Multivariate Curve Resolution: 50 years addressing the mixture analysis problem – A review

Alternating and Modified Alternating Least Squares Applied to Raman Spectra of Finished Gasolines

Extraction of components from individual refinery streams (e.g., reformates and alkylates) in finished gasoline was undertaken using Raman spectroscopy to characterize the chemical content of the finished product. Modified alternating least squares (MALS) was used for separating Raman spectroscopic data sets of the finished product into its pure individual components. The advantages of MALS over alternating least squares (ALS) for multicomponent resolution are highlighted in this study using three Raman spectroscopic data sets which provide a suitable benchmark for comparing the performance of these two methods. MALS is superior to ALS in terms of accuracy and can better resolve components than ALS, and it is also more robust toward collinear data. Finally, components near the noise level usually cannot be extracted by ALS because of instability when inverting the covariance structure which inflates the noise present in the data. However, these same components can be extracted by MALS due to the stabilization of the least squares regression with respect to the matrix inversion using modified techniques from ridge regression.

Multivariate curve resolution-alternating least-squares and second-order advantage in first-order calibration. A systematic characterisation for three-component analytical systems

BACKGROUND

Recent interest has been focused on the application of multivariate curve resolution-alternating least-squares (MCR-ALS) to systems involving the measurement of first-order and non-bilinear second-order data. The latter pose important challenges to bilinear decomposition models, due to the phenomenon of rotational ambiguity in the solutions, even under the application of the full set of chemical constraints that is usually employed in MCR-ALS calibration.

RESULTS

After the analysis of several simulated and experimental datasets, important conclusions regarding the role of the selectivity patterns in the constituent spectra have been drawn concerning the achievement of the second-order advantage. Theoretical considerations based on the calculation of the areas of feasible solutions helped to support the observations regarding the predictive ability of MCR- ALS in the various datasets.

SIGNIFICANCE

The understanding of the impact of rotational ambiguity in obtaining the second-order advantage with both first-order and non-bilinear second-order data is of paramount importance in the future development of analytical protocols of complex samples.

Widefield Super-Resolution Infrared Spectroscopy and Imaging of Autofluorescent Biological Materials and Photosynthetic Microorganisms Using Fluorescence Detected Photothermal Infrared (FL-PTIR)

We have demonstrated high-speed, super-resolution infrared (IR) spectroscopy and chemical imaging of autofluorescent biomaterials and organisms using camera-based widefield photothermal detection that takes advantage of temperature-dependent modulations of autofluorescent emission. A variety of biological materials and photosynthetic organisms exhibit strong autofluorescence emission under ultraviolet excitation and the autofluorescent emission has a very strong temperature dependence, of order 1%/K. Illuminating a sample with pulses of IR light from a wavelength-tunable laser source causes periodic localized sample temperature increases that result in a corresponding transient decrease in autofluorescent emission. A low-cost light-emitting diode-based fluorescence excitation source was used in combination with a conventional fluorescence microscopy camera to detect localized variations in autofluorescent emission over a wide area as an indicator of localized IR absorption. IR absorption image stacks were acquired over a range of IR wavelengths, including the fingerprint spectral range, enabling extraction of localized IR absorption spectra. We have applied widefield fluorescence detected photothermal IR (FL-PTIR) to an analysis of autofluorescent biological materials including collagen, leaf tissue, and photosynthetic organisms including diatoms and green microalgae cells. We have also demonstrated the FL-PTIR on live microalgae in water, demonstrating the potential for label-free dynamic chemical imaging of autofluorescent cells.

Investigating the quality of extraction and quantification of bioactive compounds in berries through liquid chromatography and multivariate curve resolution

Berries are a rich source of natural antioxidant compounds, which are essential to profile, as they add to their nutritional value. However, the complexity of the matrix and the structural diversity of these compounds pose challenges in extraction and chromatographic separation. By relying on multivariate curve resolution alternating least squares (MCR-ALS) ability to extract components from complex spectral mixtures, our study evaluates the contributions of various extraction techniques to interference, extractability, and quantifying different groups of overlapping compounds using liquid chromatography diode array detection (LC-DAD) data. Additionally, the combination of these methods extends its applicability to evaluate polyphenol degradation in stored berry smoothies, where evolving factor analysis (EFA) is also used to elucidate degradation products. Results indicate that among the extraction techniques, ultrasonication-assisted extraction employing 1% formic acid in methanol demonstrated superior extractability and selectivity for the different phenolic compound groups, compared with both pressurized liquid extraction and centrifugation of the fresh berry smoothie. Employing MCR-ALS on the LC-DAD data enabled reliable estimation of total amounts of compound classes with high spectral overlaps. Degradation studies revealed significant temperature-dependent effects on anthocyanins, with at least 50% degradation after 7 months of storage at room temperature, while refrigeration and freezing maintained fair stability for at least 12 months. The EFA model estimated phenolic derivatives as the main possible degradation products. These findings enhance the reliability of quantifying polyphenolic compounds and understanding their stability during the storage of berry products.

In Situ Raman Hyperspectral Analysis of Microbial Colonies for Secondary Metabolites Screening

Since the discovery of penicillin, a vast array of microbial antibiotics has been identified and applied in the medical field. Globally, the search for drug candidates via microbial screening is ongoing. Traditional screening methods, however, are time-consuming and require labor-intensive sample processing, significantly reducing throughput. This research introduces a Raman spectroscopy-based screening system tailored to the in situ analysis of microbial colonies on solid culture media. Employing multivariate curve resolution-alternating least-squares (MCR-ALS) for spectral decomposition, our approach reveals the production of secondary metabolites at the single colony level. We enhanced the microbial culture method, enabling direct, high signal-to-noise (S/N) ratio Raman spectroscopic measurements of colonies of Escherichia coli and actinomycetes species. Through semisupervised MCR analysis using the known spectra of actinorhodin and undecylprodigiosin as references, we accurately assessed the production of these compounds by Streptomyces coelicolor A3(2). Furthermore, we herein successfully detected the production of amphotericin B by Streptomyces nodosus, even in the absence of prior spectral information. This demonstrates the potential of our technique in the discovery of secondary metabolites. In addition to enabling the detection of the above-mentioned compounds, this analysis revealed the heterogeneity of the spatial distribution of their production in each colony. Our technique makes a significant contribution to the advancement of microbial screening, offering a rapid, efficient alternative to conventional methods and opening avenues for secondary metabolites discovery.

