Quantitative analysis of powder mixtures by Raman spectrometry: the influence of particle size and its correction

Developing robust food composition models: Strategies for handling temperature and packaging variations in dry-cured ham using near infrared spectrometry

Low-cost near infrared devices intended for consumers able to easily determine composition and quality of food products may boost adoption of sustainable healthy diets. However, predictive algorithms robust to external variations are needed. The aim of this work was to evaluate different data analysis strategies to develop robust predictive models for food composition when using spectrometric data subjected to external variations, specifically temperature and packaging material, acquired using low-cost sensors. Usefulness of global modelling (GM), Generalised least squares weighting (GLSW), Loading space standardisation (LSS), Multiplicative Effects Model (MEM) were explored, and the effect of samples heterogeneity evaluated. To do so, two low-cost handheld NIR-based devices with different spectral ranges and resolutions were used. The food matrix samples were obtained from different anatomical muscles of commercial dry-cured ham. Spectra were acquired on two types of packaging films at different temperatures to further explore the usefulness of global modelling (GM), generalised least squares weighting (GLSW), loading space standardisation (LSS), and multiplicative effects model (MEM) to retrieve these effects. Results show that the inherent food sample heterogeneity produces as much spectral variability as temperature and packaging materials. For temperature compensation, LSS did not decrease the predictive error caused by this factor probably due to the heterogeneity of the samples used. In contrast, the GLSW method decreased the predictive errors from 0.52% to 0.46% for salt and from 2.10% to 1.40% for water.. Only a slight effect of packaging was observed, and GM models were found to be the best strategy to compensate it, showing a decrease of bias from -1.35 to 0.012. The examined compensation strategies could facilitate the deployment of low-cost spectrometers for consumer use, as they offer an effective means to mitigate or eliminate variations from any source in the data that are unrelated to the properties of interest.

Calibration free approaches for rapid polymorph discrimination via low frequency (THz) Raman spectroscopy

Application of multivariate curve resolution to non-invasive Raman spectra has been investigated for rapid on-line analysis of crystallisation processes and high-throughput screening. Exploring quantification of mefenamic acid solid forms (form I, form II, and dimethylformamide solvate) from the Raman spectra indicated excellent agreement with off-line X-ray analysis.

Synthesis, Structural, and Raman Investigation of Lanthanide Nitride Powders (Ln = La, Ce, Nd, Sm, Gd, Tb, Dy, Er, Lu)

Lanthanide nitride (LnN) materials have garnered significant interest in recent years due to their promising potential as heterogeneous catalysts for green ammonia synthesis under low temperature and pressure reaction conditions. Here, we report on the synthesis of an extended series of lanthanide (Ln) nitride powders (Ln = lanthanum, cerium, neodymium, samarium, gadolinium, terbium, dysprosium, erbium, lutetium) and their structural and vibrational properties. Polycrystalline powders were fabricated using a ball milling mechanochemical process, and their structural properties were assessed by X-ray diffraction (XRD) and transmission electron microscopy (TEM). The experimental lattice constants deduced from XRD and TEM were compared with density functional theory-based calculated lattice constants using the Perdew-Burke-Ernzerhof exchange-correlation functional. We show that the calculated lattice constants are within 1-1.5% of the experimental values for the majority of the LnN species-a notable increase in accuracy over prior computational approaches. The frequencies of Raman scattering from the LO(Γ) phonon are reported across the series and compare well with published thin-film data on a smaller selection of the series. As expected, there is a linear relationship between the LO(Γ) phonon frequency and atomic number. Finally, we demonstrate that Raman spectroscopy can be used to detect the presence of a nanoscale oxide layer on the surface of ErN powders.

