The Catalan Surveillance Network of SARS-CoV-2 in Sewage: design, implementation, and performance

Wastewater-based epidemiology has shown to be an efficient tool to track the circulation of SARS-CoV-2 in communities assisted by wastewater treatment plants (WWTPs). The challenge comes when this approach is employed to help Health authorities in their decision-making. Here, we describe the roadmap for the design and deployment of SARSAIGUA, the Catalan Surveillance Network of SARS-CoV-2 in Sewage. The network monitors, weekly or biweekly, 56 WWTPs evenly distributed across the territory and serving 6 M inhabitants (80% of the Catalan population). Each week, samples from 45 WWTPs are collected, analyzed, results reported to Health authorities, and finally published within less than 72 h in an online dashboard ( https://sarsaigua.icra.cat ). After 20 months of monitoring (July 20-March 22), the standardized viral load (gene copies/day) in all the WWTPs monitored fairly matched the cumulative number of COVID-19 cases along the successive pandemic waves, showing a good fit with the diagnosed cases in the served municipalities (Spearman Rho = 0.69). Here we describe the roadmap of the design and deployment of SARSAIGUA while providing several open-access tools for the management and visualization of the surveillance data.The authors wish to thank the staff from all the WWTPs monitored for their help and technical support during the sampling campaigns. The authors acknowledge the funding received from the ACA and the ASPCAT from the Catalan Government (Generalitat de Catalunya). ICRA authors acknowledge the funding provided by the Generalitat de Catalunya through the Consolidated Research Group grants ICRA-ENV 2017 SGR 1124 and ICRA-TiA 2017 SGR 1318. ICRA researchers also thank the funding from the CERCA program of the Catalan Government.Peer reviewe

Estimation of chromatographic peak purity and multivariate curve resolution of overlapped peaks

International audienc

D-PES methodology for the analysis of therapeutic antibodies,

International audienc

Méthodologie intra-capillaire pour la protéolyse et la séparation d'anticorps monoclonaux à usage thérapeutique,

International audienc

In vitro metabolism of decabromo diphenyl ether using rat microsomes

International audienc

Benefits of chemometrics to analyze large-scale raw Time-Domain NMR data, an alternative to classical signal processing

International audienc

Multiblock analysis applied to TD-NMR of butters and related products

International audienceThis work presents a novel and rapid approach to predict fat content in butter products based on nuclear magnetic resonance longitudinal (T-1) relaxation measurements and multi-block chemometric methods. The potential of using simultaneously liquid (T-1L) and solid phase (T-1S) signals of fifty samples of margarine, butter and concentrated fat by Sequential and Orthogonalized Partial Least Squares (SO-PLS) and Sequential and Orthogonalized Selective Covariance Selection (SO-CovSel) methods was investigated. The two signals (T-1L and T-1S) were also used separately with PLS and CovSel regressions. The models were compared in term of prediction errors (RMSEP) and repeatability error (sigma(rep)). The results obtained from liquid phase (RMSEP approximate to 1.33% and sigma(rep)approximate to 0.73%) are better than those obtained with solid phase (RMSEP approximate to 5.27% and sigma(rep) approximate to 0.69%). Multiblock methodologies present better performance (RMSEP approximate to 1.00% and sigma(rep) approximate to 0.47%) and illustrate their power in the quantitative analysis of butter products. Moreover, SO-Covsel results allow for proposing a measurement protocol based on a limited number of NMR acquisitions, which opens a new way to quantify fat content in butter products with reduced analysis times

ATR-MIR and MCR-ALS as a tool for monitoring wine alcoholic fermentation and detecting bacterial spoilage

International audienc

Methods for drip irrigation clogging detection, analysis and understanding: State of the art and perspectives

International audienceClogging is one of the main ageing factors in drip irrigation. Clogging can be physical, chemical or biological. The adequate maintenance procedures to be applied differ according to the type of clogging involved. It is therefore noteworthy to first determine the nature and amount of clogging in the field before managing maintenance operations. Laboratory-based methods can help to understand the clogging mechanisms. However, these methods require either the extraction of samples and thus the destruction of drippers or a specific installation such as transparent flow cells to observe clogging. In the field, several methods based on hydraulic measurements can estimate the presence of clogging. However they provide an indirect estimation and do not necessarily reflect the early stages of clogging. There is currently no direct method for detecting clogging in the field in the drippers. The difficulty arises both from the variety of clogging materials to be detected and from the small section of the dripper channels of the order of 1 mm². This review analyses the pros and cons of several types of sensors (electrical, mechatronic, acoustic and optical) that offer potential perspectives to meet this need

Detection of bacterial spoilage during wine alcoholic fermentation using ATR-MIR and MCR-ALS

International audienceA new methodology is proposed to describe the evolution of the main chemical compounds of grape must during wine alcoholic fermentation using Attenuated Total Reflectance Mid Infrared (ATR-MIR) spectra in combination with Multivariate Curve Resolution Alternating Least-Squares (MCR-ALS). In addition, we have developed a process control strategy to detect differences between fermentations running under Normal Operation Conditions (NOC) and fermentations intentionally spoiled with lactic acid bacteria at the beginning of alcoholic fermentation (MLF) to promote deviations from NOC.MCR-ALS models on these data showed a good data fit (R2 = 99.95% and lack of fit = 2.31%). It was possible to resolve the spectral profiles of relevant molecules involved in the alcoholic fermentation process, including the one related to bacterial spoilage (lactic acid). MSPC charts were built based on the concentration profiles obtained from the MCR-ALS models and using T2 and Q statistics. Spoiled wines showed off-limit values for T2 after 96 h, making it possible to detect lactic acid bacteria spoilage in early stages of alcoholic fermentation.Thus, the use of ATR-MIR spectra and MCR-ALS analysis shows a great potential for a rapid control of the state of the alcoholic fermentation process, making it possible to early detect the appearance of undesired molecules during the process, which allows the winemaker to apply corrective measures and obtain a good final product