Applied Spectroscopy最新文献

Partial least squares discriminant analysis (PLS-DA) is often used for data sets that consist of a large number of potential predictors but relatively few observations such that chance correlations between predictors and response can occur that lead to false conclusions. Hence, there is a need for data adequacy testing before model building but currently no such method exists. In this work we propose one where we used random permutations to destroy the correlation structure between predictor and response data. This produced normal distributions of chance correlation coefficients that were used to find correlation coefficients in the non-permuted data that differed significantly from chance occurrences. Based on these distributions, we defined two novel null hypotheses to control for when a true null hypothesis is incorrectly rejected and the other for when a false null hypothesis is not rejected. To counter false positive errors, the standard significance levels were adjusted with predictor-based Bonferroni corrections. To counter false negative errors, we compared the true and permuted correlation coefficients in distribution tails. The outcomes of the hypothesis tests then indicated whether or not PLS-DA models could be successfully built from these data sets. We also investigated how to determine the number of samples needed for a data set with a given number of predictors. Simulations showed that our method produced significantly fewer false positives than PLS-DA (P = 0.0018, our method error rate 12× less than PLS-DA error rate) but significantly more false negatives (P = 0.0003, our method error rate 4.5× more than PLS-DA error rate). Data from Raman spectroscopy showed that the method transferred to real data. By pre-screening such data, our method can aid in assessing whether to proceed with model building and, when there is a need to increase the sample size, we show by how much.

The growing interest in high-purity bioplastics for emerging specialized applications motivated us to investigate the crystallization of poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (PHBHx) from a chloroform solution using advanced spectroscopic techniques. Previous study of the carbonyl stretching region of time-dependent attenuated total reflection infrared (ATR-IR) spectra had revealed that solution crystallization process of PHBHx involving distinct intermediate species was markedly different from the more traditional melt crystallization. IR study of PHBHx solution crystallization is now extended to more complex C-H stretching and fingerprint regions. Characteristic IR bands of the system showing the least correlated and most independent behaviors with each other were identified using a new technique based on a two-dimensional (2D) discrimination spectrum. Correlation filters based on the characteristic bands were used to selectively attenuate interfering spectral contributions in conjunction with the hetero-mode two-dimensional correlation spectroscopy (2D-COS) analysis. Traditional classification of decreasing and increasing IR bands, respectively, during the crystallization process of PHBHx copolymers simply to the amorphous and crystalline components probably needs to be reconsidered. Dynamics of bands in the C-H stretching and fingerprint regions are not fully synchronized with the behavior of amorphous or crystalline species characterized by the carbonyl stretching region. The result strongly suggests the presence of some intermediate species appearing after the consumption of amorphous components and prior to the formation of crystals.

Microplastics (MPs), as an emerging pollutant, have become a potential threat to the global ecological environment and human health due to their small particle size, wide distribution, easy interaction with other pollutants in the environment, and transmission along the food chain. As a powerful analytical technique, two-dimensional correlation spectroscopy (2D-COS) can provide information on intermolecular interactions and dynamic changes, thus exhibiting unique advantages in MPs research. This review focuses on 2D-COS applications in MPs studies, including chemical structure and functional group characterization, aging or degradation mechanism elucidation, MPs-pollutants interaction analysis, and novel applications in biogeochemical processes (e.g., plant-MPs interactions, biofilm-modulated transformation). Finally, we discuss the current limitations of 2D-COS and prospect its future application trends in MPs research.

A polarized near-infrared (NIR) study for characterizing the orientation distribution of polymer chains is described. A low-density polyethylene (LDPE) sheet prepared with a hot rolling press is gradually rotated while being probed with a high-speed polarized NIR spectrometer based on an acousto-optic tunable filter (AOTF) to obtain a series of NIR spectra. During the change in the rotation angle, the spectral feature varies depending on the direction of the molecular vibration within the sample. The fine features of the spectral changes are readily analyzed with the two-trace two-dimensional (2T2D) correlation method by using the spectrum measured at 0° as a sample spectrum. The asynchronous correlation intensities between the crystalline and amorphous bands of the LDPE exhibited a sinusoidal variation depending on the selected reference spectrum (i.e., polarization angle). In particular, the intensities provide local maximum points at 90° and 270° where the direction of the polarized NIR light becomes parallel to the hot rolling direction. Consequently, the cyclic pattern of the asynchronous correlation intensity suggests that the hot rolling treatment substantially induces the reorientation of the polymer chains and subsequent additional crystallization, which makes the LDPE hard and brittle.

