A Study on Comparison of Phycocyanin Extraction Methods for Hyperspectral Remote Sensing of Cyanobacteria in Turbid Inland Waters

Ha, Rim; Shin, Hyunjoo; Nam, Gibeom; Park, Sang‐Hyun; Ting, Kang; Song, Hyunoh; Lee, Hyuk

doi:10.15681/kswe.2016.32.6.520

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…For determination of the PC concentration, the pigment was extracted using the freezing and thawing technique from [34], and the concentration was calculated using the equation from [35]. The PC extraction was based on a previously published method [36]. Pre-concentrated samples were homogenized with a 200 W Ultra-Sonicator (SIBATA Inc., Seoul, Korea).…”

Section: Pigment Extractionmentioning

confidence: 99%

Quantification of Phycocyanin in Inland Waters through Remote Measurement of Ratios and Shifts in Reflection Spectral Peaks

Nam

Shin

et al. 2021

Remote Sensing

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This study introduces a semi-empirical algorithm to estimate the extent of the phycocyanin (PC) concentration in eutrophic freshwater bodies; this is achieved by studying the reflectance characteristics of the red and near-red spectral regions, especially the shifting of the peak near 700 nm towards longer wavelengths. Spectral measurements in a darkroom environment over the pure-cultured cyanobacteria Microcystis showed that the shift is proportional to the algal biomass. A similar proportional trend was found from extensive field measurement data. The data also showed that the correlation of the magnitude of the shift with the PC concentration was greater than that with chlorophyll-a. This indicates that the characteristic can be a useful index to quantify cyanobacterial biomass. Based on these observations, a new PC algorithm was proposed that uses the remote sensing reflectance of the peak band around 700 nm and the trough band around 620 nm, and the magnitude of the peak shift near 700 nm. The efficacy of the algorithm was tested with 300 sets of field data, and the results were compared to select algorithms for the PC concentration prediction. The new algorithm performed better than the other algorithms with respect to most error indices, especially the mean relative error, indicating that the algorithm can reduce errors when PC concentrations are low. The algorithm was also applied to a hyperspectral dataset obtained through aerial imaging, in order to predict the spatial distribution of the PC concentration in an approximately 86 km long reach of the Nakdong River.

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Section: Pigment Extractionmentioning

confidence: 99%

Quantification of Phycocyanin in Inland Waters through Remote Measurement of Ratios and Shifts in Reflection Spectral Peaks

Nam

Shin

et al. 2021

Remote Sensing

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Classification Technique of Algae Using Hyperspectral Images of Algae Culture Media

Kim

Gwon

Oh³

et al. 2023

Applied Sciences

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Increases in algal growth have been reported in rivers, reservoirs, and other water resources worldwide, including Korea. Algal overgrowth can result in algal bloom, which has several negative impacts, such as ecosystem degradation and economic losses. Mitigation measures employed in Korea include an algal warning system and survey-based water quality forecast systems. However, these methods are time-consuming and require sample collection from the site. On the other hand, remote sensing techniques that use chlorophyll a are unable to distinguish between different types of algal species. In this paper, we aimed to identify a classification technique based on remote sensing methods that can be used to distinguish between blue-green algae and green algae. We acquired and prepared an algal culture solution and used a hyperspectral sensor to obtain an algae spectrum. Thereafter, we measured the absorption and emission spectra of blue-green and green algae and distinguished them using the instantaneous slope change of the spectrum. The absorption spectra for green algae showed two peaks at 417–437 nm and 661–673 nm, whereas those of blue-green algae showed three peaks at 449–529 nm, 433–437 nm, and 669–677 nm. The results of this study could form a basis for developing mitigation measures for algal overgrowth.

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The Estimation of Blue-green algae Cell Count and Phycocyanin Concentration using Fluorescent Concentration analyzer

Choi¹,

Park²,

Lee³

et al. 2022

J Korean Soc Environ Eng

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Objectives:The purpose of this study is to provide basic data for the development of a rapid and easy to measure alternative indicator for estimating the number of cyanobacteria cells in the field.Methods:Phycocyanin, algal cell number, and water quality items were measured at the left, middle, and right points of Maery Bridge near the Mulgeum and Maery algae monitoring network located downstream of the Nakdong River. The phycocyanin concentration(PC) was measured in the field using the LISST-HAB sensor, which is a fluorescence concentration analyzer, and comparative analysis was performed with the number of algal cells by algae species.Results and Discussion:As a limiting factor for blue-green algae occurrence, the correlation coefficient (r) between T-P and Blue-green algae (BGA) was 0.58 (≥0.3). Phycocyanin and Blue-green algae showed a high significant correlation at 0.96 (≥0.3). Prob (F-statistics) of the blue-green algae cell count estimation formula using phycocyanin was less than 0.05, showing a significant result. The estimation formula was estimated BGA(cells/mL) = 326.95×PC+50,484. As a result of the analysis of the predicted and measured values of Blue-green algae, r2 was 0.988, indicating a high coefficient of determination and 92.5% accuracy.Conclusion: The correlation analysis with the number of cyanobacteria showed a high significant correlation with phycocyanin of 0.96 (≥0.3), followed by T-P, a limiting factor for the occurrence of cyanobacteria, of 0.58 (≥0.3) indicating a significant correlation. In addition, the formula for estimating the number of blue-green algae cells using phycocyanin was found to be BGA (cells/mL)=326.95×PC+50,484 in which r2 was 0.927, and Prob (F-statistics) was less than 0.05. As a result of analyzing the predicted and measured values of cyanobacteria cell numbers, r2 was 0.988, indicating a high coefficient of determination, with accuracy of 92.5%. Through this results, it reveals that the number of cyanobacteria cells can be estimated using the sensor of the fluorescence concentration analyzer at the target point by substituting the phycocyanin concentration into the blue-green algae cell number estimation formula.

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A Study on Comparison of Phycocyanin Extraction Methods for Hyperspectral Remote Sensing of Cyanobacteria in Turbid Inland Waters

Cited by 3 publications

References 7 publications

Quantification of Phycocyanin in Inland Waters through Remote Measurement of Ratios and Shifts in Reflection Spectral Peaks

Quantification of Phycocyanin in Inland Waters through Remote Measurement of Ratios and Shifts in Reflection Spectral Peaks

Classification Technique of Algae Using Hyperspectral Images of Algae Culture Media

The Estimation of Blue-green algae Cell Count and Phycocyanin Concentration using Fluorescent Concentration analyzer

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