BIOINOVAR - Biocatalysis, Bioproducts and Bioenergy, Paulo de Góes Institute of Microbiology, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil.
Federal University of Rio de Janeiro (UFRJ), Institute of Microbiology Paulo de Góes, LABEM - Laboratory of Microbial Biotechnology and Ecology, Rio de Janeiro, Brazil.
Talanta. 2020 Nov 1;219:121238. doi: 10.1016/j.talanta.2020.121238. Epub 2020 Jun 14.
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 RCV = 0.939, RP = 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 RCV = 0.862, RP = 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.
本研究报告了一种基于激发-发射矩阵（EEM）测量的麦角固醇自体荧光来识别和定量真菌生物量的方法的开发。在这项工作的第一阶段，通过 APCI-MS 评估了几种麦角固醇提取方法，其中超声辅助程序显示出最佳效果。在实验设计之后，将几种真菌 Schizophyllum commune 的干燥菌丝与巴巴苏（Orbignya sp.）油工业的淀粉固体残渣（BBR）混合，并对这些样品进行了几种麦角固醇提取方法的处理。对样品的 EEM 光谱数据进行主成分分析（PCA），结果表明可以通过麦角固醇自体荧光定性评估样品中麦角固醇的存在，而无需添加任何试剂。为了评估使用麦角固醇自体荧光定量真菌生物量的可行性，使用偏最小二乘法（PLS）回归对 EEM 光谱数据和已知量的真菌生物量进行建模，并应用向后选择预测因子的程序（AutoPLS）选择提供最低预测误差的激发-发射波长对。结果表明，含有干扰物质（BBR）的样品中真菌生物量的数量可以通过 RCV=0.939、RP=0.936、RPDcv=4.07、RPDp=4.06、RMSECV=0.0731 和 RMSEP=0.0797 进行准确预测。为了获得易于理解的方程，以表达真菌生物量和荧光强度之间的关系，应用多元线性回归（MLR）对 AutoPLS 方法选择的 VIP 变量进行分析。选择的 MLR 模型仅使用 2 个变量，表现出非常好的性能，RCV=0.862、RP=0.809、RPDcv=2.18、RPDp=2.35、RMSECV=0.137 和 RMSEP=0.138。本研究表明，即使与农业工业残留物（在这种情况下为 BBR）混合，麦角固醇自体荧光也可以成功用于定量真菌生物量。
