Evaluation and classification of fentanyl-related compounds using EC-SERS and machine learning

Multiple analytical techniques for the screening of fentanyl-related compounds exist. High discriminatory methods such as GC-MS and LC-MS are expensive, time-consuming, and less amenable to onsite analysis. Raman spectroscopy provides a rapid, inexpensive alternative. Raman variants such as electrochemical surface-enhanced Raman scattering (EC-SERS) can provide signal enhancements with 1010 magnitudes, allowing for the detection of low-concentration analytes, otherwise undetected using conventional Raman. Library search algorithms embedded in instruments utilizing SERS may suffer from accuracy when multicomponent mixtures involving fentanyl derivatives are analyzed. The complexing of machine learning techniques to Raman spectra demonstrates an improvement in the discrimination of drugs even when present in multicomponent mixtures of various ratios. Additionally, these algorithms are capable of identifying spectral features difficult to detect by manual comparisons. Therefore, the goal of this study was to evaluate fentanyl-related compounds and other drugs of abuse using EC-SERS and to process the acquired data using machine learning-convolutional neural networks (CNN). The CNN was created using Keras v 2.4.0 with Tensorflow v 2.9.1 backend. In-house binary mixtures and authentic adjudicated case samples were used to evaluate the created machine-learning models. The overall accuracy of the model was 98.4 ± 0.1% after 10-fold cross-validation. The correct identification for the in-house binary mixtures was 92%, while the authentic case samples were 85%. The high accuracies achieved in this study demonstrate the advantage of using machine learning to process spectral data when screening seized drug materials comprised of multiple components.

Extreme Point Sort Transformation Combined With a Long Short-Term Memory Network Algorithm for the Raman-Based Identification of Therapeutic Monoclonal Antibodies

Therapeutic monoclonal antibodies (mAbs) are a new generation of protein-based medicines that are usually expensive and thus represent a target for counterfeiters. In the present study, a method based on Raman spectroscopy that combined extreme point sort transformation with a long short-term memory (LSTM) network algorithm was presented for the identification of therapeutic mAbs. A total of 15 therapeutic mAbs were used in this study. An in-house Raman spectrum dataset for model training was created with 1,350 spectra. The characteristic region of the Raman spectrum was reduced in dimension and then transformed through an extreme point sort transformation into a sequence array, which was fitted for the LSTM network. The characteristic array was extracted from the sequence array using a well-trained LSTM network and then compared with standard spectra for identification. To demonstrate whether the present algorithm was better, ThermoFisher OMNIC 8.3 software (Thermo Fisher Scientific Inc., U.S.) with two matching modes was selected for comparison. Finally, the present method was successfully applied to identify 30 samples, including 15 therapeutic mAbs and 15 other injections. The characteristic region was selected from 100 to 1800 cm⁻¹ of the full spectrum. The optimized dimensional values were set from 35 to 53, and the threshold value range was from 0.97 to 0.99 for 15 therapeutic mAbs. The results of the robustness test indicated that the present method had good robustness against spectral peak drift, random noise and fluorescence interference from the measurement. The areas under the curve (AUC) values of the present method that were analysed on the full spectrum and analysed on the characteristic region by the OMNIC 8.3 software’s built-in method were 1.000, 0.678, and 0.613, respectively. The similarity scores for 15 therapeutic mAbs using OMNIC 8.3 software in all groups compared with that of the relative present algorithm group had extremely remarkable differences (p < 0.001). The results suggested that the extreme point sort transformation combined with the LSTM network algorithm enabled the characteristic extraction of the therapeutic mAb Raman spectrum. The present method is a proposed solution to rapidly identify therapeutic mAbs.

Through-container quantitative analysis of hand sanitizers using spatially offset Raman spectroscopy

The COVID-19 pandemic created an increased demand for hygiene supplies such as hand sanitizers. In response, a large number of new domestic or imported hand sanitizer products entered the US market. Some of these products were later found to be out of specification. Here, to quickly assess the quality of the hand sanitizer products, a quantitative, through-container screening method was developed for rapid and non-destructive screening. Using spatially offset Raman spectroscopy (SORS) and support vector regression (SVR), active ingredients (e.g., type of alcohol) of 173 commercial and in-house products were identified and quantified regardless of the container material or opacity. Alcohol content in hand sanitizer formulations were predicted with high accuracy [Formula: see text] using SVR and [Formula: see text] of the substandard test samples were identified. In sum, a SORS-SVR method was developed and used for testing medical countermeasures used against COVID-19, demonstrating a potential for high-volume testing during public health threats.

Quantitative analysis of excipient dominated drug formulations by Raman spectroscopy combined with deep learning

Owing to the growing interest in the application of Raman spectroscopy for quantitative purposes in solid pharmaceutical preparations, an article on the identification of compositions in excipient dominated drugs based on Raman spectra is presented. We proposed label-free Raman spectroscopy in conjunction with deep learning (DL) and non-negative least squares (NNLS) as a solution to overcome the drug fast screening bottleneck, which is not only a great challenge to drug administration, but also a major scientific challenge linked to falsified and/or substandard medicines. The result showed that Raman spectroscopy remains a cost effective, rapid, and user-friendly method, which if combined with DL and NNLS leads to fast implantation in the identification of lactose dominated drug (LDD) formulations. Meanwhile, Raman spectroscopy with the peak matching method allows a visual interpretation of the spectral signature (presence or absence of active pharmaceutical ingredients (APIs) and low content APIs).

