Research progress on pesticide residue detection based on microfluidic technology

The problem of pesticide residue contamination has attracted widespread attention and poses a risk to human health. The current traditional pesticide residue detection methods have difficulty meeting rapid and diverse field screening requirements. Microfluidic technology integrates functions from sample preparation to detection, showing great potential for quick and accurate high-throughput detection of pesticide residues. This paper reviews the latest research progress on microfluidic technology for pesticide residue detection. First, the commonly used microfluidic materials are summarized, including silicon, glass, paper, polydimethylsiloxane, and polymethyl methacrylate. We evaluated their advantages and disadvantages in pesticide residue detection applications. Second, the current pesticide residue detection technology based on microfluidics and its application to real samples are summarized. Finally, we discuss this technology's present challenges and future research directions. This study is expected to provide a reference for the future development of microfluidic technology for pesticide residue detection.

Miniaturized analytical methods for determination of environmental contaminants of emerging concern - A review

The determination of contaminants of emerging concern (CECs) in environmental samples has become a challenging and critical issue. The present work focuses on miniaturized analytical strategies reported in the literature for the determination of CECs. The first part of the review provides brief overview of CECs whose monitoring in environmental samples is of particular significance, namely personal care products, pharmaceuticals, endocrine disruptors, UV-filters, newly registered pesticides, illicit drugs, disinfection by-products, surfactants, high technology rare earth elements, and engineered nanomaterials. Besides, an overview of downsized sample preparation approaches reported in the literature for the determination of CECs in environmental samples is provided. Particularly, analytical methodologies involving microextraction approaches used for the enrichment of CECs are discussed. Both solid phase- and liquid phase-based microextraction techniques are highlighted devoting special attention to recently reported approaches. Special emphasis is placed on newly developed materials used for extraction purposes in microextraction techniques. In addition, recent contributions involving miniaturized analytical flow techniques for the determination of CECs are discussed. Besides, the strengths, weaknesses, opportunities and threats of point of need and portable devices have been identified and critically compared with chromatographic methods coupled to mass chromatography. Finally, challenging aspects regarding miniaturized analytical methods for determination of CECs are critically discussed.

Pesticide Residues Identification by Optical Spectrum in the Time-Sequence of Enzyme Inhibitors Performed on Microfluidic Paper-Based Analytical Devices (µPADs)

Pesticides vary in the level of poisonousness, while a conventional rapid test card only provides a general “absence or not” solution, which cannot identify the various genera of pesticides. In order to solve this problem, we proposed a seven-layer paper-based microfluidic chip, integrating the enzyme acetylcholinesterase (AChE) and chromogenic reaction. It enables on-chip pesticide identification via a reflected light intensity spectrum in time-sequence according to the different reaction efficiencies of pesticide molecules and assures the optimum temperature for enzyme activity. After pretreatment of figures of reflected light intensity during the 15 min period, the figures mainly focused on the reflected light variations aroused by the enzyme inhibition assay, and thus, the linear discriminant analysis showed satisfying discrimination of imidacloprid (Y = −1.6525X − 139.7500), phorate (Y = −3.9689X − 483.0526), and avermectin (Y = −2.3617X − 28.3082). The correlation coefficients for these linearity curves were 0.9635, 0.8093, and 0.9094, respectively, with a 95% limit of agreement. Then, the avermectin class chemicals and real-world samples (i.e., lettuce and rice) were tested, which all showed feasible graphic results to distinguish all the chemicals. Therefore, it is feasible to distinguish the three tested kinds of pesticides by the changes in the reflected light spectrum in each min (15 min) via the proposed chip with a high level of automation and integration.