An aberration-free line scan confocal Raman imager and type classification and distribution detection of microplastics.

An aberration-free line scanning confocal Raman imager (named AFLSCRI) is developed to achieve rapid Raman imaging. As an application example, various types and sizes of MPs are identified through Raman imaging combined with a machine learning algorithm. The system has excellent performance with a spatial resolution of 2 µm and spectral resolution of 4 cm-1. Compared to traditional point-scanning Raman imaging systems, the detection speed is improved by 2 orders of magnitude. The pervasive nature of MPs results in their infiltration into the food chain, raising concerns for human health due to the potential for chemical leaching and the introduction of persistent organic pollutants. We conducted a series of experiments on various types and sizes of MPs. The system can give a classification accuracy of 98% for seven different types of plastics, and Raman imaging and species identification for MPs as small as 1 µm in diameter were achieved. We also identified toxic and harmful substances remaining in plastics, such as Dioctyl Phthalate (DOP) residues. This demonstrates a strong performance in microplastic species identification, size recognition and identification of hazardous substance contamination in microplastics.

Determination of geographic origins and types of Lindera aggregata samples using a portable short-wave infrared hyperspectral imager.

A portable short-wavelength infrared microscope hyperspectral imager (SMHI) combined with machine learning algorithms for the purpose of classifying geographical origins as well as root types of Lindera aggregata is developed. The spectral range of the SMHI system is 1090-1820 nm (5500-9100 cm-1) with spectral and spatial resolutions of 4 nm and 27.3 µm, respectively. Utilizing PCA-RF algorithms, the geographic origin of tuberous roots and leaves from five different origins were classified with accuracies of 97.5% and 97.8%, respectively. In addition, spatial identification of tuberous root and taproot tubers in a mixed sample was done with an accuracy of 98.98%. The accuracy of origin classification and spatial identification are high enough which indicate the significant potential of applying SMHI system into the non-invasive spatial mapping and rapid quality assessment of medicinal herbs.

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.