Applied Spectroscopy最新文献

The increasing concern about the presence of pesticides in vegetable leaves has underscored an urgent need for real-time, nondestructive, and accurate detection methods. Traditional methods are reliable but laboratory-based, costly, and unsuitable for field monitoring. In this study, we propose an efficient learning model pipeline that uses hyperspectral reflectance signatures to detect pesticide residue in plant leaves. We extract a comprehensive set of 39 domain-specific features based on vegetation indices, red-edge metrics, spectral statistics, and derivative profiles. To enhance the performance, use a multilayer perceptron to extract more features. A feature fusion module is used to combine both domain-specific features and features extracted by a multilayer perceptron. Further refinement is achieved through a feed-forward attention scoring module that dynamically weights important features. The efficiency of the system is evaluated using an enhanced extra trees classifier, which shows superior classification performance and stability across different feature formats. With cross-validation, our model achieves an accuracy of 94.69%, significantly outperforming conventional classifiers such as convolutional neural networks, support vector machines, and ensemble models such as random forest and extra trees. This framework not only improves interpretability and performance but also provides a foundation for a real-time, on-site pesticide monitoring solution.

Attenuated total reflection infrared-far-infrared (ATR IR-FIR) spectra (4000-50 cm^-1) and Raman spectra (2000-50 cm^-1) were measured for twelve types of biogenic minerals (shells), including Corbicula sandai (aragonite), Corbicula fluminea (aragonite), Corbicula japonica (aragonite), Ruditapes philippinarum (aragonite), and Mytilus galloprovincialis (aragonite and calcite) from different origins and growing environments. In this study, we investigated the crystal structures of these biogenic minerals, the water contents and structure in them, and the differences in the crystal structures among the aragonite forms of these minerals. In the 4000-3000 cm^-1 region and around the 1650 cm^-1 band region in the IR spectra, the proportion of the IR absorption bands related to weak and strong hydrogen bonds was significantly different among the shellfish species investigated. Therefore, it has been found that IR spectroscopy is useful for discriminating among shells based on the content and structure of water such as hydrogen bonds. In the low-frequency region below 500 cm^-1, where bands corresponding to lattice vibrational modes are observed, we investigated the lattice vibration modes of aragonite of shells and discussed particularly the full width at half-maximum (FWHM) of the bands at around 267 cm^-1 in the FIR spectra and the intensity of the side band at around 140 cm^-1 in the Raman spectra. As a result, we demonstrated that using both IR and Raman spectroscopies including the low-frequency regions allows us to distinguish various biogenic minerals from different habitats and growing environments. Additionally, it suggests that both IR and Raman spectroscopies including low-frequency regions are useful for characterizing habitats of shellfish.

Arthroscopic procedures rely on qualitative methods for cartilage assessment, such as tissue visualization and mechanical probing. visible-near-infrared (Vis-NIR) spectroscopy offers the potential to include compositional tissue characterization which could improve surgical guidance. The primary objective of this study was to assess the feasibility of using a fiber optic Vis-NIR probe in environments typically experienced during arthroscopy. Given the geometric constraints of articulating joints, a probe was fabricated with a 90-degree bend at the tip to enable movement and access to tissues. Absorbances from arthroscopic irrigation fluid (saline) are prominent in the NIR spectral region and thus need to be minimized during spectral collection. The current study aims to identify spectral data where the probe was not in contact with the tissues and/or where environmental saline contributed to the spectra. Porcine patella tissues were used to model how spectra collection in various conditions (probe offset from tissue and presence of fluid) impact spectra. Spectra were collected from cartilage, bone, and osteochondral tissues (n = 6 each) in experimental configurations with and without tissue contact and/or saline. Additionally, arthroscopic spectra collection in an equine stifle joint was investigated. Spectra collected while the fiber optic probe was in contact with the tissues resulted in minimal impact of environmental saline. Principal component analysis of spectra resulted in the separation of groups based on experimental configuration, demonstrating the potential for the development of more advanced machine learning algorithms focused on exclusion of spectra without appropriate tissue contact and with saline interference.

A technique for quenching-independent, two-photon absorption laser-induced fluorescence (TALIF) measurements of atomic nitrogen in high-enthalpy facilities is presented. The technique relies on high-laser intensity, which results in the photoionization rate dominating other depopulation channels for the induced excited state. The photoionization-dominated technique is applied here to study the distribution of atomic nitrogen in the vicinity of an ablating graphite sample in a plasma plume. Three different plasma conditions are investigated: a 17 MJ/kg air plasma delivering 145 W/cm² to the graphite surface, an 18 MJ/kg air plasma delivering 195 W/cm², and a 24 MJ/kg nitrogen plasma delivering 85 W/cm². The number density of atomic nitrogen is measured along the stagnation streamline of the flow on the graphite sample in each case. The highest number densities are observed in the nitrogen plume, as would be expected given the pure nitrogen composition and high enthalpy. The 195 W/cm² air condition has the second-highest atomic nitrogen distribution, followed by the 145 W/cm² air condition. This technique may provide a valuable method for studying atomic nitrogen, an important species to air/carbon gas-surface interaction at hypersonic-relevant conditions, in high-enthalpy facilities.

