Environmentally-friendly technology for rapid identification and quantification of emerging pollutants from wastewater using infrared spectroscopy.

The monitoring of emerging pollutants in wastewaters is nowadays an issue of special concern, with the classical quantification methods being time and reagent consuming. In this sense, a FTIR transmission spectroscopy based chemometric methodology was developed for the determination of eight of these pollutants. A total of 456 samples were, therefore, obtained, from an activated sludge wastewater treatment process spiked with the studied pollutants, and analysed in the range of 200 cm-1 to 14,000 cm-1. Then, a k-nearest neighbour (kNN) analysis aiming at identifying each sample pollutant was employed. Next, partial least squares (PLS) and ordinary least squares (OLS) modelling approaches were employed in order to obtain suitable prediction models. This procedure resulted in good prediction abilities regarding the estimation of atrazine, desloratadine, paracetamol, ß-estradiol, ibuprofen, carbamazepine, sulfamethoxazole and ethynylestradiol concentrations in wastewaters. These promising results suggest this technology as a fast, eco-friendly and reagent free alternative methodology for the quantification of emerging pollutants in wastewaters.

Quantification of pharmaceutical compounds in wastewater samples by near infrared spectroscopy (NIR).

The quantification of pollutants, as pharmaceuticals, in wastewater is an issue of special concern. Usually, typical methods to quantify these products are time and reagent consuming. This paper describes the development and validation of a Fourier transform near-infrared (FT-NIR) spectroscopy methodology for the quantification of pharmaceuticals in wastewaters. For this purpose, 276 samples obtained from an activated sludge wastewater treatment process were analysed in the range of 200 cm-1 to 14,000 cm-1, and further treated by chemometric techniques to develop and validate the quantification models. The obtained results were found adequate for the prediction of ibuprofen, sulfamethoxazole, 17ß-estradiol and carbamazepine with coefficients of determination (R2) around 0.95 and residual prediction deviation (RPD) values above four, for the overall (training and validation) data points. These results are very promising and confirm that this technology can be seen as an alternative for the quantification of pharmaceuticals in wastewater.

New PLS analysis approach to wine volatile compounds characterization by near infrared spectroscopy (NIR).

This work aims to explore the potential of near infrared (NIR) spectroscopy to quantify volatile compounds in Vinho Verde wines, commonly determined by gas chromatography. For this purpose, 105 Vinho Verde wine samples were analyzed using Fourier transform near infrared (FT-NIR) transmission spectroscopy in the range of 5435 cm-1 to 6357 cm-1. Boxplot and principal components analysis (PCA) were performed for clusters identification and outliers removal. A partial least square (PLS) regression was then applied to develop the calibration models, by a new iterative approach. The predictive ability of the models was confirmed by an external validation procedure with an independent sample set. The obtained results could be considered as quite good with coefficients of determination (R2) varying from 0.94 to 0.97. The current methodology, using NIR spectroscopy and chemometrics, can be seen as a promising rapid tool to determine volatile compounds in Vinho Verde wines.

Quantitative image analysis as a tool for Yarrowia lipolytica dimorphic growth evaluation in different culture media.

Yarrowia lipolytica, a yeast strain with a huge biotechnological potential, capable to produce metabolites such as γ-decalactone, citric acid, intracellular lipids and enzymes, possesses the ability to change its morphology in response to environmental conditions. In the present study, a quantitative image analysis (QIA) procedure was developed for the identification and quantification of Y. lipolytica W29 and MTLY40-2P strains dimorphic growth, cultivated in batch cultures on hydrophilic (glucose and N-acetylglucosamine (GlcNAc) and hydrophobic (olive oil and castor oil) media. The morphological characterization of yeast cells by QIA techniques revealed that hydrophobic carbon sources, namely castor oil, should be preferred for both strains growth in the yeast single cell morphotype. On the other hand, hydrophilic sugars, namely glucose and GlcNAc caused a dimorphic transition growth towards the hyphae morphotype. Experiments for γ-decalactone production with MTLY40-2P strain in two distinct morphotypes (yeast single cells and hyphae cells) were also performed. The obtained results showed the adequacy of the proposed morphology monitoring tool in relation to each morphotype on the aroma production ability. The present work allowed establishing that QIA techniques can be a valuable tool for the identification of the best culture conditions for industrial processes implementation.

Quantitative image analysis for the characterization of microbial aggregates in biological wastewater treatment: a review.

Quantitative image analysis techniques have gained an undeniable role in several fields of research during the last decade. In the field of biological wastewater treatment (WWT) processes, several computer applications have been developed for monitoring microbial entities, either as individual cells or in different types of aggregates. New descriptors have been defined that are more reliable, objective, and useful than the subjective and time-consuming parameters classically used to monitor biological WWT processes. Examples of this application include the objective prediction of filamentous bulking, known to be one of the most problematic phenomena occurring in activated sludge technology. It also demonstrated its usefulness in classifying protozoa and metazoa populations. In high-rate anaerobic processes, based on granular sludge, aggregation times and fragmentation phenomena could be detected during critical events, e.g., toxic and organic overloads. Currently, the major efforts and needs are in the development of quantitative image analysis techniques focusing on its application coupled with stained samples, either by classical or fluorescent-based techniques. The use of quantitative morphological parameters in process control and online applications is also being investigated. This work reviews the major advances of quantitative image analysis applied to biological WWT processes.

