Analyte Spectra Research Articles

Enhancement of broadband terahertz trace absorption spectrum based on angular multiplexing of trapezoidal dielectric grating

Terahertz spectroscopy provide a fast, label-free, and non-destructive method for detecting bio-medical molecules, with wide applications in national security, pharmacy, and healthcare. However, detecting terahertz absorption spectra of trace analytes still faces significant challenges. In recent years, metamaterial parameter multiplexing technology has been used to detect the terahertz fingerprint spectra of trace substances. This paper proposes a new structure based on the angle-multiplexing enhancement principle of a trapezoidal dielectric grating to enhance the terahertz absorption spectra of thin-film analytes. By scanning the incident wave angle, a series of resonance absorption peaks can be obtained. The enhanced absorption spectrum formed by the envelope of absorption spectrum peaks increases by 141 times compared to directly measuring the absorption spectrum of the same film. This structure provides a new and effective means for terahertz trace detection using metamaterial parameter multiplexing technology.

Local Modeling by Adapting Source Calibration Models to Analyte Shifted Target Domain Samples Without Reference Values.

Spectral multivariate calibration aims to derive models characterizing mathematical relationships between sample analyte amounts and corresponding spectral responses. These models are effective at predicting target domain sample analyte amounts when target samples are within the analyte and spectral calibration source domain. Models fail when target samples shift (analyte amounts and/or spectra) from the original calibration domain model. A total recalibration solution requires acquisition of new sample reference values and spectra. However, obtaining enough reference values to distinguish the target domain may be challenging or expensive. A simpler approach adapts the original model to the target domain using target sample spectra without analyte reference values (unlabeled). Analytical chemists have developed several machine learning algorithms using unlabeled regression domain adaptation processes. Unfortunately, prediction accuracy declines for these methods depending on how much the target domain analyte distribution has shifted from the calibration distribution, and regression transfer learning methods are instead needed. Regression domain adaptation and transfer learning are often referred to as model updating in analytical chemistry, but regression domain adaptation only applies to spectral shifts. The regression transfer learning method presented in this paper named null augmentation regression constant analyte (NARCA) leverages unlabeled repeat spectra of a single target sample to update an original calibration model to the shifted target domain sample. With sample repeat spectra, the analyte amount can be assumed constant or nearly constant for NARCA and because models are formed for one sample, NARCA operates as a local modeling method. The performance of NARCA as a regression transfer learning method is evaluated using five near-infrared data sets.

Imaging-based terahertz pixelated metamaterials for molecular fingerprint sensing.

With the rapid development of terahertz-enabled devices, the study of miniaturized and integrated systems has attracted significant attention. We experimentally demonstrate an imaging-based pixelated metamaterial for detecting terahertz molecular fingerprints related to intermolecular vibrations and large-amplitude intramolecular modes, including chemical identification and compositional analysis. The compact THz sensor consists of a 4 × 4 pixelated filter-detector array with transmission resonances tuned to discrete frequencies. The absorption spectra of analytes are computationally reconstructed from different spectral responses of meta-pixels, and the resulting information is characterized via near-field imaging. Due to the spectrometer-less operation principle, such imaging-based approaches provide an alternative method for developing sensitive, versatile, and miniaturized THz biosensors, especially for practical field deployment applications.

Automated identification of pesticide mixtures via machine learning analysis of TLC-SERS spectra

Identification of components in pesticide mixtures has been a major challenge in spectral analysis. In this paper, we assembled monolayer Ag nanoparticles on Thin-layer chromatography (TLC) plates to prepare TLC-Ag substrates with mixture separation and surface-enhanced Raman scattering (SERS) detection. Spectral scans were performed along the longitudinal direction of the TLC-Ag substrate to generate SERS spectra of all target analytes on the TLC plate. Convolutional neural network classification and spectral angle similarity machine learning algorithms were used to identify pesticide information from the TLC-SERS spectra. It was shown that the proposed automated spectral analysis method successfully classified five categories, including four pesticides (thiram, triadimefon, benzimidazole, thiamethoxam) as well as a blank TLC-Ag data control. The location of each pesticide on the TLC plate was determined by the intersection of the information curves of the two algorithms with 100 % accuracy. Therefore, this method is expected to help regulators understand the residues of mixed pesticides in agricultural products and reduce the potential risk of agricultural products to human health and the environment.

