A spectral bias-error stepwise correction method of plasma image-spectrum fusion based on deep learning for improving the performance of LIBS.

Poor spectral stability seriously hinders the wide application of laser-induced breakdown spectroscopy (LIBS), so how to improve its stability is the focus, hotspot, and difficulty of current research. In this study, to achieve high precision quantitative analysis under complex detection conditions, utilizing the fusion of multi-dimensional plasma information and the integration of physical models and algorithmic models, a spectral bias-error stepwise correction method of plasma image-spectrum fusion based on deep learning (SBESC-PISF) was proposed. In this method, based on the statistical properties of LIBS spectra, the actual obtained spectra were decomposed into three parts: the ideal spectral intensity related only to the element concentration, and the spectral bias and spectral error caused by the fluctuation of complex high-dimensional plasma parameters. Further, the deep learning methods were used to fully excavate all the effective features in the plasma images and spectra to invert the complex high-dimensional plasma parameters according to the physical models. Finally, the estimation models of spectral bias and spectral error were established based on these features, to realize the high-precision correction of spectral intensity. To verify the feasibility of SBESC-PISF, the spectra of aluminum alloy samples obtained under three complex detection conditions were used for analysis. Under the experimental condition of laser energy fluctuation, after correction by SBESC-PISF, R2 of the three calibration curves was all increased to 0.999, RMSE and STD of the validation set (RMSEV, STDV) were reduced by 55.246 % and 50.167 %, respectively. Under the experimental condition of defocusing amount fluctuation, R2 was also all increased to 0.999, RMSEV and STDV were decreased by 58.201 % and 51.006 %, respectively. When the laser energy and defocusing amount fluctuate simultaneously, R2 was increased to 0.999, 0.996 and 0.988, RMSEV and STDV were reduced by 58.776 % and 54.397 %, respectively. These experimental results demonstrate that the spectral fluctuation correction of SBESC-PISF under complex detection conditions is effective and has wide applicability.

Sensitivity and stability improvement on slippery surface-aggregated substrate for trace heavy metals detection using NELIBS.

The sensitive and stable detection of trace heavy metals in liquid is crucial given its profound impact on various aspects of human life. Currently, nanoparticle-enhanced laser-induced breakdown spectroscopy (NELIBS) with dried droplet method (DDM) is widely applied for heavy metals detection. Nevertheless, the coffee ring effect (CRE) in DDM affects the stability, accuracy, and sensitivity of NELIBS. Here, we developed a slippery surface-aggregated substrate (SS substrate) to suppress the CRE and enrich analytes, and form a plasmonic platform for NELIBS detection. The SS substrate was prepared by infiltrating perfluorinated lubricant into the pores of PTFE membrane. The droplet, with targeted elements and gold nanoparticles, was dried on the SS substate to form the plasmonic platform for NELIBS analysis. Then, trace heavy metal elements copper (Cu) and manganese (Mn) were analyzed by NELIBS. The results of Cu (RSD = 5.60%, LoD = 3.72 µg/L) and Mn (RSD = 7.42%, LoD = 6.37 µg/L), illustrated the CRE suppression and analytes enrichment by the SS substrate. The results verified the realization of stable, accurate and sensitive NELIBS detection. And the LoDs succeeded to reach the standard limit of China (GB/T 14848-2017). Furthermore, the results for groundwater detection (relative error: 5.92% (Cu) and 4.74% (Mn)), comparing NELIBS and inductively coupled plasma mass spectrometry (ICP-MS), validated the feasibility of the SS substrate in practical applications. In summary, the SS substrate exhibits immense potential for practical application such as water quality detection and supervision.

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.

Blood cancer diagnosis using hyperspectral imaging combined with the forward searching method and machine learning.

Hyperspectral imaging (HSI), a widely used biosensing technique, has been applied to tumor detection. Rapid, accurate, and low-cost detection of blood cancer using hyperspectral technology remains a challenge. We developed a new method to discriminate blood cancer using hyperspectral imaging (HSI) and the forward searching method (FSM). Four commonly used classification models are applied for four types of blood cancer spectra recognition. The support vector machine (SVM) model with the highest recognition accuracy (94.5%) combined with HSI achieves high-precision tumor identification. For higher recognition accuracy and lower hardware barriers, based on the selection probabilities of spectral lines calculated by a multi-objective atomic orbital search method, the FSM is proposed for HSI feature selection. With the proposed method, the wavelength band range of the spectrum is reduced by at least 50%. Compared with the traditional dimensionality reduction methods, the FSM can obtain a higher accuracy rate with lower hardware requirements. These results show that our proposed method can achieve non-invasive rapid screening of blood cancers with lower hardware requirements. Therefore, HSI assisted with FSM and SVM hybrid models can be a powerful and promising tool for blood cancer detection.

Self-absorption correction method for one-point calibration laser-induced breakdown spectroscopy.

