LIBS-Acoustic Mid-Level Fusion Scheme for Mineral Differentiation under Terrestrial and Martian Atmospheric Conditions

Mid-level data fusion strategy based on urinary nucleosides SERS spectra and blood CEA levels for enhanced preoperative detection of lymph node metastasis in colorectal cancer

BACKGROUND

Preoperative prediction of lymph node metastasis (LNM) plays a crucial role in the treatment and prognosis of colorectal cancer (CRC). The traditional histopathological examination is invasive and time-consuming, providing pathological features only postoperatively. Preoperative serum carcinoembryonic antigen (CEA) is strongly correlated with postoperative LN status. However, the detection accuracy of LNM based on a single preoperative CEA level is low. Therefore, developing a more powerful and sensitive diagnostic tool would be of great clinical value for improving the accurate preoperative prediction of LNM in CRC patients.

RESULTS

This study aimed to develop a mid-level fusion approach using urinary nucleosides Raman spectra and blood CEA data to enhance the preoperative discrimination of CRC patients with and without LNM. Surface-enhanced Raman scattering (SERS) spectra of urinary modified nucleosides, isolated by affinity chromatography, were first acquired from 48 patients with LNM and 49 patients without LNM. The principal component analysis (PCA) scores obtained from the SERS spectra were then combined with preoperative blood CEA values to create a fused data array. The discriminant accuracy based on either dataset alone or the fused data was evaluated using three machine learning algorithms: linear discriminant analysis, k-nearest neighbors, and support vector machine. Results showed that the fused data could discriminate between the two groups with an accuracy of up to 91 %, outperforming SERS alone (86 %) and CEA alone (69 %).

SIGNIFICANCE

To our knowledge, this is the first report of mid-level data fusion of urinary nucleosides SERS spectra with blood CEA levels for the preoperative prediction of LNM in CRC. This work demonstrates that the mid-level data fusion strategy aided by SVM algorithm can greatly improve the preoperative prediction accuracy of LNM. This is crucial for therapeutic decision-making and prognostic assessment in CRC.

Data Fusion of Acoustic and Optical Emission from Laser-Induced Plasma for In Situ Measurement of Rare Earth Elements in Molten LiCl-KCl

Molten salts have a significant potential for use as next-generation nuclear reactor coolants and in pyroprocessing for the recycling of used nuclear fuel. However, the molten salt composition needs to be known at all times, and high temperatures and intense ionizing radiation pose challenges for the monitoring instrumentation. Although the technique of laser-induced breakdown spectroscopy (LIBS) has been studied for in situ measurements of molten salts, trials to improve its monitoring accuracy using chemometrics are lacking. In this study, a data fusion technique using the LIBS optical and laser-induced acoustic (LIA) signals was investigated to enhance the measurement accuracy for molten salt monitoring. Prediction models were constructed using the partial least-squares method, and the variable importance in projection scores was analyzed to evaluate the effect of incorporating the LIA signal into the analysis. This study investigates rare earth elements Eu, Er, and Pr found not only in nuclear but also in other settings such as laser and magnetic materials. The analysis of LIBS data without data fusion resulted in a root-mean-square error of prediction (RMSEP) of 0.0774-0.0913 wt %, whereas the prediction model using data fusion led to approximately 18-40% enhanced RMSEP (0.0461-0.0679 wt %). The results suggest that fusing the LIBS data with the simultaneously recorded LIA data can improve the monitoring accuracy of rare earth element composition in molten salts.

Enhancement of spectral model transferability in LIBS systems through LIBS-LIPAS fusion technique

BACKGROUND

Laser-induced breakdown spectroscopy (LIBS) is extensively utilized a range of scientific and industrial detection applications owing to its capability for rapid, in-situ detection. However, conventional LIBS models are often tailored to specific LIBS systems, hindering their transferability between LIBS subsystems. Transfer algorithms can adapt spectral models to subsystems, but require access to the datasets of each subsystem beforehand, followed by making individual adjustments for the dataset of each subsystem. It is clear that a method to enhance the inherent transferability of spectral original models is urgently needed.

RESULTS

We proposed an innovative fusion methodology, named laser-induced breakdown spectroscopy fusion laser-induced plasma acoustic spectroscopy (LIBS-LIPAS), to enhance the transferability of support vector machine (SVM) original models across LIBS systems with varying laser beams. The methodology was demonstrated using nickel-based high-temperature alloy samples. Here, the area-full width at half maximum (AFCEI) Composite Evaluation Index was proposed for extracting critical features from LIBS. Further enhancing the transferability of the model, the laser-induced plasma acoustic signal was transformed from the time domain to the frequency domain. Subsequently, the feature-level fusion method was employed to improve the classification accuracy of the transferred LIBS system to 97.8 %. A decision-level fusion approach (amalgamating LIBS, LIPAS, and feature-level fusion models) achieved an exemplary accuracy of 99 %. Finally, the adaptability of the method was demonstrated using titanium alloy samples.

SIGNIFICANCE AND NOVELTY

In this work, based on plasma radiation models, we simultaneously captured LIBS and LIPAS, and proposed the fusion of these two distinct yet origin-consistent signals, significantly enhancing the transferability of the LIBS original model. The methodology proposed holds significant potential to advance LIBS technology and broaden its applicability in analytical chemistry research and industrial applications.

Feasibility of a Low-Power, Low-Resolution Laser-Induced Breakdown Spectroscopy Instrument for Analysis of Nickel Alloys: Quantification of the Major Alloying Elements and Classification

A simple cost-effective laser-induced breakdown spectroscopy (LIBS) instrument was used for quantification of major elements in several nickel alloys and also sorting them. A compact low-power diode-pumped solid-state laser and a miniature low-resolution spectrometer were assembled for the LIBS instrument. Material properties of the nickel alloys depend mainly on the composition of the major elements, Ni, Cr, and Fe, ranging from a few to ∼60 wt%. The emission peaks at 547.7 nm, 520.4 nm, and 438.1 nm for Ni, Cr, and Fe, respectively, were chosen for this analysis. The analytical performance was found to be enough for the quantification of Ni, Cr, and Fe in the nickel alloys. Limits of detection and accuracy were estimated to be a few weight percent (wt%) and measurement precisions were less than 10% in terms of relative standard deviation. The calibration performance of this intensity-based method was compared with that of the "ratio method" which is used in conventional optical emission spectroscopy analyses. The comparison indicates that the intensity-based method is more appropriate with the low-performance LIBS instrument that detects emission peaks of only a few major elements. Also, multivariate modeling of the six different nickel alloy samples based on the emission peak intensities of Ni, Cr, and Fe was performed using k-nearest neighbors (KNN) and linear discriminant analysis (LDA). The KNN and ordinary LDA models showed 95.0% and 98.3% classification correctness for the separate test data set, respectively. To improve classification performance further, the two-step LDA model was trained. In this approach, the two closest sample classes responsible for the decrease in the classification correctness were separately modeled in the second step to exploit their difference effectively. The two-step LDA model showed 100% correctness in classifying the test objects. Our results indicate that such a low-performance LIBS instrument can be effectively utilized for quantitative analysis of the major elements in the nickel alloys and their rapid identification or sorting in combination with an appropriate multivariate modeling algorithm.