Deep Learning Enabled SERS Identification of Gaseous Molecules on Flexible Plasmonic MOF Nanowire Films.

Through the capture of a target molecule at the metal surface with a highly confined electromagnetic field induced by surface plasmon, surface enhanced Raman spectroscopy (SERS) emerges as a spectral analysis technology with high sensitivity. However, accurate SERS identification of a gaseous molecule with low density and high velocity is still a challenge due to its difficulty in capture. In this work, a flexible paper-based plasmonic metal-organic framework (MOF) film consisting of Ag nanowires@ZIF-8 (AgNWs@ZIF-8) is fabricated for SERS detection of gaseous molecules. Benefiting from its micronanopores generated by the nanowire network and ZIF-8 shell, the effective capture of the gaseous molecule is achieved, and its SERS spectrum is obtained in this paper-based flexible plasmonic MOF nanowire film. With optimal structure parameters, spectra of gaseous 4-aminothiophenol, 4-mercaptophenol, and dithiohydroquinone demonstrate that this film has good SERS performance, which could maintain obvious Raman signals within 30 days during reproducible detection. To realize SERS identification of gaseous molecules, deep learning is performed based on the SERS spectra of the mixed gaseous analyte obtained in this flexible porous film. The results point out that an artificial neural network algorithm could identify gaseous aldehydes (gaseous biomarker of colorectal cancer) in simulated exhaled breath with high accuracy at 93.7%. The integration of the flexible paper-based film sensors with deep learning offers a promising new approach for noninvasive colorectal cancer screening. Our work explores SERS applications in gaseous analyte detection and has broad potential in clinical medicine, food safety, environmental monitoring, etc.

Reference-free quantitative mass spectrometry in the presence of nonlinear distortion caused by in situ chemical reactions among constituents.

Materials performance is primarily influenced by chemical composition, making compositional analysis (CA) essential in materials science. Traditional quantitative mass spectrometry, which deconvolutes analyte spectra into reference spectra, struggles with reactive systems where spectral variations occur, such as peak shifts and new peak emergences. Additionally, obtaining reference spectra for all pure constituents is often impractical. To address these challenges, I propose nonlinear reference-free quantitative mass spectrometry (NL-RQMS). This method simultaneously determines composition, reference spectra, and nonlinear interaction effects directly from a spectral dataset of mixtures, eliminating the need for prior reference spectra. In a benchmark test on ternary reactive polymers of epoxy and amines, NL-RQMS inferred compositions with an error margin of just 3 wt%, significantly outperforming the 6 wt% error margin of linear RQMS. The inferred interaction terms clearly indicate in situ reactions between epoxy and amine moieties. This framework enables accurate compositional analysis without prior knowledge of the constituents, even in systems with interactive components, and holds significant potential for applications such as grading recycled plastics, where pristine materials, degradation compounds, and stabilizers interact complexly, causing nonlinear spectral distortions.

Rapid detection of Pseudomonas syringae pv. actinidiae by electrochemical surface-enhanced Raman spectroscopy

Bacterial cancer caused by Pseudomonas syringae pv. actinidiae (Psa) is a major threat to kiwifruit in the world, and there is still a lack of effective control measures. The field of bacterial detection needs a fast, easy-to-use and sensitive identification platform. The current bacterial identification methods are lack of time efficiency, which brings problems to many sectors of society. Surface-enhanced Raman spectroscopy (SERS) and electrochemistry (EC) have been studied as possible candidates for bacterial detection because of their high sensitivity for the detection of biomolecules. In this work, SERS, EC and electrochemical surface-enhanced Raman spectroscopy (EC-SERS) were used for the first time to study the adsorption and EC behavior of Psa on the surface of nanostructured silver electrodes. Two different Raman spectra of a single analyte were obtained, and this dual detection was realized. Silver nanoparticles with iodide and calcium ions (Ag@ICNPs) were synthesized as SERS substrates significantly enhanced the characteristic signal peaks of Psa, and the limit of detection (LOD) is as low as 1.0 × 102 cfu/mL. Chemical imaging results show that the application of negative voltage can significantly improve the spectrum quality, showing a higher signal at −0.8 V, indicating that Psa molecules may have potential-induced reorientation on the electrode surface. Therefore, EC-SERS has the ability to greatly improve the SERS performance of bacteria in terms of peak intensity and spectral richness.

