# Applications of Raman Microscopy/Spectroscopy-Based Techniques to Plant Disease Diagnosis
## 9 Why Raman Techniques Are Important in Diagnosis
Cluster 1 (red circles) is defined by 109 keywords, with keywords including \"surface enhanced Raman spectroscopy\", which contributed 2.693 TLS, or 3.55%; \"procedures\", which contributed 2.150 TLS, or 2.83%; \"chemistry\", which contributed 2.273 TLS, or 0.68%, and \"bacterium detection\", which contributed 1.462 TLS, or 1.93%. Cluster 2 (green circles) is defined by 89 keywords, with keywords including \"bacteria\", which contributed 3.716 TLS, or 4.90%; \"diagnosis\", which contributed 3.595 TLS, or 4.74%; \"Raman spectroscopy\", which contributed 2.667 TLS, or 3.52%; \"Escherichia coli\", which contributed 1.772 TLS, or 2.34%, and \"Raman scattering\", which contributed 1.742 TLS, or 2.30%  
Cluster 3 (blue circles) is defined by 88 keywords, with keywords including \"Raman spectrometry\" contributing 3.679 TLS, or 4.85%; \"human\" contributing 3.245 TLS, or 4.28%; \"nonhuman\" contributing 2.882 TLS, or 3.80%; \"humans\" contributing 2.703 TLS, or 3.56%, and \"bacterium\", which contributed 1.815, or 2.39%. Cluster 4 (mustard color circles) is defined by 55 keywords, with keywords including \"surface scattering\" contributing 1.426 TLS, or 1.88%; \"metal nanoparticles\" contributing 1.399 TLS, or 1.84%; \"staphylococcus aureus\" contributing 1156 TLS, or 1.52%; \"enhanced Raman scattering\" contributing 802 TLS, or 1.06%, and \"pathogens\" contributing 646 TLS, or 0.85%. Cluster 5 (purple) is defined by 43 keywords, with keywords including \"isolation and purification\" contributing 1.041 TLS, or 1.37%, \"polymerase chain reaction\" contributing 698 TLS, or 0.92%, \"genetics\" contributing 492 TLS, or 0.65%, \"sensitivity analysis\" contributing 318 TLS, or 0.42%, and \"rapid detection\" contributing 285 TLS, or 0.38%. Cluster 6 (sky blue) is defined by 26 keywords, with keywords including \"microfluidics\" contributing 587 TLS or 0.77%, \"biological marker\" contributing 470 TLS or 0.62%, \"metabolism\" contributing 416 TLS or 0.55%, \"clinical diagnosis\" contributing 290 TLS or 0.38%, and \"pathogenicity\" contributing 263 TLS or 0.35%. Cluster 7 (orange) is defined by four keywords, with keywords including \"devices\" contributing 399 TLS, or 0.53%, and \"surface properties\" contributing 123 TLS, or 0.16%. In Figure 2, Raman spectroscopy was utilized to identify pathogenic bacteria such as _Staphylococcus aureus_. Despite the limitations of Raman spectroscopy as a diagnostic tool, such as the lack of commercial databases for identifying bacterial spectra, application of Raman spectroscopy to various clinical _Staphylococcus_ species enables successful identification. Overall, the use of Raman spectroscopy is a reliable tool for _Staphylococci_  
Figure 2: Co-keyword network visualization based on “Raman” AND “diagnosis” AND “bacteria”. identification, making it an important diagnostic method in microbiological research for successful identification in a single measurement [59]. In addition, Figure 2 includes Raman spectroscopy and the polymerase chain reaction as important methods for bacterial diagnosis. The significance of Raman spectroscopy in diagnosing Huanglongbing (yellow dragon disease), previously known as citrus greening, a severe citrus tree disease was reported [67]. Raman spectroscopy was reported to be more sensitive in diagnosing Huanglongbing on both orange and grapefruit trees compared to qPCR, the \"golden standard\" in pathogen diagnostics according to [PERSON] et al. (2020). Furthermore, Raman spectroscopy was described as an important diagnostic tool for both biotic and abiotic plant disease stresses [68].