Adaptive Hit-Quality Index for Raman Spectrum Identification

The recognition capability of the identification system using Raman spectroscopy is increasing with the demands in the field. Among the various approaches that determine the identity of a target, signal correlation using a moving window is one of the most effective and intuitive methods. In this paper, we report a new correlation method that is robust to spectral intensity variations. Using the peak distribution of a given spectrum, this method adaptively determines meaningful spectral regions for the identification target. Three commercial Raman spectrometer and a 14 033 library were included in the study, which was used for a library-based chemical discrimination test and mixed material analysis experiments. According to the identification experimental results, the proposed method correctly identified all of the spectra and maintained a mean correlation score above 0.95 while maintaining the correlation score of nontarget materials as low as possible.

Portable and benchtop Raman spectrometers coupled to cluster analysis to identify quinine sulfate polymorphs in solid dosage forms and antimalarial drug quantification in solution by AuNPs-SERS with MCR-ALS

This paper proposes for the first time: (a) a qualitative analytical method based on portable and benchtop backscattering Raman spectrometers coupled to hierarchical cluster analysis (HCA) and multivariate curve resolution - alternating least-squares (MCR-ALS) to identify two polymorphs of antimalarial quinine sulfate in commercial pharmaceutical tablets in their intact forms and (b) a quantitative analytical method based on gold nanoparticles (AuNPs) as active substrates for surface-enhanced Raman scattering (SERS) in combination with MCR-ALS to quantify quinine sulfate in commercial pharmaceutical tablets in solution. The pure concentration and spectral profiles recovered by MCR-ALS proved that both formulations present different polymorphs. These results were also confirmed by two clusters observed in the HCA model, according to their similarities within and among the samples that provided useful information about the homogeneity of different pharmaceutical manufacturing processes. AuNPs-SERS coupled to MCR-ALS was able to quantify quinine sulfate in the calibration range from 150.00 to 200.00 ng mL-1 even with the strong overlapping spectral profile of the background SERS signal, proving that it is a powerful ultrahigh sensitivity analytical method. This reduced linearity was validated throughout a large calibration range from 25.00 to 175.00 μg mL-1 used in a reference analytical method based on high performance liquid chromatography with a diode array detector (HPLC-DAD) coupled to MCR-ALS for analytical validation purposes, even in the presence of a coeluted compound. The analytical methods developed herein are fast, because second-order chromatographic data and first-order SERS spectroscopic data were obtained in less than 6 and 2 min, respectively. Concentrations of quinine sulfate were estimated with low root mean square error of prediction (RMSEP) values and a low relative error of prediction (REP%) in the range 1.8-4.5%.

Forensics: evidence examination via Raman spectroscopy

Forensic science can be broadly defined as the application of any of the scientific method to solving a crime. Within forensic science there are many different disciplines, however, for the majority of them, five main concepts shape the nature of forensic examination: transfer, identification, classification/individualization, association, and reconstruction. The concepts of identification, classification/individualization, and association rely greatly on analytical chemistry techniques. It is, therefore, no stretch to see how one of the rising stars of analytical chemistry techniques, Raman spectroscopy, could be of use. Raman spectroscopy is known for needing a small amount of sample, being non-destructive, and very substance specific, all of which make it ideal for analyzing crime scene evidence. The purpose of this chapter is to show the state of new methods development for forensic applications based on Raman spectroscopy published between 2015 and 2017.

Peak window correlation method for drug screening using Raman spectroscopy

Modern portable and hand-held Raman spectrometers that recently have become widespread in drug quality screening have good reproducibility and are able to detect small concentrations of substances in mixtures of several components or distinguish compounds similar in structure and having minimal differences in spectrum with appropriate mathematical processing methods. Among other spectrum comparison approaches, the peak search at their location is the most important task of spectral imaging of the studied samples. In this work, the Raman spectra of liquid drugs involved in the governmental non-destructive quality screening program performed by 8 mobile laboratories equipped with Raman spectrometers with uncooled detector and a 532 nm laser were compared with reference sample spectra using the peak windows correlation (PWC) algorithm developed in this work by authors. The proposed method provides accurate identification, detection of composition changes, and presence of foreign components in drugs formulations even if their contribution to the overall signal is negligible. The spectral correlation method called hit-quality index (HQI) method conventionally used for such portable spectrometers was specified as comparative method.

Eliminating Non-linear Raman Shift Displacement Between Spectrometers via Moving Window Fast Fourier Transform Cross-Correlation

Obtaining consistent spectra by using different spectrometers is of critical importance to the fields that rely heavily on Raman spectroscopy. The quality of both qualitative and quantitative analysis depends on the stability of specific Raman peak shifts across instruments. Non-linear drifts in the Raman shifts can, however, introduce additional complexity in model building, potentially even rendering a model impractical. Fortunately, various types of shift correction methods can be applied in data preprocessing in order to address this problem. In this work, a moving window fast Fourier transform cross-correlation is developed to correct non-linear shifts for synchronization of spectra obtained from different Raman instruments. The performance of this method is demonstrated by using a series of Raman spectra of pharmaceuticals as well as comparing with data obtained by using an existing standard Raman shift scattering procedure. The results show that after the removal of shift displacements, the spectral consistency improves significantly, i.e., the spectral correlation coefficient of the two Raman instruments increased from 0.87 to 0.95. The developed standardization method has, to a certain extent, reduced instrumental systematic errors caused by measurement, while enhancing spectral compatibility and consistency through a simple and flexible moving window procedure.

Raman spectrum identification based on the correlation score using the weighted segmental hit quality index

In this paper, we consider a novel method for identification of Raman spectra recorded on different instruments with different wavelengths.