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.

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.

Microbial testing plays a crucial role in medical and biological research, serving as a significant direction in disease prevention and control studies. Traditional manual testing, which involves identifying colonies on petri dishes, is the best method, but it is not only expensive but also time-consuming. However, the application and development of deep learning can address these issues. However, a crucial condition is that deep learning models require a large amount of labeled medical imaging data. Obtaining high-resolution images is time-consuming and cannot generate a large amount of data in a short time. Therefore, we designed a Double DCGAN (D-DCGAN) to construct a dataset. Under the DCGAN adversarial network, synthetic colonies are generated by overlapping small single colonies and composite colonies to establish a random synthetic colony model, and a DCGANB generation model is established for the synthesis of complete colonies. In this experiment, we evaluated the performance of the D-DCGAN model and verified the effectiveness of the generated dataset using Faster R-CNN and YOLOv12. A second-order evaluation model based on extensive evaluation metrics was constructed, and the optimal matching data volume was calculated to be 1320 out of the original 236 data points. We demonstrated that the proposed framework effectively enhances the image dataset and improves detection performance, achieving an FID score of 37.1 and an accuracy rate of 0.975, effectively proving the feasibility of dataset enhancement.