Multiple excitation wavelength fluorescence emission spectra technique for discrimination of phytoplankton

Hu, Xupeng; Su, Rongguo; Zhang, Fang; Wang, Xiulin; Wang, Hongtao; Zheng, Zhixi

doi:10.1007/s11802-010-0016-x

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…The linear independence of the factory-made and species-specific SFS was checked by the row reduction echelon form method using Gauss Jordan elimination with partial pivoting (MATLAB function "rref ", Lindfield and Penny, 2012). The degree of linear independence was further studied using multivariate regression models that were based on non-negative least-squares restrictions (MATLAB function "lsqnonneg") on the weighting factors (Hu et al, 2010).…”

Section: Data Analysesmentioning

confidence: 99%

Quantifying phytoplankton communities using spectral fluorescence: the effects of species composition and physiological state

Escoffier

Bernard

Hamlaoui

et al. 2014

Journal of Plankton Research

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In situ fluorometers are the optimal means of providing high-frequency estimates of phytoplankton communities. However, they may be subjected to measurement biases originating from variations in the physiological states of cells, the use of spectral fluorescence signatures (SFS) defined on the basis of inappropriate phytoplankton groups and the lack of linear independence between selected sets of SFS. We assessed correction procedures for measurement biases in mono and mixed cultures of five freshwater phytoplankton species. We investigated the impacts of total Chla levels, the lack of linear independence between SFS and varying physiological states on the accuracy of the Chla estimates that were provided by the FluoroProbe (bbe Moldaenke GmbH, Germany). The use of species-specific SFS allowed for the correction of quantification and classification biases. In some cases, the procedure led to a lack of linear independence between SFS, which significantly reduced estimation accuracies. A convenient method to evaluate linear independence between SFS is provided. Differences in the physiological states of phytoplankton cultures following light pre-acclimation and/or N-starvation appeared to be species specific. Light pre-acclimation led to an underestimation of biomass (up to 228.5%) through fluorescence quenching. The responses of the phytoplankton cultures to N-starvation varied depending on the species (from 240.3 to þ336% biases in Chla quantification). Overall, the application of appropriate corrective measures increased data accuracy. However, optimal data reliability can only be achieved by estimating phytoplankton community composition and associated environmental conditions.

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Section: Data Analysesmentioning

confidence: 99%

Quantifying phytoplankton communities using spectral fluorescence: the effects of species composition and physiological state

Escoffier

Bernard

Hamlaoui

et al. 2014

Journal of Plankton Research

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“…In lower plants, fluorescence excitation or emission spectra have recently been used mainly as a method for the differentiation of algal populations in vivo and in situ , selective detection and quantification of cyanobacteria and eukaryotic algae , or for the identification of phytoplankton . In higher plants, measurements of blue‐green and red and far‐red fluorescence can be used to detect direct and indirect plant responses to stress.…”

Section: Introductionmentioning

confidence: 99%

Metabolic activity, the chemical composition of biomass and photosynthetic activity ofChlorella vulgarisunder different light spectra in photobioreactors

Kula

Rys

Możdżeń

et al. 2014

Engineering in Life Sciences

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Omitting the far‐red in LED lighting for bioreactors is inexplicable because it affects photosynthetic efficiency and photomorphogenetic activity. Therefore, this work compares three light sources (fluorescent—white light (WL), and LED: blue + red—BRL and blue + red + far‐red—BRFRL) for cultures of Chlorella vulgaris. Metabolic activity was determined by isothermal calorimetry. Changes in the chemical composition of biomass were examined by spectrofluorimetry and Raman spectroscopy. Maximum quantum yield of photosystem II was determined on the basis of chlorophyll a fluorescence parameters. The algae grown under BRL produced significantly more thermal energy than those cultured under BRFRL and WL. The Raman spectra of cultures showed characteristic bands for carotenoids, chlorophyll, phenolics, lipids, aliphatic carbohydrates, pectins, and disaccharides. According to the cluster analysis, the chemical composition of cultures grown under BRL and BRFRL was very similar, whereas the WL represented a distinct group. BRL and BRFRL stimulated the biosynthesis of an unidentified compound(s) with fluorescence maximum at 614 nm. At the beginning of the cultivation, photosystem II had very weak photochemical activity. Under BRFRL, ratios of Fv/Fm reached the maximum after 4 days, whereas under BRL and WL, after 6 days of cultivation. The results point to the favorable influence of the far‐red on the metabolism of microalgae.

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“…In 2010, Hu et al utilized the fact that different algae species have different ratios of antenna pigments, which results in different fluorescence emission spectra [13]. More specifically, Hu et al illuminated twenty different algae from six algae divisions (Dinophyta, Bacillariophyta, Chrysophyta, Cyanophyta, Cryptophyta, and Chlorophyta) at four different wavelengths (440 nm, 470 nm, 530 nm, and 580 nm), and then measured the emission spectra from 600 nm -750 nm with a 5 nm resolution.…”

Section: Related Workmentioning

confidence: 99%

The Feasibility of Automated Identification of Six Algae Types Using Feed-Forward Neural Networks and Fluorescence-Based Spectral-Morphological Features

et al. 2019

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Harmful algae blooms (HABs), which produce lethal toxins, are a growing global concern since they negatively affect the quality of drinking water and have major negative impact on wildlife, the fishing industry, as well as tourism and recreational water use. The goldstandard process employed in the field to identify and enumerate algae requires highly trained professionals to manually observe algae under a microscope, which is a very time-consuming and tedious task. Therefore, an automated approach to identify and enumerate these micro-organisms is much needed. In this study, we investigate the feasibility of leveraging machine learning and fluorescence-based spectral-morphological features to enable the identification of six different algae types in an automated fashion. More specifically, a custom multi-band fluorescence imaging microscope is used to capture fluorescence imaging data of a water sample at six different excitation wavelengths ranging from 405 nm -530 nm. Automated data processing and segmentation was performed on the captured fluorescence imaging data to isolate different micro-organisms from the water sample. A number of morphological and spectral fluorescence features are then extracted from the isolated micro-organism imaging data, and used to train neural network classification models designed for the purpose of identification of the six algae types given an isolated micro-organism. Experimental results using three different neural network classification models (one trained on morphological features, one trained on fluorescencebased spectral features, and one trained on fluorescencebased spectral-morphological features) showed that the use of either fluorescence-based spectral features or fluorescence-based spectral-morphological features to train neural network classification models led to statistically significant improvements in identification accuracy when compared to the use of morphological features (with average identification accuracies of 95.7% ± 3.5% and 96.1% ± 1.5%, respectively). These preliminary results are quite promising, given that the identification accuracy of human taxonomists are typically between the range of 67% and 83%, and thus illustrates the feasibility of leveraging machine learning and fluorescence-based spectral-morphological features as a viable method for automated identification of different algae types.

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Multiple excitation wavelength fluorescence emission spectra technique for discrimination of phytoplankton

Cited by 6 publications

References 17 publications

Quantifying phytoplankton communities using spectral fluorescence: the effects of species composition and physiological state

Quantifying phytoplankton communities using spectral fluorescence: the effects of species composition and physiological state

Metabolic activity, the chemical composition of biomass and photosynthetic activity ofChlorella vulgarisunder different light spectra in photobioreactors

The Feasibility of Automated Identification of Six Algae Types Using Feed-Forward Neural Networks and Fluorescence-Based Spectral-Morphological Features

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