Journal of Applied Spectroscopy最新文献

Fluorescent dissolved organic matter (FDOM) — particularly tryptophan (Trp), tyrosine (Tyr), and humic acid (HA) — serves as a crucial indicator in environmental monitoring. This study introduced a novel quantitative analysis approach for analyzing three-dimensional excitation–emission matrix spectra (3D-EEMs) of FDOM using convolutional neural networks (CNNs). The performance of the CNN model was evaluated and compared with the self-weighting alternating trilinear decomposition (SWATLD) algorithm. Results revealed that the proposed model significantly outperforms the SWATLD algorithm. Specifically, the CNN model achieved R², RMSE, and MAPE values of 0.964, 0.047, and 14.950%, respectively, while for the SWATLD algorithm, these values were 0.944, 0.062, and 17.439%. Augmentation of the original spectral dataset did not yield a substantial improvement in the performance of the SWATLD algorithm, but it significantly enhanced the prediction ability of the CNN model. This enhancement was evident in the improved R², RMSE, and MAPE values of 0.989, 0.030, and 12.837%, highlighting the critical role of data augmentation in boosting the performance of the CNN model, especially when dealing with a limited dataset. Application of the CNN model to water samples from Laizhou Bay yielded satisfactory results, enabling a simple and rapid analysis of FDOM in seawater. Therefore, an accurate and convenient analytical model was developed based on EEMs and CNNs, which can swiftly determine the concentration of FDOM in the environment and provide valuable references for environmental monitoring and early warning.

Rapid detection of defects in metal additive manufacturing (AM) components remains a challenge. In this paper, laser-induced breakdown spectroscopy (LIBS) technology was used to establish a rapid identification of metal AM defects and defect-free control groups. The corresponding spectral acquisition of metal AM components with defects and without defects was carried out, and the research elements (Fe, Cr, Mn, and Ti) and corresponding spectral lines were obtained in combination with the NIST database. The spectral lines with features of importance greater than the average value are selected by random forest (RF). The selected spectral lines were used as the input variables of the k-nearest neighbor (KNN) model and the backpropagation neural network (BPNN) model. The classification performance and verification results of KNN, RF–KNN, and RF–BPNN models were compared. The results showed that the RF–BPNN model exhibited the best accuracy, sensitivity, and specificity in the training set, test set and validation set, with accuracies of 99.4, 97.2, and 96.67%, respectively. This indicates that LIBS combined with RF–BPNN can be used for the detection of defects in metal AM components.

This study presents the eco-friendly synthesis of copper-doped zinc oxide nanoparticles (Cu–ZnO NPs) using Asystasia gangetica leaf extracts as a reducing and stabilizing agent. To reveal their structural, morphological, and optical properties, the synthesized nanoparticles were characterized by X-ray diffraction, Fourier-transform infrared spectroscopy, scanning electron microscopy, ultraviolet-visible spectroscopy, and energy-dispersive X-ray spectroscopy. The dye degradation efficiency of Cu–ZnO NPs was evaluated for Rhodamine B dye under varying pH, nanoparticle concentration, dye concentration, and reaction time using response surface methodology. Statistical analysis using a central composite design showed that lower nanoparticle concentrations (50 ppm), higher dye concentrations (30 ppt), alkaline pH 9, and extended reaction times (120 min) resulted in optimal dye degradation. The ANOVA results, with a significant F-value and R² values indicating a good fit, confirmed the model’s adequacy. This green synthesis approach offers a sustainable method for nanoparticle production and its practical application in environmental remediation, particularly in the degradation of synthetic dyes. These findings contribute to the advancement of nanotechnology for eco-friendly applications, with potential implications for wastewater treatment and environmental sustainability.

K_2–xSm_xAl₂B₂O₇ (0.0025 ≤ x ≤ 0.04) phosphors were synthesized by a solution combustion method. The X-ray powder diff raction (XRD) method was used to study the phase formation of K₂Al₂B₂O₇. The bond structure of the K₂Al₂B₂O₇ was obtained by using infrared spectrum measurements. In order to investigate the photoluminescence characteristics of the K₂Al₂B₂O₇:Sm³⁺ phosphors, measurements of the excitation and emission spectra were made. The standard 4f–4f electronic transitions of Sm³⁺ were seen in the excitation spectrum at ambient temperature. The emission spectrum of K₂Al₂B₂O₇:Sm³⁺ exhibited four emission peaks corresponding to ⁴G_5/2 → ⁶H_5/2 (564 nm), ⁴G_5/2 → ⁶H_7/2 (600 nm), ⁴G_5/2 → ⁶H_9/2 (646 nm), and ⁴G_5/2 → ⁶H_11/2 (706 nm) transitions of Sm³⁺. Lastly, the correlation between emission intensity and Sm³⁺ content was thoroughly investigated.

