Bacterial Bioluminescent Biosensor Characterisation for On-line Monitoring of Heavy Metals Pollutions in Waste Water Treatment Plant Effluents

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Raman Spectroscopy: Toward a Portable Food Quality-Warning System

Food waste is one of the main problems contributing to climate change as its piling up in landfills produces the greenhouse gas methane. Food waste occurs at every stage of food production; however, the major source of food waste occurs at businesses that supply food to consumers. Industry 4.0 technologies have shown promises in helping reduce food waste in food supply chains. However, more innovative technologies such as Raman spectroscopy holds great promise in helping reduce food waste, but this has largely been ignored in the literature. In this context, we propose a portable Raman platform to monitor food quality during transportation. The developed system was tested in conditions mimicking those present in a refrigerated truck by analyzing chicken samples stored at temperatures of 4 °C. Raman spectra were acquired for non-packaged and packaged samples over the duration of 30 days resulting in 6000 spectra. The analysis of Raman spectra revealed that the system was able to detect noticeable changes in chicken quality starting day six. The main Raman bands contributing to this change were amide I and tyrosine. The proposed system will offer the potential to reduce food losses during transportation by consistently checking the food quality over time

Online Detection of Metals in Environmental Samples: Comparing Two Concepts of Bioluminescent Bacterial Biosensors

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Design of a toxicity biosensor based on Aliivibrio fischeri entrapped in a disposable card

International audienceThe degradation of the marine environment is a subject of concern for the European authorities primarily because of its contamination by hydrocarbons. The traditional methods (ISO 11348 standard) of general toxicity assessment are unsuitable in a context of in situ monitoring, such as seaports or bathing zones. Consequently, to address this issue, bacterial biosensors appear to be pertinent tools. This article presents the design of an innovative bioluminescent biosensor dedicated to in situ toxicity monitoring. This biosensor is based on the entrapment of the wild marine bioluminescent bacterial strain Aliivibrio fischeri ATCC® 49387™ in an agarose matrix within a disposable card. A pre-study was needed to select the most biological parameters. In particular, the regenerating medium’s composition and the hydrogel concentration needed for the bacterial entrapment (mechanical resistance) were optimized. Based on these data, the ability of the bacterial reporter to assess the sample toxicity was demonstrated using naphthalene as a chemical model. The biosensor’s results show a lower sensitivity to naphthalene (EC50 = 95 mg/L) compared with the results obtained using the reference method (EC50 = 43 mg/L). With this architecture, the biosensor is an interesting compromise among low maintenance, ease of use, appropriate sensitivity, relatively low cost and the ability to control online toxicity

Main Technological Advancements in Bacterial Bioluminescent Biosensors Over the Last Two Decades.

International audienceEnvironmental quality assessment is an extensive field of research due to the permanent increase of the stringency imposed by the legislative framework. To complete the wide panel of measurement methods, essentially based on physicochemical tools, some scientists focused on the development of alternative biological methods such as those based on the use of bioluminescent bacteria biosensors. The first report dedicated to the development of such biosensors dates back to 1967 and describes an analytical system designed to address the problem of air toxicity assessment. Nevertheless the available technologies in the photosensitive sensors field were not mature enough and, as a result, limited biosensor development possibilities. For about 20 years, the wide democratisation of photosensors coupled with advances in the genetic engineering field have allowed the expansion of the scope of possibilities of bioluminescent bacterial biosensors, allowing a significant emergence of these biotechnologies. This chapter retraces the history of the main technological evolutions that bacterial bioluminescent biosensors have known over the last two decades. Graphical Abstract

Detection of Metal and Organometallic Compounds with Bioluminescent Bacterial Bioassays.

International audienceChemical detection of metal and organometallic compounds is very specific and sensitive, but these techniques are time consuming and expensive. Although these techniques provide information about the concentrations of compounds, they fail to inform us about the toxicity of a sample. Because the toxic effects of metals and organometallic compounds are influenced by a multitude of environmental factors, such as pH, the presence of chelating agents, speciation, and organic matter, bioassays have been developed for ecotoxicological studies. Among these bioassays, recombinant luminescent bacteria have been developed over the past 20 years, and many of them are specific for the detection of metals and metalloids. These bioassays are simple to use, are inexpensive, and provide information on the bioavailable fraction of metals and organometals. Thus, they are an essential complementary tool for providing information beyond chemical analysis. In this chapter, we propose to investigate the detection of metals and organometallic compounds with bioluminescent bacterial bioassays and the applications of these bioassays to environmental samples. Graphical Abstract

A microsystem approach to measure the oxygen consumption of bacteria. Towards a precise evaluation of the BOD (Biological Oxygen Demand) parameter of wastewater

International audienceThe quantity of organic pollutants present in wastewater is classically evaluated by measuring the quantity of dissolved oxygen during five days; it is quantified by the so called BOD5 parameter (Biological Oxygen Demand) [1]. This work constitutes the first step of an overall strategy targeting to improve the monitoring of BOD5. We focus on the development of a microsystem approach allowing monitoring the O2 consumption induced by the biodegradation process of organic matter. To evaluate the organic pollutants concentration, we use Escherichia coli as bacterial indicator, confined in a PDMS-glass chip. Their metabolic activity in presence of organic molecules is deduced from their oxygen consumption. These measurements are ensured by optical sensors present in each of the five instrumented chambers of the chip. The results show that the microsystem approach is suitable to measure simultaneously different concentrations of organic load, and that it is possible to reduce the analysis time. By examining the O 2 diffusion towards the walls of the device, we analyse the different part of the experimental results; it allows predicting, in the future, a precise evaluation of the BOD value within a few hours

Improvement of the Identification of Four Heavy Metals in Environmental Samples by Using Predictive Decision Tree Models Coupled with a Set of Five Bioluminescent Bacteria

International audienceA primary statistical model based on the crossings between the different detection ranges of a set of five bioluminescent bacterial strains was developed to identify and quantify four metals which were at several concentrations in different mixtures: cadmium, arsenic III, mercury, and copper. Four specific decision trees based on the CHAID algorithm (CHi-squared Automatic Interaction Detector type) which compose this model were designed from a database of 576 experiments (192 different mixture conditions). A specific software, 'Metalsoft', helped us choose the best decision tree and a user-friendly way to identify the metal. To validate this innovative approach, 18 environmental samples containing a mixture of these metals were submitted to a bioassay and to standardized chemical methods. The results show on average a high correlation of 98.6% for the qualitative metal identification and 94.2% for the quantification. The results are particularly encouraging, and our model is able to provide semiquantitative information after only 60 min without pretreatments of samples