Applied Spectroscopy最新文献

Growing demand for pesticides has created an environment prone to deceptive activities, where counterfeit or adulterated pesticide products infiltrate the market, often escaping rapid detection. This situation presents a significant challenge for sensor technology, crucial in identifying authentic pesticides and ensuring agricultural safety practices. Raman spectroscopy emerges as a powerful technique for detecting adulterants. Coupling the electrochemical techniques allows a more specific and selective detection and compound identification. In this study, we evaluate the efficacy of spectroelectrochemical measurements by coupling a potentiostat and Raman spectrograph to identify paraquat, a nonselective herbicide banned in several countries. Our findings demonstrate that applying -0.70 V during measurements yields highly selective Raman spectra, highlighting the primary vibrational bands of paraquat. Moreover, the selective Raman signal of paraquat was discernible in complex samples, including tap water, apple, and green cabbage, even in the presence of other pesticides such as diquat, acephate, and glyphosate. These results underscore the potential of this technique for reliable pesticide detection in diverse and complex matrices.

How to identify bloodstains and obtain some potential evidence is of great significance for solving criminal cases. First, the spectral data of different species of bloodstain samples (human blood and animal blood) were acquired by using a hyperspectral imager. Then, an extreme learning machine (ELM) algorithm was used to build the training models of different species of bloodstain samples. Meanwhile, two traditional support vector machine and random forest classification algorithms were also compared with the ELM algorithm. The prediction results showed that the precision, sensitivity, specificity, and F1 score of the ELM algorithm were the highest. This indicates that hyperspectral technology, together with an ELM algorithm, could identify bloodstain species rapidly, non-destructively, and accurately. It has provided a new technical reference for bloodstain detection and identification.

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.

An infrared squaraine dye was utilized to detect Cu²⁺ in solvents based on H-aggregates of squaraine dye. H-aggregates are a type of aggregation with enhanced photophysical properties compared to monomers. In the presence of a Ca²⁺ solution, F-Cl offers exceptional H-aggregators that can be transformed into monomers by adding Cu²⁺. Furthermore, this mode successfully demonstrated fluorescence changes in HeLa cells cultured in vitro after the addition of Ca²⁺ or Cu²⁺. A highly specific detection of Cu²⁺ was achieved using this transformation mode.

Fluorescence spectroscopy is an attractive candidate for analyzing samples of nylon. Impurities within the polymers formed during the synthesis and processing of nylons give rise to the observed fluorescence, allowing for nylons to be analyzed based on their impurities. Nylons from the same source are expected to display similar fluorescence profiles, and nylons with different fluorescence are expected to be from different sources. This paper investigates an important case where different nylons displayed similar fluorescence, preventing easy discrimination. Samples of Nylon 6 and Nylon 6/12 had visually indistinguishable excitation-emission matrices (EEM), excitation spectra, fluorescence spectra, and synchronous fluorescence spectra at larger Δλ. By collecting synchronous fluorescence spectra at smaller Δλ, additional features in the fluorescence profiles were identified that allowed for some discrimination between the two nylons. Combining the EEM and synchronous fluorescence data with chemometric algorithms provided a clearer differentiation between the two nylons. parallel factor analysis, principal component analysis, and common dimension partial least squares (ComDim-PLS) showed two distinct clusters in the data, with ComDim-PLS providing the greatest distinction between the clusters. The loadings revealed the variables of interest to the ComDim-PLS were the 400 nm and 335 nm bands for all synchronous fluorescence spectra, the 460 nm and 310 nm bands for the Δλ = 20 nm and Δλ = 30 nm synchronous fluorescence spectra, and the 440 nm band for the Δλ = 20 nm synchronous fluorescence spectra. The linear discriminant analysis performed with the PLS data yielded a classification accuracy of 95% with the EEM data and 100% with the synchronous fluorescence data, displaying the power of this technique to differentiate two different nylons with visually indistinguishable fluorescence spectra.