In Situ Quantitative Monitoring of Adsorption from Aqueous Phase by UV-vis Spectroscopy: Implication for Understanding of Heterogeneous Processes

The development of in situ techniques to quantitatively characterize the heterogeneous reactions is essential for understanding physicochemical processes in aqueous phase. In this work, a new approach coupling in situ UV-vis spectroscopy with a two-step algorithm strategy is developed to quantitatively monitor heterogeneous reactions in a compact closed-loop incorporation. The algorithm involves the inverse adding-doubling method for light scattering correction and the multivariate curve resolution-alternating least squares (MCR-ALS) method for spectral deconvolution. Innovatively, theoretical spectral simulations are employed to connect MCR-ALS solutions with chemical molecular structural evolution without prior information for reference spectra. As a model case study, the aqueous adsorption kinetics of bisphenol A onto polyamide microparticles are successfully quantified in a one-step UV-vis spectroscopic measurement. The practical applicability of this approach is confirmed by rapidly screening a superior adsorbent from commercial materials for antibiotic wastewater adsorption treatment. The demonstrated capabilities are expected to extend beyond monitoring adsorption systems to other heterogeneous reactions, significantly advancing UV-vis spectroscopic techniques toward practical integration into automated experimental platforms for probing aqueous chemical processes and beyond.

PFAS remediation: Evaluating the infrared spectra of complex gaseous mixtures to determine the efficacy of thermal decomposition of PFAS

Due to their widespread production and known environmental contamination, the need for the detection and remediation of per- and polyfluoroalkyl substances (PFAS) has grown quickly. While destructive thermal treatment of PFAS at low temperatures (e.g., 200-500 °C) is of interest due to lower energy and infrastructure requirements, the range of possible degradation products remains underexplored. To better understand the low temperature decomposition of PFAS species, we have coupled gas-phase infrared spectroscopy with a multivariate curve resolution (MCR) analysis and a database of high-resolution PFAS infrared reference spectra to characterize and quantify a complex mixture resulting from potassium perfluorooctanesulfonate (PFOS-K) decomposition. Beginning at 375 °C, nine prevalent decomposition products (namely smaller perfluorocarbon species) are identified and quantified.

A chemometric-assisted method for automatic, rapid and non-targeted detection of multi-pesticides in plant-derived foods by gas chromatography-mass spectrometry

An automatic, rapid and non-targeted detection method for multi-pesticides in plant-derived foods was developed by gas chromatography-mass spectrometry and chemometrics. In this method, a novel algorithm named moving window iterative target transformation factor analysis was proposed. Although there are challenges of peak overlapping and background interference, the retention time and corrected mass spectra of unknown pesticides can be automatically obtained through iteration calculation in the 'moving window' with reference to the pesticide mass spectral library. One mixed pesticide standard and nine varieties of plant-derived foods were investigated with the proposed method. By contrast, a fast temperature programme was used to shorten detection time compared to the standard temperature programme. For the mixed standard, the mass spectra and retention times of all 39 pesticides were successfully obtained from the overlapping signal. Furthermore, all spiked pesticides were successfully detected in plant-derived foods within 10 min using a fast temperature programme.

Iron nuclearity in mineral fibres: Unravelling the catalytic activity for predictive modelling of toxicity

Chronic inflammation induced in vivo by mineral fibres, such as asbestos, is sustained by the cyclic formation of cytotoxic/genotoxic oxidant species that are catalysed by iron. High catalytic activity is observed when iron atoms are isolated in the crystal lattice (nuclearity=1), whereas the catalytic activity is expected to be reduced or null when iron forms clusters of higher nuclearity. This study presents a novel approach for systematically measuring iron nuclearity across a large range of iron-containing standards and mineral fibres of social and economic importance, and for quantitatively assessing the relation between nuclearity and toxicity. The multivariate curve resolution (MCR) empirical approach and density functional theory (DFT) calculations were applied to the analysis of UV-Vis spectra to obtain information on the nature of iron and nuclearity. This approach led to the determination of the nuclearity of selected mineral fibres which was subsequently used to calculate a toxicity-related index. High nuclearity-related toxicity was estimated for chrysotile samples, fibrous glaucophane, asbestos tremolite, and fibrous wollastonite. Intermediate values of toxicity, corresponding to a mean nuclearity of 2, were assigned to actinolite asbestos, amosite, and crocidolite. Finally, a low nuclearity-related toxicity parameter, corresponding to an iron-cluster with a lower catalytic power to produce oxidants, was assigned to asbestos anthophyllite.

Distinct spectral signatures unfold ECM stiffness-triggered biochemical changes in breast cancer cells

Cancer progression often accompanies the stiffening of extracellular matrix (ECM) in and around the tumor, owing to extra deposition and cross-linking of collagen. Stiff ECM has been linked with poor prognosis and is known to fuel invasion and metastasis, notably in breast cancer. However, the underlying biochemical or metabolic changes and the cognate molecular signatures remain elusive. Here, we explored Raman spectroscopy to unveil the spectral fingerprints of breast cancer cells in response to extracellular mechanical cues. Using stiffness-tuneable hydrogels, we showed that cells grown on stiff ECM displayed morphological changes with high proliferation. We further demonstrated that Raman Spectroscopy, a label-free and non-invasive technique, could provide comprehensive information about the biochemical environment of breast cancer cells in response to varying ECM stiffness. Raman spectroscopic analysis classified the cells into distinct clusters based on principal component-based linear discriminant analysis (PC-LDA). Multivariate curve resolution-alternating least squares (MCR-ALS) analysis indicated that cells cultured on stiff ECM exhibited elevated nucleic acid content and lesser lipids. Interestingly, increased intensity of Raman bands corresponding to cytochrome-c was also observed in stiff ECM conditions, suggesting mitochondrial modulation. The key findings harboured by spectral profiles were also corroborated by transmission electron microscopy, confirming altered metabolic status as reflected by increased mitochondria number and decreased lipid droplets in response to ECM stiffening. Collectively, these findings not only give the spectral signatures for mechanoresponse but also provide the landscape of biochemical changes in response to ECM stiffening.

Serum Raman spectroscopy: Evaluation of tumour load variations in experimental carcinogenesis

Several serum Raman spectroscopy (RS) studies have demonstrated its potential as an oral cancer screening tool. This study investigates influence of low tumour load (LTL) and high tumour load (HTL) on serum RS using hamster buccal pouch model of experimental oral carcinogenesis. Sera of untreated control, LTL, and HTL groups at week intervals during malignant transformation were employed. Serum Raman spectra were subjected to multivariate analyses-principal component analysis, principal component-based linear discriminant analysis (for stratification of study groups), and multivariate curve resolution-alternating least squares (MCR-ALS) (to comprehend biomolecular differences). Multivariate analysis revealed misclassifications between LTL and HTL at all week intervals. MCR-ALS components showed statistically significant abundances between control versus LTL and control versus HTL, but could not discern LTL and HTL. MCR-ALS components exhibited spectral mixtures of proteins, lipids, heme and nucleic acids. Thus, these findings support use of serum RS as a screening tool as varying tumour load is not a confounding factor influencing the technique.