THz-TDS as a PAT tool for monitoring blend homogeneity in pharmaceutical manufacturing of solid oral dosage forms: A proof-of-concept study

The process analytical technology (PAT) framework is well established and integral to facilitate process understanding, enable a transition from batch to continuous manufacturing, and improve product quality. Near-infrared (NIR) spectroscopy has been established as a standard PAT tool for many process analytical challenges, including monitoring powder blend homogeneity. However, alternative technologies for monitoring powder blending are of interest due to the importance of the blending step in manufacturing solid oral dosage forms. Terahertz time-domain spectroscopy (THz-TDS) is therefore explored in this study as an alternative tool for monitoring blend homogeneity with the potential for endpoint control in a batch blending process. Powder blends of microcrystalline cellulose (MCC) and dibasic calcium phosphate dihydrate and blends of MCC and granulated α-lactose monohydrate were investigated non-invasively at various compositions using THz-TDS in transmission mode for acquiring spectra from samples enclosed in the blending container. It was found that attenuation- and phase-related parameters acquired with THz-TDS could reliably resolve physical changes related to the homogeneity of the blend. Further evaluations revealed that changes in the bulk density of the blend, in addition to the intrinsic optical properties of the materials, played a critical role in the observed trends for both systems. In contrast, the scattering contribution of the powder was mainly crucial for the attenuation-related parameter in blends with materials of high refractive indices. Finally, THz-TDS measurements were acquired throughout a blending process mimicking a continuous acquisition. The method could follow blending dynamics and resulted in reasonable predictive errors of the content of 0.5 - 2.5 %. Relative standard deviations for high content blends (20 %) were acceptable (3 - 7 %) whereas at low contents (5 %) significantly higher values (9 - 35 %) were found. Based on these findings, THz-TDS is a feasible PAT tool for monitoring blend homogeneity and controlling high content blend processes, although precision and accuracy is considered to improve with a more suitable interface.

Enhancing phase identification in waste-to-energy fly ashes: Role of Raman spectroscopy, background fluorescence, and photobleaching

Waste-to-Energy (WTE) facilities incinerate ∼11% (∼ 222 Mt) of global solid waste, generating bottom and fly ashes. Landfilling these ashes is costly, and risks releasing contaminants into the environment. Instead, using WTE ashes in secondary industrial applications can circumvent such environmental risks. However, their secondary use is restricted by their inconsistent mineralogy, which may vary due to fluctuating waste composition and combustion conditions. Therefore, there is a need for rapid and reliable monitoring of WTE fly ash mineralogy. Here, we evaluate the employment of Raman spectroscopy for that purpose. Our initial investigation of 12 unique WTE fly ashes resulted in excessive fluorescence, rendering key Raman peaks obscure. To address this issue, we report that a mere 2 min of photobleaching can significantly reduce this fluorescence, facilitating the detection of calcite, calcium sulfate, zincite, and carbon - phases previously undetectable in original spectra. These results show the potential of Raman spectroscopy for rapid monitoring of WTE fly ash mineralogy, which could be beneficial in diverting these ashes from landfill.

A Universal and Accurate Method for Easily Identifying Components in Raman Spectroscopy Based on Deep Learning

Raman spectroscopy has been widely used to provide the structural fingerprint for molecular identification. Due to interference from coexisting components, noise, baseline, and systematic differences between spectrometers, component identification with Raman spectra is challenging, especially for mixtures. In this study, a method entitled DeepRaman has been proposed to solve those problems by combining the comparison ability of a pseudo-Siamese neural network (pSNN) and the input-shape flexibility of spatial pyramid pooling (SPP). DeepRaman was trained, validated, and tested with 41,564 augmented Raman spectra from two databases (pharmaceutical material and S.T. Japan). It can achieve 96.29% accuracy, 98.40% true positive rate (TPR), and 94.36% true negative rate (TNR) on the test set. Another six data sets measured on different instruments were used to evaluate the performance of the proposed method from different aspects. DeepRaman can provide accurate identification results and significantly outperform the hit quality index (HQI) method and other deep learning models. In addition, it performs well in cases of different spectral complexity and low-content components. Once the model is established, it can be used directly on different data sets without retraining or transfer learning. Furthermore, it also obtains promising results for the analysis of surface-enhanced Raman spectroscopy (SERS) data sets and Raman imaging data sets. In summary, it is an accurate, universal, and ready-to-use method for component identification in various application scenarios.

