Investigating the mechanistic impact of pork soft tissue preparation techniques on the classification precision of laser-induced breakdown spectroscopy.

The investigation of the mechanism underlying the impact of biological soft tissue sample preparation methods on laser-induced breakdown spectroscopy (LIBS) signals can enhance the stability of LIBS signals. Our study focused on four specific preparation methods applied to pork samples: rapid freezing, fresh slicing, drying, and pressing. The influence of various preparation techniques on the signal-to-noise ratio and fluctuation of Ca, Na, Mg, and CN bands within the sample spectra was assessed. The signal-to-noise ratios for samples that were dried and pressed notably improved. And the pressing method effectively mitigated the uneven distribution of pork tissue components, displaying superior spectral line stability. To explain this phenomenon, we used the Saha-Boltzmann diagram to estimate the plasma temperature. Remarkably, there was a significant reduction in plasma temperature fluctuations across four pressed samples, with a standard deviation of 108.53. Furthermore, we undertook a classification analysis employing support vector machine models to corroborate the generalization efficacy of the sample preparation technique. Dried and pressed samples demonstrated notably higher classification accuracy, precision, and recall (all >93%) compared to frozen and fresh samples, where these metrics remained below 86%. The performance of the SVM model was ultimately evaluated using Receiver Operating Characteristic (ROC) curves and the Area Under the Curve (AUC). The AUC for the frozen, fresh, dried, and pressed samples was 0.854, 0.907, 0.989, and 0.996, respectively. The findings revealed that the pressing method exhibited superior performance, followed by drying, fresh slicing, and freezing, in descending order of effectiveness.

Decision-level data fusion based on laser-induced breakdown and Raman spectroscopy: A study of bimodal spectroscopy for diagnosis of lung cancer at different stages.

Lung cancer staging is crucial for personalized treatment and improved prognosis. We propose a novel bimodal diagnostic approach that integrates LIBS and Raman technologies into a single platform, enabling comprehensive tissue elemental and molecular analysis. This strategy identifies critical staging elements and molecular marker signatures of lung tumors. LIBS detects concentration patterns of elemental lines including Mg (I), Mg (II), Ca (I), Ca (II), Fe (I), and Cu (II). Concurrently, Raman spectroscopy identifies changes in molecular content, such as phenylalanine (1033 cm-1), tyrosine (1174 cm-1), tryptophan (1207 cm-1), amide III (1267 cm-1), and proteins (1126 cm-1 and 1447 cm-1), among others. The bimodal information is fused using a decision-level Bayesian fusion model, significantly enhancing the performance of the convolutional neural network architecture in classification algorithms, with an accuracy of 99.17 %, sensitivity of 99.17 %, and specificity of 99.88 %. This study provides a powerful new tool for the accurate staging and diagnosis of lung tumors.

Research on a new multiple-screening method for laser-induced plasma spectroscopy utilizing Lorentz.

In the field of Laser Induced Breakdown Spectroscopy (LIBS) research, the screening and extraction of complex spectra play a crucial role in enhancing the accuracy of quantitative analysis. This paper introduces a novel approach for multiple screenings of LIBS spectra using Lorentz Screening and Sensitivity and Volatility Analysis. Initially, Create symmetrical sampling standards for Lorentz fitting. Then the Lorentz fitting is used to uniformly screen the collected spectral information on both sides in order to eliminate adjacent interference peaks. Subsequently, Sensitivity and Volatility Analysis is employed to further remove overlapping peaks and select spectra with low volatility and high sensitivity. Sensitivity and Volatility Analysis is a spectral discrimination method proposed on the premise of intensity's correlation with concentration. It utilizes a Z-score method that incorporates both deviation and standard deviation for effective analysis. Furthermore, it meticulously selects spectral lines with minimal interference and volatility, thereby augmenting the precision of quantitative analysis. The quantitative accuracy (R2) for Chromium (Cr) and Nickel (Ni) elements can reach 0.9919 and 0.9768, respectively. Their average errors can be reduced to 0.0566 % and 0.1024 %. The study demonstrates that Lorentz Screening and Sensitivity and Volatility Analysis can select high-quality characteristic spectral lines to improve the performance of the model.

Fusion of laser-induced breakdown spectroscopy technology and deep learning: a new method to identify malignant and benign lung tumors with high accuracy.

Precisely distinguishing between malignant and benign lung tumors is pivotal for suggesting therapeutic strategies and enhancing prognosis, yet this differentiation remains a daunting task. The growth rates, metastatic potentials, and prognoses of benign and malignant tumors differ significantly. Developing specialized treatment protocols tailored to various tumor types is essential for enhancing patient survival outcomes. Employing laser-induced breakdown spectroscopy (LIBS) in conjunction with a deep learning methodology, we attained a high-precision differential diagnosis of malignant and benign lung tumors. First, LIBS spectra of malignant tumors, benign tumors, and normal tissues were collected. The spectra were preprocessed and Z score normalized. Then, the intensities of the Mg II 279.6, Mg I 285.2, Ca II 393.4, Cu II 518.3, and Na I 589.6 nm lines were analyzed in the spectra of the three tissues. The analytical results show that the elemental lines have different contents in the three tissues and can be used as a basis for distinguishing between the three tissues. Finally, the RF-1D ResNet model was constructed by combining the feature importance assessment method of random forest (RF) and one-dimensional residual network (1D ResNet). The classification accuracy, precision, sensitivity, and specificity of the RF-1D ResNet model were 91.1%, 91.6%, 91.3%, and 91.3%, respectively. And the model demonstrates superior performance with an area under the curve (AUC) value of 0.99. The above results show that combining LIBS with deep learning is an effective way to diagnose malignant and benign tumors.

A study of the spectral signal effect of self-holes in metal additive manufacturing components using laser-induced breakdown spectroscopy (LIBS).

Understanding the detection mechanism of hole defects in metal additive manufacturing (AM) components is of great significance for the detection of metal AM component defects using laser-induced breakdown spectroscopy (LIBS). In this work, the mapping relationship between the hole defects of metal AM components and the LIBS spectral signal was studied using the controlled variable method. The effect of hole defects mostly showed a suppression effect and peaked at a hole depth of 1.0 mm when the LIBS system was at its optimal excitation parameter. To explore the possible reasons behind the inhibitory effect of self-holes, the variation law of the plasma temperature with and without hole defects was further investigated. Our results showed that the plasma temperature change curve was similar to the spectral line intensity change trend. Finally, the linear relationship between the focal length effect and the hole effect, and the relationship between the constraint effect and the hole effect were studied. The minimum fitting R2 between the constraint effect and the hole effect was 0.979. We believed that the inhibition of the hole effect was mainly caused by the absorption and loss of energy in the plasma during the process of plasma radiation and shock wave reflection from the hole wall. By studying the detection mechanism of hole defects in metal additive manufacturing components excited by LIBS and finding the effective characteristics of hole defects in metal AM components, it is helpful to achieve higher precision and higher sensitivity defect detection.