Online and Offline Prioritization of Chemicals of Interest in Suspect Screening and Non-targeted Screening with High-Resolution Mass Spectrometry

Recent advances in high-resolution mass spectrometry (HRMS) have enabled the detection of thousands of chemicals from a single sample, while computational methods have improved the identification and quantification of these chemicals in the absence of reference standards typically required in targeted analysis. However, to determine the presence of chemicals of interest that may pose an overall impact on ecological and human health, prioritization strategies must be used to effectively and efficiently highlight chemicals for further investigation. Prioritization can be based on a chemical's physicochemical properties, structure, exposure, and toxicity, in addition to its regulatory status. This Perspective aims to provide a framework for the strategies used for chemical prioritization that can be implemented to facilitate high-quality research and communication of results. These strategies are categorized as either "online" or "offline" prioritization techniques. Online prioritization techniques trigger the isolation and fragmentation of ions from the low-energy mass spectra in real time, with user-defined parameters. Offline prioritization techniques, in contrast, highlight chemicals of interest after the data has been acquired; detected features can be filtered and ranked based on the relative abundance or the predicted structure, toxicity, and concentration imputed from the tandem mass spectrum (MS2). Here we provide an overview of these prioritization techniques and how they have been successfully implemented and reported in the literature to find chemicals of elevated risk to human and ecological environments. A complete list of software and tools is available from https://nontargetedanalysis.org/.

A multivariate curve resolution analysis of multicenter proton spectroscopic imaging of the prostate for cancer localization and assessment of aggressiveness

In this study, we investigated the potential of the multivariate curve resolution alternating least squares (MCR-ALS) algorithm for analyzing three-dimensional (3D) 1 H-MRSI data of the prostate in prostate cancer (PCa) patients. MCR-ALS generates relative intensities of components representing spectral profiles derived from a large training set of patients, providing an interpretable model. Our objectives were to classify magnetic resonance (MR) spectra, differentiating tumor lesions from benign tissue, and to assess PCa aggressiveness. We included multicenter 3D 1 H-MRSI data from 106 PCa patients across eight centers. The patient cohort was divided into a training set (N = 63) and an independent test set (N = 43). Singular value decomposition determined that MR spectra were optimally represented by five components. The profiles of these components were extracted from the training set by MCR-ALS and assigned to specific tissue types. Using these components, MCR-ALS was applied to the test set for a quantitative analysis to discriminate tumor lesions from benign tissue and to assess tumor aggressiveness. Relative intensity maps of the components were reconstructed and compared with histopathology reports. The quantitative analysis demonstrated a significant separation between tumor and benign voxels (t-test, p < 0.001). This result was achieved including voxels with low-quality MR spectra. A receiver operating characteristic analysis of the relative intensity of the tumor component revealed that low- and high-risk tumor lesions could be distinguished with an area under the curve of 0.88. Maps of this component properly identified the extent of tumor lesions. Our study demonstrated that MCR-ALS analysis of 1 H-MRSI of the prostate can reliably identify tumor lesions and assess their aggressiveness. It handled multicenter data with minimal preprocessing and without using prior knowledge or quality control. These findings indicate that MCR-ALS can serve as an automated tool to assess the presence, extent, and aggressiveness of tumor lesions in the prostate, enhancing diagnostic capabilities and treatment planning of PCa patients.

An exhaustive analysis of the use of image moments for second-order calibration. A comparison with multivariate curve resolution-alternating least-squares

BACKGROUND

the chemometric processing of second-order chromatographic-spectral data is usually carried out with the aid of multivariate curve resolution-alternating least-squares (MCR-ALS). Recently, an alternative procedure was described based on the estimation of image moments for each data matrix and subsequent application of multiple linear regression after suitable variable selection.

RESULTS

The analysis of both simulated and experimental data leads to the conclusion that the image moment method, although can cope with chromatographic lack of reproducibility across injections, it only performs well in the absence of uncalibrated interferents. MCR-ALS, on the other hand, provides good analytical results in all studied situations, whether the test samples contain uncalibrated interferents or not.

SIGNIFICANCE

The results are useful to assess the real usefulness of newly proposed methodologies for second-order calibration in the case of chromatographic-spectral data sets, especially when samples contain unexpected chemical constituents.

Gain deeper insights into traditional Chinese medicines using multidimensional chromatography combined with chemometric approaches

Traditional Chinese medicines (TCMs) possess a rich historical background, unique theoretical framework, remarkable therapeutic efficacy, and abundant resources. However, the modernization and internationalization of TCMs have faced significant obstacles due to their diverse ingredients and unknown mechanisms. To gain deeper insights into the phytochemicals and ensure the quality control of TCMs, there is an urgent need to enhance analytical techniques. Currently, two-dimensional (2D) chromatography, which incorporates two independent separation mechanisms, demonstrates superior separation capabilities compared to the traditional one-dimensional (1D) separation system when analyzing TCMs samples. Over the past decade, new techniques have been continuously developed to gain actionable insights from complex samples. This review presents the recent advancements in the application of multidimensional chromatography for the quality evaluation of TCMs, encompassing 2D-gas chromatography (GC), 2D-liquid chromatography (LC), as well as emerging three-dimensional (3D)-GC, 3D-LC, and their associated data-processing approaches. These studies highlight the promising potential of multidimensional chromatographic separation for future phytochemical analysis. Nevertheless, the increased separation capability has resulted in higher-order data sets and greater demands for data-processing tools. Considering that multidimensional chromatography is still a relatively nascent research field, further hardware enhancements and the implementation of chemometric methods are necessary to foster its robust development.

Analysing olive ripening with digital image RGB histograms

Digital images are commonly used to monitor processes that are based on colour changes due to their simplicity and easy capture. Colour information in these images can be analysed objectively and accurately using colour histograms. One such process is olive ripening, which is characterized by changes in chemical composition, sensory properties and can be followed by changes in physical appearance, mainly colour. The reference method to quantify the ripeness of olives is the Maturity Index (MI), which is determined by trained experts assigning individual olives into a colour scale through visual inspection. Instead, this study proposes a methodology based on Chemometrics Assisted Colour Histogram-based Analytical Systems (CACHAS) to automatically assess the MI of olives based on R, G, and B colour histograms derived from digital images. The methodology was shown to be easily transferable for routine analysis and capable of controlling the ripening of olives. The study also confirms the high potential of digital images to understand the ripening process of olives (and potentially other fruits) and to predict the MI with satisfactory accuracy, providing an objective and reproducible alternative to visual inspection of trained experts.

