Half-life determination of inorganic-organic hybrid nanomaterials in mice using laser-induced breakdown spectroscopy

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.

Analysis on dynamic changes of etizolam and its metabolites and exploration of its development prospect using UPLC-Q-exactive-MS

As one of the most widely abused designer benzodiazepines in the world, etizolam has been found in many cases in many countries. In this study, UPLC-Q-Exactive-MS was used for the first time to establish a dynamic change model of etizolam and its metabolites in rats. Compared with previous studies, the detection sensitivity and reproducibility of the instrument were higher. In the experiment, we optimized the traditional pharmacokinetic model based on Gauss function. According to the significant difference of etizolam in the plasma elimination phase of rats, a new pharmacokinetic model based on Lorentz function was established to describe the dynamic changes of etizolam more rigorously, which made the error effects lower and the accuracy of the pharmacokinetic parameters was improved. At the same time, the pharmacokinetic parameters of etizolam were compared with four other designer benzodiazepines reported in previous studies in rats, and we found the direct reason for the popularity of etizolam in the NPS market and explored the future development of etizolam for the first time. In addition, 21 metabolites were found through rat experiments to effectively detect etizolam abuse for a long time, of which 4 metabolites had the longest detection window and could be used as long-acting metabolites for experiments, which greatly prolongs the detection window and extends the time range in which etizolam was detected in real cases. This study is the first to conduct a systematic and comprehensive study on the metabolism and pharmacokinetics of etizolam and find out the direct reason for the prevalence of etizolam abuse, and we also discuss the development trend of etizolam in the future market of new psychoactive substances, which is beneficial for forensic experts to assess the trend of drug abuse and can provide reference for relevant drug control and drug treatment.

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.

Recent Development of Nanomaterials for Transdermal Drug Delivery

Nano-engineered medical products first appeared in the last decade. The current research in this area focuses on developing safe drugs with minimal adverse effects associated with the pharmacologically active cargo. Transdermal drug delivery, an alternative to oral administration, offers patient convenience, avoids first-pass hepatic metabolism, provides local targeting, and reduces effective drug toxicities. Nanomaterials provide alternatives to conventional transdermal drug delivery including patches, gels, sprays, and lotions, but it is crucial to understand the transport mechanisms involved. This article reviews the recent research trends in transdermal drug delivery and emphasizes the mechanisms and nano-formulations currently in vogue.

Visualization and accuracy improvement of soil classification using laser-induced breakdown spectroscopy with deep learning

Deep learning method is applied to spectral detection due to the advantage of not needing feature engineering. In this work, the deep neural network (DNN) model is designed to perform data mining on the laser-induced breakdown spectroscopy (LIBS) spectra of the ore. The potential of heat diffusion for an affinity-based transition embedding model is first used to perform nonlinear mapping of fully connected layer data in the DNN model. Compared with traditional methods, the DNN model has the highest recognition accuracy rate (75.92%). A training set update method based on DNN output is proposed, and the final model has a recognition accuracy of 85.54%. The method of training set update proposed in this work can not only obtain the sample labels quickly but also improve the accuracy of deep learning models. The results demonstrate that LIBS combined with the DNN model is a valuable tool for ore classification at a high accuracy rate.

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%.

A method for improving the accuracy of calibration-free laser-induced breakdown spectroscopy by exploiting self-absorption

The existence of the self-absorption effect results in a nonlinear relationship between spectral intensity and elemental concentration, which dramatically affect the quantitative accuracy of laser-induced breakdown spectroscopy (LIBS), especially calibration-free LIBS (CF-LIBS). In this work, the CF-LIBS with columnar density and standard reference line (CF-LIBS with CD-SRL) was proposed to improve the quantitative accuracy of CF-LIBS analysis by exploiting self-absorption. Our method allows using self-absorbed lines to perform the calibration-free approach directly and does not require self-absorption correction algorithms. To verify this method, the experiment was conducted both on aluminium-bronze and aluminium alloy samples. Compared with classical CF-LIBS, the average errors (AEs) of CF-LIBS with CD-SRL were decreased from 3.20%, 3.22%, 3.15% and 3.01%-0.95%, 1.00%, 1.16% and 1.78%, respectively for four aluminium-bronze alloy samples. The AEs were decreased from 0.66%, 0.70%, 0.89% and 1.30%-0.43%, 0.61%, 0.77% and 0.33%, respectively for four aluminium alloy samples. The experimental results demonstrated that CF-LIBS with CD-SRL provided higher quantitative accuracy and stronger adaptability than classical CF-LIBS, which is quite helpful for the practical application of CF-LIBS.

Meat species identification accuracy improvement using sample set portioning based on joint x-y distance and laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy (LIBS) was suitable for the identification of meat species due to fast and less sample preparation. However, the problem of low accuracy rate of the recognition model caused by improper selection of training set samples by random split has severely restricted the development of LIBS in meat detection. Sample set portioning based on the joint x-y distance (SPXY) method was applied for dividing the meat spectra into a training set and a test set. Then, the five kinds of meat samples (shrimp, chicken, beef, scallop, and pig liver) were classified by the support vector machine (SVM). With the random split method, Kennard-Stone method, and SPXY method, the recognition accuracies of the SVM model were 90.44%, 91.95%, and 94.35%, respectively. The multidimensional scaling method was used to visualize the results of the sample split for the interpretation of the classification. The results showed that the identification performance of the SPXY method combined with the SVM model was best, and the accuracy rates of shrimp, chicken, beef, scallop, and pig liver were 100.00%, 100.00%, 100.00%, 78.57%, and 92.00%, respectively. Moreover, to verify the broad adaptability of the SPXY method, the linear discriminant analysis model, the K-nearest neighbor model, and the ensemble learning model were applied as the meat species identification model. The results demonstrated that the accuracy rate of the classification model can be improved with the SPXY method. In light of the findings, the proposed sample portioning method can improve the accuracy rate of the recognition model using LIBS.

Predictive data clustering of laser-induced breakdown spectroscopy for brain tumor analysis

Limited by the lack of training spectral data in different kinds of tissues, the diagnostic accuracy of laser-induced breakdown spectroscopy (LIBS) is hard to reach the desired level with normal supervised learning identification methods. In this paper, we proposed to apply the predictive data clustering methods with supervised learning methods together to identify tissue information accurately. The meanshift clustering method is introduced to compare with three other clustering methods which have been used in LIBS field. We proposed the cluster precision (CP) score as a new criterion to work with Calinski-Harabasz (CH) score together for the evaluation of the clustering effect. The influences of principal component analysis (PCA) on all four kinds of clustering methods are also analyzed. PCA-meanshift shows the best clustering effect based on the comprehensive evaluation combined CH and CP scores. Based on the spatial location and feature similarity information provided by the predictive clustering, the PCA-Meanshift can improve diagnosis accuracy from less than 95% to 100% for all classifiers including support vector machine (SVM), k nearest neighbor (k-NN), soft independent modeling of class analogy (Simca) and random forests (RF) models.