Sotagliflozin is an oral antidiabetic medication that targets SGLT1 and SGLT2. This medication was not included in any pharmacopeia. The aim of this study was to develop, validate, and compare different methods for measuring the concentration of sotagliflozin in tablet form. These methods include spectro photometric techniques such as zero-order UV spectrophotometry, first-order derivative spectroscopy, second-order derivative UV spectrophotometry, and high-performance liquid chromatography (HPLC). Method A was a simple zero-order UV spectrophotometric method established for the determination of sotagliflozin in methanol at 254 nm. Method B is a first-order derivative spectrophotometric method, and method C is a second-order derivative spectrophotometric method involving the measurement of amplitudes at 264 and 234 nm, respectively. Method D was performed using HPLC, which was carried out using a C18 column mobile phase consisting of acetonitrile:0.1% orthophosphoric acid (40:60 v/v/v) with a flow rate of 1 mL/min and detection at 249 nm, which provided a sharp peak with a short retention time of 3.748 min. The developed analytical methods were validated statistically. The analysis conducted using liquid chromatography and spectrophotometric methods revealed that both approaches were robust, precise, and accurate, with an RSD of less than 1%. The recovery values were (98–100%) within the typical range. The four analytical techniques were compared statistically, and no significant differences were found. For the quantitative analysis of sotagliflozin, it was discovered that these established procedures are reliable, quick, accurate, and easy to use, and they may also be applied for quality control testing.

An online in situ detection system based on laser-induced breakdown spectroscopy (LIBS) is developed for the diagnosis and tracing of the rubber combustion process. The feasibility and accuracy of the system are verified by taking styrene–butadiene rubber, fluoro rubber, silicone rubber, chloroprene rubber, and natural rubber as samples. Metallic elements such as Ca, Mg, and Na are detected in the LIBS spectra of rubber combustion. The results show that the smoke produced contains different elements for different kinds of rubbers. Based on the self-developed single particle aerosol mass spectrometry system, the smoke produced by rubber combustion is detected by mass spectrometry. The mass spectrum data are compared and supplemented for LIBS spectral data. Then, the system, combining LIBS with principal component analysis (PCA) and backpropagation artificial neural network BP-ANN, achieves efficient detection and identification of different rubber smoke, as well as tracking of the rubber combustion process. The identification accuracy reached 94.00%. The combination of LIBS and algorithm helps to improve the efficiency of processing a large amount of spectral information data. Furthermore, the element differences among various types of rubber slabs are analyzed, thereby validating the accuracy of the diagnosis and traceability system. The aforementioned results indicate that the online in situ diagnosis and traceability of the rubber in the combustion process with LIBS is feasible, and using spectral detection for different types of rubber recycling is also promising.

The present research work was conducted to show the capabilities of the spectrophotometric approaches comparable to chromatographic techniques in terms of resolution, determination of spectral purity, and quantification of binary mixture present in tablet formulation. Overlapping spectra of the drugs paracetamol (PCM) and flupirtine maleate (FLU) present as a binary mixture were resolved using ratio subtraction plus the extended ratio subtraction (RS–ERS), method ratio subtraction method plus the unified constant subtraction (RS–UCS) method and ratio subtraction method plus the constant multiplication (RS–CM) method. After spectral resolution, each drug was quantified by measuring the absorbance at the respective wavelength maximum of the drug using a linear regression equation. The aforementioned three spectrophotometric approaches were initially applied to physical mixtures prepared in the laboratory; later it was extended to analyze marketed tablet formulation. The spectral purity of resolved spectra was studied by computing the spectral contrast angle (SCA) and the spectral ratio factor (SRF). Developed methods were validated as per the parameters mentioned under the International Council for Harmonization (ICH) guideline Q2 (R1). Developed methods were assigned greenness scores using the AGREE tool and successfully used for analyzing marketed tablet formulation.

We present a systematic theoretical investigation of the linear polarizations of the fluorescence radiation of 5s_1/2 → np_3/2 (n = 2, 3, 4, where n is the principal quantum number) lines after inner-shell single-photon ionization of the exemplary Xe atoms and the Xe-like W²⁰⁺ ions by (polarized and unpolarized) incoming photons within the framework of the relativistic approach and density-matrix theory. The correlation between the polarization of the incident light and the polarization of the characteristic radiation is examined. The sensitive dependence of the transfer of the polarization of fluorescence radiation emitted by the photoionization of atoms/ions is thoroughly analyzed. Our results demonstrate that incident light polarization plays a critical role in determining the polarization of fluorescence radiation. Due to polarization transfer, the polarization of the fluorescence radiation significantly changes. Our present results are in agreement with other theoretical and experimental values.

Raman imaging was used to detect the distribution of each component of wet granulation tablets and analyze their active pharmaceutical ingredient (API) particle size. The four excipients in the tablets — lactose, microcrystalline cellulose, crosslinked sodium carboxymethyl cellulose, and magnesium stearate — were significantly identified by their characteristic peaks, respectively. The average equivalent diameters of API particles in tablets 1, 2, and 3 were 4.49, 6.53, and 13.95 μm, respectively. Tablet 1 exhibited a favorable particle morphology, with minimal differences between particles and an average particle size. The greatest particle size disparities were observed in tablet 3. Furthermore, the cumulative distribution statistics ratio in the API particle system reached 90%, showing that the particle sizes of tablets 1, 2, and 3 were 5.41, 14.45, and 24.00 μm, respectively. This trend was consistent with the API powder results for raw materials measured using a particle size analyzer. The minimum detection limit of the particles was 1.68 μm. In addition, the introduction of the coefficient of variation was used to evaluate the tablets’ uniformity. Whereas tablet 3 exhibited the highest degree of variability and the poorest uniformity, tablet 2 exhibited the lowest degree of variation and the best uniformity. Raman imaging facilitated the visualization of the distribution of each component in the tablet and the API particle size analysis in a «one-stop» manner.