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      Assessment of Biotechnologically Important Filamentous Fungal Biomass by Fourier Transform Raman Spectroscopy

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          Abstract

          Oleaginous filamentous fungi can accumulate large amount of cellular lipids and biopolymers and pigments and potentially serve as a major source of biochemicals for food, feed, chemical, pharmaceutical, and transport industries. We assessed suitability of Fourier transform (FT) Raman spectroscopy for screening and process monitoring of filamentous fungi in biotechnology. Six Mucoromycota strains were cultivated in microbioreactors under six growth conditions (three phosphate concentrations in the presence and absence of calcium). FT-Raman and FT-infrared (FTIR) spectroscopic data was assessed in respect to reference analyses of lipids, phosphorus, and carotenoids by using principal component analysis (PCA), multiblock or consensus PCA, partial least square regression (PLSR), and analysis of spectral variation due to different design factors by an ANOVA model. All main chemical biomass constituents were detected by FT-Raman spectroscopy, including lipids, proteins, cell wall carbohydrates, and polyphosphates, and carotenoids. FT-Raman spectra clearly show the effect of growth conditions on fungal biomass. PLSR models with high coefficients of determination (0.83–0.94) and low error (approximately 8%) for quantitative determination of total lipids, phosphates, and carotenoids were established. FT-Raman spectroscopy showed great potential for chemical analysis of biomass of oleaginous filamentous fungi. The study demonstrates that FT-Raman and FTIR spectroscopies provide complementary information on main fungal biomass constituents.

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          Using Fourier transform IR spectroscopy to analyze biological materials.

          IR spectroscopy is an excellent method for biological analyses. It enables the nonperturbative, label-free extraction of biochemical information and images toward diagnosis and the assessment of cell functionality. Although not strictly microscopy in the conventional sense, it allows the construction of images of tissue or cell architecture by the passing of spectral data through a variety of computational algorithms. Because such images are constructed from fingerprint spectra, the notion is that they can be an objective reflection of the underlying health status of the analyzed sample. One of the major difficulties in the field has been determining a consensus on spectral pre-processing and data analysis. This manuscript brings together as coauthors some of the leaders in this field to allow the standardization of methods and procedures for adapting a multistage approach to a methodology that can be applied to a variety of cell biological questions or used within a clinical setting for disease screening or diagnosis. We describe a protocol for collecting IR spectra and images from biological samples (e.g., fixed cytology and tissue sections, live cells or biofluids) that assesses the instrumental options available, appropriate sample preparation, different sampling modes as well as important advances in spectral data acquisition. After acquisition, data processing consists of a sequence of steps including quality control, spectral pre-processing, feature extraction and classification of the supervised or unsupervised type. A typical experiment can be completed and analyzed within hours. Example results are presented on the use of IR spectra combined with multivariate data processing.
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            Biodiesel production from oleaginous microorganisms

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              ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data.

              Datasets resulting from metabolomics or metabolic profiling experiments are becoming increasingly complex. Such datasets may contain underlying factors, such as time (time-resolved or longitudinal measurements), doses or combinations thereof. Currently used biostatistics methods do not take the structure of such complex datasets into account. However, incorporating this structure into the data analysis is important for understanding the biological information in these datasets. We describe ASCA, a new method that can deal with complex multivariate datasets containing an underlying experimental design, such as metabolomics datasets. It is a direct generalization of analysis of variance (ANOVA) for univariate data to the multivariate case. The method allows for easy interpretation of the variation induced by the different factors of the design. The method is illustrated with a dataset from a metabolomics experiment with time and dose factors.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Int J Mol Sci
                Int J Mol Sci
                ijms
                International Journal of Molecular Sciences
                MDPI
                1422-0067
                23 June 2021
                July 2021
                : 22
                : 13
                : 6710
                Affiliations
                [1 ]Faculty of Science and Technology, Norwegian University of Life Sciences, P.O. Box 5003, 1432 Ås, Norway; simona.dzurendova@ 123456nmbu.no (S.D.); volha.shapaval@ 123456nmbu.no (V.S.); valeria.tafintseva@ 123456nmbu.no (V.T.); achim.kohler@ 123456nmbu.no (A.K.); dana.byrtusova@ 123456nmbu.no (D.B.)
                [2 ]Faculty of Chemistry, Brno University of Technology, Purkyňova 464/118, 61200 Brno, Czech Republic; xcszotkowski@ 123456fch.vut.cz (M.S.); marova@ 123456fch.vut.cz (I.M.)
                Author notes
                Author information
                https://orcid.org/0000-0003-2674-1328
                https://orcid.org/0000-0001-6015-0893
                https://orcid.org/0000-0001-5046-520X
                Article
                ijms-22-06710
                10.3390/ijms22136710
                8269384
                34201486
                29c518e1-df25-47ca-9bbd-d6379e03af7e
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 19 May 2021
                : 17 June 2021
                Categories
                Article

                Molecular biology
                oleaginous microorganisms,biodiesel,pigments,biopolymers,carotenoids,fatty acids,chitin,chitosan,fermentation,fungi

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