This study presents a linear charge-coupled device (CCD) spectrometer and its application to quantitative determination of rutin with UV-Vis spectrophotometry and fluorimetry detections. The spectrometer can obtain spectrum with the range of 271.44 nm from one exposure and the 1.7 nm resolution. With the same spectrometer, the determination of rutin in rutin tablet was successfully studied by measuring the absorption and laser-induced fluorescence emission. The data shows that there is no significant difference for determining rutin between the two methods. The result implies that the spectrometer, which realizes two different analytical methods, can be widely used in trace analysis.

Radiography is an essential basis for the diagnosis of fractures. For the pediatric elbow joint diagnosis, the doctor needs to diagnose abnormalities based on the location and shape of each bone, which is a great challenge for AI algorithms when interpreting radiographs. Bone instance segmentation is an effective upstream task for automatic radiograph interpretation. Pediatric elbow bone instance segmentation is a process by which each bone is extracted separately from radiography. However, the arbitrary directions and the overlapping of bones pose issues for bone instance segmentation. In this paper, we design a detection-segmentation pipeline to tackle these problems by using rotational bounding boxes to detect bones and proposing a robust segmentation method. The proposed pipeline mainly contains three parts: (i) We use Faster R-CNN-style architecture to detect and locate bones. (ii) We adopt the Oriented Bounding Box (OBB) to improve the localizing accuracy. (iii) We design the Global-Local Fusion Segmentation Network to combine the global and local contexts of the overlapped bones. To verify the effectiveness of our proposal, we conduct experiments on our self-constructed dataset that contains 1274 well-annotated pediatric elbow radiographs. The qualitative and quantitative results indicate that the network significantly improves the performance of bone extraction. Our methodology has good potential for applying deep learning in the radiography’s bone instance segmentation.

In order to real-time online monitoring the reaction substrates for biological processes, we designed an on-line Fourier transform infrared spectrometer measurement system, which has the advantages of low cost, simple and compact structure, good reliability, and strong shock resistance compared with the traditional Fourier transform infrared spectrometer. This system consists of two core components, i.e., a high-performance interferometer and an attenuated total reflectance (ATR) probe. In the interferometer, we 1) combined corner cube mirrors and flat mirrors to avoid tilting of the moving mirror; 2) set 60° incident angle to the ZnSe beam splitter to improve the luminous flux; 3) shared folded optical path with both the infrared light and the reference laser to improve compactness in structure; and 4) drive the moving mirror by a parallelogram swing flexible support module to make it move smoothly. In the ATR probe, we utilized two large diameter and inner coating light pipes to transmit the incident and outgoing light to enhance the luminous flux further. We also simulated the optical system and did some off-line and online measurement experiments. The results show the spectrometer has satisfactory reliability, large luminous flux, and online measurement capability. It can be real-time online monitoring the biological process through measuring the concentration of the related bioreactor reactant. The online analysis has wide application prospects in biological, chemical testing, material analysis, and many other fields.