In the present work, appropriate machine learning techniques coupled with LIBS have been proposed for the effective classification of multielement rock samples. To obtain the best classification efficiency most suitable emission lines were selected. Plasma on the surface of seventeen rock samples was generated using a 532 nm Q-switched neodymium-doped yttrium aluminum garnet (Nd:YAG) laser, and optical emission spectra were collected via an Avantes spectrometer. Well-isolated signature emission lines corresponding to detected elements (Ca, Mg, Na, K, Fe, Ba, Sr, Si, Al, and Li) were chosen as input for the machine learning algorithms. Three machine learning techniques, including analysis of variance (ANOVA), principal component analysis (PCA), and PCA coupled with standard normal variate (SVM), were utilized on normalized intensities of selected spectral lines of detected elements. ANOVA testing on the selected lines was employed to assess the normality and suitability of data for further machine learning techniques. The combination of laser-induced breakdown spectroscopy (LIBS) with PCA enabled a comprehensive classification of rock samples. The linearity and efficiency of PCA were enhanced by utilizing the support vector machine (SVM), resulting in the accurate classification of rock samples. This study demonstrates that to assess the effective classification of multielement rock samples the appropriate emission lines and machine learning techniques are crucial. Using this methodology results become more reliable as compared to conventional machine learning techniques.

In X-ray fluorescence spectroscopy, specific elements, particularly neighboring elements, interfere with each other. A wavelength-dispersive optical system based on double-curved crystal (DCC) with multiple faces was designed. A Johann-type graphite curved crystal was used to focus the monochromatic excitation light, and a logarithmic spiral graphite curved crystal was used to collect the target fluorescence (Cl, 2.62 keV). The experimental results show that the wavelength dispersion device based on the DCC technology has a Cl detection limit of 0.18 part per million (ppm) when used for oil products. In the detection of a 1 ppm standard sample, the detection range of the device was 0.38 ppm, demonstrating good stability. Based on the fluorescence spectroscopy and detection results, the DCC technology could completely eliminate the influence of a high S concentration on Cl detection.

Micro-Raman spectroscopic analysis of a fossil sample of Vicarya callosa japonica was performed to investigate the chemical process of fossilization. The Vicarya sample, originating from the Miocene Katsuta Group, Okayama prefecture, southwestern Japan, had a conical shell body with multiple protuberances on the outer layer. The interior of the shell was filled with a carbonate sediment. Raman mapping combined with principal component analysis (PCA) and partial least squares regression (PLSR) analysis were performed on the sample. Well-preserved, in vivo aragonite was found to be distributed on the shell and near the boundary between the internal carbonate precipitates and the shell. The internal precipitates were composed of pure calcite and black carbonates. The black-colored precipitates contained pyrite, suggesting that the carbonates were derived from the same biogenic tissue as the carbonate concretions and were the starting point for their crystallization. The rapid formation of the precipitates, also similar to that of carbonate concretions, and the suppression of the demineralization effect of the shell from pore water in the sediment may have contributed to the preservation of the aragonite. The reaction of the transition from aragonite to calcite in the shell progressed to some extent and crystallization was completed before the transition to calcite was complete.

This work implements a mid-level data fusion methodology on spectral data from handheld X-ray fluorescence and laser-induced breakdown spectroscopy analyzers to quantify plutonium surrogate (CeO) contamination in soil samples for the first time. Spectral data from each analyzer were used independently to train supervised machine learning regressions to predict Ce concentration. Fused features from both data sets were then used to train the same models, comparing prediction performance by evaluating model precision and sensitivity. Fusing principal component scores from the two sensors yielded an order of magnitude improvement in precision and sensitivity of predictions made with an artificial neural network, compared to predictions made by models trained on independent sensor data. Lastly, a boosted ensemble trained on the fused spectral features yielded an ideal predictor with root-mean-squared error on the order of 10-6 and calculated limit of detection order 10-5 wt.

Environmental fluorescence measurements sometimes use water Raman scattering as an internal standard to compensate for path length, lensing effects, and turbidity. Fluorescent dissolved organic matter (FDOM) in water may interfere strongly with the measurement of this reference. However, fluorescence in fluid solution is largely unpolarized, while the OH stretching Raman band of water is always strongly polarized. Using an environmental sample from Lake Wateree in South Carolina, USA, we demonstrate that judicious use of this polarization allows for a significant level of improvement in the contrast or visibility of the water Raman band relative to FDOM.

