The pace of scientific discovery is no longer governed solely by the physical limits of manual experimentation. We are currently witnessing a shift that is as transformative as the invention of the microscope itself. Artificial Intelligence (AI) and advanced metabolomics are reshaping how science is conducted, moving research from a “trial-and-error” model to a predictive, data-driven discipline. By combining high-resolution analytical hardware with machine learning, laboratories can now solve complex biological challenges – such as developing animal-free culture media – with unprecedented speed.

The complexity of the modern workflow

In the fast-evolving landscape of biopharmaceuticals and cell biology, the reliance on traditional methods often leads to significant hurdles. For decades, the industry has relied on fetal bovine serum (FBS) to supplement cell culture media, despite its high costs, ethical concerns, and inherent inconsistency.

Many lab teams find themselves buried under mountains of raw data from complex matrices, struggling to identify which specific molecular components actually drive performance.

When dealing with undefined raw ingredients, such as plant and microbial extracts, understanding chemical composition is critical to ensuring batch-to-batch reproducibility and process continuity when scaling.

From raw peaks to actionable insights

The challenge in modern labs isn’t a lack of data; it is the complexity of interpreting high-dimensional datasets. Manual analysis of thousands of formulations is no longer feasible. As regulatory requirements for biologics become more stringent, the demand for defined, reproducible, and regulatory-compliant media has grown.

Advanced metabolomics provides the molecular profiling required to qualify raw materials, while AI handles the broad combinatorial screening. This synergy allows researchers to tailor media composition to specific cell lines, improving yield and efficiency across the drug development lifecycle.

Unpacking the PFAS puzzle

The analytical challenge of PFAS detection

Enhanced detection

Key benefits:

• Compact Design: Saves valuable laboratory space without compromising performance.
• High Sensitivity: Detects trace levels of PFAS, ensuring accurate quantification.ScienceDirect
• Robust Performance: Handles complex matrices with minimal maintenance requirements.
• User-Friendly Interface: Simplifies operation and data analysis, reducing training time.

Comprehensive analysis

Key benefits:

• High-Resolution Detection: Accurately identifies and quantifies known and unknown PFAS compounds.
• Fast Acquisition Rates: Enhances throughput, allowing for the analysis of more samples in less time.
• Advanced Data Analysis: Facilitates complex data interpretation with integrated software tools.
• Versatility: Suitable for various applications, from environmental monitoring to product safety assessments.

Building a safer future

Partner with Chemetrix for PFAS solutions

Although there’s a rising demand for honey, the honey bee population is also under threat. Another not-so-sweet issue is the number of products labelled as honey on retail shelves that don’t meet the criteria to be classified as pure honey.

The term “adulterated honey” means any honey to which has been added honeydew, glucose, dextrose, molasses, sugar, sugar syrup, inverted sugar, or any other similar product or products other than the nectar of floral exudations of plants gathered and stored in the comb by honey bees.

Food fraud is a significant concern for consumers and producers, with research indicating that fraud accounts for up to 25% of all globally reported food safety incidents. The growing demand for food authenticity means consumers regularly pay a premium for organic and sustainably produced goods like honey. Fraudsters have been flooding markets with adulterated, low-quality, or mislabeled foodstuffs, damaging the livelihoods of legitimate businesses and potentially risking consumer health.

Increasing demand

Consumers have become quite specific in their demand for honey, focusing on unifloral honey or monofloral honey obtained predominantly from bees that feed on a single species of plant flowers. This results in a unique colour, flavour, and fragrance exclusive to each type of unifloral honey. As consumers are willing to pay more for these products, protections must ensure that they purchase what they expect.

According to data from the Food and Agriculture Organization of the United Nations, China, Mexico, Russia, Turkey, and the United States are among the major honey-producing countries accounting for approximately 55 per cent of world production. The most common form of adulteration involves extending or diluting honey with other less expensive sweeteners. Commonly identified extenders are corn, cane, and beet syrups.