Extraction of pure component spectra from ex situ illumination UV/Vis and NMR spectroscopy

Obtaining understanding of a photochemical reaction relies on the observation, identification and quantification of the compounds involved. The photochemical properties of the individual components are of particular importance, and their determination, however, is not always trivial. This is also true for the quantitative measure on the ability to absorb light, the extinction coefficient εi if more than one species i is present and two or more species absorb light of the same wavelength. In this work, it is demonstrated how pure component spectra can be obtained with a simple combination of successive and repeated ex situ illumination, UV/Vis and NMR spectroscopy. From the complementary information accessible, the wavelength-dependent extinction coefficients of all species can be calculated yielding the pure component spectra. A comparison with published data shows excellent agreement and thus proves that this approach is highly reliable.

Assessment of Essential Information in the Fourier Domain to Accelerate Raman Hyperspectral Microimaging

In the context of multivariate curve resolution (MCR) and spectral unmixing, essential information (EI) corresponds to the most linearly dissimilar rows and/or columns of a two-way data matrix. In recent works, the assessment of EI has been revealed to be a very useful practical tool to select the most relevant spectral information before MCR analysis, key features being speed and compression ability. However, the canonical approach relies on the principal component analysis to evaluate the convex hull that encapsulates the data structure in the normalized score space. This implies that the evaluation of the essentiality of each spectrum can only be achieved after all the spectra have been acquired by the instrument. This paper proposes a new approach to extract EI in the Fourier domain (EIFD). Spectral information is transformed into Fourier coefficients, and EI is assessed from a convex hull analysis of the data point cloud in the 2D phasor plots of a few selected harmonics. Because the coordinate system of a phasor plot does not depend on the data themselves, the evaluation of the essentiality of the information carried by each spectrum can be achieved individually and independently from the others. As a result, time-consuming operations like Raman spectral imaging can be significantly accelerated exploiting a chemometric-driven (i.e., based on the EI content of a spectral pixel) procedure for data acquisition and targeted sampling. The usefulness of EIFD is shown by analyzing Raman hyperspectral microimaging data, demonstrating a potential 50-fold acceleration of Raman acquisition.

Multivariate curve Resolution-Alternating least squares coupled with Raman microspectroscopy: new insights into the kinetic response of primary oral squamous carcinoma cells to cisplatin

Raman MicroSpectroscopy (RMS) is a powerful label-free tool to probe the effects of drugs at a cellular/subcellular level. It is important, however, to be able to extract relevant biochemical and kinetic spectroscopic signatures of the specific cellular responses. In the present study, a combination of Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and Principal Component Analysis (PCA) is used to analyse the RMS data for the example of exposure of primary Oral Squamous Carcinoma Cells (OSCC) to the chemotherapeutic agent cisplatin. Dosing regimens were established by cytotoxicity assays, and the effects of the drug on cellular spectral profiles were monitored from 16 to 72 hours post-exposure using an apoptosis assay, to establish the relative populations of viable (V), early (EA) and late apoptotic/dead (LA/D) cells after the drug treatment. Based on a kinetic model of the progression from V > EA > D, MCR-ALS regression analysis of the RMS responses was able to extract spectral profiles associated with each stage of the cellular responses, enabling a quantitative comparison of the response rates for the respective drug treatments. Moreover, PCA was used to compare the spectral profiles of the viable cells exposed to the drug. Spectral differences were highlighted in the early stages (16 hours exposure), indicative of the initial cellular response to the drug treatment, and also in the late stages (48-72 hours exposure), representing the cell death pathway. The study demonstrates that RMS coupled with multivariate analysis can be used to quantitatively monitor the progression of cellular responses to different drugs, towards future applications for label-free, in vitro, pre-clinical screening.

First-Order Individual Gas Sensors as Next Generation Reliable Analytical Instruments

It is conventionally expected that the performance of existing gas sensors may degrade in the field compared to laboratory conditions because (i) a sensor may lose its accuracy in the presence of chemical interferences and (ii) variations of ambient conditions over time may induce sensor-response fluctuations (i.e., drift). Breaking this status quo in poor sensor performance requires understanding the origins of design principles of existing sensors and bringing new principles to sensor designs. Existing gas sensors are single-output (e.g., resistance, electrical current, light intensity, etc.) sensors, also known as zero-order sensors (Karl Booksh and Bruce R. Kowalski, Analytical Chemistry, DOI: 10.1021/ac00087a718). Any zero-order sensor is undesirably affected by variable chemical background and sensor drift that cannot be distinguished from the response to an analyte. To address these limitations, we are developing multivariable gas sensors with independent responses, which are first-order analytical instruments. Here, we demonstrate self-correction against drift in two types of first-order gas sensors that operate in different portions of the electromagnetic spectrum. Our radiofrequency sensors utilize dielectric excitation of semiconducting metal oxide materials on the shoulder of their dielectric relaxation peak and achieve self-correction of the baseline drift by operation at several frequencies. Our photonic sensors utilize nanostructured sensing materials inspired by Morpho butterflies and achieve self-correction of the baseline drift by operation at several wavelengths. These principles of self-correction for drift effects in first-order sensors open opportunities for diverse emerging monitoring applications that cannot afford frequent periodic maintenance that is typical of traditional analytical instruments.

A new constraint to model background signals when processing chromatographic-spectral second-order data with multivariate curve resolution

BACKGROUND

the chemometric processing of second-order chromatographic-spectral data is usually carried out with the aid of multivariate curve resolution-alternating least-squares (MCR-ALS). When baseline contributions occur in the data, the background profile retrieved with MCR-ALS may show abnormal lumps or negative dips at the position of the remaining component peaks.

RESULTS

The phenomenon is shown to be due to remaining rotational ambiguity in the obtained profiles, as confirmed by the estimation of the boundaries of the range of feasible bilinear profiles. To avoid the abnormal features in the retrieved profile, a new background interpolation constraint is proposed and described in detail. Both simulated and experimental data are employed to support the need of the new MCR-ALS constraint. In the latter case, the estimated analyte concentrations agreed with those previously reported.

SIGNIFICANCE

The developed procedure helps to reduce the extent of rotational ambiguity in the solution and to better interpret the results on physicochemical grounds.

Monitoring and modelling the glutamine metabolic pathway: a review and future perspectives

BACKGROUND

Analysis of the glutamine metabolic pathway has taken a special place in metabolomics research in recent years, given its important role in cell biosynthesis and bioenergetics across several disorders, especially in cancer cell survival. The science of metabolomics addresses the intricate intracellular metabolic network by exploring and understanding how cells function and respond to external or internal perturbations to identify potential therapeutic targets. However, despite recent advances in metabolomics, monitoring the kinetics of a metabolic pathway in a living cell in situ, real-time and holistically remains a significant challenge.