Near-infrared (NIR) dyes have a unique ability to interact favorably with light in the NIR region, which is particularly interesting where stealth and camouflage are paramount, such as in military uniforms. Characterization of cotton fabric dyed with NIR-absorbing dyes using visible-NIR (Vis-NIR) and short-wave infrared (SWIR) hyperspectral imaging was done. The aim of the study was to discern spectral changes caused by variations in dye concentration and dyeing temperature as these parameters directly influence color- and crocking-fastness of fabrics impacting the camouflage effect. The fabric was dyed at three concentrations (2.5, 5, and 10%) and two dyeing temperatures (55 °C and 85 °C) and principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed on the spectra to discriminate the fabrics based on dye concentrations. The PCA models successfully segregated the fabrics based on the dye concentration and dyeing temperature, while PLS-DA models demonstrated classification accuracies between 75 and 100% in the Vis-NIR range. Spectra in the SWIR region could not be used to detect the differences in the concentrations of the NIR dyes. This finding is promising, as it aligns with the objective of creating NIR-dyed camouflage fabrics that remain indistinguishable under varying dye concentrations. These results open possibilities for further exploration in enhancing the stealth capabilities of textiles in military applications.

Spatially offset Raman spectroscopy (SORS) is typically used to non-invasively investigate stratified samples that possess features on a millimeter scale, whereas micro-SORS usually deals with micrometer-thick layered samples. However, there are many instances where these boundaries are intertwined, sometimes indicating the possibility of using both techniques as well as circumstances that present mutual exclusion to their applicability. The aim of this study is to establish an application protocol that provides better insight into their suitability for deployment in various scenarios. The differences and similarities between the two approaches are investigated highlighting their strengths and limitations considering both theoretical and practical aspects. Diverse available parameters entail prospects and restrictions of both techniques and give rise to specific instrumental effects, namely, the overlap between the collection and excitation areas, the percentage of collected area for a given spatial offset, and the accuracy in the definition of the spatial offset (spread effect). These aspects are studied and exemplified on mockup samples relevant to the field of cultural heritage. The samples are characterized by high compositional complexity comprising features ranging from micrometer to millimeter scales. The conclusions reached are also relevant to other scientific areas such as biomedical, forensic, or energy harvest.

Carbon nanofibers are a new type of carbon materials. One of the methods of obtaining them is the carbonization of a polymer precursor. They are attractive in many areas, including medicine, due to the possibility of modifying their properties in a wide range. For example, the conditions of the carbonization process result in the creation of materials with designed structures and surface parameters. In the current work, the nanoprecursor was polyacrylonitrile (PAN) fibers. Two types of carbon fibers obtained by carbonization of the PAN precursor at 1000 °C were tested. The first electrospun carbon nanofibers (ESCNFs) were cytotoxic, while the second ESCNF-f were biocompatible after functionalization. The parameters obtained from Raman tests did not clearly discriminate between the tested materials. Multiwavelength Raman studies, analyzed using the two-dimensional correlation spectroscopy (2D-COS), treating the laser energy as an external disturbance, showed a difference between both fibrous structures. 2D-COS indicates that structures resembling graphite systems, devoid of disordered carbon forms, are nontoxic.

Nondispersive ultraviolet visible gas analyzer designs were evaluated for monitoring molybdenum-containing chloride and oxychloride precursor delivery during microelectronics vapor deposition processes. The performances of three analyzer designs, which differed only in the bandpass filter employed for wavelength selection, were compared for measuring the partial pressure of molybdenum pentachloride, molybdenum oxytetrachloride (MoOCl₄), and molybdenum dioxydichloride (MoO₂Cl₂). The analyzer's optical response with a 369 nm center wavelength filter for molybdenum pentachloride was determined by measuring the molybdenum pentachloride absorbance as a function of vapor molar density. The calibrated analyzer was transferred to a process line on a deposition chamber and used to measure the molybdenum pentachloride partial pressure during delivery in a flowing carrier gas. The molybdenum pentachloride minimum detectable density was determined to be 1 × 10^-4 mol m^-3 (0.35 Pa for a cell temperature of 145 °C), for data collected at 1 kHz and referenced to a 0.2 s duration background. The analyzer optical response for molybdenum pentachloride with the two other filters and the response for MoOCl₄ and MoO₂Cl₂ with all three filters were simulated with a simple model. These data were used to evaluate the sensitivity and selectivity of analyzers incorporating the different filters to some likely combinations of analytes and interferents.

We present an attenuated total reflection (ATR) correction scheme capable of rectifying ATR spectra while considering the polarization state for arbitrary angles of incidence, provided that this angle exceeds the critical angle for the entire ATR spectrum. Due to its reliance on the weak absorption approximation, it cannot achieve perfect correction of the ATR spectra. However, comprehending its functionality may offer valuable insights into the concept behind the weak absorption approximation. Depending on the specific polarization state of an instrument accessory combination, this correction scheme may outperform the proprietary advanced ATR correction authored by ThermoFisher while being as user-friendly, but in contrast to the latter completely transparent with regard to its functionality.