Thursday, August 14, 2025

New 'forever chemicals' found in whale blubber challenge our understanding of PFAS

Stockholm University

image:
Killer whale
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Credit: Anna Roos, Swedish Museum of Natural History

Scientists from Stockholm University and the Swedish Museum of Natural history, in collaboration with partners in Greenland and Canada, have identified a previously undocumented class of PFAS* in the blubber of killer whales.

The new study, published in Environmental Science and Technology Letters, reveals the presence of five fluorotelomer sulfones—highly fluorinated, lipophilic (fat-loving) chemicals never before reported in wildlife. Unlike well studied PFAS, which typically accumulate in protein-rich tissues such as liver and blood, these new substances accumulate in fat-rich blubber.

“This is the first time that highly fluorinated PFAS have been shown to preferentially accumulate in fat,” says lead author Mélanie Lauria, formerly a doctoral student at the Department of Environmental Science at Stockholm University and currently at the Swiss Federal Institute of Aquatic Science and Technology (EAWAG). “It challenges the long-held assumption that PFAS primarily bind to proteins and accumulate in the liver or blood. Research has so far focused on a subgroup of PFAS that is “charged” or “polar”, and therefore interact mainly with proteins. The results of this study show that we have overlooked PFAS that are neutral and can interact and accumulate in neutral fats such as blubber.”

Using advanced mass spectrometry techniques, the researchers analysed tissue samples from killer whales sampled in Greenland and Sweden. The newly identified compounds accounted for up to 75 percent of fluorine-containing substances in blubber, but were undetectable in liver tissue—evidence of their fat-loving nature.

“These findings suggest that we may be underestimating the PFAS body burden in marine mammals. Blubber can represent up to half of a marine mammal’s body mass, so neglecting fat-soluble PFAS could significantly undermine the accuracy of exposure assessments,” says co-author Jonathan Benskin, professor at the Department of Environmental Science, Stockholm University.

According to the authors, monitoring efforts should be expanded to include fat-soluble PFAS in order to better understand the full extent of PFAS exposure in Arctic ecosystems.

The study raises new questions about chemical exposure in apex predators, with implications for environmental and human health—particularly in Arctic regions where marine mammals are part of traditional diets.

The research was funded by the European Union’s Horizon 2020 program and supported by Indigenous communities in Greenland, who facilitated the legal sampling of whales through subsistence hunting.

*PFAS stands for poly- and perfluorinated alkyl substances and is a collective term for a large group of organic compounds with a carbon chain where hydrogen atoms are completely (perfluorinated carbon chain) or partially (polyfluorinated carbon chain) replaced by fluorine atoms.

Contact:
Jonathan Benskin, Stockholm University
Phone: +4676 426 5490, E-mail jon.benskin@aces.su.se

Mélanie Lauria, Swiss Federal Institute of Aquatic Science and Technology (EAWAG)
Phone: +41 77 948 20 03, E-mail: melanie.lauria@eawag.ch.

More information

The article “Discovery of Fluorotelomer Sulfones in the Blubber of Greenland Killer Whales (Orcinus orca)” is published in Environmental Science and Technology Letters. https://pubs.acs.org/doi/10.1021/acs.estlett.5c00516
DOI: 10.1021/acs.estlett.5c00516

Journal

Environmental Science & Technology Letters

DOI

10.1021/acs.estlett.5c00516

Method of Research

Experimental study

Subject of Research

Animal tissue samples

Article Title

“Discovery of Fluorotelomer Sulfones in the Blubber of Greenland Killer Whales (Orcinus orca)”

Teens with higher blood levels of PFAS regain more weight after bariatric surgery, study finds

Researchers from the Keck School of Medicine of USC found that the manmade chemicals were linked to greater weight regain and increased waist circumference up to five years after surgery.

Keck School of Medicine of USC

Adolescents who undergo bariatric surgery face a higher risk of weight regain, which can undermine treatment success and long-term health, if they have elevated blood levels of per- and polyfluoroalkyl substances (PFAS) prior to the procedure, according to new USC research. PFAS are manufactured chemicals used in consumer products that accumulate in the body over time and are increasingly linked to a range of health concerns, including kidney problems, liver damage and various cancers. The findings were just published in the journal Obesity.