AIM

This review paper explores the range of analytical approaches for monitoring metabolic pathways, as well as physicochemical modeling techniques, with a focus on glutamine metabolism. We discuss the advantages and disadvantages of each method and explore the potential of label-free Raman microspectroscopy, in conjunction with kinetic modeling, to enable real-time and in situ monitoring of the cellular kinetics of the glutamine metabolic pathway.

KEY SCIENTIFIC CONCEPTS

Given its important role in cell metabolism, the ability to monitor and model the glutamine metabolic pathways are highlighted. Novel, label free approaches have the potential to revolutionise metabolic biosensing, laying the foundation for a new paradigm in metabolomics research and addressing the challenges in monitoring metabolic pathways in living cells.

The Emerging Role of Raman Spectroscopy as an Omics Approach for Metabolic Profiling and Biomarker Detection toward Precision Medicine

Omics technologies have rapidly evolved with the unprecedented potential to shape precision medicine. Novel omics approaches are imperative toallow rapid and accurate data collection and integration with clinical information and enable a new era of healthcare. In this comprehensive review, we highlight the utility of Raman spectroscopy (RS) as an emerging omics technology for clinically relevant applications using clinically significant samples and models. We discuss the use of RS both as a label-free approach for probing the intrinsic metabolites of biological materials, and as a labeled approach where signal from Raman reporters conjugated to nanoparticles (NPs) serve as an indirect measure for tracking protein biomarkers in vivo and for high throughout proteomics. We summarize the use of machine learning algorithms for processing RS data to allow accurate detection and evaluation of treatment response specifically focusing on cancer, cardiac, gastrointestinal, and neurodegenerative diseases. We also highlight the integration of RS with established omics approaches for holistic diagnostic information. Further, we elaborate on metal-free NPs that leverage the biological Raman-silent region overcoming the challenges of traditional metal NPs. We conclude the review with an outlook on future directions that will ultimately allow the adaptation of RS as a clinical approach and revolutionize precision medicine.

Maize Internode Autofluorescence at the Macroscopic Scale: Image Representation and Principal Component Analysis of a Series of Large Multispectral Images

A quantitative histology of maize stems is needed to study the role of tissue and of their chemical composition in plant development and in their end-use quality. In the present work, a new methodology is proposed to show and quantify the spatial variability of tissue composition in plant organs and to statistically compare different samples accounting for biological variability. Multispectral UV/visible autofluorescence imaging was used to acquire a macroscale image series based on the fluorescence of phenolic compounds in the cell wall. A series of 40 multispectral large images of a whole internode section taken from four maize inbred lines were compared. The series consisted of more than 1 billion pixels and 11 autofluorescence channels. Principal Component Analysis was adapted and named large PCA and score image montages at different scales were built. Large PCA score distributions were proposed as quantitative features to compare the inbred lines. Variations in the tissue fluorescence were clearly displayed in the score images. General intensity variations were identified. Rind vascular bundles were differentiated from other tissues due to their lignin fluorescence after visible excitation, while variations within the pith parenchyma were shown via UV fluorescence. They depended on the inbred line, as revealed by the first four large PCA score distributions. Autofluorescence macroscopy combined with an adapted analysis of a series of large images is promising for the investigation of the spatial heterogeneity of tissue composition between and within organ sections. The method is easy to implement and can be easily extended to other multi-hyperspectral imaging techniques. The score distributions enable a global comparison of the images and an analysis of the inbred lines' effect. The interpretation of the tissue autofluorescence needs to be further investigated by using complementary spatially resolved techniques.

Spatiotemporal succession of phosphorous accumulating biofilms during the first year of establishment

Many wastewater treatment plants are dependent on the utilization of microorganisms in biofilms. Our knowledge about the establishment of these biofilms is limited, particular with respect to biofilms involved in enhanced biological phosphorus removal (EBPR). These biofilms rely on polyphosphate-accumulating organisms (PAOs), requiring alternating oxic and anaerobic conditions for phosphorous uptake. This challenge has been solved using the Hias process, which combines moving-bed biofilm-reactor (MBBR) technology with physical transfer of biofilm-carriers from oxic to anaerobic zones. We combined biofilm fractionation with temporal analyses to unveil the establishment in the Hias process. A stable phosphorous removal efficiency of >95% was reached within 16 weeks of operation. Phosphorus removal, however, was not correlated with the establishment of known PAOs. The biofilms seemed associated with an outer microbiota layer with rapid turnover and an inner layer with a slow expansion. The inner layer showed an overrepresentation of known PAOs. In conclusion, our spatiotemporal analyses of phosphorous accumulating biofilm establishment lead to a new model for biofilm growth, while the mechanisms for phosphorous removal remain largely unresolved.

Sensitivity considerations on denoising series of spectra by singular value decomposition

When acquiring series of spectra ( T 1 , T 2 , CP buildup curves, etc.) on samples with poor SNR, we are usually faced with choosing between taking a few points with a large number of scans to maximize the SNR or more points with a smaller number of scans to maximize the information content. In this Letter, we show how low-rank decomposition can be used to denoise a series of spectra, reducing the trade-off between the number of scans and the number of experiments.

Evaluating performance of SORS-based subsurface signal separation methods using statistical replication Monte Carlo simulation

Spatially offset Raman spectroscopy (SORS) is a depth-profiling technique with deep information enhancement. However, the interference of the surface layer cannot be eliminated without prior information. The signal separation method is an effective candidate for reconstructing pure subsurface Raman spectra, and there is still a lack of evaluation means for the signal separation method. Therefore, a method based on line-scan SORS combined with improved statistical replication Monte Carlo (SRMC) simulation was proposed to evaluate the effectiveness of food subsurface signal separation method. Firstly, SRMC simulates the photon flux in the sample, generates a corresponding number of Raman photons at each voxel of interest, and collects them by external map scanning. Then, 5625 groups of mixed signals with different optical characteristic parameters were convoluted with spectra of public database and application measurement and introduced into signal separation methods. The effectiveness and application range of the method were evaluated by the similarity between the separated signals and the source Raman spectra. Finally, the simulation results were verified by three packaged foods. FastICA method can effectively separate Raman signals from subsurface layer of food and thus promote deep quality evaluation of food.

