In situ quantification of poly(3-hydroxybutyrate) and biomass in Cupriavidus necator by a fluorescence spectroscopic assay

Fluorescence spectroscopy offers a cheap, simple, and fast approach to monitor poly(3-hydroxybutyrate) (PHB) formation, a biodegradable polymer belonging to the biodegradable polyester class polyhydroxyalkanoates. In the present study, a fluorescence and side scatter-based spectroscopic setup was developed to monitor in situ biomass, and PHB formation of biotechnological applied Cupriavidus necator strain. To establish PHB quantification of C. necator, the dyes 2,2-difluoro-4,6,8,10,12-pentamethyl-3-aza-1-azonia-2-boranuidatricyclo[7.3.0.03,7]dodeca-1(12),4,6,8,10-pentaene (BODIPY493/503), ethyl 5-methoxy-1,2-bis(3-methylbut-2-enyl)-3-oxoindole-2-carboxylate (LipidGreen2), and 9-(diethylamino)benzo[a]phenoxazin-5-one (Nile red) were compared with each other. Fluorescence staining efficacy was obtained through 3D-excitation-emission matrix and design of experiments. The coefficients of determination were ≥ 0.98 for all three dyes and linear to the high-pressure liquid chromatography obtained PHB content, and the side scatter to the biomass concentration. The fluorescence correlation models were further improved by the incorporation of the biomass-related side scatter. Afterward, the resulting regression fluorescence models were successfully applied to nitrogen-deficit, phosphor-deficit, and NaCl-stressed C. necator cultures. The highest transferability of the regression models was shown by using LipidGreen2. The novel approach opens a tailor-made way for a fast and simultaneous detection of the crucial biotechnological parameters biomass and PHB content during fermentation. Key points • Intracellular quantification of PHB and biomass using fluorescence spectroscopy. • Optimizing fluorescence staining conditions and 3D-excitation-emission matrix. • PHB was best obtained by LipidGreen2, followed by BODIPDY493/503 and Nile red. Graphical abstract

Effect of different D-amino acids on biofilm formation of mixed microorganisms

This study aimed to determine the effects of D-tyrosine, D-aspartic acid, D-tryptophan and D-leucine on biofilm formation of mixed microorganisms. Results showed that, in the attachment stage, D-amino acids caused significant reduction in adhesion efficiency of mixed microorganisms to membrane surface. Moreover, D-amino acids have a promoting effect on the reversible adhesion of mixed microorganisms. The addition of D-amino acid generally inhibited the biofilm biomass, of which D-tyrosine has the best inhibition effect. With the effect of D-tyrosine, D-aspartic acid, D-tryptophan and D-leucine, the protein in extracellular polymeric substance (EPS) decreased by 8.21%, 7.65%, 3.51% and 11.31%, respectively. The carbohydrates in EPS decreased by 29.53%, 21.44%, 14.60% and 10.54%, respectively. The results of excitation-emission matrix spectra (EEMs) suggested that the structural properties of the tyrosine-like proteins, tryptophan-like protein and humic-like acid might have changed by the D-amino acids.

Fluorescence Excitation–Emission Matrix Spectroscopy and Boosting Regression Tree Model to Detect Dissolved Organic Carbon in Water

In recent years, optical methods have been proven to be a powerful tool for m onitoring dissolved organic carbon (DOC) in natural waters. However, the effectiveness of this method in marine systems with low DOC concentrations remains to be shown. Herein, a new method based on fluorescence excitation–emission matrix spectroscopy for seawater DOC quantification is proposed. Pre-processing method is investigated to achieve a high signal to noise ratio. Peak-picking operation is then performed to obtain feature peaks. In order to combine the information from sparsely located feature peaks, sparse principal component analysis is applied to identifying important variables used in the following regression procedure. Under these conditions the result of regression analysis can be obtained readily in a given data set coupling with boosting regression tree. The method was tested on samples collected from the East China Sea. Compared to the parallel factor analysis–multivariate linear regression method, experimental results show that the proposed method achieved a more consistent regression output and indicate that the boosting regression tree has potential for DOC quantification even at low concentrations.

Use of simultaneous absorbance-transmittance and excitation-emission matrix (A-TEEM) spectroscopy to monitor source water contamination for waterworks

Conventional oil-in-water analyzers used by waterworks have hydrocarbon detection limits at mg/L levels and do not identify the type of oil compounds. The objectives of this study were to evaluate a more sensitive optical instrument and the analysis method to (1) determine the signature excitation and emission matrixs of each type of oil (such as diesel, heavy oil, gasoline and kerosene) or their indicator organic compounds and enter them into the instrument's software library and (2) test out the effectiveness of the instrument in detecting the above-mentioned oil in local waterworks’ source and treated water. The patented simultaneous absorbance-transmittance excitation-emission matrix (A-TEEM) instrument method was used to identify and quantify low levels of organic contaminants present in a much higher background of other dissolved organic matter components in raw and treated water. Multivariate regression and machine learning techniques were applied and shown to have potential for alerting plant operators to organic contamination events.