Surface-enhanced Raman spectroscopy for rapid identification and quantification of Flibanserin in different kinds of wine

Zirconium-based hydrophobic-MOFs as innovative electrode modifiers for flibanserin determination: Exploring the electrooxidation mechanism using a comprehensive spectroelectrochemical study

Three different types of Zr-based MOFs derived from benzene dicarboxylic acid (BDC) and naphthalene dicarboxylic acid as organic linkers (ZrBDC, 2,6-ZrNDC, and 1,4-ZrNDC) were synthesized. They were characterized using X-ray diffraction analysis (XRD), X-ray photoelectron spectroscopy (XPS), Fourier-transform IR spectroscopy (FT-IR), and Transmission electron microscopy (TEM). Their hydrophilic/hydrophobic nature was investigated via contact angle measurements; ZrBDC MOF was hydrophilic and the other two (ZrNDC) MOFs were hydrophobic. The three MOFs were combined with MWCNTs as electrode modifiers for the determination of a hydrophobic analyte, flibanserin (FLB), as a proof-of-concept analyte. Under the optimized experimental conditions, a significant enhancement in the oxidation peak current of FLB was observed when utilizing 2,6-ZrNDC and 1,4-ZrNDC, being the highest when using 1,4-ZrNDC. Furthermore, a thorough investigation of the complex oxidation pathway of FLB was performed by carrying out simultaneous spectroelectrochemical measurements. Based on the obtained results, it was verified that the piperazine moiety of FLB is the primary site for electrochemical oxidation. The fabricated sensor based on 1,4-ZrNDC/MW/CPE showed an oxidation peak of FLB at 0.8 V vs Ag/AgCl. Moreover, it showed excellent linearity for the determination of FLB in the range 0.05 to 0.80 μmol L-1 with a correlation coefficient (r) = 0.9973 and limit of detection of 3.0 nmol L-1. The applicability of the developed approach was demonstrated by determination of FLB in pharmaceutical tablets and human urine samples with acceptable repeatability (% RSD values were below 1.9% and 2.1%, respectively) and reasonable recovery values (ranged between 97 and 103% for pharmaceutical tablets and between 96 and 102% for human urine samples). The outcomes of the suggested methodology can be utilized for the determination of other hydrophobic compounds of pharmaceutical or biological interest with the aim of achieving low detection limits of these compounds in various matrices.

Quantitative analysis of key components in Qingke beer brewing process by multispectral analysis combined with chemometrics

In order to monitor the Qingke beer brewing process in real time, this paper presents an analytical method for predicting the content of key components in the wort during the mashing and boiling stages using multi-spectroscopy combined with chemometrics. The results showed that the Neural Networks (NN) model based on Raman spectroscopy (RPD = 3.9727) and the NN model based on NIR spectroscopy (RPD = 5.1952) had the best prediction performance for the reducing sugar content in the mashing and boiling stages; The partial least Squares (PLS) model based on Raman spectroscopy (RPD = 2.7301) and the NN model based on Raman spectroscopy (RPD = 4.3892) predicted the content of free amino nitrogen best; The PLS model based on UV-Vis spectroscopy (RPD = 4.0412) and the NN model based on Raman spectroscopy (RPD = 4.0540) are most suitable for the quantitative analysis of total phenols. The results can be used as a guide for real-time control of wort quality in industrial production.

Solid chip-based detection of trace morphine in solutions via portable surface-enhanced Raman spectroscopy

