Multi-Pesticide Residue Analysis Method Designed for the Robot Experimenters

Influence of On-farm Pesticide Practices and Processing Methods on Pesticide Residue Levels in Potato Tubers (Solanum tuberosum L.) in Nyandarua County, Kenya

In Kenya, the extensive use of agrochemicals in potato farming raises concerns about pesticide residues in potato tubers and products. This study aimed to document the pesticide application practices of potato farmers in Nyandarua County, Kenya and evaluate the effect of the practices on pesticide residue levels in raw potato tubers. Also, the study evaluated the effect of various heat processing methods on the pesticide residue levels in potatoes. A cross-sectional survey using semi-structured questionnaires was conducted on 275 randomly selected farmers. Alongside, raw Shangi potato variety samples (n = 16) from respective farmers were analyzed for some of the most commonly used pesticide residues using LC-MS/MS and GC-MS/MS. The study found that 98.8% of farmers use synthetic pesticides, with 96.4% using fungicides, 68.2% insecticides, and 28.7% herbicides. Common fungicides contained mancozeb, metalaxyl, and cymoxanil as the main active ingredients. Insecticides contained α-cypermethrin and imidacloprid, while herbicides had glyphosate and 2,4-D as the main active ingredients. These chemicals were used either alone or in mixtures. Only 11.85% of farmers adhered to the recommended manufacturer's application rates, with the majority relying on advice from agrochemical retailers (74.63%) or other farmers (13.32%). The frequent mixing of pesticides and weekly applications were also common practices. Residue analysis revealed that adherence to the manufacturer's instructions resulted in lower residue levels. Mixing pesticides and frequent applications led to higher residues, particularly for fungicides containing azoxystrobin. Longer preharvest intervals generally reduced residue levels. Most of the pesticide residues were below the EU and Codex Maximum Residue Limits (MRLs) in potatoes, however, the banned insecticides containing chlorpyrifos and fenitrothion were found at levels exceeding the EU and Codex MRLs of 0.01 mg/kg. Processing methods such as frying, baking, boiling, and steaming significantly reduced pesticide residues, below the Codex MRLs. Frying and boiling were particularly effective for most pesticides. However, baking, roasting, and frying were not effective in reducing chlorpyrifos and fenitrothion below EU and Codex MRLs. The findings highlight the need for farmer education on proper pesticide use and adherence to recommended practices to minimize residue levels and ensure food safety.

Automated pretreatment of environmental water samples and non-targeted intelligent screening of organic compounds based on machine experiments

The complexity of environmental pollutants poses significant challenges for monitoring and analysis, especially with the emergence of numerous emerging contaminants. Traditional analysis methods rely mainly on laboratory analysis, which involves labor-intensive and time-consuming sample preparation procedures and non-target data analysis, greatly limiting the rapid detection of water organic pollutants. In this study, we designed a robot experimenter combined with GC × GC-TOFMS. By configuring self-developed automated analysis software, we established a fully automated process from sample collection to data characterization, for the analysis of organic pollutants. We validated the method with 111 organic standards compounds. The robot performed 2577 actions covering the entire workflow, from water sample collection to sample pre-treatment. The integration of mass spectrometry and related software enabled the automatic analysis of emerging hazardous contaminants, from sampling to the output of detection results. The results showed the automated process could qualitatively identify all compounds and demonstrated good linearity, low detection limits, and excellent quantitative ability within the range of 0.04-0.4 mg/L. The average recoveries of 82.89 % of the samples ranged from 70 % to 120 % (relative standard deviation (RSD) <15 %) at different spiked concentrations. This indicated that the established method could be used for non-targeted analysis of emerging contaminants in environmental water samples. We applied the method to samples from wastewater treatment plants and river sections, identifying 1,902 compounds across 26 categories, including 6 known hazardous contaminants found in all samples. The relative content of these characteristic compounds will inform whether treated wastewater meets discharge standards and aid in tracing the sources of pollutants. Therefore, the development of this fully automated machine experimental method enables real-time and online automatic analysis of organic pollutants in environmental water. The establishment of characteristic fingerprints can provide technical support for early warning and traceability of water quality.

Automation and Computerization of (Bio)sensing Systems

Sensing systems necessitate automation to reduce human effort, increase reproducibility, and enable remote sensing. In this perspective, we highlight different types of sensing systems with elements of automation, which are based on flow injection and sequential injection analysis, microfluidics, robotics, and other prototypes addressing specific real-world problems. Finally, we discuss the role of computer technology in sensing systems. Automated flow injection and sequential injection techniques offer precise and efficient sample handling and dependable outcomes. They enable continuous analysis of numerous samples, boosting throughput, and saving time and resources. They enhance safety by minimizing contact with hazardous chemicals. Microfluidic systems are enhanced by automation to enable precise control of parameters and increase of analysis speed. Robotic sampling and sample preparation platforms excel in precise execution of intricate, repetitive tasks such as sample handling, dilution, and transfer. These platforms enhance efficiency by multitasking, use minimal sample volumes, and they seamlessly integrate with analytical instruments. Other sensor prototypes utilize mechanical devices and computer technology to address real-world issues, offering efficient, accurate, and economical real-time solutions for analyte identification and quantification in remote areas. Computer technology is crucial in modern sensing systems, enabling data acquisition, signal processing, real-time analysis, and data storage. Machine learning and artificial intelligence enhance predictions from the sensor data, supporting the Internet of Things with efficient data management.