New method for rapid identification and quantification of fungal biomass using ergosterol autofluorescence

This research reports on the development of a method to identify and quantify fungal biomass based on ergosterol autofluorescence using excitation-emission matrix (EEM) measurements. In the first stage of this work, several ergosterol extraction methods were evaluated by APCI-MS, where the ultrasoun...

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Published in:Talanta (Oxford) 2020-11, Vol.219, p.121238-121238, Article 121238
Main Authors: Mansoldo, Felipe Raposo Passos, Firpo, Rhayssa, Cardoso, Veronica da Silva, Queiroz, Gregório Nepomuceno, Cedrola, Sabrina Martins Lage, Godoy, Mateus Gomes de, Vermelho, Alane Beatriz
Format: Article
Language:English
Subjects:
Autofluorescence
Biomass
Ergosterol
Excitation‐emission matrix
Fungal biomass quantification
Fungi
Least-Squares Analysis
Optical Imaging
Partial least squares (PLS) regression
Research Design
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Summary:This research reports on the development of a method to identify and quantify fungal biomass based on ergosterol autofluorescence using excitation-emission matrix (EEM) measurements. In the first stage of this work, several ergosterol extraction methods were evaluated by APCI-MS, where the ultrasound-assisted procedure showed the best results. Following an experimental design, various quantities of the dried mycelium of the fungus Schizophyllum commune were mixed with the starchy solid residue (BBR) from the babassu (Orbignya sp.) oil industry, and these samples were subjected to several ergosterol extraction methods. The EEM spectral data of the samples were subjected to Principal Component Analysis (PCA), which showed the possibility to qualitatively evaluate the presence of ergosterol in the samples by ergosterol autofluorescence without the addition of any reagent. In order to assess the feasibility of quantifying fungal biomass using ergosterol autofluorescence, the EEM spectral data and known amounts of fungal biomass were modeled using partial least squares (PLS) regression and a procedure of backward selection of predictors (AutoPLS) was applied to select the Excitation–Emission wavelength pairs that provide the lowest prediction error. The results revealed that the amount of fungal biomass in samples containing interfering substances (BBR) can be accurately predicted with R2CV = 0.939, R2P = 0.936, RPDcv = 4.07, RPDp = 4.06, RMSECV = 0.0731 and RMSEP = 0.0797. In order to obtain an easy-to-understand equation that expresses the relationship between fungal biomass and fluorescence intensity, multiple linear regression (MLR) was applied to the VIP variables selected by the AutoPLS method. The MLR model selected only 2 variables and showed a very good performance, with R2CV = 0.862, R2P = 0.809, RPDcv = 2.18, RPDp = 2.35, RMSECV = 0.137 and RMSEP = 0.138. This study demonstrated that ergosterol autofluorescence can be successfully used to quantify fungal biomass even when mixed with agroindustrial residues, in this case BBR. [Display omitted] •The first Excitation-Emission Matrix (EEM) analysis of ergosterol.•PLS regression was established for fungal biomass prediction using ergosterol EEM.•EEM analysis is fast and low-cost compared with traditional methods.•Fungal biomass could be accurately predicted with RPDcv >4 and RPDp >4.•MLR selected the 2 most important combinations of excitation-emission wavelengths.
ISSN:0039-9140
1873-3573
DOI:10.1016/j.talanta.2020.121238