The accurate, sensitive and portable detection of morphine is important to handle judicial cases, but remains to be a great challenge. In this work, a flexible route is presented for the accurate identification and efficient detection of trace morphine in solutions based on surface-enhanced Raman spectroscopy (SERS) and a solid substrate/chip. A gold-coated jagged silicon nanoarray (Au-JSiNA) is designed and prepared via Si-based polystyrene colloidal template-reactive ion etching and sputtering deposition of Au. Such Au-JSiNA has three-dimensional nanostructure with good structural uniformity, high SERS activity and hydrophobic surface. Adopting this Au-JSiNA as SERS chip, trace morphine in solutions could be detected and identified in both dropping and soaking ways, and the limit of detection is below 10-4 mg/mL. Importantly, such chip is especially suitable for the detection of trace morphine in aqueous solutions and even domestic sewage. The good SERS performance is attributed to the high-density nanotips and nanogaps on this chip as well as its hydrophobic surface. Additionally, the appropriate surface modification of this Au-JSiNA chip with 3-mercapto-1-propanol or 3-mercaptopropionic acid/1-(3-dimethylaminopropyl)-3-ethylcarbodiimide can further increase its SERS performances to morphine. This work provides a facile route and practical solid chip for SERS detection of trace morphine in solutions, which is significant to develop the portable and reliable instruments for on-site analysis of drugs in solutions.

Antiepileptic drug concentration detection based on Raman spectroscopy and an improved snake optimization-convolutional neural network algorithm

A method for measurement of antiepileptic drug concentrations based on Raman spectroscopy and an optimization algorithm for mathematical models are proposed and investigated. This study uses Raman spectroscopy to measure mixed antiepileptic drugs, and an Improved Snake Optimization (ISO)-Convolutional Neural Network (CNN) algorithm is proposed. Raman spectroscopy is widely used in the identification of pharmaceutical ingredients due to its sharp peaks, no pre-treatment of samples and non-destructive detection. To analyze the spectral data precisely, a machine learning method is used in this paper. The ISO algorithm is an improved intelligent swarm algorithm in which the method of generating random solutions is improved, which can ensure that a comprehensive local search of the model is performed, the global search capability is maintained at a later stage, and the convergence speed is accelerated. In this study, 360 groups of oxcarbazepine, carbamazepine, and lamotrigine drug mixtures are measured using Raman spectroscopy, and the raw spectral data after pre-processing are trained and evaluated using ISO-CNN algorithms, and the results are compared and analyzed with those obtained from other algorithms such as the Northern Goshawk Optimization algorithm, Chameleon Swarm Algorithm, and White Shark Optimizer algorithm. The results show that the best ISO-CNN algorithm training is achieved for oxcarbazepine, with a determination coefficient and root mean square error of 0.99378 and 0.0295 for the validation set, and 0.99627 and 0.0278 for the test set. The overall results suggest that Raman spectroscopy combined with machine learning algorithms can be a potential tool for drug concentration prediction.

Nitroaromatic explosives' detection and quantification using an attention-based transformer on surface-enhanced Raman spectroscopy maps

Rapidly and accurately detecting and quantifying the concentrations of nitroaromatic explosives is critical for public health and security. Among existing approaches, explosives' detection with Surface-Enhanced Raman Spectroscopy (SERS) has received considerable attention due to its high sensitivity. Typically, a preprocessed single spectrum that is the average of the entire or a selected subset of a SERS map is used to train various machine learning models for detection and quantification. Designing an appropriate averaging and preprocessing procedure for SERS maps across different concentrations is time-consuming and computationally costly, and the averaging of spectra may lead to the loss of crucial spectral information. We propose an attention-based vision transformer neural network for nitroaromatic explosives' detection and quantification that takes raw SERS maps as the input without any preprocessing. We produce two novel SERS datasets, 2,4-dinitrophenols (DNP) and picric acid (PA), and one benchmark SERS dataset, 4-nitrobenzenethiol (4-NBT), which have repeated measurements down to concentrations of 1 nM to illustrate the detection limit. We experimentally show that our approach outperforms or is on par with the existing methods in terms of detection and concentration prediction accuracy. With the produced attention maps, we can further identify the regions with a higher signal-to-noise ratio in the SERS maps. Based on our findings, the molecule of interest detection and concentration prediction using raw SERS maps is a promising alternative to existing approaches.