Chemometrics-assisted excitation-emission matrix fluorescence spectroscopy for real-time migration monitoring of multiple polycyclic aromatic hydrocarbons from plastic products to food simulants

Polycyclic aromatic hydrocarbons (PAHs), as a class of organic pollutants that have attracted much attention, are likely to be formed with the production and processing of plastic products, and they may migrate from contaminated plastic products to food, causing the risk of poisoning or cancer. In this study, migration tests were carried out on disposable plastic products for food contact, and a novel strategy that combines excitation-emission matrix (EEM) fluorescence spectroscopy with the advanced second-order calibration method based on the three-direction resection alternating trilinear decomposition (TDR-ATLD) algorithm was used to monitor the migration of three PAHs anthracene (ANT), pyrene (PYR), and phenanthrene (PHE) from the plastic products to food simulants in real-time. With the “second-order advantage”, even if the fluorescence spectra of the target analytes overlapped seriously, and other unknown substances migrated from the plastic products to food simulants, accurate qualitative and quantitative results were still obtained by the proposed method. In the static system, the coefficient of determination (R2) of the three PAHs within the calibration range were all greater than 0.99, and the average spiked recoveries were 99.5-107.1%, with the standard deviation lower than 8.9%. The figures of merit (FOMs) and intra- or inter-day precision also showed good feasibility and reliability of the method. In the simulation study of the migration kinetic process, three PAHs can be quantified in real-time in complex matrix, then the related migration equations were established. The results indicate that the proposed method can be used for real-time migration quantitative monitoring of PAHs, providing a potential and available method for the study of the migration kinetics of hazardous substances from food contact materials to food or food simulants.

Effect of pH of Ganoderma lucidum aqueous extract on green synthesis of silver nanoparticles

Perspective applications of Ag nanoparticles (NPs) stimulate intense research on their affordable synthesis, including green routes. The use of fungi extracts has advantages over other organisms, because of their relatively easy isolation and higher efficiency in a reduction of metal ions and NP formation. Here we report mycosynthesis of AgNPs based on Ganoderma lucidum fruit body extract with different pH values as the bioreducing and stabilising agent. Stable NPs colloids with distinct plasmonic resonance peaking at 408–418 nm are obtained by using photoreduction in a broad pH range (5 to 11). Synthesis efficiency drops only at very acidic conditions, pH = 2.5. The NP size and morphology are studied by dynamic light scattering and scanning electron microscopy. An analysis of FTIR spectra of pure analyte and NP sample indicates that stabilisation of the AgNPs by the components of Ganoderma lucidum extract may take place via forming chemical bonds with the NP surface. These bio-friendly AgNPs are both optically and chemically active, as inferred from surface-enhanced Raman scattering of a standard dye analytes and charge transfer-induced quenching of the photoluminescence of both dye and inorganic NPs, and can be studied for various applications which require direct access to the AgNP surface.

Smart eco-friendly mathematically manipulated UV spectroscopic methods to resolve severely overlapped spectra of a binary mixture of dapagliflozin with sitagliptin and vildagliptin

