Surface-Enhanced Raman Scattering Spectroscopy Combined With Chemical Imaging Analysis for Detecting Apple Valsa Canker at an Early Stage

Abstract

Apple Valsa canker (AVC) with early incubation characteristics is a severe apple tree disease, resulting in significant orchards yield loss. Early detection of the infected trees is critical to prevent the disease from rapidly developing. Surface-enhanced Raman Scattering (SERS) spectroscopy with simplifies detection procedures and improves detection efficiency is a potential method for AVC detection. In this study, AVC early infected detection was proposed by combining SERS spectroscopy with the chemometrics methods and machine learning algorithms, and chemical distribution imaging was successfully applied to the analysis of disease dynamics. Results showed that the samples of healthy, early disease, and late disease sample datasets demonstrated significant clustering effects. The adaptive iterative reweighted penalized least squares (air-PLS) algorithm was used as the best baseline correction method to eliminate the interference of baseline shifts. The BP-ANN, ELM, Random Forest, and LS-SVM machine learning algorithms incorporating optimal spectral variables were utilized to establish discriminative models to detect of the AVC disease stage. The accuracy of these models was above 90%. SERS chemical imaging results showed that cellulose and lignin were significantly reduced at the phloem disease-health junction under AVC stress. These results suggested that SERS spectroscopy combined with chemical imaging analysis for early detection of the AVC disease was feasible and promising. This study provided a practical method for the rapidly diagnosing of apple orchard diseases.

Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics

“…SERS have been used in a variety of different applications for the detection and therapy of disease. For example, detecting the activity of intracellular enzymes, 300–304 breast cancer 305,306 and more. Chou and co-workers demonstrated the detection of the β-amyloid peptide, which is a key component in plaques in sufferers of Alzheimer's disease.…”

Nondestructive determination of carotenoids in kiwifruit leaves infected with Pseudomonas syringae pv. actinidiae by surface‐enhanced Raman spectroscopy combined with chemical imaging

“…However, RS is often disturbed by fluorescence caused by chromophores in plant tissues, and changes in composition under disease stress may lead to RS band broadening or drift (Mukherjee et al, 2017; Weng et al, 2021). This can lead to significant deviations in biochemical composition analysis of RS data (Fang et al, 2022).…”

“…Zhang, Li, et al (2022) used aptamer sensors based on SERS and applied them to the highly sensitive detection of the plant hormone abscisic acid. Fang et al (2022) demonstrated that SERS combined with chemical imaging analysis enabled early diagnosis of apple tree rot disease, showing that under apple Valsa canker stress, cellulose and lignin at the phloem disease–healthy junction significantly reduced.…”

Detection and Classification of Temporal Changes for Citrus Canker Growth Rate Using Deep Learning

“…Early detection of citrus canker disease at its initial stages of development is very important as it could be helpful for designing plan for control of the disease before it attain threshold level and sever productivity and financial loss. Early identification of citrus canker during the initial growth phase can prevent disease transmission to other plant parts, particularly fruits, which are a vital economic product, thereby enabling significant cost savings (Fang et al, 2022). Accurate estimation of citrus fruit production prior to harvest is highly crucial for citrus producer community and decision makers.…”