The monitoring of acetone in exhaled breath is expected to provide a noninvasive and painless method for dynamic monitoring of summarized physiological metabolic status during obesity treatment. Although the commonly used Mass Spectrometry (MS) technology has high accuracy, the long detection time and large equipment size limit the application of daily bedside detection. As for the real-time and accurate detection of acetone, the gas sensor has become the best choice of gas detection technology, but it is easy to be disturbed by water vapor in breath gas. An integrated breath gas detection system based on cavity ring-down spectroscopy (CRDS) is reported in this paper, which is a laser absorption spectroscopy technique with high-sensitivity detection and absolute quantitative analysis. The system uses a 266 nm single-wavelength ultraviolet laser combined with a breath gas pretreatment unit to effectively remove the influence of water vapor. The ring-down time of this system was 1.068 μs, the detection sensitivity was 1 ppb, and the stability of the system was 0.13%. The detection principle of the integrated breath gas detection system follows Lambert–Beer’s law, which is an absolute measurement with very high detection accuracy, and was further validated by Gas Chromatography–Mass Spectrometer (GC-MS) testing. Significant differences in the response of the integrated breath gas detection system to simulated gases containing different concentrations of acetone indicate the potential of the system for the detection of trace amounts of acetone. Meanwhile, the monitoring of acetone during obesity treatment also signifies the feasibility of this system in the dynamic monitoring of physiological indicators, which is not only important for the optimization of the obesity treatment process but also promises to shed further light on the interaction between obesity treatment and physiological metabolism in medicine.

Against the background of globalization, the circulation range of traditional Chinese medicinal materials is constantly expanding, and the phenomena of mixed origins and counterfeiting are becoming increasingly serious. Tracing the origin of traditional Chinese medicinal materials is of great significance for ensuring their quality, safety, and effectiveness. Laser-induced breakdown spectroscopy (LIBS), as a rapid and non-destructive element analysis technique, can be used for the origin tracing of traditional Chinese medicinal materials. Deep learning can not only handle non-linear relationships but also automatically extract features from high-dimensional data. In this paper, LIBS is combined with deep learning, and a Double-Multi Convolutional Neural Network LIBS Analysis System (DMC-LIBSAS) is proposed for the origin tracing of the traditional Chinese medicinal material Angelica dahurica. The system consists of a LIBS signal generation module, a spectral preprocessing module, and an algorithm analysis module—Double-Multi Convolutional Neural Network (DMCNN)—achieving a direct mapping from input data to output results. And the ability of DMCNN to extract characteristic peaks is demonstrated by the 1D Gradient-weighted Class Activation Mapping (1D-Grad-CAM) method. The tracing accuracy of DMC-LIBSAS for Angelica dahurica reaches 95.25%. To further verify the effectiveness of the system, it is compared with six classic methods including LeNet, AlexNet, Resnet18, K-nearest neighbors (KNN), Random Forest (RF), and Decision Tree (DT) (with accuracies of 68%, 75%, 72.5%, 79.7%, 86.7%, and 75.5%, respectively), and the tracing effects are all much lower than that of DMC-LIBSAS. The results show that DMC-LIBSAS can effectively and accurately trace the origin of Angelica dahurica, providing a new technical support for the quality supervision of traditional Chinese medicinal materials.

Cavity ring-down spectroscopy has significant potential for detecting trace volatile organic compounds, owing to its long absorption path and high sensitivity. However, in practical measurements, noise severely decreases the accuracy of decay curves and the reliability of concentration retrieval. To address this, we developed a deep learning-based denoising model called decay-upsampling FC-Net. Experimental results showed that the model improved the signal-to-noise ratio from 13.86 dB to 26.79 dB and processed a single decay curve in only 0.000207 s on average. Moreover, under high-noise conditions, it determined the ring-down time more accurately than conventional methods. This study provides an effective signal processing solution to enhance the practical reliability of Cavity ring-down spectroscopy gas detection systems.

In recent years, pesticides have been widely applied in the commercial cultivation of traditional Chinese medicinal plants to increase the yield of medicinal materials. Xinhui dried tangerine peel (Citri Reticulatae Pericarpium), a common ingredient in traditional Chinese medicine, utilizes the citrus peel as its medicinal part. During cultivation, the peel is directly exposed to pesticides, making it susceptible to pesticide residue accumulation. To enable the rapid identification of pesticide types and their targeted removal, this study integrated laser-induced breakdown spectroscopy with ensemble learning algorithms. Three lightweight neural network models—1D-CNN, Res-CNN, and LIBS-UNet—were developed and trained using either a single loss function or a composite loss function. The 1D-CNN, Res-CNN, and LIBS-UNet models achieved accuracies of 97.50% and 98.69%, 95.00% and 95.73%, and 74.06% and 76.88% for the single loss and composite loss functions, respectively. During the model ensemble stage, individual models were weighted according to their classification accuracy and test similarity matrices. Through this approach, the pesticide identification accuracy reached 99.99%. This study demonstrates that ensemble learning can effectively integrate the strengths of multiple weak classifiers, thereby significantly enhancing classification performance and providing a novel approach for the rapid detection of pesticide residues in traditional Chinese medicine ingredients.