Background
With the widespread use of pesticides in modern agriculture, issues such as food contamination, ecological damage, and public health risks have become increasingly prominent. Pesticide residues accumulate through the food chain and may cause neurotoxicity, endocrine disorders, and even increase the risk of cancer. Globally, tens of thousands of foodborne illnesses are linked to pesticide exposure each year. Traditional pesticide detection methods, such as HPLC and mass spectrometry, offer high precision but require complex equipment, are time-consuming, and depend on laboratory settings. This makes them unsuitable for rapid on-site testing. Therefore, there is an urgent need to develop new detection technologies that are highly sensitive, specific, and portable.

Research Progress
The research team of Xu Yan and Hongxia Li from Jilin University systematically reviewed the fundamental theories and latest breakthroughs in optical biosensors for pesticide detection. Their work offers new ideas for tackling pesticide residue monitoring challenges. This review thoroughly covers key optical sensing techniques, including fluorescence (FL), colorimetry (CL), surface-enhanced Raman scattering (SERS), surface plasmon resonance (SPR), and chemiluminescence. It also examines the working mechanisms of various molecular recognition elements, such as enzymes, antibodies, aptamers, molecularly imprinted polymers (MIPs), and supramolecular host–guest complexes (Fig. 1).
The research team systematically analyzed numerous cutting-edge case studies, clearly illustrating the unique advantages and practical challenges of various optical sensing platforms. They also highlighted the central role played by different biorecognition elements in these systems.
The exceptional performance of FL sensors, such as achieving femtogram-level detection sensitivity with quantum dots or carbon dots, fundamentally relies on the precise guidance provided by specific recognition elements. Key biorecognition elements form the cornerstone for achieving high-selectivity detection. These include enzyme-mediated systems that amplify signals through catalytic activity, high-affinity antibody binding based on the "lock-and-key" principle , and aptamer recognition known for its high specificity and stability. However, these elements also have their limitations. Enzyme activity is susceptible to environmental factors, antibodies are costly with batch-to-batch variations, and aptamer selection is complex, sometimes resulting in insufficient binding affinity for small-molecule targets.
Colorimetric sensors excel in their simplicity, enabling rapid on-site screening through visible color changes without complex instruments. This advantage is tightly linked to their recognition cores. The catalytic properties of enzymes, particularly novel nanozymes, are directly converted into observable color changes. Meanwhile, antibodies translate specific antigen-antibody binding into clear band signals, as seen in immunochromatographic strips. While nanozymes overcome the stability issues of natural enzymes, colorimetric methods generally have lower sensitivity compared to FL or SERS. They are also prone to errors from subjective color interpretation and background interference, especially with colored samples, leading to potential false positives.
The leap towards "fingerprint-level" identification of pesticide molecules by SERS stems from the integration of two parallel pathways. The first is the biorecognition-guided path, where antibodies or aptamers specifically capture and bring the target pesticide close to plasmonic nanostructures. This grants SERS its powerful target selectivity for picking out specific molecules from complex mixtures. The second is the non-biological substrate enhancement path, which focuses on designing gold or silver nanostructures to maximize the physical enhancement effect. A major bottleneck for SERS lies in stably and reproducibly combining high-specificity recognition with intense, uniform signal enhancement from the substrate.
To balance sensitivity, specificity, and reliability, dual-mode sensors have been developed. These platforms ingeniously integrate two complementary optical detection techniques, such as FL with CL, or CL with SERS. They create an internal cross-verification mechanism that effectively counters signal interference from complex sample matrices, which is common in single-mode detection. This significantly improves the accuracy and reliability of results. Of course, this integration also introduces new challenges, including more complex system design and increased difficulty in probe synthesis and signal coordination.

Future Prospects
Based on current progress, future research will focus on three key directions. First, developing higher-performance intelligent sensing materials, such as environmentally responsive degradable sensors and more stable biomimetic recognition elements. Second, promoting deeper integration with artificial intelligence by using machine learning algorithms to automatically interpret complex spectra, enable simultaneous and accurate identification of multiple residues, and predict pollution trends. Third, building Internet of Things (IoT) networks to create intelligent systems for real-time on-site pesticide monitoring.

In addition, the development of green sensors and the establishment of unified performance evaluation standards will lay a solid foundation for advancing the industrialization of this field and safeguarding global food safety and ecological sustainability.

The complete study is accessible via DOI:10.34133/research.1060