We present the first implementation of complex-valued classical least squares (CLS) regression in spectroscopy. Although the results indicate that complex-valued CLS does not outperform methods that utilize only the more suitable part of the complex refractive index spectra, it includes an error detection feature that enables a self-correction mechanism. This mechanism decreases the mean absolute error (MAE) to approximately 26% relative to using only the mid-infrared (MIR) absorption index (k) spectra for CLS, and to about 46% relative to using only the MIR refractive index (n) spectra of benzene-toluene mixtures. For benzene-cyclohexane mixtures, the MAE was reduced to approximately 75% relative to the k spectra and 58% relative to the n spectra. In contrast, for benzene-carbon tetrachloride (CCl₄) mixtures, i.e., a system that exhibits particularly large deviations from Beer's law, no improvement over the n spectra was observed; the n-based MAE was 81% relative to the k spectra. These percentages may further vary based on the complexity of the system, the spectral regions selected for CLS and the corresponding deviations from Beer's approximation.

This study assessed the feasibility of using portable infrared and handheld Raman devices for the rapid screening of alcohol-based gel hand sanitizers to detect potential adulteration or misbranding. Alcohol potency was estimated by analyzing the concentration-dependent hydrogen bond-induced peak shifting characteristic of alcohol-water mixtures. Specifically, alcohol concentration in water (v/v%) was plotted as a function of the ratio of two characteristic peak positions affected by this shifting, yielding linear responses between 30%-100% for infrared spectroscopy and 40%-100% for Raman spectroscopy. Calibration equations derived from these curves were applied to estimate alcohol concentration, resulting in average errors (± standard deviations) of 1.6% (1.2%) for infrared spectroscopy and 2.4% (1.7%) for Raman spectroscopy, compared to gas chromatography with flame ionization detection (GC-FID). A total of 24 products were analyzed using this screening workflow, with results used to prioritize samples for further analysis via official compendial methods. All 21 samples identified as violative or presumptively violative by the rapid screening devices were confirmed as violative using GC-FID, while all three samples classified as presumptively non-violative were confirmed as non-violative. This method may be suitable for field deployment at locations such as mail facilities, points of entry, and express courier hubs, where expedited screening of these products is beneficial. Its implementation could enhance regulatory enforcement efforts and support consumer safety by identifying non-compliant products more efficiently.

Currently, there is increasing interest in identifying the mechanistic characteristics of the α-synuclein amyloid protein aggregation during its early stages. The initiation of amyloid protein incubation was investigated by applying the concepts of hydrophobic hydration in the early-formed protein aggregates and the light transport in the protein samples by using near-infrared light. These are unexplored concepts in amyloid protein aggregation research. Early-formed protein aggregates develop solvent-exposed hydrophobic residue segments, and intramolecular and intermolecular interactions can be identified by hydrophobic hydration, while consecutive intramolecular interactions can cancel this effect. In the light transport within protein samples, at low protein concentrations, the early-formed protein aggregates achieve stability, whereas at higher concentrations, such as those found in neuronal synapses (∼50  µM), the early-formed aggregates continue to develop.

Given fungi's critical role in public health and their impact during pandemics such as COVID-19, precise identification and classification are essential. Additionally, fungi hold significant value in medical and economic applications. For this work, fungi were isolated from various fruit. The fungi were initially identified based on their morphological characteristics using microscopic techniques. To achieve a comprehensive characterization, the eight fungal species were analyzed using rapid and cost-effective spectroscopic techniques, including attenuated total reflectance Fourier transform infrared spectroscopy (ATR FT-IR), Raman spectroscopy (RS), and ultraviolet-visible spectroscopy (UV-Vis). Fungal samples were used in the powder form, generating distinct spectral fingerprints in the biochemical region specific to components such as proteins, lipids, polysaccharides, carbohydrates, and nucleic acids. Results demonstrated the efficacy of these spectroscopic approaches for rapid and accurate identification, enabling discrimination between fungal species and reliable classification at the genus level. The results showed the species were identified as Aspergillus parasiticus, Phytophthora spp., Chaetomium globosum, Penicillium digitatum, Penicillium sp., Penicillium italicum, Rhizoctonia solani, and Myrothecium roridum. This highlights the potential of these techniques as efficient tools for fungi identification.

This study proposes a method to remove background pixels from near-infrared hyperspectral images based on the pixel-wise standard deviation of reflectance method (px-wise SD method). This method calculates the standard deviation (SD) of reflectance in each pixel, namely each spectrum, and determines a threshold to distinguish between background and object pixels from the resulting histogram of the px-wise SD. The method effectiveness is evaluated using hyperspectral images of a leaf-like pastry with a hole placed on either a low-reflectance sheet or white paper. On white paper, the px-wise SD of reflectance exhibits a trimodal histogram with two prominent peaks and one small peak between them. The prominent peak with a lower SD corresponds to the white paper pixels, whereas the other peak with a higher SD is associated with the surface and edge pixels of the pastry. The small peak represents the pixels of the hole. The background and object pixels can be effectively separated by setting a threshold between this small peak and the prominent peak for the pastry pixels. Moreover, the mean spectrum calculated using only object pixels remains consistent, regardless of the type of background material. Conversely, the mean spectrum calculated using all pixels is distorted due to the spectral inclusion of the background material.