A sensitive, simple, inexpensive, rapid, and eco-friendly spectrofluorimetric method was created to assess the folic acid (FA) concentration in tablets using acriflavine (ACF) as an eco-friendly photoprobe. Based on its ability to quench the ACF fluorescence intensity in water at pH 8.0 and λ_ex = 460 nm. FA concentration was measured by quenching the fluorescence intensity of the ACF at λ_em = 508 nm within the linear range of 3.5×10^-6 - 30.0×10^-6 mol L^-1 with a correlation coefficient r² = 0.9991. The limit of quantification (LOQ) and the limit of detection (LOD) are 1.159 × 10^-6 mol·L^-1 and 0.383 × 10^-6 mol·L^-1, respectively. This method was easy to use and accurately and effectively evaluated FA in pharmaceutical tablet samples. No influence was observed from the excipients usually contained in pharmaceutical formulations. The method was verified as valid according to the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) guidelines. In addition, its greenness has been estimated using environmental assessment tools.

Laser-induced breakdown spectroscopy (LIBS) has broad application potential, yet its analytical accuracy is often limited by poor spectral stability. Since plasma optical signals directly reflect plasma fluctuations, they offer a promising basis for spectral correction. In a novel approach, we introduce a neuromorphic dynamic vision sensor (DVS) to capture plasma dynamics with microsecond temporal resolution. The DVS provides a 120 dB dynamic range and a low data rate (∼10 MB/s), enabling acquisition of plasma optical signals over a wide range of conditions. We further propose an event-enhanced spectroscopy correction network (EESCN), which employs a dual-stream convolutional neural network (CNN) to extract key features from spectra and plasma images, respectively. A multihead attention module then performs cross-modal fusion by dynamically weighting spectral and image features to predict and correct signal fluctuations. To emulate challenging conditions, we introduced laser energy fluctuations and selected spectral lines affected by self-absorption. EESCN substantially suppressed spectral fluctuations arising from self-absorption and laser energy fluctuations for C(I) 493.202 nm and Mn(I) 403.076 nm in carbon steel, and for Cu(I) 327.395 nm and Zn(I) 328.233 nm in copper alloys, reducing the mean relative standard deviations by 70.52%, 79.33%, 80.76%, and 72.09%, respectively. Calibration curves constructed from the corrected spectra all achieved R² values above 0.99, markedly outperforming the original spectra, normalization, and other correction methods. By integrating a low-cost, high-speed DVS with a cross-modal fusion model, this work provides a practical and powerful solution for mitigating spectral instability in LIBS and supports robust on-site analytical applications.

Sediment contamination by trace elements (TE) is a major environmental issue. In particular, TE speciation is of great importance because the form of the TE determines their mobility, bioavailability, and consequently their potential toxicity. Characterizing the chemical speciation of TEs can be complex and costly with current analytical methods. Non-destructive spectroscopic methods, which require limited sample preparation, are therefore useful tools for characterizing and possibly quantifying TEs in complex sedimentary matrices. Thus, this study explores the potential of visible and near-infrared hyperspectral imaging (HSI) to estimate the speciation of some TEs in sediments based on their spectral properties. Standard ranges of sixteen chemical species of six TEs, i.e., arsenic (As), cadmium (Cd), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn), were produced using three model sediment matrices (clay, silt, and organic matter). The results obtained show specific absorptions for each of the TE species, and nine of them could be quantified with detection limits of around 1 g/kg in the visible range and around 10 g/kg in the short-wave infrared range. This approach enables a more accurate and rapid assessment of environmental risk using HSI, in addition to conventional analytical methods.