Intermediates during the Nucleation of Platinum Nanoparticles by a Reaction with Ethylene Glycol: Operando X-ray Absorption Spectroscopy Studies with a Microfluidic Cell

Using operando X-ray absorption spectroscopy in a continuous-flow microfluidic cell, we have investigated the nucleation of platinum nanoparticles from aqueous hexachloroplatinate solution in the presence of the reducing agent ethylene glycol. By adjusting flow rates in the microfluidic channel, we resolved the temporal evolution of the reaction system in the first few seconds, generating the time profiles for speciation, ligand exchange, and reduction of Pt. Detailed analysis of the X-ray absorption near-edge structure and extended X-ray absorption fine structure spectra with multivariate data analysis shows that at least two reaction intermediates are involved in the transformation of the precursor H₂PtCl₆ to metallic platinum nanoparticles, including the formation of clusters with Pt-Pt bonding before complete reduction to Pt nanoparticles.

Resonant Raman-Active Polymer Dot Barcodes for Multiplex Cell Mapping

Resonance Raman spectroscopy is an efficient tool for multiplex imaging because of the narrow bandwidth of the electronically enhanced vibrational signals. However, Raman signals are often overwhelmed by concurrent fluorescence. In this study, we synthesized a series of truxene-based conjugated Raman probes to show structure-specific Raman fingerprint patterns with a common 532 nm light source. The subsequent polymer dot (Pdot) formation of the Raman probes efficiently suppressed fluorescence via aggregation-induced quenching and improved the dispersion stability of particles without leakage of Raman probes or particle agglomeration for more than 1 year. Additionally, the Raman signal amplified by electronic resonance and increased probe concentration exhibited over 10³ times higher relative Raman intensities versus 5-ethynyl-2'-deoxyuridine, enabling successful Raman imaging. Finally, multiplex Raman mapping was demonstrated with a single 532 nm laser using six Raman-active and biocompatible Pdots as barcodes for live cells. Resonant Raman-active Pdots may suggest a simple, robust, and efficient way for multiplex Raman imaging using a standard Raman spectrometer, suggesting the broad applicability of our strategy.

Multivariate curve resolution for kinetic modeling and scale-up prediction

Abstract

An imine synthesis was investigated in a nearly isothermal oscillating segmented flow microreactor at different temperatures using non-invasive Raman spectroscopy. Multivariate curve resolution provided a calibration-free approach for obtaining kinetic parameters. The two different multivariate curve resolution approaches, soft and hard modeling, were applied and contrasted, leading to similar results. Taking heat and mass balance into account, the proposed kinetic model was applied for a model-based scale-up prediction. Finally, the reaction was performed in a 0.5 L semi-batch reactor, followed by in-line Raman spectroscopy and off-line gas chromatography analysis. The successful scale-up was demonstrated with a good agreement between measured and predicted concentration profiles.

Highlights

• Oscillation segmented flow reactor with inline Raman spectroscopy.

• Multivariate Curve Resolution with hard and soft constraints.

• High quality kinetic model for scale-up predictions.

Graphical abstract

Raman microspectroscopy based TNM staging and grading of breast cancer

The tumor-node-metastasis (TNM) system is the most common way that doctors determine the anatomical extent of cancer on the basis of clinical and pathological criteria. In this study, a spectral histopathological study has been carried out to bridge Raman micro spectroscopy with the breast cancer TNM system. A total of seventy breast tissue samples, including healthy tissue, early, middle, and advanced cancer, were investigated to provide detailed insights into compositional and structural variations that accompany breast malignant evolution. After evaluating the main spectral variations in all tissue types, the generalized discriminant analysis (GDA) pathological diagnostic model was established to discriminate the TNM staging and grading information. Moreover, micro-Raman images were reconstructed by K-means clustering analysis (KCA) for visualizing the lobular acinar in healthy tissue and ductal structures in all early, middle and advanced breast cancer tissue groups. While, univariate imaging techniques were adapted to describe the distribution differences of biochemical components such as tryptophan, β-carotene, proteins, and lipids in the scanned regions. The achieved spectral histopathological results not only established a spectra-structure correlations via tissue biochemical profiles but also provided important data and discriminative model references for in vivo Raman-based breast cancer diagnosis.

Development of Spectral Imaging Cytometry

Spectral flow cytometry is a new technology that enables measurements of fluorescent spectra and light scattering properties in diverse cellular populations with high precision. Modern instruments allow simultaneous determination of up to 40+ fluorescent dyes with heavily overlapping emission spectra, discrimination of autofluorescent signals in the stained specimens, and detailed analysis of diverse autofluorescence of different cells-from mammalian to chlorophyll-containing cells like cyanobacteria. In this paper, we review the history, compare modern conventional and spectral flow cytometers, and discuss several applications of spectral flow cytometry.

Multivariate Curve Resolution Alternating Least Squares Analysis of In Vivo Skin Raman Spectra

In recent years, Raman spectroscopy has been used to study biological tissues. However, the analysis of experimental Raman spectra is still challenging, since the Raman spectra of most biological tissue components overlap significantly and it is difficult to separate individual components. New methods of analysis are needed that would allow for the decomposition of Raman spectra into components and the evaluation of their contribution. The aim of our work is to study the possibilities of the multivariate curve resolution alternating least squares (MCR-ALS) method for the analysis of skin tissues in vivo. We investigated the Raman spectra of human skin recorded using a portable conventional Raman spectroscopy setup. The MCR-ALS analysis was performed for the Raman spectra of normal skin, keratosis, basal cell carcinoma, malignant melanoma, and pigmented nevus. We obtained spectral profiles corresponding to the contribution of the optical system and skin components: melanin, proteins, lipids, water, etc. The obtained results show that the multivariate curve resolution alternating least squares analysis can provide new information on the biochemical profiles of skin tissues. Such information may be used in medical diagnostics to analyze Raman spectra with a low signal-to-noise ratio, as well as in various fields of science and industry for preprocessing Raman spectra to remove parasitic components.

Kinetic Model of Diclofenac Degradation Developed Using Multivariate Curve Resolution Method

This study presents the kinetic modeling of the natural long-term aging of the pharmaceutical substance as well as the intact tablets of Diclofenac. Datasets are collections of near-infrared spectra acquired from the intact tablets packed in plastic blisters and the spectra of the pure substance. Fresh samples and samples at different stages of degradation are analyzed. No methods of accelerated aging were applied. Multi-step application of MCR-ALS in its soft version followed by the kinetic modeling of the results helps to propose a generic degradation mechanism; which includes: a global kinetic model; approximations of the NIR spectra of the intermediate and product; rough estimates of rate constants. We study tablets in blister packs; exactly as they are presented in pharmacies; and this is important from a practical point of view.