Bariatric surgery, a form of weight-loss surgery that alters the digestive system, is an effective treatment for severe obesity that can reduce the risk of long-term health problems such as type 2 diabetes and cardiovascular disease. But many people who get bariatric surgery regain significant weight over time, which can limit the procedure’s long-term health benefits. Researchers believe PFAS exposure may play a role in weight regain because the chemicals are known to disrupt endocrine and metabolic processes.

“With the growing use of weight loss interventions around the world, it’s critical for us to understand the association between PFAS and successful weight loss management, including what this means for long-term outcomes,” said Brittney Baumert, PhD, MPH, a postdoctoral research fellow in population and public health sciences at the Keck School of Medicine of USC and the study’s first author.

With funding from NIH, Baumert and her colleagues conducted the first long-term study linking PFAS exposure to bariatric surgery outcomes in teens. They tracked 186 adolescents for five years, finding that those with higher PFAS blood levels before surgery regained more weight and had greater increases in waist circumference than those with lower PFAS levels. The strongest associations were observed in a class of PFAS known as sulfonic acids, which includes perfluoroctaansulfonaten (PFOS), perfluorohexane sulfonic acid (PFHxS) and perfluoroheptanesulfonic acid (PFHpS).

The findings could help health care providers tailor treatment plans to better serve patients, as well as offer hints about the biological mechanisms that underlie the health effects of PFAS. The study also underscores the need for stronger limits on PFAS levels in food packaging, water supplies and other sources of exposure, the researchers said.

“Our study shows a clear association between PFAS exposure and weight-related outcomes in bariatric surgery for adolescents,” Baumert said. “PFAS are a modifiable risk, which is why protective policies are so important to reduce exposure and safeguard public health—especially for vulnerable populations.”

Tracking long-term outcomes

The researchers analyzed data on 186 teens from the Teen-Longitudinal Assessment of Bariatric Surgery (Teen-LABS) study who underwent bariatric surgery between 2007 and 2012. Before surgery, blood samples were collected from each patient and tested for levels of seven types of PFAS. Weight, body mass index (BMI) and waist circumference were measured before surgery and six months, one year, three years and five years after surgery.

To analyze the data, the researchers divided patients into three groups based on their blood PFAS concentrations: low, medium and high exposure.

Overall, patients with higher blood levels of PFOS, PFHxS and PFHpS gained more weight and had greater increases in waist circumference one to five years post-surgery, compared to patients with lower blood levels of those chemicals.

To illustrate the difference for PFOS, adolescents with a starting body weight of 328 pounds in the low exposure group regained, on average, 35.9 pounds by the five-year mark. But teens of the same weight with the highest PFOS exposure regained, on average, 46.9 pounds.

For PFHpS, teens in the highest exposure group regained weight at an average of 4.3% of their baseline bodyweight per year, compared to 2.7% per year in the lowest exposure group.

Better patient care

The study adds to the mounting evidence supporting stronger regulation of PFAS, the researchers said—particularly in public water supplies, which are the greatest source of exposure.

It also points to potential strategies for improving patient care. Bariatric surgery is costly and invasive, so understanding who is likely to benefit can help providers make informed decisions about the best course of treatment for each individual.

“We’re also interested in exploring whether PFAS exposure is relevant to other weight loss interventions," including GLP-1 medications such as Ozempic, Baumert said.

In addition, Baumert and her team are investigating the link between PFAS and long-term metabolic health. For example: How might PFAS exposure relate to a person’s risk for type 2 diabetes and metabolic dysfunction-associated steatotic liver disease (MASLD)? The researchers are now analyzing those connections using data from the Teen-LABS study.

About this research

In addition to Baumert, the study’s other authors are Elizabeth Costello, Zhenjiang Li, Sarah Rock, Sandrah P. Eckel, Max T. Aung, Rob McConnell, David V. Conti and Lida Chatzi from the Department of Population and Public Health Sciences, Keck School of Medicine of USC, University of Southern California; Justin R. Ryder and Thomas Inge from Lurie Children’s Hospital of Chicago and Northwestern University Feinberg School of Medicine; Todd Jenkins and Stavra A. Xanthakos from Cincinnati Children’s Hospital Medical Center; Stephanie Sisley from Baylor College of Medicine; Douglas I. Walker from the Rollins School of Public Health, Emory University; Nikos Stratakis from the Barcelona Institute for Global Health, Barcelona, Spain; Damaskini Valvi from the Icahn School of Medicine at Mount Sinai; Scott M. Bartell from the Department of Environmental Medicine and Climate Science, University of California, Irvine; Angela L. Slitt from the College of Pharmacy, University of Rhode Island; Rohit Kohli from the Division of Gastroenterology, Hepatology and Nutrition, Children’s Hospital Los Angeles; and Michele A. La Merrill from the Department of Environmental Toxicology, University of California, Davis.

