Rapid Analysis of Styrene in Drinking Water and Tea Samples Using Dispersive Liquid-Liquid Microextraction Combined with Liquid Chromatography-Ultraviolet Detection

Role of Residual Monomers in the Manifestation of (Cyto)toxicity by Polystyrene Microplastic Model Particles

Polystyrene (PS) is an important model polymer for the investigation of effects of microplastic (MP) and nanoplastic (NP) particles on living systems. Aqueous dispersions of PS MP or NP contain residual monomers of styrene. In consequence, it is not clear if the effects observed in standard (cyto)toxicity studies are evoked by the polymer (MP/NP) particle or by residual monomers. We addressed that question by comparing standard PS model particle dispersions with in-house synthesized PS particle dispersions. We proposed a rapid purification method of PS particle dispersions by dialysis against mixed solvents and developed a simple method of UV-vis spectrometry to detect residual styrene in the dispersions. We found that standard PS model particle dispersions, which contain residual monomers, exerted a low but significant cytotoxicity on mammalian cells, while the in-house synthesized PS, after rigorous purification to reduce the styrene content, did not. However, the PS particles per se but not the residual styrene in both PS particle dispersions resulted in immobilization of Daphnia. Only by using freshly monomer-depleted particles, will it be possible in the future to assess the (cyto)toxicities of PS particles, avoiding an otherwise not controllable bias effect of the monomer.

Non-targeted screening and analysis of volatile organic compounds in drinking water by DLLME with GC-MS

Volatile organic compounds (VOCs) in drinking water may potentially be hazardous. We developed a novel non-targeted analysis method of VOCs in drinking water that uses dispersive liquid-liquid microextraction coupled with gas chromatography-mass spectrometry. Analysis parameters were selected from range-finding tests on the peak number and average area of the extracted compounds. The optimized method was applied to analyze VOCs in tap water samples collected from Wuhan City, China. Twenty-seven compounds with high match degrees and a high prevalence were selected for quantification and evaluation. We used structure-activity relationships to predict the carcinogenicity of these compounds. Although most of the compounds were non-toxic, compounds such as dibutyl phthalate and diacetone alcohol should be investigated further. Untargeted analysis of the tap water samples identified 75-200 VOCs, including 67 highly prevalent compounds. Industrial and pharmaceutical chemicals accounted for approximately 70% of the VOCs in the samples. This method of non-targeted analysis and in silico toxicity prediction is simple and economic, and could be used in screening VOCs in drinking water.

Quality Evaluation of Green and Dark Tea Grade Using Electronic Nose and Multivariate Statistical Analysis

Aroma assessment remains difficult and uncertain in the present sensory assessment system. It is highly desirable to develop a new assessment method to discriminate the quality of various teas in the tea market. In the present work, based on linear discriminant analysis and principal component analysis, the aroma of dry and wet samples of different Xi-hu Longjing and Pu-erh teas were tested and differentiated by electronic noses (e-nose). The results confirm that e-nose can discriminate different priced Xi-hu Longjing tea samples in the range of 80-800 RMB/500 g and varying storage years of Pu-erh tea samples. Furthermore, for the detection of both dry and wet samples of Longjing and Pu-erh teas, the results reveal that all samples have specific aroma characteristics that e-nose can recognize. More importantly, contribution analysis in sensors indicates that nitrogen oxides, methane and alcohols are the characteristic components that contribute to the fragrances of different priced Xi-hu Longjing teas, while nitrogen oxides, aromatic benzene and amines make the fragrances of Pu-erh teas with different storage years disparate. PRACTICAL APPLICATION: This work demonstrates that e-nose can rapidly distinguish tea products with different price levels and varying storage years. With the advantages of ease of use, high portability and flexibility, e-nose will be widely expanded and applied in refined processing and the development of flavored foods.

Rapid extraction of copper ions in water, tea, milk and apple juice by solvent-terminated dispersive liquid-liquid microextraction using p-sulfonatocalix (4) arene: optimization by artificial neural networks coupled bat inspired algorithm and response surface methodology

A bat inspired algorithm with the aid of artificial neural networks (ANN-BA) has been used for the first time in chemistry and food sciences to optimize solvent-terminated dispersive liquid-liquid microextraction (ST-DLLME) as a green, fast and low cost technique for determination of Cu²⁺ ions in water and food samples using p-sulfonatocalix (4) arene as a complexing reagent. For this purpose, the influence of four important factors four factors which was influenced on the extraction efficiency such as salt addition, solution pH and disperser and extraction solvent volumes were investigated. Central composite design (CCD) as a comparative technique was employed for optimization of ST-DLLME efficiency. The ANN-BA optimization technique was regarded as a superior model due to its higher value of extraction efficiency (about 7.21%) compared to CCD method. Under ANN-BA optimal conditions, the limit of quantitation (S/N = 10), limit of detection (S/N = 3) and linear range were 0.35, 0.12 and 0.35-1000 µg L^-1, respectively. In these circumstances, the percentage recoveries for drinking tea, apple juice, milk, bottled drinking water, river and well water spiked with 0.05, 0.1 and 0.2 mg L^-1 of Cu²⁺ ions were in the acceptable range (91.4-107.1%). In comparison to other methods, the developed ST-DLLME method showed the lowest solvent and sample consumption, shortest value of extraction time, most suitable determination and detection limits and linear range with simple and low cost apparatus. Additionally, the use of bat inspired algorithm as a powerful metaheuristic algorithm with the aid of artificial networks is another advantage of the present work.

