Kate Nyarko

DNA methylation is a central epigenetic regulator of genome stability, developmental plasticity, and stress adaptation in plant tissue culture systems. During dedifferentiation, callus formation, organogenesis, somatic embryogenesis, transformation, and prolonged subculture, DNA methylation patterns can be extensively remodeled, thereby contributing to regeneration competence and somaclonal variation. To detect such changes, traditional methylation-detection methods, including bisulfite-based sequencing and methylation-sensitive polymerase chain reaction, provide high analytical output but remain labor-intensive (2–3 days), destructive (requiring microgram amounts of DNA), and difficult to deploy as rapid screening tools across large tissue-culture plantlets. Surface-enhanced Raman scattering (SERS) offers a complementary route by coupling molecular fingerprinting with plasmonic signal amplification. Controlled systems have demonstrated highly sensitive detection of methylation-associated molecular signatures, with reported detection limits reaching the nanomolar-to-submicromolar range depending on analyte type, substrate architecture, and measurement conditions. However, these quantitative values primarily come from synthetic DNA or model systems and should be interpreted cautiously when applied to complex plant tissue culture samples. This review critically examines current SERS-based strategies for DNA methylation analysis, emphasizing substrate selection, signal-enhancement mechanisms, SERS enhancement mechanisms, surface functionalization, and computational spectral analysis. SERS has emerged as a promising complementary technology for DNA methylation analysis due to its molecular fingerprinting capability and high sensitivity. However, direct demonstrations of SERS-based DNA methylation detection in plant tissue culture systems remain limited. Current evidence largely originates from synthetic DNA models, purified nucleic acid systems, and non-plant biological applications, providing methodological foundations rather than complete plant validation. Future studies are required to establish plant-specific substrates, calibration strategies, and biological validation workflows.

The presence of phenolic compounds in honey can serve as potential authenticity markers for honey’s botanical or geographical origins. The composition and properties of honey can vary greatly depending on the floral and geographical origins. This study focuses on identifying the specific markers that can distinguish honey based on their geographical areas in the United States. The main approach presented in this study to identify the geographic origins of honey involves chemometric methods combined with phenolic compound fingerprinting. Sample clean-up and phenolic compound extraction was carried out using solid phase extraction (SPE). Reversed phase liquid chromatography in combination with tandem mass spectrometry were utilized for the separation of the compounds. The honey physicochemical qualities were predominantly determined via spectrophotometric methods. Multivariate statistical tools such as principal component analysis (PCA), analysis of variance (ANOVA), and partial-least squares discriminant analysis (PLS-DA) were employed as both classification and feature selection tools. Overall, the present study was able to identify the presence of 12 potential markers to differentiate the honey’s geographical origins. The total phenolic content ranged from 81.6 to 105.7 mg GAE/100 g corresponding to honey from Colorado and Washington, respectively (GAE: gallic acid equivalents). The regression analysis shows a tendency for the total phenolic content of honey to increase as the color of honey increases. The most important result obtained in this study is the demonstration that the geographical origin of honey plays a critical role in predicting the physical properties and phenolic composition of honey.