The binary formulations consisting of dapagliflozin with sitagliptin and vildagliptin are prescribed for the management of diabetes. The current study presents novel eco-friendly ultraviolet spectrophotometric methods for concurrent quantification of dapagliflozin with sitagliptin or vildagliptin in their binary formulations. The ratio spectra of series of sitagliptin, vildagliptin, and dapagliflozin were generated using dapagliflozin 7.5 µg/mL, sitagliptin 10 µg/mL, and vildagliptin 10 µg/mL as divisor than the ratio difference and ratio first derivative techniques were established. Also, the parent spectra of analytes were extracted from the mixture spectrum by successive mathematical filtration for quantification of individual analytes from the binary mixture without prior separation. The procedure showed good correlation in the series of 5–100 µg/mL for sitagliptin, 7.5–75 µg/mL for vildagliptin, and 1–15 µg/mL for dapagliflozin with an excellent correlation coefficient and good levels of detection and quantification. For the quantification of dapagliflozin levels in combination with different analytes, a single calibration curve was constructed. To create repeatable methods, the wavelength selection for measuring the peak amplitude and analyte concentration of divisor spectra was carefully optimized. The explored methods showed a low relative error percentage of 0.44–1.52, 0.20–0.94, and 0.02–1.22 and relative standard deviation percentages of 0.47–1.44, 1.16–1.41, and 0.94–1.81 for SGT, VGT, and DAP respectively, which confirmed the trueness and precision of the established processes. The validity of the ultraviolet methods was confirmed by quantifying the laboratory mixtures with varying dapagliflozin, sitagliptin, and vildagliptin ratios in addition to the standard addition technique. Additionally, the method's greenness evaluated by hexagonal and AGREE models and whiteness by the red–greenblue model demonstrated greater environmental sustainability in terms of safety and waste generated. Thus, utilizing environmentally friendly UV spectrophotometric techniques, a binary formulation including dapagliflozin + sitagliptin and dapagliflozin + vildagliptin can be evaluated for routine quality control.

Towards non-target proactive food safety: identification of active compounds in convenience tomato products by ten-dimensional hyphenation with integrated simulated gastrointestinal digestion

Current strategies for non-target food screening focus mainly on known hazardous chemicals (adulterants, residues, contaminants, packaging migrants, etc.) instead of bioactive constituents in general and exclude the biological effect detection. To widen the perspective, a more proactive non-target effect-directed strategy is introduced to complement food safety in order to detect not only known but also unknown bioactive compounds. The developed 10-dimensional hyphenation included on-surface digestion (1D), planar chromatographic separation (2D), visualization using white light (3D), UV light (4D), fluorescence light (5D), effect-directed assay analysis (6D), heart-cut zone elution to an orthogonal reversed phase column chromatography including online desalting (7D) with subsequent diode array detection (8D), high-resolution mass spectrometry (9D), and fragmentation (10D). Metabolism, i.e., intestinal digestion of each sample, was simulated and integrated on the same adsorbent surface to study any changes in the compound profiles. As proof of principle, nine convenience tomato products and a freshly prepared tomato soup were screened via five different planar assays in a non-targeted mode. Non-digested and digested samples were compared side by side. In their effect-directed profiles, 14 bioactive compounds from classes of lipids, plant hormones, spices, and pesticides were identified. In particular, bioactive compounds coming from the lipid class were increased by gastrointestinal digestion, while spices and pesticides remained unaffected. With regard to food safety, the determination of the two dinitrophenol herbicides dinoterb and dinoseb in highly processed tomato products should be given special attention. The hyphenation covered a broad analyte spectrum and showed robust and reliable results.Graphical

Silver nanoparticles - laser induced graphene (Ag NPs - LIG) hybrid electrodes for sensitive electrochemical-surface enhanced Raman spectroscopy (EC-SERS) detection.

This paper presents a novel approach for the fabrication of low cost Electrochemical-Surface Enhanced Raman Scattering (EC-SERS) sensing platforms. Laser Induced Graphene (LIG) electrodes were readily fabricated by direct laser writing of polyimide tapes and functionalized with silver nanoparticles (Ag NPs) to obtain hybrid Ag NPs - LIG electrodes suitable for EC-SERS analysis. Detection was achieved by coupling a handheld potentiostat with a Raman spectrograph, enabling measurement of SERS spectra of target analytes generated during voltage sweeps in the 0.0 to -1.0 V interval range. The sensing capabilities of the fabricated system were first tested with model molecule 4-aminobenzenethiol (4-ABT). Following sensitive detection of 4-ABT, EC-SERS analysis of food contaminant melamine in milk and antibiotic difloxacin hydrochloride (DIF) in river water was demonstrated, achieving sensitive detection of both analytes without pre-treatment steps. The easiness of fabrication, versatility of design, rapid analysis time and potential miniaturization of the system make Ag NPs - LIG electrodes suitable for a large range of in situ applications in the field of food monitoring and for environmental analysis.

