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      Application of regression and artificial neural network analysis of Red-Green-Blue image components in prediction of chlorophyll content in microalgae

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          Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations

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            Consistent sets of spectrophotometric chlorophyll equations for acetone, methanol and ethanol solvents.

            A set of equations for determining chlorophyll a (Chl a) and accessory chlorophylls b, c2, c1 + c2 and the special case of Acaryochloris marina, which uses Chl d as its primary photosynthetic pigment and also has Chl a, have been developed for 90% acetone, methanol and ethanol solvents. These equations for different solvents give chlorophyll assays that are consistent with each other. No algorithms for Chl c compounds (c2, c1 + c2) in the presence of Chl a have previously been published for methanol or ethanol. The limits of detection (and inherent error, +/- 95% confidence limit), for chlorophylls in all organisms tested, was generally less than 0.1 microg/ml. The Chl a and b algorithms for green algae and land plants have very small inherent errors (< 0.01 microg/ml). Chl a and d algorithms for Acaryochloris marina are consistent with each other, giving estimates of Chl d/a ratios which are consistent with previously published estimates using HPLC and a rarely used algorithm originally published for diethyl ether in 1955. The statistical error structure of chlorophyll algorithms is discussed. The relative error of measurements of chlorophylls increases hyperbolically in diluted chlorophyll extracts because the inherent errors of the chlorophyll algorithms are constants independent of the magnitude of absorbance readings. For safety reasons, efficient extraction of chlorophylls and the convenience of being able to use polystyrene cuvettes, the algorithms for ethanol are recommended for routine assays of chlorophylls. The methanol algorithms would be convenient for assays associated with HPLC work.
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              Algae as potential feedstock for the production of biofuels and value-added products: Opportunities and challenges

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                Author and article information

                Contributors
                Journal
                Bioresource Technology
                Bioresource Technology
                Elsevier BV
                09608524
                February 2023
                February 2023
                : 370
                : 128503
                Article
                10.1016/j.biortech.2022.128503
                36535615
                2f95d1e9-440b-49f2-b3e5-a33d0d70cf6e
                © 2023

                https://www.elsevier.com/tdm/userlicense/1.0/

                https://doi.org/10.15223/policy-017

                https://doi.org/10.15223/policy-037

                https://doi.org/10.15223/policy-012

                https://doi.org/10.15223/policy-029

                https://doi.org/10.15223/policy-004

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