Design and daytime performance of laser-induced fluorescence spectrum lidar for simultaneous detection of multiple components, dissolved organic matter, phycocyanin, and chlorophyll in river water

Abstract

In this work, we developed mobile laser-induced fluorescence spectrum (LIFS) lidar based on preliminary experiments on the excitation emission matrix of a water sample and a method for reducing solar background light using the synchronous detection technique. The combination of a UV short-pulse laser (355 nm, 6 ns) for fluorescence excitation with a 10-100 ns short-time synchronous detection using a gated image-intensified multi-channel CCD of the fluorescence made the LIFS lidar operation possible even in daytime. The LIFS lidar with this construction demonstrated the potential of natural river/lake water quality monitoring at the Tenryu River/Lake Suwa. Three main components in the fluorescence data of the water, dissolved organic matter, phycocyanin, and chlorophyll, were extracted by spectral analysis using the standard spectral functions of these components. Their concentrations were estimated by adapting experimentally calibrated data. Results of long-term field observations using our LIFS lidar from 2010 to 2012 show the necessity of simultaneous multi-component detection to understand the natural water environment.

Remote Detection of the Fluorescence Spectrum of Natural Pollens Floating in the Atmosphere Using a Laser-Induced-Fluorescence Spectrum (LIFS) Lidar

“…If we average 100 signals using a 10 Hz laser, which needs several minutes to process and save data manually, we can obtain the fluorescence spectrum every 4-10 min, as shown in Figure 5a. We have already succeeded in delivering LIFS lidar data to a personal mobile phone from an observation place directly using a commercial communication system [24]. On the other hand, the gravitational method using a Durham's sampler that has been used as a traditional and basic method of pollen particle counting needs to one day inform the pollen status to the public.…”

“…In the previous reports on fluorescence lidar observation in daytime, observation targets were in high concentration/dense conditions, including hard targets, because it was easier to achieve a signal to noise ratio under such conditions. These observations included plant constituents monitoring [21,22], water quality monitoring [23,24], construction status monitoring [25,26], land monitoring [27], insects [28] and bird [29] detections. The aerosol fluorescence measurement was done only in nighttime [11,30].…”

“…We introduced a synchronous-delay detection technique [24] to be able to operate during daytime. If a detector gate opens late during the arrival of a fluorescence emission, because a fluorescence emission is short, the solar background light is significantly decreased.…”

Distribution Map of Plant Fluorescence Spectrum in Three-Dimensions Created by a Laser-Induced Fluorescence Spectrum (LIFS) Lidar Observations

“…1) To enable daytime and outdoor operations, we adapted the synchronous detection technique [7] and prepared a photonic multi-channel analyzer system (PMA-12 (C10029-01), Hamamatsu Photonics, Japan) whose detector was a gated intensified CCD. The CCD gate used as a lidar detector opens to coincide only with the arrival of fluorescence emissions corresponding to laser irradiations.…”

Near-Infrared Spectral Characteristic Extraction and Qualitative Analysis Method for Complex Multi-Component Mixtures Based on TRPCA-SVM

“…As early as the 1980s, some scholars used visible spectroscopy, ultraviolet spectroscopy, and infrared spectroscopy to detect metal ions and RNA mixtures, and they have carried out corrections of background noise in the spectrum and quantitative analyses of multi-components [5,6]. With the continuous deepening of research, multi-component quantitative analysis of complex bodies based on spectral detection technology has been rapidly developed [7][8][9]. The quantitative analysis method takes the component content and spectral data as the training set and uses chemometric algorithms to establish the predictive model to realize rapid and non-destructive quantitative detection [10][11][12].…”