Simultaneous extraction of Cu2+ and Cd2+ ions in water, wastewater, and food samples using solvent-terminated dispersive liquid-liquid microextraction: optimization by multiobjective evolutionary algorithm based on decomposition

Solvent-terminated dispersive liquid-liquid microextraction (ST-DLLME) as a simple, fast, and low-cost technique was developed for simultaneous extraction of Cd²⁺ and Cu²⁺ ions in aqueous solutions. Multiobjective evolutionary algorithm based on decomposition with the aid of artificial neural networks (ANN-MOEA/D) was used for the first time in chemistry, environment, and food sciences to optimize several independent variables affecting the extraction efficiency, including disperser volume and extraction solvent volume, pH, and salt addition. To perform the ST-DLLME operations, xylene, methanol, and dithizone were utilized as an extraction solvent, disperser solvent, and chelating agent, respectively. Non-dominated sorting genetic algorithm versions II and III (NSGA II and NSGA III) as multiobjective metaheuristic algorithms and in addition central composite design (CCD) were studied as comparable optimization methods. A comparison of results from these techniques revealed that ANN-MOEA/D model was the best optimization technique owing to its highest efficiency (97.6% for Cd²⁺ and 98.3% for Cu²⁺). Under optimal conditions obtained by ANN-MOEAD, the detection limit (S/N = 3), the quantitation limit(S/N = 10), and the linear range for Cu²⁺ were 0.05, 0.15, and 0.15-1000 μg L^-1, respectively, and for Cd²⁺ were 0.07, 0.21, and 0.21-750 μg L^-1, respectively. The real sample recoveries at a spiking level of 0.05, 0.1, and 0.3 mg L^-1 of Cu²⁺ and Cd²⁺ ions under the optimal conditions obtained by ANN-MOEA/D ranged from 94.8 to 105%.

Rapid Analysis of Acrylamide in Tap and Well Water Samples by Solvent Terminated Dispersive Liquid-Liquid Microextraction Followed by GC-FID

A fast, green and low cost method for analysis of acrylamide in tap and well water has been presented for the first time using solvent terminated-dispersive liquid liquid microextraction (ST-DLLME) with a simple equipment which does not need centrifugation step followed by GC-FID. The use of one variable at a time optimization method revealed that methanol and octanone were the superior disperser and extraction solvents, respectively. A central composite design (CCD) as a response surface methodology was used for multivariate optimization of five independent factors (volumes of extraction and dispersive solvents, pH, salt addition and extraction time) on the extraction efficiency. Under CCD optimal conditions, the linear range, detection limit (S/N = 3) and quantitation limit (S/N = 10) were 0.1, 0.3 and 0.3-550 ng mL^-1, respectively. In these circumstances, the recoveries for real samples (tap and well water) spiked with 0.5, 1 and 10 ng g^-1 were in the acceptable range (90.8%-94.1%). In comparison with other methods in the literature, the suggested ST-DLLME approach showed the best analytical performance. The presented green method has potential application as a routine method in the environmental and analytical laboratories for analysis of acrylamide in water samples.

Optimization of Solvent Terminated Dispersive Liquid-Liquid Microextraction of Copper Ions in Water and Food Samples Using Artificial Neural Networks Coupled Bees Algorithm

A multivariate method based on solvent terminated dispersive liquid-liquid microextraction was developed for the determination of Cu²⁺ ions in aqueous samples. In the proposed approach, di-2-ethylhexylphosphoric acid, xylene and acetone were used as chelating agent, dispersive and extraction solvents, respectively. The effects of various factors on the extraction efficiency such as extraction and dispersive solvent volumes, salt addition and pH were studied using central composite design (CCD) and artificial neural networks coupled bees algorithm (ANN-BA). Upon comparison of these techniques, ANN-BA model was considered to be better optimization method due to its higher percentage relative recovery (about 5%) as compared to the CCD approach. The linear range and the limits of detection (S/N = 3) and quantitation (S/N = 10) were 0.22-140, 0.08 and 0.22 µg L^-1, respectively. Under the optimal conditions, the recoveries for real samples spiked with 0.1 and 0.3 mg L^-1 were in the range of 85-98%.