Characterization of activated sludge abnormalities by image analysis and chemometric techniques.

This work focuses on the use of chemometric techniques for identifying activated sludge process abnormalities. Chemometric methods combined with image analysis can improve activated sludge systems monitoring and minimize the need of analytical measurements. For that purpose data was collected from aggregated and filamentous biomass, biomass composition on Gram-positive/Gram-negative bacteria and viable/damaged bacteria, and operational parameters. Principal component analysis (PCA) was subsequently applied to identify activated sludge abnormalities, allowing the identification of several disturbances, namely filamentous bulking, pinpoint flocs formation, and zoogleal bulking as well as normal conditions by grouping the collected samples in corresponding clusters.

Identifying different types of bulking in an activated sludge system through quantitative image analysis.

The present study proposes an image analysis methodology for the identification of different types of disturbances in wastewater treatment activated sludge systems. Up to date, most reported image analysis methodologies have been used in activated sludge processes with the aim of filamentous bulking detection, however, other disturbances could be foreseen in wastewater treatment plants. Such disturbances can lead to fluctuations in the biomass contents, affecting the mixed liquor suspended solids (MLSS), and in the sludge settling ability, affecting the sludge volume index (SVI). Therefore, this work focuses on predicting the MLSS and SVI parameters for different types of disturbances affecting an activated sludge system. Four experiments were conducted simulating filamentous bulking, zoogleal or viscous bulking, pinpoint floc formation, and normal operating conditions. Alongside the MLSS and SVI determination, the aggregated and filamentous biomass contents and morphology were studied as well as the biomass Gram and viability status, by means of image analysis.

Dilution and magnification effects on image analysis applications in activated sludge characterization.

The properties of activated sludge systems can be characterized using image analysis procedures. When these systems operate with high biomass content, accurate sludge characterization requires samples to be diluted. Selection of the best image acquisition magnification is directly related to the amount of biomass screened. The aim of the present study was to survey the effects of dilution and magnification on the assessment of aggregated and filamentous bacterial content and structure using image analysis procedures. Assessments of biomass content and structure were affected by dilutions. Therefore, the correct operating dilution requires careful consideration. Moreover, the acquisition methodology comprising a 100× magnification allowed data on aggregated and filamentous biomass to be determined and smaller aggregates to be identified and characterized, without affecting the accuracy of lower magnifications regarding biomass representativeness.

A Comparison between bright field and phase-contrast image analysis techniques in activated sludge morphological characterization.

Different approaches using microscopy image analysis procedures were employed for characterization of activated sludge systems. The approaches varied mainly on the type of visualization and acquisition method used for collection of data. In this context, this study focused on the comparison of the two most common acquisition methods: bright field and phase-contrast microscopy. Images were acquired from seven different wastewater treatment plants for a combined period of two years. Advantages and disadvantages of each acquisition technique and the results are discussed. Bright field microscopy proved to be more simple and inexpensive and provided the best overall results.

Correlation between sludge settling ability and image analysis information using partial least squares.

In the last years there has been an increase on the research of the activated sludge processes, and mainly on the solid-liquid separation stage, considered of critical importance, due to the different problems that may arise affecting the compaction and the settling of the sludge. Furthermore, image analysis procedures are, nowadays considered to be an adequate method to characterize both aggregated and filamentous bacteria, and increasingly used to monitor bulking events in pilot plants. As a result of that, in this work, image analysis routines were developed in Matlab environment, allowing the identification and characterization of microbial aggregates and protruding filaments. Moreover, the large amount of activated sludge data collected with the image analysis implementation can be subsequently treated by multivariate statistical procedures such as PLS. In the current work the implementation of image analysis and PLS techniques has shown to provide important information for better understanding the behavior of activated sludge processes, and to predict, at some extent, the sludge volume index. As a matter of fact, the obtained results allowed explaining the strong relationships between the sludge settling properties and the free filamentous bacteria contents, aggregates size and aggregates morphology, establishing relevant relationships between macroscopic and microscopic properties of the biological system.

Monitoring of activated sludge settling ability through image analysis: validation on full-scale wastewater treatment plants.

In recent years, a great deal of attention has been focused on the research of activated sludge processes, where the solid-liquid separation phase is frequently considered of critical importance, due to the different problems that severely affect the compaction and the settling of the sludge. Bearing that in mind, in this work, image analysis routines were developed in Matlab environment, allowing the identification and characterization of microbial aggregates and protruding filaments in eight different wastewater treatment plants, for a combined period of 2 years. The monitoring of the activated sludge contents allowed for the detection of bulking events proving that the developed image analysis methodology is adequate for a continuous examination of the morphological changes in microbial aggregates and subsequent estimation of the sludge volume index. In fact, the obtained results proved that the developed image analysis methodology is a feasible method for the continuous monitoring of activated sludge systems and identification of disturbances.