Attenuated total reflection (ATR) spectroscopy in infrared is a standard tool used in most analytical labs, as it allows a rapid chemical analysis with virtually no sample preparation. However, when the sample contains materials with a high refractive index, special care must be taken as the resulting data may be severely biased. This article reports a theoretical approach to correcting distorted ATR spectra. Starting from Snell's law, Lorenz model and Fresnel's equations are combined to obtain the complex relationship between optical constants. With calculating the real and imaginary parts, that is, and , respectively, of the complex refractive index from the absorption spectrum, a model for mixtures comprising of a liquid and a solid is established. The effects of distortion and potential misinterpretation of the data are discussed. Proof-of-concept experiments with mixtures of carbonaceous materials and toluene confirm the theoretically predicted observations.

Raman spectroscopy is a chemical process that utilizes the interaction between light and matter to get significant insights into the structure or characteristics of matter. Raman spectroscopy techniques, such as quantitative evaluation, early diagnostic capabilities, and elucidation of the spectral properties of tissues, are excellent candidates for use in research. In cancer, changes in genes and proteins expressed by related genes are associated with a poor prognosis and aggressive tumor characteristics. Due to modifications and regulatory steps in protein translation, the results of the messenger RNA (mRNA) expression of genes may not correctly reflect the results of protein expression. For this reason, the mRNA and protein expressions of genes are studied in parallel in molecular studies on cancer. In our study, the breast cancer gene type 1 (BRCA1) gene, which is frequently studied in breast cancer and is relatively more difficult to measure by traditional methods due to its high molecular weight, was selected, and protein quantification was performed in tissue samples by Raman spectroscopy. With Raman spectroscopy, it is possible to obtain rapid and precise quantitative results even with a small amount of sample, so it is quite advantageous compared to traditional methods. In our study, we performed surface-enhanced Raman spectroscopy (SERS) to analyze the quantitative protein amount. SERS is a highly sensitive method for detecting compounds at low concentrations. For this purpose, magnetic nanoparticles modified with protein antibodies were used, and the target protein was withdrawn from the complex environment and transferred to an appropriate buffer environment. The calibration curve for BRCA1, which plots Raman intensity against concentration, was derived by calculating the average response reading from duplicate assays conducted under identical conditions. The BRCA1 protein levels of cells were determined from the regression curve of the BRCA1 protein. The relation between the concentration of BRCA1 protein and SERS spectrum intensity was determined to be logarithmic in the range of 300 µg·mL-1 to 292 ng·mL-1 (R2 = 0.9928, limit of detection = 10.41 µg·mL-1, and limit of quantitation = 31.24 µg·mL-1).

Alcohols from biological waste sources or renewable electricity (electrofuels) are gaining attention in hard-to-decarbonize sectors such as transport. Adding alcohol to conventional fuels has positive environmental effects on automotive applications, requiring minimal engine adjustments. Employing a combination of terahertz (THz) and gigahertz (GHz) spectroscopies, a comprehensive analysis of model fitting is presented for diesel-like fuels, pure alcohols (ethanol and n-butanol), and alcohol-fuel blends. Through the integration of data from both spectroscopic techniques, new Debye parameters are introduced to improve the accuracy of fitting for various fuels. This research demonstrates that THz spectroscopy alone is valuable for reasonable fits, particularly for alcohols. However, integrating THz and GHz spectroscopies leads to improved fitting, and to better potential to understand the behavior of fuel properties. In addition, the effect of alcohol concentration on the dielectric constant spectra in blends was investigated, highlighting the importance of molecular interactions. The results reveal a linear relationship between fitted parameters and alcohol content in the blends. However, the study acknowledges limitations, including challenges in achieving satisfactory fits at low alcohol concentrations and the necessity for assumptions in the modeling process. These findings provide a basis for future research and advances in fuel property modeling.

In this paper, a single-step synthetic approach is presented for the development of bimetallic molybdenum-copper nanoclusters (Mo-CuNCs), shielded by a small molecule 6-thioguanine (6-TG). The Mo-CuNCs possessed a small size, high fluorescence, stable behavior, and good solubility in water. The 6-TG-Mo-CuNCs exhibit strong blue fluorescence emission at 410 nm after exciting at 330 nm as compared to its monometallic nanoclusters. Utilizing 6-TG-Mo-CuNCs superior biochemical stability, uric acid (UA) can be specifically detected as an oxidative stress biomarker using an inner filter effect mechanism. The probe demonstrated good sensing capability for detecting UA within the range of 0.09-5.00 μM and a detection limit of 0.237 μM. The method feasibility is further validated by quantifying UA in urine and plasma samples.