Raman spectroscopy-in situ characterization of reversibly intercalated oxygen vacancies in α-MoO3

This work reports on the in situ strategy to reversibly generate or suppress oxygen vacancies on α-MoO₃ which were probed by Raman spectroscopy. Reversible changes in two features of the α-MoO₃ Raman spectrum could be correlated to the generation of oxygen vacancies: displacement of the T_b band frequency and the intensity decrease of the symmetrical stretching (ν_s) band. These two features could be used to qualitatively describe oxygen vacancies. Raman results also indicate that oxygen vacancies are located in the interlayer region of the α-MoO₃ lattice. This observation is corroborated by in situ X-ray diffraction, which also indicates the absence of nonstoichiometric phase transitions.

This work reports on the in situ strategy to reversibly generate or suppress oxygen vacancies on α-MoO₃ which were probed by Raman spectroscopy.

Evaluation of Photocatalytic Activity in Water Pollutants and Cytotoxic Response of α-Fe2O3 Nanoparticles

α-Fe₂O₃ samples were manufactured by means of the polymeric precursor method. The powders were sintered and calcined at temperatures of 300-700 °C for 2 h, respectively. In the X-ray diffraction results, the formation of the rhombohedral phase without secondary phases was exhibited. The size of the particle increased after calcination at 700 °C, exhibiting a slightly more irregular morphology for the samples calcined with the addition of NH₄OH in the synthesis process. From the field-emission scanning electron microscopy measurements, the particle size was determined, showing a smaller size for the samples without NH₄OH in the synthesis process. The samples calcined at 600 °C had a size of 100 nm, with the sizes for lower temperatures being smaller. The size of the nanoparticle agglomerates was largest for the samples with NH₄OH; however, the zeta potential was slightly lower over time for these samples. The phase study of the α-Fe₂O₃ nanoparticles was confirmed by means of Raman spectroscopy, without additional bands of another crystal structure. In addition, the synthesized nanoparticles exhibited good photocatalytic activity in the degradation of rhodamine B (RhB) and atrazine (ATZ) within 40 min, with a maximum degradation of 59% for ATZ and 40% for rhodamine. The best responses in the degradation were for the samples without the addition of NH₄OH in the synthesis process and in proportions lower than 0.1 g. The cytotoxic effects of the nanoparticles obtained at 600 °C were evaluated in apical cells of onion roots. The results are promising for future applications because no changes were observed in the mitosis of the cells.

Deep learning-based component identification for the Raman spectra of mixtures

Raman spectroscopy is widely used as a fingerprint technique for molecular identification. However, Raman spectra contain molecular information from multiple components and interferences from noise and instrumentation. Thus, component identification using Raman spectra is still challenging, especially for mixtures. In this study, a novel approach entitled deep learning-based component identification (DeepCID) was proposed to solve this problem. Convolution neural network (CNN) models were established to predict the presence of components in mixtures. Comparative studies showed that DeepCID could learn spectral features and identify components in both simulated and real Raman spectral datasets of mixtures with higher accuracy and significantly lower false positive rates. In addition, DeepCID showed better sensitivity when compared with the logistic regression (LR) with L1-regularization, k-nearest neighbor (kNN), random forest (RF) and back propagation artificial neural network (BP-ANN) models for ternary mixture spectral datasets. In conclusion, DeepCID is a promising method for solving the component identification problem in the Raman spectra of mixtures.

