Neonicotinoid residues in fruits, vegetables, and honey: advances in ultra-trace analysis, matrix-effect mitigation, and environmental risk monitoring

In the quiet corners of the global food supply, a silent tension exists between the need to grow enough food for billions and the need to keep that food safe from invisible chemical threats. Farmers rely on a specific class of insecticides to protect their crops from pests that eat leaves and suck sap, ensuring that harvests remain steady and abundant. These chemicals are designed to be systemic, meaning they are absorbed by the plant itself, traveling from the roots or seeds up into the fruit, leaves, and even the nectar that bees collect. While this protects the crop, it also means the chemicals can end up on the dinner plate or in the honey jar. The challenge for scientists and regulators is not just to find these traces, but to measure them with absolute certainty in foods that are chemically complex. Fruits, vegetables, and honey are not empty containers; they are rich mixtures of sugars, pigments, acids, and waxes that can hide or distort the very signals scientists are trying to detect. If the measurement is off, a safe food might be wrongly rejected, or a dangerous one might slip through.

A new review brings together the latest thinking on how to solve this problem, focusing on the years between 2019 and 2026. The authors, a team of researchers from institutions across Indonesia, China, Malaysia, and Europe, argue that the way we monitor these insecticides must evolve from simple detection to a sophisticated, risk-based system. They examine how scientists are improving the way they pull these chemicals out of complex foods like honey and leafy greens, how they are using advanced machines to see them more clearly, and how they are building a global framework to ensure that a measurement taken in one country means the same thing in another. The paper does not claim to have found a single magic solution, but rather maps out a path forward where better chemistry, smarter data, and unified rules work together to protect both human health and the ecosystems that support our food.

The core of the issue lies in the nature of the food itself. When a scientist tries to measure a tiny amount of insecticide residue in a jar of honey or a bunch of spinach, they are fighting a battle against the food's own ingredients. Honey is thick with sugar and pollen, while leafy vegetables are packed with green pigments and waxes. When these foods are processed in a lab, these natural components can interfere with the machines used to detect the chemicals, often making the insecticide look like it is there in larger or smaller amounts than it really is. This interference is known as a matrix effect, and it is the primary reason why getting an accurate reading is so difficult. The review highlights that standard methods used for one type of food often fail when applied to another. For instance, a technique that works well for a watery tomato might produce unreliable results for a sticky, sugar-heavy honey sample. To fix this, researchers have refined their extraction methods, creating specialized cleaning steps that remove the interfering sugars and pigments without losing the insecticide traces they are trying to find.

Once the sample is prepared, the next challenge is seeing the invisible. The paper details how modern laboratories are moving toward ultra-sensitive machines that can detect these chemicals at levels far below what was possible a decade ago. These machines, often described as mass spectrometers, act like incredibly precise scales that weigh molecules to identify them. The most common tool is a device that separates chemicals based on how they move through a liquid and then weighs them to confirm their identity. This method is now the standard for routine checks because it is fast and reliable. However, the review points out that for the most difficult cases, scientists are turning to even more powerful tools that can not only weigh the molecules but also look at their exact structure. This higher level of detail allows them to spot not just the original insecticide, but also the broken-down pieces, or metabolites, that the chemical turns into inside the plant or the environment. These metabolites can sometimes be just as toxic as the original chemical, and catching them is essential for a true picture of the risk.

Beyond the laboratory, the paper explores a growing frontier: the ability to test for these residues right where the food is grown or sold. For years, accurate testing required sending samples to a central lab, a process that took days or weeks. Now, new portable devices are emerging that can give a quick answer in the field. These include handheld sensors that use light to identify chemicals, strips that change color when a residue is present, and small electronic devices that can be plugged into a smartphone. While these tools are not yet precise enough to replace the heavy-duty lab machines for official legal decisions, they serve a vital role as a first line of defense. They allow inspectors to screen hundreds of samples quickly, flagging the ones that look suspicious so that only those need to be sent to the lab for a full, expensive analysis. This approach saves time and money, allowing regulators to focus their resources on the foods that pose the highest risk.

The review also emphasizes that technology alone is not enough; the rules governing how these tests are done must be the same everywhere. Currently, different countries have different limits for how much residue is allowed, and they use different methods to measure it. This creates confusion and can block trade, as a shipment of fruit might be rejected in one country simply because the testing method used there is more sensitive than the one used in the country where it was grown. The authors argue for a global harmonization of standards, where laboratories agree on how to prepare samples, how to calibrate their machines, and how to report their results. They suggest that without this agreement, the data collected by different countries cannot be compared, making it impossible to understand the true global picture of contamination.

Looking to the future, the paper suggests that the next leap forward will come from combining these physical tools with digital intelligence. The authors propose using artificial intelligence to analyze the vast amounts of data generated by these tests. Instead of just reacting to contamination after it is found, these smart systems could predict where problems are likely to occur by looking at patterns in weather, farming practices, and historical data. This would allow regulators to target their testing efforts more effectively, checking the right crops at the right time. The review concludes that protecting our food supply from these chemicals requires a holistic approach. It is not just about building better machines, but about creating a system where science, regulation, and data work in unison to ensure that the food we eat is safe, and that the pollinators and ecosystems that make our food possible are not harmed in the process.