Raman spectroscopy (RS) is a label-free, non-destructive optical modality that provides a detailed profile of the molecular composition of a sample. There is growing interest in the clinical application of RS to characterize biomolecular signatures associated with radiotherapy response in tumor cells and tissues. A critical step before analyzing Raman data consists of performing spectral pre-processing to increase the quality of the measurements. Spectral pre-processing comprises baseline subtraction, signal smoothing, cosmic ray (CR) correction, and removal of poor-quality measurements. Herein, we present a convolutional autoencoder (AE) for single-step, automated pre-processing of Raman spectra obtained from tumor cells and tumor tissue. We trained two separate models using the same proposed architecture, one for eliminating spectral artifacts from preclinical single-cell line and xenografted tissue spectra exposed to single-fraction radiation, and the other for correcting clinical prostate tumor biopsy spectra collected from patients receiving high-dose-rate brachytherapy (HDR-BT). The autoencoder demonstrated fast, excellent performance in removing baseline, noise, and CRs. For the preclinical data, the model obtained a root mean squared error (RMSE), and a percentage root mean squared difference (PRD) of 7.1 × 10^-5 and 3.1%, respectively, between the AE-corrected spectra and their corresponding target data (pre-processed by our current baseline-removal algorithm). Also, the autoencoder successfully removed 94.0% of CRs from the spectra. For the clinical biopsy data, the AE achieved an RMSE and a PRD of 8.1 × 10^-5 and 3.7%, respectively, and a CR removal rate of 90.2%. Overall, the AE corrected approximately 11 000 spectra within 2.4 s without the need of a GPU. Furthermore, comparative supervised learning-based post-processing data analyses were performed separately on the spectra pre-processed by the autoencoder versus the target data, and we show consistency in the biochemical radiation response profiles extracted. Finally, the AE architecture was leveraged to train a reconstruction AE to facilitate semi-automated identification of poor-quality prostate biopsy spectra, and we demonstrate 96.4% agreement between AE and manually removed outliers. These results support the development of a deep learning framework for efficient, automated pre-processing of tumor cell and tissue Raman spectra collected for radiation response monitoring studies.

In attenuated total reflection (ATR) spectroscopy, the presence of an evanescent field penetrating the sample is generally considered crucial. However, according to wave optics, this evanescent field vanishes when the rarer medium is absorbing, and the attenuation of total reflection results from transmission into this medium. While the evanescent field may not play a significant role in this scenario, a closer examination of the relevant relationships reveals that the system's properties vary smoothly with both the angle of incidence and the imaginary part of the dielectric function. This effect can be further illustrated by comparing electric field maps and spectra for semi-infinite rarer media with those for rarer media composed of layers with thicknesses on the order of the wavelength. In the latter case, ATR spectra can be recorded well below the critical angle, where no evanescent field exists. If the layer is vacuum and the underlying semi-infinite medium is assumed to have the same refractive index but is weakly absorbing, tunneling and frustrated total reflection can be observed. Reflecting on our results, we can now define the critical angle in the presence of absorption as the point at which the real and imaginary parts of the perpendicular component of the wavevector become equal. Overall, we conclude that evanescent waves play little to no significant role. Any deviation from total reflection can be attributed to transmission through the ATR crystal-medium interface.

Butterfly wings exhibit optical phenomena resulting from pigments as well as from intricate nanostructures of the scales that plays an important role in their ecology mainly, communication, thermoregulation as well as mating. In our study, we examined the optical behavior of butterfly wing scales by analyzing their percent reflectance, absorbance, percent transmittance, and effective refractive index using ultraviolet-visible near-infrared (UV-Vis-NIR) spectroscopy which is a valuable analytical technique that provide details of the optical properties of materials. In the study conducted with 10 butterflies, the UV, visible, and NIR regions are highlighted to determine the optical properties of butterflies. From the study, it is explored that the UV region exhibit major absorbance, the visible region exhibits major reflectance, and infrared regions exhibit minor reflectance. Optical parameters other than reflectance and absorbance are derived from the spectroscopic data and plotted using Origin software. The percent reflectance, absorbance, percent transmittance, effective refractive index, and their respective wavelength of butterflies studied vary across species. Ariadne merione is observed to have the highest percent reflectance and the lowest is observed in the Eurema hecabe. The overall percentage of reflectance observed in the study ranges between 46%-68%. The absorbance is observed highest for Parantica aglea and lowest for Ypthima huebneri with optimum absorbance ranging between 1.23-0.82. The highest transmittance percentage is observed for Tirumala septentrionis, and the lowest value is observed in Mycalesis mineus and E. hecabe with optimum transmittance ranging between 63% to 47%, respectively. The refractive index was analyzed using the Fresnel equation, followed by an empirical Cauchy dispersion fit to characterize its wavelength dependence. The results revealed unusually high refractive index values for a biological specimen, indicating an effective refractive index behavior influenced by structural, pigmentation and optical complexity rather than representing the intrinsic material refractive index. This study is the first record on comprehensively determining the optical properties of Indian butterflies especially effective refractive index using UV-Vis-NIR spectroscopy.