Dual-Beam Photothermal Spectroscopy Employing a Mach-Zehnder Interferometer and an External Cavity Quantum Cascade Laser for Detection of Water Traces in Organic Solvents.

We report on a mid-infrared (mid-IR) photothermal spectrometer for liquid-phase samples for the detection of water in organic solvents, such as ethanol or chloroform, and in complex mixtures, such as jet fuel. The spectrometer is based on a Mach-Zehnder interferometer (MZI) employing a He-Ne laser, a mini-flow cell with two embedded channels placed in the interferometer's arms, and a tunable external cavity quantum cascade laser (EC-QCL) for selective analyte excitation in a collinear arrangement. In this study, the bending vibration of water in the spectral range 1565-1725 cm-1 is targeted. The interferometer is locked to its quadrature point (QP) for most stable and automated operation. It provides a linear response with respect to the water content in the studied solvents and photothermal analyte spectra, which are in good agreement with FTIR absorbance spectra. The method is calibrated and validated against coulometric Karl Fischer (KF) titration, showing comparable performance and sensitivity. Limits of detection (LODs) for water detection in the single-digit ppm range were obtained for chloroform and jet fuel due to their low background absorption, whereas lower sensitivity has been observed for water detection in ethanol due to pronounced background absorption from the solvent. In contrast to KF titration, which requires toxic reagents and produces waste, the developed method works reagent-free. It can be applied in an online format in the chemical industry as well as for fuel quality control, being industrial applications where traces of water need to be accurately determined, preferably in real-time. It thus holds great promise as a green alternative to the offline KF titration method, which is the current standard method for this application.

A Data Set Comparison Method Using Noise Statistics Applied to VUV Spectrum Match Determinations.

It has been demonstrated that a pair of spectra exhibiting a coefficient of determination (R2) as low as 0.976 can originate from the same chemical species in one example, while a different pair of spectra exhibiting an R2 up to 0.9997 can originate from different chemical species. The R2 between spectra overlays depends on the signal-to-noise ratio, while the residual between any two spectra should look like noise only when the two spectra originate from the same chemical species. Numerical characteristics of the residual between two high-resolution spectra are invaluable toward the definitive elimination of many plausible matches of reference spectra to the sample spectra of analytes eluted from two-dimensional gas chromatography. Additionally, numerical characteristics beyond R2 facilitate a logical ranking of all plausible matches, making positive identification of a single-component analyte possible provided a reference spectrum exists for all plausible matches. Specifically, the experimental background noise is shown to follow a Gaussian distribution at all wavelengths, and a method is described to normalize the data such that the numerically adjusted noise distributions are independent of wavelength. The differences between matching spectra are further shown to exhibit numerical characteristics consistent with the background noise's Gaussian distribution, common to all wavelengths. Seven criteria are described for judging the similarity between spectra: R2 between the two spectra, R2 of a Q-Q plot with one axis being ideal Gaussian quantiles and the other axis being the distribution of the numerically adjusted residual quantiles, the maximum count of consecutive (by wavelength) signs in the residual, and the first four moments of the residuals. One exemplar application of the methodology is a definitive match of n-undecane, n-dodecane, and n-tridecane sample spectra to their corresponding reference spectrum, which is among the most challenging set of species within the volatility range of jet fuel to differentiate by spectral methods. While this example is a significant stress test of the approach, the utility of the methodology generally is in the subtle math and transparent criteria that unambiguously identify mismatches because the distributions of residuals between mismatching spectra are very clearly not Gaussian and have a high consecutive sign count, even in cases where the R2 between the compared spectra is ambiguous.

