Optimisation using the finite element method of a filter-based microfluidic SERS sensor for detection of multiple pesticides in strawberry.

This study developed an in-field analytical technique for food samples by integrating filtration into a surface-enhanced Raman spectroscopy (SERS) microchip. This microchip embedded a filter membrane in the chip inlet to eliminate interfering particulates and enrich target analytes. The design and geometry of the channel were optimised by finite-elemental method (FEM) to tailor variations of flow velocity (within 0-24 μL/s) and facilitate efficient mixing of the filtrate with nanoparticles in two steps. Four pesticides (thiabendazole, thiram, endosulfan, and malathion) were successfully detected either individually or as a mixture in strawberries using this sensor. Strong Raman signals were obtained for the four studied pesticides and their major peaks were clearly observable even at a low concentration of 5 µg/kg. Limits of detection of four pesticides in strawberry extract were in the range of 44-88 μg/kg, showing good sensitivity of the sensor to the target analytes. High selectivity of the sensor was also proved by successful detection of each individual pesticide as a mixture in strawberry matrices. High recoveries (90-122%) were achieved for the four pesticides in the strawberry extract. This sensor is the first filter-based SERS microchip for identification and quantification of multiple target analytes in complex food samples.