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Spectral intensity drift is a frequent issue in analytical processes, especially in long time excitation scanning for large size metal materials, which can significantly adversely impact the accuracy and stability of analysis results. Spectral intensity drift correction is the process of preprocessing spectral data using mathematical algorithms in order to facilitate the subsequent qualitative and quantitative analysis of spectra, especially in combination with stoichiometric methods. Up to now, spectral intensity drift correction within prolonged excitation has not been reported yet. We propose an intensity drift correction method for element content of large-size samples using the Spark Mapping Analysis for Large Samples (SMALS) technique. By considering the row-by-row and column-by-column mapping modes of the SMALS, this includes curve fitting baseline correction for in-row and in-column correction, as well as total average value correction for inter-row and inter-column correction. The final measurement values are derived by coupling rows with columns. The careful implementation of correction steps can enhance baseline correction performance, effectively reducing measurement errors a drift errors. Application of this method to characterize the cross and longitudinal sections of an oversized steel billet indicates high agreement with composition distribution obtained by micro-beam X-ray fluorescence (μ-XRF). The corrected longitudinal and cross-sectional data also exhibit strong alignment. Comparison of statistical analysis results pre- and post-correction demonstrates significant improvements in the clarity of elements segregation pattern. This intensity drift correction method not only enhances the spectral quality but also improves the accuracy and robustness of quantitative and qualitative spectral analysis. This study contributes to establishing a robust foundation for component characterization of large-size metal materials using the SMALS technique. The novel spectral intensity correction method shows theoretical significance and practical value for large-scale, long-duration excitation scanning analysis. [Display omitted] • The measurement error was eliminated by in-row/in-column, inter-row/inter-column correction and complementary coupling. • The intensity drift correction method significantly improves the accuracy and stability of analysis results of the SMALS. • This work laid a firm foundation of component characterization for large-size metal material using the SMALS. [ABSTRACT FROM AUTHOR]

The utilization of near-infrared (NIR) spectroscopy, in conjunction with chemometric techniques, has been widely used in a variety of sectors, including agricultural production and pharmaceutical production. Nevertheless, the laborious and arduous procedure of gathering samples and evaluating their physicochemical properties leads to relatively limited training set sizes for modeling. This problem severely limits the optimization and practical application of NIR spectrum analysis models. The Safer Active Semi-Supervised Sample Augmentation Learning Model (Safer-AS3A) proposed in this paper tries to address the problem by incorporating active learning (AL) and semi-supervised learning (SSL) techniques. Experiments were conducted on two sets of publicly available NIR spectral datasets, and the Safer-AS3A model was compared to other models with similar characteristics. The experimental results indicate that the Safer-AS3A model proposed in our study outperforms comparable models in terms of accuracy and robustness when dealing with scenarios having a limited number of labeled samples. Furthermore, after the training set was expanded with the Safer-AS3A model, the Partial least squares regression (PLSR), Bayesian ridge regression (BRR), and Support vector regression (SVR) models on the Diesel dataset improved their R 2 on the test set by 5.923 %, 3.018 %, and 7.331 %, respectively, compared to the models using only the labeled sample set. On the other hand, the Ridge regression (RR), BRR, and SVR models on the test set on the Shoot dataset improved the R 2 by 4.169 %, 4.449 %, and 11.597 %, respectively. Overall, the Safer-AS3A model can effectively expand the NIR spectral dataset and considerably improve the performance of the NIR spectral analysis model. Using the AL method, the SSL method, and the co-training method together, a novel and effective method is presented for generating high-quality pseudo-labels. This method opens up new avenues for enhancing the efficiency and precision of NIR spectrum analysis. It also provides novel perspectives on sample diversification and prospective applications in other disciplines. [Display omitted] • A deep active learning method is developed to analyze near-infrared spectroscopy data. • The stability principle and voting mechanism are added to the confidence evaluation of pseudo-labeling. • The co-training method effectively alleviates the problem of insufficient labeled spectral samples. [ABSTRACT FROM AUTHOR]

