Abstract
Background: Apple Valsa Canker (AVC) with early incubation characteristics is a severe apple tree disease. Therefore, early detection of the infected trees is necessary to prevent the rapid development of the disease. Surface enhanced Raman Scattering (SERS) spectroscopy is a promising technique that simplifies detection procedures and reduces detection time. Meanwhile, SERS enhance signals at low laser powers and suppress biological fluorescence. In this study, the early detection of the AVC disease was carried out by combining SERS spectroscopy with the chemometrics methods and machine learning algorithms, and then chemical distribution imaging was successfully applied to the analysis of disease dynamics.Results: Firstly, the microstructure, UV-Vis spectrum, and Raman spectrum of SERS metallic nano-substrates were proved to investigate the enhancement effects of the synthesized AgNPs. Secondly, the multiple spectral baseline correction (MSBC), the asymmetric least squares (AsLS), and the adaptive iterative reweighted penalized least squares (air-PLS) were adopted to eliminate the disturbances of the baseline offset. The correlation analysis method was employed to identify the best baseline correction algorithm, which was the air-PLS algorithm herein. Meanwhile, principal component analysis (PCA) was used to perform clustering analysis based on the healthy, early disease, and late disease sample datasets, demonstrating obvious clustering effects. After that, optimal spectral variables were selected to build machine learning models to detect AVC disease, incorporating the BP-ANN, ELM, RForest, and LS-SVM algorithms. The accuracy of these models was above 90%, showing excellent discriminant performance. Finally, SERS chemical imaging provided the spatiotemporal dynamic characteristics of changes in the cellulose and lignin of the phloem disease-health junction under AVC stress. The results suggested that cellulose and lignin in the cell walls of infected tissues reduced significantly.Conclusions: SERS spectroscopy combining with chemical imaging analysis for early detection of the AVC disease was considered feasible and promising. This study provided a practical method for the rapid diagnosis of apple orchard diseases.
Chemical imaging analysis of active pharmaceutical ingredient in dissolving microneedle arrays by Raman spectroscopy