A high-throughput multi-microfluidic crystal generator (MMicroCryGen) platform for facile screening of polymorphism and crystal morphology for pharmaceutical compounds

In this work, a novel multi-microfluidic crystallization platform called MMicroCryGen is presented, offering a facile methodology for generating individual crystals for fast and easy screening of the polymorphism and crystal habit of solid compounds. The MMicroCryGen device is capable of performing 8 × 10 cooling crystallization experiments in parallel using 8 disposable microcapillary film strips, each requiring less than 25 μL of solution. Compared to traditional microfluidic systems, the MMicroCryGen platform does not require complex fluid handling; it can be directly integrated with a 96-well microplate and it can also work in a "dipstick" mode. The produced crystals can be safely and directly observed inside the capillaries by optical and spectroscopic techniques. The platform was validated by performing a number of independent experimental runs for: (1) polymorph and hydrate screening of ortho-aminobenzoic acid, succinic acid and piroxicam; (2) co-crystal form screening of the p-toluenesulfonamide/triphenylphosphine oxide system; (3) studying the effect of o-toluic acid on ortho-aminobenzoic cooling crystallization (effect of structurally related additives). In all three cases, all known solid forms were identified with a single experiment using ∼200 μL of solvent and just a few micrograms of the solid material. The MMicroCryGen is simple to use, inexpensive and it provides increased flexibility compared to traditional crystallization techniques, being an effective new microfluidic solution for solid form screening in pharmaceutical, fine chemicals, food and agrochemical industries.

Optimizing laser crater enhanced Raman scattering spectroscopy

The laser crater enhanced Raman scattering (LCERS) spectroscopy technique has been systematically studied for chosen sampling strategy and influence of powder material properties on spectra intensity enhancement. The same nanosecond pulsed solid state Nd:YAG laser (532 nm, 10 ns, 0.1-1.5 mJ/pulse) was used for laser crater production and Raman scattering experiments for l-aspartic acid powder. Increased sampling area inside crater cavity is the key factor for Raman signal improvement for the LCERS technique, thus Raman signal enhancement was studied as a function of numerous experimental parameters including lens-to-sample distance, wavelength (532 and 1064 nm) and laser pulse energy utilized for crater production. Combining laser pulses of 1064 and 532 nm wavelengths for crater ablation was shown to be an effective way for additional LCERS signal improvement. Powder material properties (particle size distribution, powder compactness) were demonstrated to affect LCERS measurements with better results achieved for smaller particles and lower compactness.

Time-Gated Raman Spectroscopy for Quantitative Determination of Solid-State Forms of Fluorescent Pharmaceuticals

Raman spectroscopy is widely used for quantitative pharmaceutical analysis, but a common obstacle to its use is sample fluorescence masking the Raman signal. Time-gating provides an instrument-based method for rejecting fluorescence through temporal resolution of the spectral signal and allows Raman spectra of fluorescent materials to be obtained. An additional practical advantage is that analysis is possible in ambient lighting. This study assesses the efficacy of time-gated Raman spectroscopy for the quantitative measurement of fluorescent pharmaceuticals. Time-gated Raman spectroscopy with a 128 × (2) × 4 CMOS SPAD detector was applied for quantitative analysis of ternary mixtures of solid-state forms of the model drug, piroxicam (PRX). Partial least-squares (PLS) regression allowed quantification, with Raman-active time domain selection (based on visual inspection) improving performance. Model performance was further improved by using kernel-based regularized least-squares (RLS) regression with greedy feature selection in which the data use in both the Raman shift and time dimensions was statistically optimized. Overall, time-gated Raman spectroscopy, especially with optimized data analysis in both the spectral and time dimensions, shows potential for sensitive and relatively routine quantitative analysis of photoluminescent pharmaceuticals during drug development and manufacturing.