Background: Hand, foot, and mouth (HMFD) disease caused by enterovirus 71 (EV 71), is closely associated with severe clinical manifestations and can be deadly. Early detection of EV 71 can be achieved by detecting the increment in miR296 and miR16 in the serum. Using HCR to amplify signals and convert biological signals into metal nanoparticle signals detectable by ICP-MS is a detection method that can collect more accurate and reliable information, compared with traditional methods, in the detection of biological samples., Results: We described a strategy for the simultaneous detection of miR296 and miR16 by ICP-MS based on metal nanoparticles (NPs) labeling with HCR. Briefly, single-stranded DNA (ssDNA) and magnetic beads (MBs), as well as NPs and signal probes for miRNA (Sp -miR ) were firstly conjugated via the streptavidin-biotin recognition system, constituting ssDNA-MBs and NPs-Sp -miR complex, respectively. The latter complex then hybridized with the former through HCR, generating the nanosensors for targets. Then, the targets were added and hybridized with ssDNA, and the HCR complex with NPs was released into the solution. Finally, the corresponding signals of the NPs were measured by ICP-MS. Results demonstrated that the developed method had good sensitivity and satisfactory selectivity and precision. Furthermore, when applied to biological samples with a complex matrix, the developed method also showed good recovery (88 % - 92 %) and reproducibility (RSD<10 %)., Significance: This method contributes to the early diagnosis of HFMD and opens up ideas for the further development of high-throughput biomarker detection. The strategy has practical potential for miR296 and miR16 detection in biological samples and provides a promising tool for multiple miRNA detection., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Recent papers on LA-ICP-MS have reported that certain elements are transported in particulate form, others in gaseous form and still others in a combination of both upon ablation of C-based materials. These two phases display different transport behaviour characteristics, potentially causing smearing in elemental maps, and could be processed differently in the ICP which raises concerns as to accuracy of quantification and emphasizes the need for matrix-matching of external standards. This work aims at a better understanding of two-phase sample transport by evaluating the peak profile changes observed upon varying parameters such as laser energy density and wavelength., Results: It is demonstrated that upon ablation of gelatin, elements are transported predominantly in particulate phase, but already at low laser energy density, a significant fraction of some elements is transported in the gaseous phase, which is even more expressed at higher energy density. This behaviour is element-specific since the ratio of the signal intensity for the analyte element transported in gas phase to the total signal intensity was 0 % for 23 Na, 43 % for 66 Zn and as high as 95 % for 13 C using a 193 nm laser. The results also suggest an effect of the laser wavelength, as all elements show either the same or higher amount of gas phase formation upon ablating with 213 nm versus 193 nm. It was even established that elements that fully occur in particulate form upon ablation using 193 nm laser radiation are partly converted into gaseous phase when using 213 nm., Significance: This work provides a thorough investigation of the underexposed phenomenon of two-phase sample transport upon ablation of biological samples upon via LA-ICP-MS. It is shown that for some elements a fraction of the ablated material is converted and transported in the gas phase, which can lead to significant smearing effects. As such, it is important to evaluate element-specific peak profiles on beforehand and, if necessary, adapt instrument settings and slow down data acquisition., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Frank Vanhaecke has patent #EP3195346B1 licensed to Teledyne Photon Machines., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: Miniaturized microplasma-based atomic emission spectrometry (AES) has been extensively used for element analysis in recent years due to the advantages of low power consumption, low gas consumption, relatively low manufacturing and running cost, and the potential for real-time and field analysis. However, few applications in bioassay detection have been reported based on microplasma AES systems because of their relatively low sensitivity and the absence of indirect analytical strategies. It is still a challenge to develop a simple, sensitive, and portable microplasma-based AES bioassay approach., Results: In this work, a portable analytical system was designed based on point discharge chemical vapor generation atomic emission spectrometry (PD-CVG-AES) coupling with gold filament enrichment. The detection of ascorbic acid (AA) was realized indirectly by means of the highly sensitive analysis of Hg 2+ . The measurement was based on Ag  +  can decrease the concentration of Hg 2+ by forming Ag-Hg amalgam in the presence of the reductant SnCl 2 , while AA can pre-reduce Ag  +  to Ag 0 , leading to the generation of silver nanoparticles (Ag NPs). The pre-reduce procedure can decrease the generation of Ag-Hg amalgam, resulting in the recovery of Hg 2+ signal. The dissociative Hg 2+ was further detected by PD-CVG-AES combination of gold filament enrichment, which significantly improved the detection sensitivity for both Hg 2+ and AA. Under optimal conditions, the limit of detection (LOD) of AA is as low as 19 nM with a relative standard deviation (RSD, n = 5) of 0.7 %., Significance: The developed novel analytical strategy obviously broadens the application of microplasma-based AES, and it is well demonstrated by the determination of AA in several traditional Chinese medicines (TCMs), offering a higher level of sensitivity compared to current AA detection techniques. It has potential for future application in point-of-care testing (POCT) assays., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)