution of five different chemically defined (CD) commercial media components used in a Chinese Hamster Ovary (CHO) cell manufacturing process for recombinant proteins.The chemometric processing of the Raman spectral data is the key factor in developing robust methods. Here, we discuss the optimum methods for eliminating baseline drift, background fluctuations, and other instrumentation artifacts to generate reproducible spectral data. Principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) were then employed in the development of a robust routine for both identification and quality evaluation of the five different media components. These methods have the potential to be extremely useful in an industrial context for “in‐house” sample handling, tracking, and quality control. Biotechnol. Bioeng. 2010;107: 290–301.

Classification of narcotics in solid mixtures using principal component analysis and Raman spectroscopy

Eighty-five solid samples consisting of illegal narcotics diluted with several different materials were analyzed by near-infrared (785 nm excitation) Raman spectroscopy. Principal Component Analysis (PCA) was employed to classify the samples according to narcotic type. The best sample discrimination was obtained by using the first derivative of the Raman spectra. Furthermore, restricting the spectral variables for PCA to 2 or 3% of the original spectral data according to the most intense peaks in the Raman spectrum of the pure narcotic resulted in a rapid discrimination method for classifying samples according to narcotic type. This method allows for the easy discrimination between cocaine, heroin, and MDMA mixtures even when the Raman spectra are complex or very similar. This approach of restricting the spectral variables also decreases the computational time by a factor of 30 (compared to the complete spectrum), making the methodology attractive for rapid automatic classification and identification of suspect materials.

Assessing the Maturity of Crude Petroleum Oils Using Total Synchronous Fluorescence Scan Spectra

There have been many applications of fluorescence methods for the analysis of crude petroleum oils down through the years. However, none of these studies has yielded a robust qualitative or quantitative method for quantifying the chemical composition, or assessing the maturity of crude oils. Simple fluorescence parameters such as lifetime, intensity, and intensity ratios do not correlate well with chemical composition particularly for medium weight crude oils [A. G. Ryder, T. J. Glynn, and M. Feely (2003). Proc. SPIE-Int. Soc. Opt. Eng.4876, 1188–1195.]. A better approach may be to use the Total Synchronous Fluorescence Scan (TSFS) method to fully interrogate the complex chemical composition of the oils [D. Patra and A. K. Mishra (2002). Anal. Bioanal. Chem.373, 304–309.]. We present TSFS spectra from 18 crude petroleum oils of varying composition, sourced from around the world. The TSFS plots of these oils are very complex, with the contours being spread over the full 250–700 nm wavelength range (λex) and 40–200 nm wavelength interval (Δλ) sampled. The 3-D contour maps tend to two contour concentrations one at λem < 300 nm, Δλ = 120–200 nm, and a second near λex ∼ 380–400 nm, Δλ = 40–60 nm. The first feature represents fluorescence emission originating mainly from energy transfer processes with the second, longer wavelength feature originating from fluorescence emission generated by a higher proportion of direct excitation as opposed to emission resulting from energy transfer. The topography of the 3D contour plots is therefore influenced by the balance between energy transfer and direct fluorescence emission, which is governed by the chemical composition of the crude oils. We discuss how the gross chemical composition affects TSFS spectra and how TSFS can be used to assess oil maturity with a view to developing quantitative methods.

Characterization of crude oils using fluorescence lifetime data

The average fluorescence lifetimes of nine North Sea crude oils with API gravities of between 20 and 51 were measured using a modular, filter based, instrument developed in-house. Two pulsed light emitting diode (LED) excitation sources (460 and 510 nm) were used to excite fluorescence, the lifetime of which was measured at a range of emission wavelengths. Fluorescence lifetimes were found to vary from 1.8 to 8.2 ns with confidence intervals of +/- 0.11 ns. The average lifetimes at all emission wavelengths were linearly correlated with API gravity and with aromatic concentration with the best results being obtained with the 460 nm excitation source. Predictive models with an accuracy of +/- 7.6 API degrees were generated using partial least-squares methods from average fluorescence lifetimes measured at an emission wavelength of 500 nm using 460 nm excitation. A better correlation was found between the aromatic concentration of the oils and the ratio of the average fluorescence lifetimes at measured at 550 and 650 nm using 460 nm excitation. This led to a quantitative model with an accuracy of +/- 5.4% for aromatic concentration.

A comparative study of the use of powder X-ray diffraction, Raman and near infrared spectroscopy for quantification of binary polymorphic mixtures of piracetam

Diffraction and spectroscopic methods were evaluated for quantitative analysis of binary powder mixtures of FII(6.403) and FIII(6.525) piracetam. The two polymorphs of piracetam could be distinguished using powder X-ray diffraction (PXRD), Raman and near-infrared (NIR) spectroscopy. The results demonstrated that Raman and NIR spectroscopy are most suitable for quantitative analysis of this polymorphic mixture. When the spectra are treated with the combination of multiplicative scatter correction (MSC) and second derivative data pretreatments, the partial least squared (PLS) regression model gave a root mean square error of calibration (RMSEC) of 0.94 and 0.99%, respectively. FIII(6.525) demonstrated some preferred orientation in PXRD analysis, making PXRD the least preferred method of quantification.

Quantitative polymorph contaminant analysis in tablets using Raman and near infra-red spectroscopies

The detection and quantification of alternate polymorphs of active pharmaceutical ingredients (APIs), particularly at low concentrations is a key issue for the manufacture and analysis of solid-state formulations. Each polymorph can possess unique physical and chemical properties which in turn can directly affect factors such as solubility and bioavailability. Near infra-red (NIR) and Raman spectroscopies can be used for the rapid characterisation and quantification of polymorphs in solid samples. In this study we have generated a model tablet system with two excipients and a 10% API concentration, where the API is a mixture of the FII and FIII polymorphs of piracetam. Using transmission Raman spectroscopy (TRS) and NIR spectroscopy it was possible to detect FII polymorph contamination in these model tablets with limits of detection (LODs) of 0.6 and 0.7%, respectively, with respect to the total tablet weight (or ∼6-7% of the API content). The TRS method is the superior method because of the speed of analysis (∼6s per sample), better sampling statistics, and because the sharper, more resolved bands in the Raman spectra allowed for easier interpretation of the spectral data. In addition the TRS used here provides facile access to the low frequency wavenumber region for analysis of solid-state lattice modes.