Quantitative determinations using portable Raman spectroscopy

Raman spectroscopy combined with partial least squares (PLS) based on hybrid spectral preprocessing and backward interval PLS (biPLS) for quantitative analysis of four PAHs in oil sludge

Polycyclic aromatic hydrocarbons (PAHs) contained in a large amount of oily sludge produced in petroleum and petrochemical production has become one of the main environmental protection concerns in the industry. The accurate determination of PAHs is of great significance in the field of petroleum geochemistry and environmental protection. In this study, Raman spectroscopy combined with partial least squares (PLS) based on different hybrid spectral preprocessing methods and variable selection strategies was proposed for quantitative analysis of phenanthrene, fluoranthrene, fluorene and naphthalene (Phe, Flt, Flu and Nap) in oil sludge. At first, PAHs in oily sludge was extracted by solid-liquid extraction with methanol as extractant, and Raman spectra of 21 oily sludge samples were collected by portable Raman spectrometer. And then, the influence of first derivative (D1st), wavelet transform (WT) and their hybrid spectral preprocessing on the predictive performance of the PLS calibration model was discussed. Thirdly, biPLS (backward interval partial least squares) was used to optimize the input variables before and after the hybrid spectral preprocessing methods, and the influence of biPLS and the hybrid spectral preprocessing sequence on the predictive performance of the PLS calibration model was discussed. Finally, the predictive performance of the PLS calibration model was optimized according to the results of leave-one-out cross-validation (LOOCV) method. The results show that the biPLS-D1st-WT-PLS calibration model established by using biPLS first to select the characteristic variables, followed by hybrid spectral preprocessing of the characteristic variables, has better prediction performance for Flt (determination coefficient of prediction (R2P) = 0.9987, and the mean relative error of prediction (MREP) = 0.0606). For Phe, Flu and Nap, the WT-biPLS-PLS calibration model has a better predictive effect (R2P are 0.9995, 0.9996 and 0.9983, and MREP are 0.0426, 0.0719 and 0.0497, respectively). In general, portable Raman spectroscopy combined with PLS calibration model based on different hybrid spectral preprocessing and variable selection strategies has achieved good prediction results for quantitative analysis of four PAHs in oily sludge. It is a new strategy to firstly select the characteristic variables of the original spectra, and secondly to preprocess the characteristic variables by the hybrid spectral preprocessing, which will provide a new idea for the establishment of quantitative analysis methods for PAHs in oily sludge.

Quantitative analysis of polycyclic aromatic hydrocarbons (PAHs) in water by surface-enhanced Raman spectroscopy (SERS) combined with Random Forest

Polycyclic aromatic hydrocarbons (PAHs) have strong carcinogenicity, teratogenicity, mutagenicity and other adverse effects on human beings. They are one of the most dangerous pollutants, which have attracted great attention in the past decades. In this work, aiming at the actual problems that water environment is polluted and human health is threatened by PAHs, surface enhanced Raman spectroscopy (SERS) combined with Random Forest (RF) calibration models were used to quantitative analysis of phenanthrene and fluoranthene in water. Firstly, the SERS data was collected after samples mixed with Ag NPs, after 31 PAHs samples were prepared. Secondly, it was discussed how spectral preprocessing integration strategies affect on the prediction performance of the RF calibration models. And then, the effect of mutual information (MI) variable selection method on the performance of RF calibration models was explored. Finally, the RF calibration models were established for phenanthrene and fluoranthene. For the prediction set, a lowest mean relative error (MRE) and a largest determination coefficient (R²) were obtained. For quantitative analysis of phenanthrene, the final prediction performance results show that R²_p is 0.9780, and MRE_p is 0.0369 based on the D1st-WT-RF calibration model. For fluoranthene, WT-D1st-MI-RF is a better calibration model, and corresponding to R²_p and MRE_p are 0.9770 and 0.0694, respectively. Hence, a rapid and accurate quantitative method of PAHs is established for the real-time detection of water environmental pollution, which is intended to provide new ideas and methods for the quantitative analysis of PAHs in water.

Raman Mapping-Based Reverse Engineering Facilitates Development of Sustained-Release Nifedipine Tablet

The development of generic preparations that are bioequivalent to a reference listed drug (RLD) is faced with challenges because some critical attributes of RLDs are commonly unknown to developers. In order to determine these attributes, Raman mapping-based reverse engineering in this study to analyze a model sustained-release tablet of nifedipine. The Raman mapping results indicate that the size and size distribution of nifedipine are critical to its release pattern and bioavailability. The tablets with a particle size of nifedipine comparable to that of a commercial product, Adalat^®-L, showed similar in vitro release profiles to the RLD. Moreover, a pharmacokinetic study in human volunteers proved the bioequivalence of the two preparations. In conclusion, Raman mapping-based reverse engineering has the potential to facilitate the development of generic preparations.

A Review of Portable High-Performance Liquid Chromatography: the Future of the Field?

There is a growing need for chemical analyses to be performed in the field, at the point of need. Tools and techniques often found in analytical chemistry laboratories are necessary in performing these analyses, yet have, historically, been unable to do so owing to their size, cost and complexity. Technical advances in miniaturisation and liquid chromatography are enabling the translation of these techniques out of the laboratory, and into the field. Here we examine the advances that are enabling portable liquid chromatography (LC). We explore the evolution of portable instrumentation from its inception to the most recent advances, highlighting the trends in the field and discussing the necessary criteria for developing in-field solutions. While instrumentation is becoming more capable it has yet to find adoption outside of research.

Quantitative Raman assays for on-site analysis of stockpiled drugs

We present a rapid Raman assay for on-site analysis of stockpiled drugs in aqueous solution. This approach was tested on Tamiflu (oseltamivir phosphate). Tamiflu is a drug approved by the FDA for treatment of influenza and is the most common antiviral included in stockpiles for use in the event of a national emergency. Rapid assays were performed on three concentrations (30, 45, and 75 mg) of oseltamivir using three different portable & handheld Raman instruments. PLS regression models were developed to establish a calibration curve and applied to the Tamiflu samples. Raman assay values were compared against the standard HPLC assay to demonstrate the viability of this approach, yielding an average assay value within 0.3% of that obtained from the HPLC analysis for the 35 different capsules analyzed. The Raman method demonstrates the potential for rapid screening of stockpiled pharmaceuticals on-site using portable Raman instrumentation and readily available consumables for sample preparation. In addition to routine screening to ensure product quality past the expiration date, this approach could also be used to assist in rapid deployment of such medications in the case of a national emergency.