Applied Spectroscopy最新文献

Modern developments in autonomous chemometric machine learning technology strive to relinquish the need for human intervention. However, such algorithms developed and used in chemometric multivariate calibration and classification applications exclude crucial expert insight when difficult and safety-critical analysis situations arise, e.g., spectral-based medical decisions such as noninvasively determining if a biopsy is cancerous. The prediction accuracy and interpolation capabilities of autonomous methods for new samples depend on the quality and scope of their training (calibration) data. Specifically, analysis patterns within target data not captured by the training data will produce undesirable outcomes. Alternatively, using an immersive analytic approach allows insertion of human expert judgment at key machine learning algorithm junctures forming a sensemaking process performed in cooperation with a computer. The capacity of immersive virtual reality (IVR) environments to render human comprehensible three-dimensional space simulating real-world encounters, suggests its suitability as a hybrid immersive human-computer interface for data analysis tasks. Using IVR maximizes human senses to capitalize on our instinctual perception of the physical environment, thereby leveraging our innate ability to recognize patterns and visualize thresholds crucial to reducing erroneous outcomes. In this first use of IVR as an immersive analytic tool for spectral data, we examine an integrated IVR real-time model selection algorithm for a recent model updating method that adapts a model from the original calibration domain to predict samples from shifted target domains. Using near-infrared data, analyte prediction errors from IVR-selected models are reduced compared to errors using an established autonomous model selection approach. Results demonstrate the viability of IVR as a human data analysis interface for spectral data analysis including classification problems.

A combination of a highly efficient 213 nm Rayleigh rejection filter (RRF) and a miniaturized 213 nm neodymium-doped vanadate laser enables portable deep ultraviolet (UV) Raman spectrometers. We demonstrate the high efficiency of 213 nm RRF manufactured by Green Optics Co., Ltd by utilizing our compact 213 nm vanadate laser to measure high signal-to-noise ratio UV Raman spectra of Teflon and UV resonance Raman (UVRR) spectra of solid ammonium nitrate. We also demonstrate UVRR detection of trace amounts of ammonia formed during ammonium nitrate UV photolysis. We roughly estimate the ammonia UVRR detection limit of ∼10 ng under our experimental conditions.

The perceived color of human skin is the result of the interaction of environmental lighting with the skin. Only by resorting to human skin spectral reflectance, it is possible to obtain physical outcomes of this interaction. The purpose of this work was to provide a cured and validated database of hyperspectral images of human faces, useful for several applications, such as psychophysics-based research, object recognition, and material modeling. The hyperspectral imaging data from 29 human faces with different skin tones and sexes, under constant lighting and controlled movements, were described and characterized. Each hyperspectral image, which comprised spectral reflectance of the whole face from 400 to 720 nm in 10 nm steps at each pixel, was analyzed between and within nine facial positions located at different areas of the face. Simultaneously, spectral measurements at the same nine facial positions using conventional local point and/or contact devices were used to ascertain the data. It was found that the spectral reflectance profile changed between skin tones, subjects, and facial locations. Important local variations of the spectral reflectance profile showed that extra care is needed when considering average values from conventional devices at the same area of measurement.

This study aims to identify different types of stress on maize leaves using feature selection and spectral index methods. Spectral data were collected from leaves under heavy metal, water, fertilizer stress, as well as under normal healthy conditions. Preprocessing steps such as continuum removal (CR), standard normal variable (SNV) transformation, multiple scattering correction (MSC), detrend correction (DT), and first-order derivative (FOD) were applied to the raw spectra. Various feature selection methods including ReliefF, chi-square test, recursive feature elimination (FRE), mutual information (MI), random forest (RF), and gradient boosting tree (GBT) were employed to determine the importance scores of different spectral bands, thus identifying sensitive spectral features capable of distinguishing various stress types. Spectral indices for stress type differentiation were constructed using label correlation method. Classification models were built using support vector machine (SVM), K-nearest neighbors (KNN), Gaussian naive Bayes (GNB), extreme gradient boosting (XGBoost), RF, and adaptive boosting (AdaBoost) algorithms. Results indicate that the characteristic spectral bands for differentiating stress types are primarily distributed around the red edge (near 700-800 nm) and water absorption valley (near 1900 nm). Spectral indices constructed using combinations of spectral bands around the near-infrared plateau absorption valley (near 1185 nm) and water absorption valley (near 1460 nm) effectively differentiate maize stress types. Among the modeling classification algorithms, RF and AdaBoost algorithms exhibited optimal performance, demonstrating high classification accuracy on both training and validation sets. These findings hold promise for providing new technical support for maize stress monitoring and diagnosis in agricultural production.

