Early on–site detection of strawberry anthracnose using portable Raman spectroscopy

• Published in: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Vol. 303; p. 123150
• Main Authors: Kim, Soogeun, Hong, Sang-Hyun, Kim, Joon Heon, Oh, Myoung-Kyu, Eom, Tae Joong, Park, Yang Ho, Shin, Gil Ho, Yim, Sang-Youp
• Format: Journal Article
• Language: English
• Published: England Elsevier B.V 15.12.2023
• Subjects: C. gloeosporioides; Early detection; Infection; On–site; Portable Raman; Strawberry anthracnose; Infection; On–site; Portable Raman; Strawberry anthracnose; Early detection; C. gloeosporioides
• ISSN: 1386-1425; 1873-3557
• DOI: 10.1016/j.saa.2023.123150

[Display omitted] •We achieved unambiguous identification of strawberry anthracnose as early as 3 days after exposure to Colletotrichum gloeosporioides.•We found molecular markers associated with C. gloeosporioides and plant metabolites, such as fluorescence background, amide III, fatty acids, aromatic rings, and terpenoids.•Early on–site detection of strawberry anthracnose is possible using potable Raman system even without visible symptoms. We developed a method for the early on–site detection of strawberry anthracnose using a portable Raman system with multivariate statistical analysis algorithms. By using molecular markers based on Raman spectra, the proposed method can detect anthracnose in strawberry stems 3 days after exposure to Colletotrichum gloeosporioides. A fiber–optic probe was applied for the portable Raman system, and the acquisition time was 10 s. We found that the molecular markers were closely related to the following subjects: i) an increase in amide III and fatty acids of C. gloeosporioides invading strawberry stems (Raman bands at 1180–1310 cm−1) and ii) a decrease in metabolites in strawberry plants, such as phenolic compounds and terpenoids (Raman bands at 760, 800, and 1523 cm−1). We also found that the increased fluorescence background caused by various chromophores within the invading C. gloeosporioides could serve as a marker. A two–dimensional cluster plot obtained by principal component analysis (PCA) showed that the three groups (control, fungal infection, and pathogen) were distinguishable. The linear discriminant analysis (LDA)–based prediction algorithm could identify C. gloeosporioides infection with a posterior probability of over 40%, even when no symptoms were visible on the inoculated strawberry plants.