Cristiano Leal

Prediction of intracellular storage polymers using quantitative image analysis in enhanced biological phosphorus removal systems

The present study focuses on predicting the concentration of intracellular storage polymers in enhanced biological phosphorus removal (EBPR) systems. For that purpose, quantitative image analysis techniques were developed for determining the intracellular concentrations of PHA (PHB and PHV) with Nile blue and glycogen with aniline blue staining. Partial least squares (PLS) were used to predict the standard analytical values of these polymers by the proposed methodology. Identification of the aerobic and anaerobic stages proved to be crucial for improving the assessment of PHA, PHB and PHV intracellular concentrations. Current Nile blue based methodology can be seen as a feasible starting point for further enhancement. Glycogen detection based on the developed aniline blue staining methodology combined with the image analysis data proved to be a promising technique, toward the elimination of the need for analytical off-line measurements.

Polyhydroxyalkanoate granules quantification in mixed microbial cultures using image analysis: Sudan Black B versus Nile Blue A staining.

Polyhydroxyalkanoates (PHA) can be produced and intracellularly accumulated as inclusions by mixed microbial cultures (MMC) for bioplastic production and in enhanced biological phosphorus removal (EBPR) systems. Classical methods for PHA quantification use a digestion step prior to chromatography analysis, rendering them labor intensive and time-consuming. The present work investigates the use of two quantitative image analysis (QIA) procedures specifically developed for PHA inclusions identification and quantification. MMC obtained from an EBPR system were visualized by bright-field and fluorescence microscopy for PHA inclusions detection, upon Sudan Black B (SBB) and Nile Blue A (NBA) staining, respectively. The captured color images were processed by QIA techniques and the image analysis data were further treated using multivariate statistical analysis. Partial least squares (PLS) regression coefficients of 0.90 and 0.86 were obtained between QIA parameters and PHA concentrations using SBB and NBA, respectively. It was found that both staining procedures might be seen as alternative methodologies to classical PHA determination.

Image analysis for automatic characterization of polyhydroxyalcanoates granules

A new monitoring approach for polyhydroxyalcanoates (PHA) granules identification and characterization based on image analysis procedures is proposed. PHA granules were analyzed by Sudan Black B (SBB) staining in an enhanced biological phosphorus removal (EBPR) system. Color images captured on an optical microscope were analyzed through quantitative image analysis. The distribution of PHA granules was estimated by determination of the proportion of blue-black pixels. A relationship was found between image analysis parameters and PHA concentration. In conclusion, it may be inferred that the present image analysis procedure is suitable to quantify PHA granules in SBB staining images and a promising alternative to standard analysis.

Use of chemometric analyses to assess biological wastewater treatment plants by protozoa and metazoa monitoring

Protozoa and metazoa biota communities in biological wastewater treatment plants (WWTP) are known to be dependent of both the plant type (oxidation ditch, trickling filter, conventional activated sludge, among others) and the working operational conditions (incoming effluent characteristics, toxics presence, organic load, aeration, hydraulic and sludge retention times, nitrification occurrence, etc.). Thus, for analogous WWTP operating in equivalent operating conditions, similar protozoa and metazoa communities can be found. Indeed, the protozoa and metazoa biota monitoring can be considered a quite useful tool for assessing the functioning of biological WWTP. Furthermore, the use of chemometric techniques in WWTP monitoring is becoming widespread to enlighten interrelationships within the plant, especially when a large collection of data can be obtained. In the current study, the protozoa and metazoa communities of three different types of WWTP, comprising one oxidation ditch, four trickling filters, and three conventional activated sludge plants, were monitored. For that purpose, metazoa, as well as the main protozoa groups (flagellates, free-swimming, crawling and sessile ciliates, and testate amoeba) were determined in terms of contents and relative abundance. The collected data was further processed by chemometric techniques, such as cross-correlation, principal components, multivariate ANOVA, and decision trees analyses, allowing to successfully identify, and characterize, the different studied WWTP, and thus, being able to help monitoring and diagnosing operational problems.