Polarized Raman spectroscopy was used to analyze aqueous solutions of sodium orthophosphate and orthovanadate over a wide concentration range (0.00891-0.702 mol/L) at 23 °C. The isotropic scattering profiles were obtained by measuring polarized Raman scattering spectra. Furthermore, R-normalized spectra were calculated and presented. The tetrahedral ions, VO₄^3-(aq) and PO₄^3-(aq), demand four Raman active bands which have been subsequently characterized and assigned. For the PO₄^3-(aq) ion, the deformation modes ν₂(e) and ν₄(f₂) appear at 415 and 557 cm^-1, and these modes are depolarized. In the P-O stretching region, the strongest Raman band appears at 936.5 cm^-1, which is totally polarized with a depolarization ratio (ρ-value) of 0.002. The broad and depolarized mode at 1010 cm^-1 constitutes the antisymmetric stretching band ν₃(f₂). The Raman spectrum of VO₄^3- shows two depolarized deformation modes ν₂(e) and ν₄(f₂) at 327 and 345.6 cm^-1, which are severely overlapped. These bands are very weak. The strongest band in the Raman spectrum of VO₄^3-(aq) is the symmetric stretching mode ν₁(a₁) at 820.2 cm^-1 which is totally polarized with a ρ-value at 0.004. The depolarized antisymmetric stretching mode ν₃(f₂) appeared at 785 cm^-1 as a broad and weak band. Both anions are strongly hydrated and showed extensive hydrolysis in an aqueous solution. Orthovanadate is a much stronger base than orthophosphate in aqueous solution. Therefore, a large amount of NaOH was used to suppress the hydrolysis of VO₄^3-(aq) sufficiently, so, it was possible to characterize the VO₄^3- modes. Quantitative Raman spectroscopy was applied to follow the hydrolysis of PO₄^3- over a wide concentration range from 0.00891 to 0.592 mol/L. The hydrolysis data allowed the calculation of the pK_a3 value for H₃PO₄ to be 12.330 ± 0.02 (25 °C). The hydrolysis of the VO₄^3- ion is ∼21 times larger than that of the PO₄^3-. The pK_a3 value for H₃VO₄ is estimated to be 13.65 ± 0.1 (25 °C).

In this study, a new system was developed to carry out simultaneous near-infrared (NIR) and small-angle X-ray scattering (SAXS) measurements. Aged polypropylene (PP) was examined with the NIR-SAXS system to demonstrate how it can be utilized to derive pertinent information about the polymer structure. Pairs of SAXS profiles and NIR spectra of PP in its initial state and after aging were measured to derive an in-depth understanding of the aging phenomenon. The SAXS profiles of the PP samples showed a clear shift of the SAXS peak to the lower q direction induced by the thermal aging, indicating an increase in the length of the long-period structure. Two-trace two-dimensional (2T2D) asynchronous correlation spectra derived from NIR spectra clearly revealed that the aging treatment leads to a substantial increase in the spectral intensity of the regularity bands representing the longer helix present in a folded lamellar structure. In other words, it suggests that the long helix structure is more abundantly present than the short helix structure in the aged PP than in the initial PP. By combining the information derived from the SAXS profiles and NIR spectra, the details of the aging-induced variation were clearly determined. Namely, aging causes additional crystallization of the PP by developing more helical structures, which involves an increase in the lamellar thickness as well as a decrease in the amorphous region. The growth of the rigid crystalline phase restricts the elastic deformation in the amorphous structure, which eventually induces the deterioration of PP by making the polymer hard but brittle. Such observation, in turn, implies that retarding or accelerating the crystallized structure of PP substantially works to control the progress of aging.

In this work, we derive a simple method for calibrating Raman bandwidths for the Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) instrument onboard NASA's Perseverance rover. Raman bandwidths and shapes reported by an instrument contain contributions from both the intrinsic Raman band (IRB) and instrumental artifacts. To directly correlate bandwidth to sample properties and to compare bandwidths across instruments, the IRB width needs to be separated from instrumental effects. Here, we use the ubiquitous bandwidth calibration method of modeling the observed Raman bands as a convolution of a Lorentzian IRB and a Gaussian instrument slit function. Using calibration target data, we calculate that SHERLOC has a slit function width of 34.1 cm^-1. With a measure of the instrument slit function, we can deconvolve the IRB from the observed band, providing the width of the Raman band unobscured by instrumental artifact. We present the correlation between observed Raman bandwidth and intrinsic Raman bandwidth in table form for the quick estimation of SHERLOC Raman intrinsic bandwidths. We discuss the limitations of using this model to calibrate Raman bandwidth and derive a quantitative method for calculating the errors associated with the calibration. We demonstrate the utility of this method of bandwidth calibration by examining the intrinsic bandwidths of SHERLOC sulfate spectra and by modeling the SHERLOC spectrum of olivine.

Ongoing technological advancements in the field of mid-infrared (MIR) spectroscopy continuously yield novel sensing modalities, offering capabilities beyond traditional techniques like Fourier transform infrared spectroscopy (FT-IR). One such advancement is MIR dispersion spectroscopy, utilizing a tunable quantum cascade laser and Mach-Zehnder interferometer for liquid-phase analysis. Our study assesses the performance of a custom MIR dispersion spectrometer at its current development stage, benchmarks its performance against FT-IR, and validates its potential for time-resolved chemical reaction monitoring. Unlike conventional methods of IR spectroscopy measuring molecular absorptions using intensity attenuation, our method detects refractive index changes (phase shifts) down to a level of 6.1 × 10^-7 refractive index units (RIU). This results in 1.5 times better sensitivity with a sevenfold increase in analytical path length, yielding heightened robustness for the analysis of liquids compared to FT-IR. As a case study, we monitor the catalytic activity of invertase with sucrose, observing the formation of resultant monosaccharides and their progression toward thermodynamic equilibrium. Anomalous refractive index spectra of reaction mixtures, with substrate concentrations ranging from 2.5 to 25 g/L, are recorded, and analyzed at various temperatures, yielding Michaelis-Menten kinetics findings comparable to the literature. Additionally, the first-time application of two-dimensional correlation spectroscopy on the recorded dynamic dispersion spectra correctly identifies the mutarotation of reaction products (glucose and fructose). The results demonstrate high precision and sensitivity in investigating complex time-dependent chemical reactions via broadband refractive index changes.