The increasing market demand for fresh produce promotes a keen interest in developing a rapid, sensitive and reliable method for monitoring plant health and determining the shelf-life of postharvest produce. The objective of this study is to explore the capability of Surface-enhanced Raman spectroscopy (SERS) in these applications. SERS integrates Raman spectroscopy which measures molecular vibrations and nanotechnology which enhances the weak Raman signals. Herein, we developed two SERS methods based on a surface detection approach using nanoparticles solution and a headspace detection approach using gold nanoparticles (AuNPs) fibers, to detect biochemical changes during postharvest storage of arugula leaves. Compared with surface detection, the headspace detection revealed significant spectral changes during the storage, particularly in the shifts around 500, 950 and 1030 cm-1. These changes analyzed using principal component analysis (PCA) to establish a prediction model for shelf-life determination. Through analyzing reference standard compounds, we identified the dimethyl disulfide (DMDS), 1-propanethiol and methanethiol (MT) were most likely to account for the signature spectra of headspace arugula at the late storage period due to the activities of spoilage bacteria. The headspace detection method was also applied to monitor the stress responses of living basil to abiotic stresses (pesticide/salinity). However, the volatile analysis of the basil plants response to abiotic stresses (pesticide/salinity) showed indistinctive results. In conclusion, the headspace detection based on SERS provides a new strategy for quality monitoring of fresh produce in the food industry.
| Identifer | oai:union.ndltd.org:UMASS/oai:scholarworks.umass.edu:masters_theses_2-1979 |
| Date | 15 July 2020 |
| Creators | Du, Xinyi |
| Publisher | ScholarWorks@UMass Amherst |
| Source Sets | University of Massachusetts, Amherst |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Masters Theses |
Page generated in 0.0087 seconds