Wastewater monitoring is necessary to preserve the quality of water in publicly water bodies. In particular, the presence of colored pollutants in wastewater inhibits the photosynthesis of seaweed, and therefore requires adequate treatment for its removal.We developed a surface-enhanced Raman scattering (SERS) sensor for the detection of color-polluted water. Silver particles on a quartz substrate were grown via mist chemical vapor deposition; this substrate was built into the sensor to enhance its Ramanscattering signals. Aqueous solutions colored with a single dye were detected at concentrations as low as the ppb level, and the intensity of their SERS spectra decreased with the dye concentration. Additionally, we evaluated the performance of the SERS sensor using mixed dyes dissolved in river-water, making assumptions suited to practical occurrences results demonstrated that our sensor depicts the molecular features of each dye in the river-water sample, clearly detecting them at concentrations as low as the ppb level. Therefore, this study proved that SERS can be applied in an environmental spectroscopic sensor.

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Herein, physical evaluation of a mass ionization assist agent prepared using mist deposition method and the visualization of natural occurring poisons in plants were investigated. An -cyano-4-hydroxy-cinnamic acid (CHCA) crystal layers were formed on sliced botanical samples deposited on a glass slide for a mass ionization assist agent of matrix assisted laser desorption/ionization(MALDI) imaging. The formed CHCA crystal layer exhibits a homogeneous surface at nozzle temperatures over 200 ℃, and its chemical stability is confirmed via Raman spectroscopy. -chaconine and -solanine in sample were detected as H + adduct localized around of root in sliced potato sample using MALDI-fourier transform ion cyclotron resonance mass spectrometry and mass spectrometry imaging. Mist deposition method is effective for the mass ionization assist preparation and promising for utilize to visualization of target components in samples.