Development of Lateral Flow Test-System for the Immunoassay of Dibutyl Phthalate in Natural Waters

Detection of Dibutyl Phthalate in Surface Water by Fluorescence Polarization Immunoassay

Dibutyl phthalate (DBP) is widely used as a plasticizer in the production of polymeric materials to give them flexibility, strength and extensibility. However, due to its negative impact on human health, in particular reproductive functions and fetal development, the content of DBP must be controlled in food and the environment. The present study aims to develop a sensitive, fast and simple fluorescence polarization immunoassay (FPIA) using monoclonal antibodies derived against DBP (MAb-DBP) for its detection in open waters. New conjugates of DBP with various fluorescein derivatives were obtained and characterized: 5-aminomethylfluorescein (AMF) and dichlorotriazinylaminofluorescein (DTAF). The advantages of using the DBP-AMF conjugate in the FPIA method are shown, the kinetics of binding of this chemical with antibodies are studied, the analysis is optimized, and the concentration of monoclonal antibodies is selected for sensitivity analysis-16 nM. The calibration dependence of the fluorescence polarization signal for the detection of DBP was obtained. The observed IC50 (DBP concentration at which a 50% decrease in the fluorescence polarization signal occurs, 40 ng/mL) and the limit of detection (LOD, 7.5 ng/mL) values were improved by a factor of 45 over the previously described FPIA using polyclonal antibodies. This technique was tested by the recovery method, and the high percentage of DBP discovery in water ranged from 85 to 110%. Using the developed method, real water samples from Lake Onega were tested, and a good correlation was shown between the results of the determination of DBP by the FPIA method and GC-MS. Thus, the FPIA method developed in this work can be used to determine DBP in open-water reservoirs.

A review on solution- and vapor-responsive sensors for the detection of phthalates

Phthalic acid esters, largely referred to as phthalates, are today acknowledged as important pollutants used in the manufacture of polyvinyl chloride (PVC)-based plastics, whose use extends to almost every aspect of modern life. The risk of exposure to phthalates is particularly relevant as high concentrations are regularly found in drinking water, food-contact materials and medical devices, motivating an immense body of research devoted to methods for their detection in liquid samples. Conversely, phthalate vapors have only recently been acknowledged as potentially important atmospheric pollutants and as early fire indicators; additionally, deposition of these vapors can pose significant problems to the proper functioning of spacecraft and diverse on-board devices, leading to major space agencies recognizing the need of developing vapor-responsive phthalate sensors. In this manuscript we present a literature survey on solution- and vapor-responsive sensors and analytical assays for the detection of phthalates, providing a detailed analysis of a vast array of analytical data to offer a clear idea on the analytical performance (limits of detection and quantification, linear range) and advantages provided by each class of sensor covered in this review (electrochemical, optical and vapor-responsive) in the context of their potential real-life applications; the manuscript also gives detailed fundamental information on the various physicochemical responses exploited by these sensors and assays that could potentially be harnessed by new researchers entering the field.

Colorimetric Characterization of Color Imaging System Based on Kernel Partial Least Squares

Colorimetric characterization is the basis of color information management in color imaging systems. In this paper, we propose a colorimetric characterization method based on kernel partial least squares (KPLS) for color imaging systems. This method takes the kernel function expansion of the three-channel response values (RGB) in the device-dependent space of the imaging system as input feature vectors, and CIE-1931 XYZ as output vectors. We first establish a KPLS color-characterization model for color imaging systems. Then we determine the hyperparameters based on nested cross validation and grid search; a color space transformation model is realized. The proposed model is validated with experiments. The CIELAB, CIELUV and CIEDE2000 color differences are used as evaluation metrics. The results of the nested cross validation test for the ColorChecker SG chart show that the proposed model is superior to the weighted nonlinear regression model and the neural network model. The method proposed in this paper has good prediction accuracy.