Optimizing laser crater enhanced Raman spectroscopy

Raman signal enhancement by laser crater production was systematically studied for 785 nm continuous wave laser pumping. Laser craters were produced in L-aspartic acid powder by a nanosecond pulsed solid state neodymium-doped yttrium aluminum garnet laser (532 nm, 8 ns, 1 mJ/pulse), while Raman spectra were then acquired by using a commercial spectrometer with 785 nm laser beam pumping. The Raman signal enhancement effect was studied in terms of the number of ablating pulses used, the lens-to-sample distance, and the crater-center-laser-spot offset. The influence of the experiment parameters on Raman signal enhancement was studied for different powder materials. Maximum Raman signal enhancement reached 11 fold for loose powders but decreased twice for pressed tablets. Raman signal enhancement was demonstrated for several diverse powder materials like gypsum or ammonium nitrate with better results achieved for the samples tending to give narrow and deep craters upon the laser ablation stage. Alternative ways of cavity production (steel needle tapping and hole drilling) were compared with the laser cratering technique in terms of Raman signal enhancement. Drilling was found to give the poorest enhancement of the Raman signal, while both laser ablation and steel needle tapping provided comparable results. Here, we have demonstrated for the first time, to the best of our knowledge, that a Raman signal can be enhanced 10 fold with the aid of simple cavity production by steel needle tapping in rough highly reflective materials. Though laser crater enhancement Raman spectroscopy requires an additional pulsed laser, this technique is more appropriate for automatization compared to the needle tapping approach.

Laser crater enhanced Raman spectroscopy

Raman signal enhancement by multiple scattering inside laser crater cones was observed for the first time, to the best of our knowledge. Laser crater enhanced Raman spectroscopy (LCERS) yielded a 14-fold increase in the Raman spectra bands due to efficient multiple scattering of laser irradiation within the laser crater walls. The same pulsed Nd:YAG laser (532 nm, 10 ns) was used for both laser crater formation and Raman scattering experiments by varying the output pulse energy. First, powerful pulses are used to produce the laser crater; then low-energy pulses are used to perform Raman scattering measurements. The laser crater profile and its alignment with the laser beam waist were found to be the key parameters for the optimization of the Raman spectrum intensity enhancement. Raman intensity enhancement resulted from increased surface scattering area at the crater walls, rather than spatially offset Raman scattering. The increased signal-to-noise ratio resulted in limits of detection improvement for quantitative analysis using LCERS.

Spectroscopic probe to contribution of physicochemical transformations in the toxicity of aged ZnO NPs to Chlorella vulgaris: new insight into the variation of toxicity of ZnO NPs under aging process

Zinc oxide nanoparticles (ZnO NPs) are one of the most abundantly applied nanomaterials in nanotechnology-based industries and they may cause unexpected environmental and health risks with their physicochemical transformations in the environment. Currently, there is still a lack of the in-depth understanding of the toxicity of aged ZnO NPs to aquatic organisms, particularly demanding quantitative analysis of the physicochemical transformations to distinguish their contributions in the toxicity assessment. For this purpose, therefore, we initiated the study of the toxicity of aged ZnO NPs to the model aquatic microalga, i.e. Chlorella vulgaris, and with the aid of spectroscopic tools for characterization and quantification of the physicochemical transformations, we scrutinized the toxicity variations for ZnO NPs with different aging times. As a result, we found that the toxicity altered in an abnormal manner with the aging time, i.e. the toxicity of aged ZnO NPs for 30 days showed the higher toxicity to the green alga than the fresh ZnO NPs or the ZnO NPs aged for longer time (e.g. 120 and 210 days). Through spectroscopic tools such as XRD, FTIR and Raman spectroscopy, we made both the qualitative and quantitative assessments of the physicochemical changes of the ZnO NPs, and confirmed that in the early stage, the toxicity mainly stemmed from the release of zinc ions, but with longer aging time, the neoformation of the nanoparticles played the critical role, leading to the overall reduced toxicity due to the less toxic hydrozincite and zinc hydroxide in the transformed compounds.