Comprehensive detection of analytes in large chromatographic datasets by coupling factor analysis with a decision tree

Abstract. Environmental samples typically contain hundreds or thousands of unique organic compounds, and even minor components may provide valuable insight into their sources and transformations. To understand atmospheric processes, individual components are frequently identified and quantified using gas chromatography–mass spectrometry. However, due to the complexity and frequently variable nature of such data, data reduction is a significant bottleneck in analysis. Consequently, only a subset of known analytes is often reported for a dataset, and large amounts of potentially useful data are discarded. We present an automated approach of cataloging and potentially identifying all analytes in a large chromatographic dataset and demonstrate the utility of our approach in an analysis of ambient aerosols. We use a coupled factor analysis–decision tree approach to deconvolute peaks and comprehensively catalog nearly all analytes in a dataset. Positive matrix factorization (PMF) of small subsections of multiple chromatograms is applied to extract factors that represent chromatographic profiles and mass spectra of potential analytes, in which peaks are detected. A decision tree based on peak parameters (e.g., location, width, and height), relative ratios of those parameters, peak shape, noise, retention time, and mass spectrum is applied to discard erroneous peaks and combine peaks determined to represent the same analyte. With our approach, all analytes within the small section of the chromatogram are cataloged, and the process is repeated for overlapping sections across the chromatogram, generating a complete list of the retention times and estimated mass spectra of all peaks in a dataset. We validate this approach using samples of known compounds and demonstrate the separation of poorly resolved peaks with similar mass spectra and the resolution of peaks that appear in only a fraction of chromatograms. As a case study, this method is applied to a complex real-world dataset of the composition of atmospheric particles, in which more than 1100 unique chromatographic peaks are resolved, and the corresponding peak information along with mass spectra are cataloged.

Chemometrics for Raman Spectroscopy Harmonization.

Raman spectroscopy is used in a wide variety of fields, and in a plethora of different configurations. Raman spectra of simple analytes can often be analyzed using univariate approaches and interpreted in a straightforward manner. For more complex spetral data such as time series or line profiles (1D), Raman maps (2D), or even volumes (3D), multivariate data analysis (MVDA) becomes a requirement. Even though there are some existing standards for creation, implementation, and validation of methods and models employed in industry and academics, further research and development in the field must contribute to their improvement. This review will cover, in broad terms, existing techniques as well as new developments for MVDA for Raman spectroscopic data, and in particular the use associated with instrumentation and data calibration. Chemometric models are often generated via fusion of analytical data from different sources, which enhances model discrimination and prediction abilities as compared to models derived from a single data source. For Raman spectroscopy, raw or unprocessed data is rarely ever used. Instead, spectra are usually corrected and manipulated,1 often by case-specific rather than universal methods. Calibration models can be used to characterize qualitatively and/or quantitatively samples measured with the same instrumentation that was used to create the model. However, regular validation is required to ensure that aging or incorrect maintenance of the instrument does not alter the model's predictions, particularly when applied in regulated fields such as pharmaceuticals. Furthermore, a model transfer may be required for different reasons, such as replacement or significant repair of the instrumentation. Modeling can also be used to consistently harmonize Raman spectroscopic data across several instrumental designs, accounting for variations in the resulting spectrum induced by different components. Data for Raman harmonization models should be processed in a protocolled manner, and the original data accessible to allow for model reconstruction or transfer when new data is added. Important processing steps will be the calibration of the spectral axes and instrument dependent effects, such as spectral resolution. In addition, data fusion and model transfer are essential for allowing new instrumentation to build on existing models to harmonize their own data. Ideally, an open access database would be created and maintained, for the purpose of allowing for continued harmonization of new Raman instruments using an outlined and accepted protocol.

Environmental sustainable mathematically processed UV spectroscopic methods for quality control analysis of remogliflozin and teneligliptin: Evaluation of greenness and whiteness

Environmental sustainable analytical methods were developed by mathematical modification of UV absorption spectra for quality control study of multicomponent formulations consisting of remogliflozin (REM) and teneligliptin (TEN), with good sensitivity and selectivity. Then analytes were quantified by measuring the peak amplitude of the first derivative spectra at zero crossing points at 230.2 nm and 213.8 nm for REG and TEN in the first derivative method. The second method involves the formation of ratio spectra and taking the absorption difference at two selected wavelengths of peak and trough of a spectrum. In the ratio first derivative method peak amplitudes were measured at 235.2 nm and 259.1 nm for simultaneous quantification of REM and TEN respectively. The fourth method was based on the measurement of the peak amplitude of zero-order spectra of analytes generated from the mixture spectrum by subtraction of a constant from the ratio spectrum followed by multiplication with divisor spectrum, Further, the proposed methods were validated systematically to confirm the linearity, precession, accuracy, sensitivity, and selectivity. Finally, validated UV spectroscopic methods were applied for simultaneous quantification of REM and TEN from formulation, and laboratory mixed solutions and statistically compared with the reported HPLC method. Further, recently developed AGREE, Hexagonal greenness and white analytical chemistry, a whiteness evaluation tools were applied to the proposed UV spectroscopic methods and found to be safer analytical methods, compared to the reported expensive, time-consuming and toxic HPLC method. Hence, proposed UV spectroscopic methods could be used for routine quality control of formulations containing REM and TEN.