Deep learning approach to overcome signal fluctuations in SERS for efficient On-Site trace explosives detection

Surface-enhanced Raman spectroscopy (SERS) is an improved Raman spectroscopy technique to identify the analyte under study uniquely. At the laboratory scale, SERS has realised a huge potential to detect trace analytes with promising applications across multiple disciplines. However, onsite detection with SERS is still limited, given the unwanted glitches of signal reliability and blinking. SERS has inherent signal fluctuations due to multiple factors such as analyte adsorption, inhomogeneous distribution of hotspots, molecule orientation etc. making it a stochastic process. Given these signal fluctuations, validating a signal as a representation of the analyte often relies on an expert's knowledge. Here we present a neural network-aided SERS model (NNAS) without expert interference to efficiently identify reliable SERS spectra of trace explosives (tetryl and picric acid) and a dye molecule (crystal violet). The model uses the signal-to-noise ratio approach to label the spectra as representative (RS) and non-representative (NRS), eliminating the reliability of the expert. Further, experimental conditions were systematically varied to simulate general variations in SERS instrumentation, and a deep-learning model was trained. The model has been validated with a validation set followed by out-of-sample testing with an accuracy of 98% for all the analytes. We believe this model can efficiently bridge the gap between laboratory and on-site detection using SERS.

Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends

Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, and microbiology. Although SERS is often limited by the lack of selectivity in the analysis of samples with complex matrices, the use of multivariate statistics and mathematical tools has been demonstrated to be an efficient strategy to circumvent this issue. Importantly, since the rapid development of artificial intelligence has been promoting the implementation of a wide variety of advanced multivariate methods in SERS, a discussion about the extent of their synergy and possible standardization becomes necessary. This critical review comprises the principles, advantages, and limitations of coupling SERS with chemometrics and machine learning for both qualitative and quantitative analytical applications. Recent advances and trends in combining SERS with uncommonly used but powerful data analysis tools are also discussed. Finally, a section on benchmarking and tips for selecting the suitable chemometric/machine learning method is included. We believe this will help to move SERS from an alternative detection strategy to a general analytical technique for real-life applications.

Study on the classification and identification of various carbonate and sulfate mineral medicines based on Raman spectroscopy combined with PCA-SVM algorithm

The efficacy of mineral medicines varies greatly between different origins. Therefore, investigating a method to quickly identify similar mineral medicines is meaningful. In this paper, a visual classification and identification model of Raman spectroscopy combined with principal component analysis (PCA) and support vector machine (SVM) algorithms was developed to rapidly classify and identify carbonate and sulfate mineral medicines. The results reveal that although the Raman spectra are too similar to distinguish by naked eye, the PCA-SVM algorithm can perform accurate classification and identification, and its accuracy, precision, recall and F1-score parameters all reach 100%. The proposed method is rapid, accurate, nondestructive, convenient, portable, and low cost, and has important application value for the classification, identification and quality supervision of various carbonate and sulfate mineral medicines.

Emphasis on the incorporation of Tropaeolin OO dye and silver nanoparticles for voltammetric estimation of flibanserin in bulk form, tablets and human plasma

A novel electrochemical sensor based on the electro-deposition of silver nanoparticles (AgNPs) on Tropaeolin OO (poly-TO) layers over pencil graphite electrode (PGE) surface was fabricated for the first time for voltammetric determination of flibanserin (FBS); a drug enhances female sexual performance. Further characterization studies using cyclic voltammetry (CV), square wave voltammetry (SWV), electrochemical impedance spectroscopy (EIS) and scanning electron microscopy (SEM) were conducted. The AgNPs synergistic effect on poly-TO layers facilitates the FBS electro-oxidation in phosphate buffer solution (pH 6.0) and its determination in bulk form, tablets and in human plasma. Following ICH guidelines, validation of the proposed SWV method for FBS analysis was successfully achieved using the fabricated sensor (AgNPs@poly-TO/PGE). Under the optimal instrumental and experimental conditions, the anodic oxidation peak current was directly proportional to FBS concentration in the range from 0.1 to 8.5 μmol L^-1 with low detection and quantitation limits (0.0286 and 0.0867 μmol L^-1, respectively). High sensitivity, selectivity as well as easiness of fabrication are the main advantages of the modified sensor.