As an important variant of calibration-free laser-induced breakdown spectroscopy (CF-LIBS), one-point calibration LIBS (OPC-LIBS) corrects the Boltzmann plot of the unknown sample by using one known sample and obtains higher quantitative accuracy than CF-LIBS. However, the self-absorption effect restricts its accuracy. In this work, a new self-absorption correction (SAC) method for OPC-LIBS is proposed to solve this problem. This method uses an algorithm to correct the self-absorption and does not require the calculation of the self-absorption coefficient. To verify the effectiveness of this SAC method, Ti, V, and Al elements in two titanium alloys were determined by classical OPC-LIBS and OPC-LIBS with SAC. The average relative errors (AREs) of all elements in the two samples were decreased from 8.78% and 9.28% to 8.07% and 7.56%, respectively. The results demonstrated the effectiveness of this SAC method for OPC-LIBS.

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 spectral-image laser-induced breakdown spectroscopy for precise qualitative and quantitative analysis of milk powder quality by fully excavating the matrix information.

A novel and effective method named time-resolved spectral-image laser-induced breakdown spectroscopy (TRSI-LIBS) was proposed to achieve precise qualitative and quantitative analysis of milk powder quality. To verify the feasibility of TRSI-LIBS, qualitative and quantitative analysis of milk powder quality was carried out. For qualitative analysis of foreign protein adulteration, the accuracy of models based on TRSI-LIBS was higher than those based on LIBS, with an accuracy improvement of about 5% to 10%. For the quantitative analysis of foreign protein adulteration and element content, the quantitative analysis models based on TSRI-LIBS also had better effect. For instance, limit of detection (LOD),determination coefficient of prediction (R2p), root-mean-square error of prediction (RMSEP) and average relative error of prediction (AREP) of quantitative model of calcium (Ca) content based on TRSI-LIBS improved from 1.47 mg/g, 0.95, 0.35 mg/g and 23.29% to 0.81 mg/g, 0.98, 0.20 mg/g and 12.60%.

Accuracy improvement of single-sample calibration laser-induced breakdown spectroscopy with self-absorption correction.

The single sample calibration laser-induced breakdown spectroscopy (SSC-LIBS) is quite suitable for the fields where the standard sample is hard to obtain, including space exploration, geology, archaeology, and jewelry identification. But in practice, the self-absorption effect of plasma destroys the linear relationship of spectral intensity and element concentration based on the Lomakin-Scherbe formula which is the guarantee of the high accuracy of the SSC-LIBS. Thus, the self-absorption effect limits the quantitative accuracy of SSC-LIBS greatly. In this work, an improved SSC-LIBS with self-absorption correction (SSC-LIBS with SAC) is proposed for the promotion of quantitative accuracy of SSC-LIBS. The SSC-LIBS with SAC can correct the intensity ratio of spectral lines in the calculation of SSC-LIBS through relative self-absorption coefficient K without complicated preparatory information. The alloy samples and pressed ore samples were used to verify the effect of the SSC-LIBS with SAC. Compared with SSC-LIBS, for alloy samples, the average RMSEP and average ARE of SSC-LIBS with SAC decreased from 0.83 wt.% and 13.75% to 0.40 wt.% and 4.06%, respectively. For the pressed ore samples, the average RMSEP and average ARE of SSC-LIBS with SAC decreased from 4.77 wt.% and 90.48% to 2.34 wt.% and 14.60%. The experimental result indicates that SSC-LIBS with SAC has a great improvement of quantitative accuracy and better universality compared with traditional SSC-LIBS, which is a mighty promotion of the wide application of SSC-LIBS.

Stable sensing platform for diagnosing electrolyte disturbance using laser-induced breakdown spectroscopy.

Electrolyte disturbance is very common and harmful, increasing the mortality of critical patients. Hence, rapid and accurate detection of electrolyte levels is vital in clinical practice. Laser-induced breakdown spectroscopy (LIBS) has the advantage of rapid and simultaneous detection of multiple elements, which meets the needs of clinical electrolyte detection. However, the cracking caused by serum drying and the effect of the coffee-ring led to the unstable spectral signal of LIBS and inaccurate detection results. Herein, we propose the ordered microarray silicon substrates (OMSS) obtained by laser microprocessing, to solve the disturbance caused by cracking and the coffee-ring effect in LIBS detection. Moreover, the area of OMSS is optimized to obtain the optimal LIBS detection effect; only a 10 uL serum sample is required. Compared with the silicon wafer substrates, the relative standard deviation (RSD) of the serum LIBS spectral reduces from above 80.00% to below 15.00% by the optimized OMSS, improving the spectral stability. Furthermore, the OMSS is combined with LIBS to form a sensing platform for electrolyte disturbance detection. A set of electrolyte disturbance simulation samples (80% of the ingredients are human serum) was prepared for this platform evaluation. Finally, the platform can achieve an accurate quantitative detection of Na and K elements (Na: RSD < 6.00%, R2 = 0.991; K: RSD < 4.00%, R2 = 0.981), and the detection time is within 5 min. The LIBS sensing platform has a good prospect in clinical electrolyte detection and other blood-related clinical diagnoses.