Tile-Based Pairwise Analysis of GC × GC-TOFMS Data to Facilitate Analyte Discovery and Mass Spectrum Purification.

A new tile-based pairwise analysis workflow, termed 1v1 analysis, is presented to discover and identify analytes that differentiate two chromatograms collected using comprehensive two-dimensional (2D) gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS). Tile-based 1v1 analysis easily discovered all 18 non-native analytes spiked in diesel fuel within the top 30 hits, outperforming standard pairwise chromatographic analyses. However, eight spiked analytes could not be identified with multivariate curve resolution-alternating least-squares (MCR-ALS) nor parallel factor analysis (PARAFAC) due to background contamination. Analyte identification was achieved with class comparison enabled-mass spectrum purification (CCE-MSP), which obtains a pure analyte spectrum by normalizing the spectra to an interferent mass channel (m/z) identified from 1v1 analysis and subtracting the two spectra. This report also details the development of CCE-MSP assisted MCR-ALS, which removes the identified interferent m/z from the data prior to decomposition. In total, 17 out of 18 spiked analytes had a match value (MV) > 800 with both versions of CCE-MSP. For example, MCR-ALS and PARAFAC were unable to decompose the pure spectrum of methyl decanoate (MVs < 200) due to its low 2D chromatographic resolution (∼0.34) and high interferent-to-analyte signal ratio (∼30:1). By leveraging information gained from 1v1 analysis, CCE-MSP and CCE-MSP assisted MCR-ALS obtained a pure spectrum with an average MV of 908 and 964, respectively. Furthermore, tile-based 1v1 analysis was applied to track moisture damage in cacao beans, where 86 analytes with at least a 2-fold concentration change were discovered between the unmolded and molded samples. This 1v1 analysis workflow is beneficial for studies where multiple replicates are either unavailable or undesirable to save analysis time.

The Use of Constituent Spectra and Weighting in Extended Multiplicative Signal Correction in Infrared Spectroscopy

Extended multiplicative signal correction (EMSC) is a widely used preprocessing technique in infrared spectroscopy. EMSC is a model-based method favored for its flexibility and versatility. The model can be extended by adding constituent spectra to explicitly model-known analytes or interferents. This paper addresses the use of constituent spectra and demonstrates common pitfalls. It clarifies the difference between analyte and interferent spectra, and the importance of orthogonality between model spectra. Different normalization approaches are discussed, and the importance of weighting in the EMSC is demonstrated. The paper illustrates how constituent analyte spectra can be estimated, and how they can be used to extract additional information from spectral features. It is shown that the EMSC parameters can be used in both regression tasks and segmentation tasks.

Plasma Discharge Ionization Emission Spectra of Analytes and SARS-CoV-2 for Label-Free Sensing Applications

Ionization spectra of substances are extensively used in their label free detection. Here we demonstrate the possibility of using plasma ionization to detect airborne and saliva SARS-COV-2 viruses through their emission spectra. It consists of an ionization chamber monitored by a fiber-optic UV-VIS spectrometer. The technique is completely label-free and can be programmed in real-time to detect different viral particles through their ionization emission spectra. Its average sensitivity for detecting deoxyribonucleic acid (DNA) bases in water is 20%/g in 1 mL of water. Its selectivity for DNA bases is through their relative emission peaks for adenine at 439.5 nm, cytosine at 440, thymine at 440.5, and guanine at 421.5 nm. The emission spectra of different electrode materials were also obtained to account for their contributions to the emission spectra of analytes. Gold electrodes were used owing to their resistance to corrosion and very low reaction with ionized species. The technique has the potential to be used in the point-of-care diagnostic and testing applications.