Evaluation of CO2 and H2O Adsorption on a Porous Polymer Using DFT and In Situ DRIFT Spectroscopy

Numerous hyper-cross-linked polymers (HCPs) have been developed as CO₂ adsorbents and photocatalysts. Yet, little is known of the CO₂ and H₂O adsorption mechanisms on amorphous porous polymers. Gaining a better understanding of these mechanisms and determining the adsorption sites are key to the rational design of improved adsorbents and photocatalysts. Herein, we present a unique approach that combines density functional theory (DFT), in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), and multivariate spectral analysis to investigate CO₂ and H₂O adsorption sites on a triazine-biphenyl HCP. We found that CO₂ and H₂O adsorb on the same HCP sites albeit with different adsorption strengths. The primary amines of the triazines were identified as favoring strong CO₂ binding interactions. Given the potential use of HCPs for CO₂ photoreduction, we also investigated CO₂ and H₂O adsorption under transient light irradiation. Under irradiation, we observed partial CO₂ and H₂O desorption and a redistribution of interactions between the H₂O and CO₂ molecules that remain adsorbed at HCP adsorption sites.

Simultaneous Determination of Adulterants in Dietary Food Supplements Using Multivariate Data Analysis after Preconcentration with Novel Nanosorbents and Chromatographic Measurement

Background

The increasing popularity of dietary supplements and, consequently, related adulteration emphasizes the rising need to examine the association of food supplements with fraud. Intentional or unintentional fraud in food supplements by hazardous chemicals compounds is a problem that many countries are struggling with. Much effort have been made to effectively and reliably control the quality of food supplements.

Objective

Due to the importance of the subject, an analytical method for the simultaneous and reliable detection and quantitative determination of three key adulterants in dietary food supplements was developed. The proposed method benefits from analytical methods and multivariate calibration methods to progress the determination of adulterants in a complex matrix.

Methods

HPLC assisted by multivariate curve resolution-alternating least square (MCR-ALS) analysis was used to detect adulterants in real samples after separation and preconcentration using novel mesoporous carbon nanoparticles. Solid-phase extraction (SPE) optimization was accomplished by central composite design (CCD). In order to obtain the best results, the MCR-ALS model was compared with the parallel factor analysis 2 (PARAFAC2) model and validated by estimation of linearity, detection limits, and recovery.

Results

The detection limits and linear dynamics were calculated as 1.5, 4.27, and 4.77 µg/mL, and 1–50, 5–20, and 5–20 µg/mL for caffeine, ephedrine, and fluoxetine, respectively. Mean recovery for determination of caffeine, ephedrine, and fluoxetine using the developed method was reported as 101.75, 91.7, and 92.36, respectively.

Conclusion

The results showed that to avoid negative health outcomes associated with the excessive consumption of adulterated food supplements releasing such products should be carefully regulated. The developed method was validated using statistical factors and showed acceptable and reliable results.

Highlights

(1) The application of MCR-ALS coupled with HPLC-Diode-Array Detection data sets allowed the simultaneous identification and quantification of three key adulterants (caffeine, ephedrine, and fluoxetine) in dietary food supplements. (2) A small amount of the novel adsorbent was successfully used to preconcentrate the trace amounts of adulterants in samples. (3) This method benefits from the chemometrics tools and experimental design to significantly reduce the use of toxic solvents and complicated instruments to propose a less time-consuming method for quantification of multicomponents in the presence of uncalibrated interferents.

Cancer Stem Cell Derived Extracellular Vesicles with Self-Functionalized 3D Nanosensor for Real-Time Cancer Diagnosis: Eliminating the Roadblocks in Liquid Biopsy

Liquid biopsy for determining the presence of cancer and the underlying tissue of origin is crucial to overcome the limitations of existing tissue biopsy and imaging-based techniques by capturing critical information from the dynamic tumor heterogeneity. A newly emerging liquid biopsy with extracellular vesicles (EVs) is gaining momentum, but its clinical relevance is in question due to the biological and technical challenges posed by existing technologies. The biological barriers of existing technologies include the inability to generate fundamental details of molecular structure, chemical composition as well as functional variations in EVs by gathering simultaneous information on multiple intra-EV molecules, unavailability of holistic qualitative analysis, in addition to the inability to identify tissue of origin. Technological barriers include reliance on EV isolation with a few labeled biomarkers, resulting in the inability to generate comprehensive information on the disease. A more favorable approach would be to generate holistic information on the disease without the use of labels. Such a marker-free diagnosis is impossible with the existing liquid biopsy due to the unavailability clinically validated cancer stem cells (CSC)-specific markers and dependence of existing technologies on EV isolation, undermining the clinical relevance of EV-based liquid biopsy. Here, CSC EVs were employed as an independent liquid biopsy modality. We hypothesize that tracking the signals of CSCs in peripheral blood with CSC EVs will provide a reliable solution for accurate cancer diagnosis, as CSC are the originators of tumor contributing to tumor heterogeneity. We report nanoengineered 3D sensors of extremely small nano-scaled probes self-functionalized for SERS, enabling integrative molecular and functional profiling of otherwise undetectable CSC EVs. A substantially enhanced SERS and ultralow limit of detection (10 EVs per 10 μL) were achieved. This was attributed to the efficient probe-EV interaction due to the 3D networks of nanoprobes, ensuring simultaneous detection of multiple EV signals. We experimentally demonstrate the crucial role of CSC EVs in cancer diagnosis. We then completed a pilot validation of this modality for cancer detection as well as for identification of the tissue of origin. An artificial neural network distinguished cancer from noncancer with 100% sensitivity and 100% specificity for three hard to detect cancers (breast, lung, and colorectal cancer). Binary classification to distinguish one tissue of origin against all other achieved 100% accuracy, while simultaneous identification of all three tissues of origin with multiclass classification achieved up to 79% accuracy. This noninvasive tool may complement existing cancer diagnostics, treatment monitoring as well as longitudinal disease monitoring by validation with a large cohort of clinical samples.

Coherent anti-Stokes Raman scattering cell imaging and segmentation with unsupervised data analysis

Coherent Raman imaging has been extensively applied to live-cell imaging in the last 2 decades, allowing to probe the intracellular lipid, protein, nucleic acid, and water content with a high-acquisition rate and sensitivity. In this context, multiplex coherent anti-Stokes Raman scattering (MCARS) microspectroscopy using sub-nanosecond laser pulses is now recognized as a mature and straightforward technology for label-free bioimaging, offering the high spectral resolution of conventional Raman spectroscopy with reduced acquisition time. Here, we introduce the combination of the MCARS imaging technique with unsupervised data analysis based on multivariate curve resolution (MCR). The MCR process is implemented under the classical signal non-negativity constraint and, even more originally, under a new spatial constraint based on cell segmentation. We thus introduce a new methodology for hyperspectral cell imaging and segmentation, based on a simple, unsupervised workflow without any spectrum-to-spectrum phase retrieval computation. We first assess the robustness of our approach by considering cells of different types, namely, from the human HEK293 and murine C2C12 lines. To evaluate its applicability over a broader range, we then study HEK293 cells in different physiological states and experimental situations. Specifically, we compare an interphasic cell with a mitotic (prophase) one. We also present a comparison between a fixed cell and a living cell, in order to visualize the potential changes induced by the fixation protocol in cellular architecture. Next, with the aim of assessing more precisely the sensitivity of our approach, we study HEK293 living cells overexpressing tropomyosin-related kinase B (TrkB), a cancer-related membrane receptor, depending on the presence of its ligand, brain-derived neurotrophic factor (BDNF). Finally, the segmentation capability of the approach is evaluated in the case of a single cell and also by considering cell clusters of various sizes.