Recent Developments in Surface-Enhanced Raman Spectroscopy and Its Application in Food Analysis: Alcoholic Beverages as an Example

Surface-enhanced Raman spectroscopy (SERS) is an emerging technology that combines Raman spectroscopy and nanotechnology with great potential. This technology can accurately characterize molecular adsorption behavior and molecular structure. Moreover, it can provide rapid and sensitive detection of molecules and trace substances. In practical application, SERS has the advantages of portability, no need for sample pretreatment, rapid analysis, high sensitivity, and ‘fingerprint’ recognition. Thus, it has great potential in food safety detection. Alcoholic beverages have a long history of production in the world. Currently, a variety of popular products have been developed. With the continuous development of the alcoholic beverage industry, simple, on-site, and sensitive detection methods are necessary. In this paper, the basic principle, development history, and research progress of SERS are summarized. In view of the chemical composition, the beneficial and toxic components of alcoholic beverages and the practical application of SERS in alcoholic beverage analysis are reviewed. The feasibility and future development of SERS are also summarized and prospected. This review provides data and reference for the future development of SERS technology and its application in food analysis.

Machine learning for rapid quantification of trace analyte molecules using SERS and flexible plasmonic paper substrates

Given the intrinsic nature of low reproducibility and signal blinking in the surface enhanced Raman scattering (SERS) technique, especially while detecting trace/ultra-trace amounts, it remains a major challenge to quantify the analyte under study. Here we present a simple and economically viable, flexible hydrophobic plasmonic filter paper-based SERS substrate for the quantification of two trace analytes [crystal violet (CV) and picric acid (PA)] using machine learning techniques and SERS data. The wettability of the substrate was modified with an easy and low-cost technique of coating it with silicone oil. Gold nanoparticles were synthesized using a femtosecond laser ablation in water technique. The prepared nanoparticles were characterized using UV, TEM, and SEM techniques and subsequently loaded onto filter papers before using them for SERS studies. We have considered the SERS intensities of the analytes at different concentrations with over 900 spectra to train the model. Principal component analysis (PCA) was used to reduce the dimensionality and, hence, the complexity of the model. Furthermore, support vector regression was used to quantify the analyte molecules and we achieved an R² error of 0.9629 for CV and 0.9472 for PA. In conjunction with a portable Raman spectrometer and a computation time of less than <10 s, we believe that this is an affordable and rapid method for quantification of analytes using the SERS technique.

Development and validation of a novel evaporation setup-assisted TLC method with fluorescence detection for determination of flibanserin in pharmaceutical and biological samples

A specific and sensitive thin layer chromatographic method coupled with fluorescence detection for determination of flibanserin (FLN) that treats woman hypoactive sexual desire disorder was developed. The proposed method depends on the enhancement of FLN native fluorescence intensity via the exposure of the developed TLC plate to concentrated hydrochloric acid vapors. Herein, an evaporation setup needed for HCl vapors exposure step was designed for the first time to ensure a uniform distribution of the vapors throughout the developed bands on the plate. Chloroform: methanol (9.5: 0.5, v/v) was the optimum mobile phase that gave a compact band (R_f= 0.44 ± 0.02) using TLC aluminium plates precoated with silica gel G 60F₂₅₄ as a stationary phase. After exposure of the developed TLC plate to HCl vapors, the FLN bands emission intensities were measured after excitation at 275 nm. Conferring ICH guidelines, the linearity range was 20.0 - 1500.0 ng/band with a good linear relationship (r= 0.9998). Detection and quantitation limits were 5.12 and 15.50 ng/band, respectively. Also, the method was validated for accuracy, precision, robustness, specificity and selectivity. Statistical analysis verified the suitability of the proposed method for estimation of FLN in tablets and in human plasma with acceptable recoveries (98.07-101.45%).

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).