Multi-Pesticide Residue Analysis Method Designed for the Robot Experimenters

Abstract

Robots replacing humans as the executioners is crucial work for intelligent multi-pesticide residue analysis to maximize reproducibility and throughput while minimizing the expertise required to perform the entire process. Traditional analysis methods are predicated on manual execution, so we configured our robot experimenter, automated the analytical workflow, and achieved the goal of robotics execution. Our robot experimenter with an X−Y−Z axis robotic arm was interfaced with seven modules and ultra-performance liquid chromatography−tandem mass spectrometry (UPLC−MS/MS) for automated standard solution preparation, sample pre-treatment, and UPLC−MS/MS detection. An algorithm was established to make the prepared matrixmatched standard solutions meet the monitoring requirements. The strategy was demonstrated and validated for the detection of 325 pesticides in 4 typical food matrices, suggesting that the developed method is applicable for the analysis of pesticide residues in vegetables and tea as part of regulatory monitoring programs and other purposes.

Automation and Computerization of (Bio)sensing Systems

“…In chemical analysis, robotic systems offer capabilities such as dispensing reagents, mixing, performing serial dilutions, centrifugation, pipetting, labeling, and transferring aliquots between different pieces of labware. These capabilities enable the implementation of robotic sampling and sample preparation in various fields, including bioanalysis, 98 clinical analysis, 99,100 pharmaceutical analysis, 101 food analysis, 102,103 environmental analysis, 104,105 and forensic toxicology. 106 With the advancements in robotic technologies, robotic systems are now readily integrated with other equipment and instruments to achieve fully automated sensing systems, from either sample handling or preparation to detection.…”

“…In chemical analysis, robotic systems offer capabilities such as dispensing reagents, mixing, performing serial dilutions, centrifugation, pipetting, labeling, and transferring aliquots between different pieces of labware. These capabilities enable the implementation of robotic sampling and sample preparation in various fields, including bioanalysis, clinical analysis, , pharmaceutical analysis, food analysis, , environmental analysis, , and forensic toxicology …”

Colorimetric Nanozyme Sensor Array Based on Metal Nanoparticle-Decorated CNTs for Quantification of Pesticides in Real Water and Soil Samples

“…Although frequently employed conventional “lock-and-key” analysis techniques possess excellent sensitivity, they require specific receptors that have a significant affinity to recognize a particular pesticide, leading to costly and lengthy procedures due to the need for numerous antibodies for multipesticide assays. , Additionally, specialized equipment was required for numerous chromatography–mass spectrometry methods as well as flow injection analysis. , Compared with the above conventional methods, array-based pattern recognition has shown great interest in detection of multiple analytes in the sensing field, which places more emphasis on group discrimination than single analyte detection. − Unlike the lock-and-key sensing mode that relies on individual receptors, this strategy utilizes artificial arrays of cross-reactive sensor elements to generate discrete patterns specific to each analyte. The examination of the patterns acquired via the use of multivariate algorithms in machine-learning approaches unveils the specific identification and concentration of the substance being analyzed, enabling the concurrent detection of several substances. − Array-based sensing has been extensively utilized for quantification and the analysis of several toxic substances such as heavy metal ions, thiols, bacteria, biogenic amines, toxic gases, etc.…”

“…6,7 Additionally, specialized equipment was required for numerous chromatography−mass spectrometry methods as well as flow injection analysis. 8,9 Compared with the above conventional methods, array-based pattern recognition has shown great interest in detection of multiple analytes in the sensing field, which places more emphasis on group discrimination than single analyte detection. 10−12 Unlike the lock-and-key sensing mode that relies on individual receptors, this strategy utilizes artificial arrays of cross-reactive sensor elements to generate discrete patterns specific to each analyte.…”