Estimating more than two pure component spectra from only two mixture spectra using two-dimensional correlation

A procedure is described to estimate the pure component spectra of mixtures from only a pair of available spectra even when there are more than two component species present in the system. In contrast, traditional multivariate curve resolution (MCR) technique cannot be used for such a case. The method relies on the use of two-trace two-dimensional (2T2D) correlation spectroscopy. Asynchronous 2T2D spectrum is used to identify the characteristic bands most strongly associated with the individual mixture component species. Correlation coefficients derived from the synchronous 2T2D spectrum are used to obtain a set of correlative filtering functions to distribute the spectral intensity of the average spectrum among the estimates of the pure component spectra. Efficacy of the method was demonstrated using a pair of ATR IR spectra obtained for two solution mixtures containing three main ingredients with very similar compositions. Relatively congested and overlapped spectral region was used first for the demonstration, and reasonable resolution was accomplished yielding a set of the estimates of pure component spectra with most of the expected pertinent features included. The analysis was then extended to a broader spectral region containing well-isolated spectral signatures of individual components for positive validation. While traditional MCR technique seems to perform better with a large number of spectra, this technique can be effectively used in conjunction with MCR to improve its stability and performance, especially under some challenging conditions.

A packaged food internal Raman signal separation method based on spatially offset Raman spectroscopy combined with FastICA

Raman spectroscopy attempts to reflect food quality by characterizing molecular vibration and rotation. However, the blocking of optical signals by packaging materials and the interference of the optical signal generated by the packaging itself make the detection of internal food quality without destroying packaging highly difficult. In this regard, this paper proposes a novel packaged food internal signal separation based on spatially offset Raman spectroscopy (SORS) coupled with improved fast independent component analysis (FastICA). Firstly, the Raman scattering image of the packaged food with offset laser incident point was obtained. Then, the movable quadratic mean of information entropy was used to select the observation feature region of the image. Thirdly, the main independents decomposed by the optimized FastICA method were identified by spectral attenuation characteristics of the SORS peak signal. Finally, the non-negativity of the separated signal was ensured by baseline recognition and correction. The effectiveness of this method was verified by refactoring the similarity between the signal and the reference signal by testing three different packaging and four internal materials under standard experimental conditions. The applicability of the method was proved by the internal signal separation of three packaged foods on sale. The experimental results indicate that the proposed method can separate the Raman signal of packaged food and can be used as a pretreatment method and auxiliary analysis means for the detection of packaged food.

A tutorial on multi-way data processing of excitation-emission fluorescence matrices acquired from semiconductor quantum dots sensing platforms

This tutorial demonstrates how to exploit the second-order advantage on excitation-emission fluorescence matrices (EEFMs) acquired from sensing platforms based on analyte-triggered semiconductor quantum dots (QDs) fluorescence modulation (quenching/enhancing). The advantage in processing such second-order EEFMs data from complex samples, seeking successful quantification, is comprehensively addressed. It is worth emphasizing that, aiming to exploit the second-order advantage, the selection of the most appropriate advanced chemometric model should rely on the matching between the data structure and the physicochemical chemometric model assumption. In this sense, the achievement of second-order advantage after EEFMs' processing is extensively addressed throughout this tutorial taking into consideration three different analytical situations, each involving a specific data structure: i) parallel factor analysis (PARAFAC), which is applied in a real dataset stacked in a three-way data array containing a trilinear data structure acquired from QDs-based detection with non-selective species; ii) multivariate curve resolution - alternating least-squares (MCR-ALS), which is also employed in a real dataset arranged in an augmented data matrix containing non-trilinear data structure acquired from QDs-based detection with a single breaking mode caused by background signals; iii) unfolded partial least-squares with residual bilinearization (U-PLS/RBL), which is applied in a dataset containing non-trilinear data acquired from a classical fluorescence system with two breaking modes caused by inner filter effect (IFE) in both instrumental modes (excitation and emission). The latter challenging data structure can be acquired via fluorescence quenching from IFE-based sensing platforms and chemometrically handled in two main steps. First, a set of calibration EEFMs data is converted into an unfolded data matrix during the unfolding process, followed by applying U-PLS model. Second, a post-calibration procedure using RBL analysis is applied to a test sample of EEFM maintained in its matrix form, in order to handle potential interferents. In the last section, the state-of-the-art of second-order EEFMs data acquired from semiconductor QDs-based sensing platforms and coupled to multi-way fluorescence data processing to accomplish a successful quantification, even with substantial interfering species, is critically reviewed.

Towards Raman imaging of centimeter scale tissue areas for real-time opto-molecular visualization of tissue boundaries for clinical applications

Raman spectroscopy combined with augmented reality and mixed reality to reconstruct molecular information of tissue surface.

Random swapping, an effective and efficient way to boost the intensities of cross peaks in a 2D asynchronous spectrum

Analysis of mixture via chromatographic-spectroscopic and analogous experiments is a common task in analytical chemistry. A 2D/nD asynchronous spectrum is effective in retrieving spectra of pure substances even if different components cannot be separated. However, noise in the 2D/nD asynchronous spectrum becomes a bottleneck in the analysis. Finding a suitable sequence of the 1D spectra used in constructing the 2D/nD asynchronous spectrum is helpful to improve the signal-to-noise level. A 2D/nD asynchronous spectrum is often produced via a large number of 1D spectra. The resultant colossal number of the possible sequences makes stochastic search the only possible way to find a suitable sequence. Random changing (RC) and random swapping (RS) are two ways to obtain a new sequence. We found that the possibility of finding a better sequence via an RS is significantly higher than that via an RC in the advanced stage of stochastic searching. This is the reason why the performance of RS is superior to that of RC in two model systems where 2D asynchronous spectra are used. We applied the RS approach on the analysis of water/isopropanol mixtures, and satisfactory sequences are acquired with affordable computational cost. Thus, the RS approach brings about an opportunity increase the signal-to-noise level of a 2D asynchronous spectrum in the analysis of the bilinear data from complex mixed samples.