Plasma parameters correction method based on plasma image-spectrum fusion for matrix effect elimination in LIBS.

Matrix effect is one of the obstacles that hinders the rapid development of laser-induced breakdown spectroscopy (LIBS), and it is currently a hot, challenging, and focal point in research. To eliminate the matrix effect, this study proposed a plasma parameters correction method based on plasma image-spectrum fusion (PPC-PISF). This method corrects the total number density, plasma temperature, and electron number density variations caused by matrix effect using effective features in plasma images and spectra. To verify the feasibility of this method, experiments were conducted on pressed and metal samples, and the results were compared with those corrected by image-assisted LIBS (IA-LIBS). For the pressed samples, after correction by PPC-PISF, the R2 of the calibration curves all improved to above 0.993, the average root-mean-square error (RMSE) decreased by 41.05%, and the average relative error (ARE) decreased by 59.35% evenly in comparison to IA-LIBS. For the metal samples, after correction by PPC-PISF, the R2 of the calibration curves all increased to above 0.997. Additionally, the RMSE decreased by 29.63% evenly, the average ARE decreased by 38.74% compared to IA-LIBS. The experimental results indicate that this method is an effective method for eliminating the matrix effect, promoting the further development of LIBS in industrial detection.

Staging classification of omicron variant SARS-CoV-2 infection based on dual-spectrometer LIBS (DS-LIBS) combined with machine learning.

Effective differentiation of the infection stages of omicron can provide significant assistance in transmission control and treatment strategies. The combination of LIBS serum detection and machine learning methods, as a novel disease auxiliary diagnostic approach, has a high potential for rapid and accurate staging classification of Omicron infection. However, conventional single-spectrometer LIBS serum detection methods focus on detecting the spectra of major elements, while trace elements are more closely related to the progression of COVID-19. Here, we proposed a rapid analytical method with dual-spectrometer LIBS (DS-LIBS) assisted with machine learning to classify different infection stages of omicron. The DS-LIBS, including a broadband spectrometer and a narrowband spectrometer, enables synchronous collection of major and trace elemental spectra in serum, respectively. By employing the RF machine learning models, the classification accuracy using the spectra data collected from DS-LIBS can reach 0.92, compared to 0.84 and 0.73 when using spectra data collected from single-spectrometer LIBS. This significant improvement in classification accuracy highlights the efficacy of the DS-LIBS approach. Then, the performance of four different models, SVM, RF, IGBT, and ETree, is compared. ETree demonstrates the best, with cross-validation and test set accuracies of 0.94 and 0.93, respectively. Additionally, it achieves classification accuracies of 1.00, 0.92, 0.92, and 0.89 for the four stages B1-acute, B1-post, B2, and B3. Overall, the results demonstrate that DS-LIBS combined with the ETree machine learning model enables effective staging classification of omicron infection.

A plasma image-spectrum fusion correction strategy for improving spectral stability based on radiation model in laser induced breakdown spectroscopy.

Spectral fluctuation is one of the main obstacles affecting the further development of LIBS, and it is also the current research hotspot and difficulty. To meet the requirements of industrial monitoring, a novel method named plasma image-spectrum fusion laser induced breakdown spectroscopy (PISF-LIBS) was proposed to correct the spectral fluctuation and improve the quantitative accuracy. In this method, by systematically analyzing the spectral radiation model, six main factors affecting the spectral stability were obtained. Further, the standard spectrum in the ideal plasma state which is not affected by these six factors was calculated, and the deviation from the actual spectrum was obtained. According to the above analysis, the calculated deviation was mainly affected by these six factors and can be estimated through them. Therefore, this study creatively proposed to use the effective information in the plasma images and spectra to indirectly characterize the deviation, so as to realize the correction of spectral fluctuation. To verify the wide applicability of PISF-LIBS in experimental conditions, the LIBS spectra of aluminum alloy obtained under four different experimental conditions were used. After PISF-LIBS correction, the R2 increased to more than 0.974, and the RMSE, MAPE and RSD of the prediction set decreased by 44.789%, 47.854% and 51.687% on average. To further verify the wide applicability of PISF-LIBS in experimental samples, alloy steel samples and pressed samples were also used. For alloy steel samples, after PISF-LIBS correction, the R2 increased to more than 0.996, and the RMSE, MAPE and RSD of the prediction set decreased by 48.337%, 52.856% and 25.819% evenly. For pressed samples, the R2 increased over 0.992, and the RMSE, MAPE and RSD of the prediction set decreased by 61.493%, 61.080% and 39.945% averagely. The experimental results prove the effectiveness and wide applicability of PISF-LIBS in spectral fluctuation correction.

Time-Resolved Laser-Induced Breakdown Spectroscopy for Accurate Qualitative and Quantitative Analysis of Brown Rice Flour Adulteration.

To solve the adulteration problem of brown rice flour in the commodity market, a novel, accurate, and stable detection method based on time-resolved laser-induced breakdown spectroscopy (TR-LIBS) is proposed. Qualitative and quantitative analysis was used to detect five adulterants and seven different adulterant ratios in brown rice flour. Being able to excavate more information from plasma by obtaining time-resolved spectra, TR-LIBS has a stronger performance, which has been further verified by experiments. For the qualitative analysis of adulterants, the traditional machine learning models based on TR-LIBS, linear discriminant analysis (LDA), naïve Bayes (NB) and support vector machine (SVM) have significantly better classification accuracy than those based on traditional LIBS, increasing by 3-11%. The deep learning classification model based on TR-LIBS also achieved the same results, with an accuracy increase of more than 8%. For the quantitative analysis of the adulteration ratio, compared with traditional LIBS, the quantitative model based on TR-LIBS reduces the limit of detection (LOD) of five adulterants from about 8-51% to 4-19%, which effectively improves the quantitative detection performance. Moreover, t-SNE visualization proved that there were more obvious boundaries between different types of samples based on TR-LIBS. These results demonstrate the great prospect of TR-LIBS in the identification of brown rice flour adulteration.