Raman spectroscopy in pharmaceutical product design

Almost 100 years after the discovery of the Raman scattering phenomenon, related analytical techniques have emerged as important tools in biomedical sciences. Raman spectroscopy and microscopy are frontier, non-invasive analytical techniques amenable for diverse biomedical areas, ranging from molecular-based drug discovery, design of innovative drug delivery systems and quality control of finished products. This review presents concise accounts of various conventional and emerging Raman instrumentations including associated hyphenated tools of pharmaceutical interest. Moreover, relevant application cases of Raman spectroscopy in early and late phase pharmaceutical development, process analysis and micro-structural analysis of drug delivery systems are introduced. Finally, potential areas of future advancement and application of Raman spectroscopic techniques are discussed.

Quantitative Macro-Raman Spectroscopy on Microparticle-Based Pharmaceutical Dosage Forms

Quantitative macro-Raman spectroscopy was applied to the analysis of the bulk composition of pharmaceutical drug powders. Powders were extracted from seven commercial lactose-carrier-based dry-powder inhalers: Flixotide 50, 100, 250, and 500 μg/dose (four concentrations of fluticasone propionate) and Seretide 100, 250, and 500 μg/dose (three concentrations of fluticasone propionate, each with 50 μg/dose salmeterol xinafoate ). Also, a carrier-free pressurized metered-dose inhaler of the same combination product, Seretide 50 (50 μg fluticasone propionate and 25 μg salmeterol xinafoate per dose) was tested. The applicability of a custom-designed dispersive macro-Raman instrument with a large sample volume of 0.16 μL was tested to determine the composition of the multicomponent powder samples. To quantify the error caused by sample heterogeneity, a Monte Carlo model was developed to predict the minimum sample volume required for representative sampling of potentially heterogeneous samples at the microscopic level, characterized by different particle-size distributions and compositions. Typical carrier-free respirable powder samples required a minimum sample volume on the order of 10(-4) μL to achieve representative sampling with less than 3% relative error. In contrast, dosage forms containing non-respirable carriers (e.g., lactose) required a sample volume on the order of 0.1 μL for representative measurements. Error analysis of the experimental results showed good agreement with the error predicted by the simulation.

Matrix Effects in Quantitative Assessment of Pharmaceutical Tablets Using Transmission Raman and Near-Infrared (NIR) Spectroscopy

Raman spectroscopy can be an alternative to near-infrared spectroscopy (NIR) for nondestructive quantitative analysis of solid pharmaceutical formulations. Compared with NIR spectra, Raman spectra have much better selectivity, but subsampling was always an issue for quantitative assessment. Raman spectroscopy in transmission mode has reduced this issue, since a large volume of the sample is measured in transmission mode. The sample matrix, such as particle size of the drug substance in a tablet, may affect the Raman signal. In this work, matrix effects in transmission NIR and Raman spectroscopy were systematically investigated for a solid pharmaceutical formulation. Tablets were manufactured according to an experimental design, varying the factors particle size of the drug substance (DS), particle size of the filler, compression force, and content of drug substance. All factors were varied at two levels plus a center point, except the drug substance content, which was varied at five levels. Six tablets from each experimental point were measured with transmission NIR and Raman spectroscopy, and their concentration of DS was determined for a third of those tablets. Principal component analysis of NIR and Raman spectra showed that the drug substance content and particle size, the particle size of the filler, and the compression force affected both NIR and Raman spectra. For quantitative assessment, orthogonal partial least squares regression was applied. All factors varied in the experimental design influenced the prediction of the DS content to some extent, both for NIR and Raman spectroscopy, the particle size of the filler having the largest effect. When all matrix variations were included in the multivariate calibrations, however, good predictions of all types of tablets were obtained, both for NIR and Raman spectroscopy. The prediction error using transmission Raman spectroscopy was about 30% lower than that obtained with transmission NIR spectroscopy.