Testing for authenticity to mitigate honey fraud

Global e-commerce is placing honey sales outside regulatory oversight more frequently—a trend expected to continue. This, combined with increased fraudulent activities, makes tackling the problem critical. This is why it is important to identify these substances quickly, efficiently, and consistently. The food industry requires analytical instruments and testing techniques to consistently and rapidly analyze food and identify trace chemicals.

Analytical testing is essential for assessing food authenticity, which is important to protect consumers’ health, the brand, and producers’ income. Testing is a necessary part of an overall strategy to mitigate fraud risk, and methods for authenticity testing are rapidly evolving, with innovative technologies now available for developing robust food testing techniques.

Agilent 1290 Infinity II LC System

For example, it has been demonstrated in recent years that coupling high-performance liquid chromatography with quadrupole time-of-flight (LC/Q-TOF), such as the Agilent 1290 Infinity II LC System with Agilent 6545 LC/Q-TOF, provides a sensitive method to reveal the chemical composition of honey samples. Using this method with a non-targeted approach enables the identification of new types and sources of fraud through the chemical markers in the honey, highlighting which kind of fraudulent activity is occurring. Since this technique evaluates multiple markers in honey to determine authenticity, it is very difficult for fraudsters to cheat by adding one or a few adulterants. This innovative technique is called honey fingerprinting.

Agilent 6545 LC/Q-TOF

Determining honey’s unique chemical composition

Honey fingerprinting is the practice of using a suitable technique to record as much information as possible on the chemical composition of a particular honey sample. In the same way, a human fingerprint is unique to individuals, this fingerprinting method unlocks and records the unique molecular composition of authentic honey samples. This enables the mapping of food components in an unprecedented fashion that will revolutionize how honey is regulated for quality, safety, and authenticity.

Utilizing a non-targeted workflow begins with identifying other compounds, including pesticides, molecules that indicate freshness, like a compound called HMF (which suggests thermal processing or age if present in high numbers), and phenolic compounds, which are related to the floral origin of honey. The advantage of using LC/Q-TOF for this technique is its efficiency: higher molecular/trace information levels can be obtained from just one sample in less time versus targeted methods focusing on just a few parameters.

Standardising honey fingerprinting methods

Although previous work has been done developing case studies for fingerprinting foodstuffs, including honey, the approaches among laboratories have been different regarding sample preparation and instrumental condition. There are also differences in terms of data processing and analysis. As a result, two laboratories analyzing the same sample may obtain slightly different results. Ideally, developing a standardized fingerprinting method that could be used across all LC/MS-based workflows, enabling the same testing technique to be used across multiple laboratories, would be optimal and where future work is aimed.

When addressing the issues of food safety, product quality, and authenticity, each may be governed by separate sets of regulations. For example, looking at the residues of contaminants in honey, such as pesticides, there may be differences globally. Countries may have their own restrictions for the maximum limit for specific compounds. Contaminants are a part of the picture when considering fingerprinting for honey, but permitted levels may vary between countries.

Additionally, as samples come from the field to the lab for testing, there is potential interest in reversing this and bringing the lab out into the field instead. This interesting but not yet recognised capability would enable regulators and the global food industry to respond more quickly to honey contamination and food fraud.

Taking a global approach to ensure honey purity

As the food supply chain becomes increasingly globalized, raising the opportunity for food fraud, experts predict that testing, such as those described above, will become more accessible, increasingly automated, and easier to perform. Fingerprinting methods—in which the entire molecular profile of food can be obtained—will be a feature of future fraud prevention and identification systems.

A positive step forward is the focus on building a library of authentic honey samples and making it an accessible, open database so that honey fingerprinting information is available across multiple stakeholders in the global supply chain. With increased knowledge, more scientists will be able to adopt techniques such as LC/Q-TOF and could also use this testing for other types of food—for example, maple syrup.

The ultimate goal is for food testing laboratories to confidently measure contaminants that threaten the global food chain and tackle food fraud head-on to ensure that consumers can access authentic and safe honey.

(This article has been modified from its original appearance on the Agilent website)