Line laser scanning microscopy based on the Scheimpflug principle for high-resolution topography restoration and quantitative measurement.

A line laser scanning microscopy system with a larger depth of field based on the Scheimpflug principle is proposed for high-resolution surface topography restoration and quantitative measurement on miniature non-transparent samples. An imaging model based on the Scheimpflug principle is established, and a calibration method without system parameters is derived, which is further extended to a microscopic system. The measuring range of the system is 5m m×4m m×x m m, where x is the movement distance of the displacement stage. In the z-axis direction, the relative error of measurement is about 1% when z is of the millimeter level and less than 7% when z is of the micron level, and the spatial resolution is better than 3.8 µm. In the y-axis direction, the relative error of measurement is less than 5%. Finally, three-dimensional scanning of two samples with different surfaces is carried out to verify the feasibility of the system. The experimental results show that our system has the capability of high-resolution topography restoration and can be applied in industrial production scenarios such as automatic measurement and intelligent identification.

Machine learning classification of origins and varieties of Tetrastigma hemsleyanum using a dual-mode microscopic hyperspectral imager.

A dual-mode microscopic hyperspectral imager (DMHI) combined with a machine learning algorithm for the purpose of classifying origins and varieties of Tetrastigma hemsleyanum (T. hemsleyanum) was developed. By switching the illumination source, the DMHI can operate in reflection imaging and fluorescence detection modes. The DMHI system has excellent performance with spatial and spectral resolutions of 27.8 µm and 3 nm, respectively. To verify the capability of the DMHI system, a series of classification experiments of T. hemsleyanum were conducted. Captured hyperspectral datasets were analyzed using principal component analysis (PCA) for dimensional reduction, and a support vector machine (SVM) model was used for classification. In reflection microscopic hyperspectral imaging (RMHI) mode, the classification accuracies of T. hemsleyanum origins and varieties were 96.3% and 97.3%, respectively, while in fluorescence microscopic hyperspectral imaging (FMHI) mode, the classification accuracies were 97.3% and 100%, respectively. Combining datasets in dual mode, excellent predictions of origin and variety were realized by the trained model, both with a 97.5% accuracy on a newly measured test set. The results show that the DMHI system is capable of T. hemsleyanum origin and variety classification, and has the potential for non-invasive detection and rapid quality assessment of various kinds of medicinal herbs.