Interference correction by extracting the information of interference dominant regions: application to near-infrared spectra

Interference such as baseline drift and light scattering can degrade the model predictability in multivariate analysis of near-infrared (NIR) spectra. Usually interference can be represented by an additive and a multiplicative factor. In order to eliminate these interferences, correction parameters are needed to be estimated from spectra. However, the spectra are often mixed of physical light scattering effects and chemical light absorbance effects, making it difficult for parameter estimation. Herein, a novel algorithm was proposed to find a spectral region automatically that the interesting chemical absorbance and noise are low, that is, finding an interference dominant region (IDR). Based on the definition of IDR, a two-step method was proposed to find the optimal IDR and the corresponding correction parameters estimated from IDR. Finally, the correction was performed to the full spectral range using previously obtained parameters for the calibration set and test set, respectively. The method can be applied to multi target systems with one IDR suitable for all targeted analytes. Tested on two benchmark data sets of near-infrared spectra, the performance of the proposed method provided considerable improvement compared with full spectral estimation methods and comparable with other state-of-art methods.

Raman spectroscopy explores molecular structural signatures of hidden materials in depth: Universal Multiple Angle Raman Spectroscopy

Non-invasive 3D imaging in materials and medical research involves methodologies such as X-ray imaging, MRI, fluorescence and optical coherence tomography, NIR absorption imaging, etc., providing global morphological/density/absorption changes of the hidden components. However, molecular information of such buried materials has been elusive. In this article we demonstrate observation of molecular structural information of materials hidden/buried in depth using Raman scattering. Typically, Raman spectroscopic observations are made at fixed collection angles, such as, 90°, 135°, and 180°, except in spatially offset Raman scattering (SORS) (only back scattering based collection of photons) and transmission techniques. Such specific collection angles restrict the observations of Raman signals either from or near the surface of the materials. Universal Multiple Angle Raman Spectroscopy (UMARS) presented here employs the principle of (a) penetration depth of photons and then diffuse propagation through non-absorbing media by multiple scattering and (b) detection of signals from all the observable angles.

Multiplicative effects model with internal standard in mobile phase for quantitative liquid chromatography-mass spectrometry

Liquid chromatography-mass spectrometry assays suffer from signal instability caused by the gradual fouling of the ion source, vacuum instability, aging of the ion multiplier, etc. To address this issue, in this contribution, an internal standard was added into the mobile phase. The internal standard was therefore ionized and detected together with the analytes of interest by the mass spectrometer to ensure that variations in measurement conditions and/or instrument have similar effects on the signal contributions of both the analytes of interest and the internal standard. Subsequently, based on the unique strategy of adding internal standard in mobile phase, a multiplicative effects model was developed for quantitative LC-MS assays and tested on a proof of concept model system: the determination of amino acids in water by LC-MS. The experimental results demonstrated that the proposed method could efficiently mitigate the detrimental effects of continuous signal variation, and achieved quantitative results with average relative predictive error values in the range of 8.0-15.0%, which were much more accurate than the corresponding results of conventional internal standard method based on the peak height ratio and partial least squares method (their average relative predictive error values were as high as 66.3% and 64.8%, respectively). Therefore, it is expected that the proposed method can be developed and extended in quantitative LC-MS analysis of more complex systems.

Microfluidics and Raman microscopy: current applications and future challenges

Raman microscopy systems are becoming increasingly widespread and accessible for characterising chemical species. Microfluidic systems are also progressively finding their way into real world applications. Therefore, it is anticipated that the integration of Raman systems with microfluidics will become increasingly attractive and practical. This review aims to provide an overview of Raman microscopy-microfluidics integrated systems for researchers who are actively interested in utilising these tools. The fundamental principles and application strengths of Raman microscopy are discussed in the context of microfluidics. Various configurations of microfluidics that incorporate Raman microscopy methods are presented, with applications highlighted. Data analysis methods are discussed, with a focus on assisting the interpretation of Raman-microfluidics data from complex samples. Finally, possible future directions of Raman-microfluidic systems are presented.