# Applications of Raman Microscopy/Spectroscopy-Based Techniques to Plant Disease Diagnosis
## 9 Why Raman Techniques Are Important in Diagnosis  
To provide a scientific possible answer to \"why are Raman techniques important in diagnosis?\", we accessed the Scopus database on 30 May 2024. The Scopus bibliographic database yielded a total of 398,574, 1442 and 384 documents using the criteria terms \"Raman\", \"Raman AND microscopy AND diagnosis\" and \"Raman AND diagnosis AND bacteria\", respectively. The results are presented as graph-based maps by creating maps based on bibliographic data and distance-based maps to reflect the strength of the relation between the items [65; 66]. All the distance-based and graph-based maps were analyzed using the following methods of analysis: (i) the type of analysis: co-occurrence; (ii) the unit of analysis: all keywords and (iii) the counting method: full counting. Figures 1 and 2 show the co-keyword networks of the keywords visualized using the bibliometric analysis software VOSviewer (1.6.20). In detail, the co-keyword network visualizations were \"Raman\" AND \"microscopy\" AND \"diagnosis\" (Figure 1) and \"Raman\" AND \"diagnosis\" AND \"bacteria\" (Figure 2). The size of a keyword node represents the keyword's frequency of occurrence. A link between two nodes represents a co-occurrence relationship, with the thickness indicating the length strength. The keywords \"Raman\" AND \"microscopy\" AND \"diagnosis\" were presented as five clusters, defined by 999 keywords (items), which contributed a Total Link Strength (TLS) of 465.805, or 100%, as presented in Figure 1.  
Cluster 1 (red circles) is defined by 314 keywords, with keywords including \"review\", which contributed 6.496 TLS, or 1.39%; \"fluorescence microscopy\", which contributed 3.557 TLS, or 0.76%; \"human cell\", contributing 3.178 TLS, or 0.68%, and \"nanomaterial\", which contributed 3.022 TLS, or 0.65%. Cluster 2 (green circles) is defined by 305 keywords, with keywords including \"diagnosis\", contributing 15.300 TLS, or 3.28%; \"chemistry\", contributing 10.185 TLS, or 2.18%; \"scanning electron microscopy\", contributing 8.679 TLS, or 1.34%; \"transmission electron microscope\", contributing 7.910 TLS, or 1.86%, and \"biosensing techniques\", contributing 5.755 TLS, or 1.23%  
Cluster 3 (blue circles) is defined by 272 keywords, with keywords including \"Raman spectroscopy\" contributing 21.950 TLS, or 6.94%; \"human\" contributing 21.734 TLS, or 1.92%; \"humans\" contributed 16.555 TLS, or 1.78%; \"spectrum analysis, Raman\" contributing 11.889 TLS, or 0.92%, and \"Raman spectroscopy\" contributed 8.339, or 0.84%. Cluster 4 (mustard color circles) is defined by 272 keywords, with keywords including \"procedure\" contributing 10.400 TLS, or 2.32%; \"surface enhanced Raman spectroscopy\" contributing 6.891 TLS, or 1.48; \"surface scattering\" contributing 3.889 TLS, or 0.83%; \"proteins\" contributing 1.980 TLS, or 0.81%, and \"early cancer diagnosis\" contributing 1.250 TLS, or 0.43%. Cluster 5 (purple) is defined by 75 keywords, with keywords including \"metabolism\" contributing 3.522 TLS, or 0.76%; \"cells\" contributing 2.186 TLS, or 0.47%; \"cell line, tumor\" contributing 1.798 TLS, or 0.39%; \"tumor cell line\" contributing 1.504 TLS, or 0.32%, and \"breast neoplasms\" contributing 928 TLS, or 0.199%. From the above, Raman spectroscopy is an advanced analytical technique used for the identification of the molecular composition and structure of microbes. The technique is based on the inelastic scattering of photons, known as Raman scattering, and it has shown promise in pathology, with an emphasis on the early diagnosis of cancer. The keywords for \"Raman\" AND \"diagnosis\" AND \"bacteria\" were presented as seven clusters, defined by 414 keywords (items), which contributed a Total Link Strength (TLS) of 75.838, or 100%, as presented in Figure 2.  
Figure 1: Co-keyword network visualization based on “Raman” AND “microscopy” AND “diagnosis”.