This work was supported by the National Institute of Environmental Health Sciences of the National Institutes of Health (R01ES030691, R01ES029944, R01ES030364, U01HG013288, P30ES007048, R01ES030691, R01ES030364, T32-ES013678, U01HG013288, P30ES007048, U01HG013288, R01ES033688, R21ES035148, P30ES023515, R01ES030691, U2CES030859, R01ES032831, P30ES007048, P2C ES033433, R01ES030364, R21ES029681, R01ES030691, R01ES030364, R01ES029944, P01CA196569, P30ES007048, R01DK128117−01A1 and P42ES027706].

Journal

Obesity

DOI

10.1002/oby.70009

Method of Research

Meta-analysis

Subject of Research

People

Article Title

PFAS Exposure and Postoperative Weight Regain in Adolescents After Bariatric Surgery: Findings from the Teen-LABS Study

Article Publication Date

14-Aug-2025

Can extremely high-temperature weather forecast oil prices?

Higher Education Press

Fig 1 — image:
Framework of our model.
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Credit: Donglan ZHA , Shuo ZHANG , Yang CAO

1 Introduction

As crude oil becomes increasingly influenced by market dynamics, fluctuations in its price have a significant effect on the global economic and financial landscape (Naser, 2016). Accurate forecasts of crude oil prices play a crucial role in providing scientific support for energy-intensive enterprises and helping investors optimize their portfolios to effectively manage risks (Tian et al., 2023; Zhang and Wang, 2022).

To develop precise projections, it is essential to uncover the underlying factors driving fluctuations in crude oil prices. Previous research has indicated that the long-term trend of oil prices is determined by market supply and demand fundamentals (Dées et al., 2007; Hamilton, 2009), whereas short-term volatility may be affected by external factors such as stock performance (Bouri et al., 2022), exchange rates (Sun et al., 2022), and investor sentiment (Dai et al., 2022a). The increasing occurrence of extreme weather events in recent years has increased the vulnerability of oil products to climate-related risks (Cruz and Krausmann, 2013; Wen et al., 2021; Tumala et al., 2023), including the impact of global warming caused by the greenhouse effect (Kweku et al., 2018; Zhang et al., 2024). The demand for crude oil and other fossil fuels tends to rise during periods of extreme hot weather (van Ruijven et al., 2019). Moreover, elevated temperatures can disrupt operations at drilling and refinery sites (Yalew et al., 2020; Qui et al., 2023) and pose challenges to the integrity of oil transportation infrastructure, such as pipelines (Izaguirre et al., 2021), potentially affecting the oil supply. Consequently, stakeholders in oil markets must consider extremely high temperatures when assessing market conditions and making pricing decisions. However, existing research on crude oil forecasting has not adequately considered extremely high weather conditions. Although recent studies have highlighted the importance of extreme weather information in predicting crude oil prices (Xu et al., 2023), their reliance on media reports introduces subjective bias. Microscale meteorological observations have the potential to provide oil market managers with precise weather information (Katopodis and Sfetsos, 2019). Our objective is to contribute additional empirical evidence regarding the relationship between extreme weather events and oil price forecasts by utilizing precise meteorological data from storage and supply locations in specific target oil markets.

There are two primary types of models used for forecasting oil prices. The first type consists of traditional statistical models, such as exponential smoothing (ES) (Azevedo and Campos, 2016; He, 2018), the autoregressive integrated moving average (ARIMA) (Xiang and Zhuang, 2013; Zhao and Wang, 2014), and vector autoregression (VAR) models (Baumeister and Kilian, 2014). However, these models face challenges in capturing the inherent nonlinearity in oil price dynamics (Gao and Lei, 2017). The second category of methodologies comprises emerging machine learning models (Zhao et al., 2017), which are primarily represented by support vector regression (SVR) (Wang et al., 2020), recurrent neural networks (RNNs) (Chaitanya Lahari et al., 2018), and long short-term memory (LSTM) models (Güleryüz and Özden, 2020). These models have advantages in characterizing the nonlinear relationship between influencing factors and crude oil prices, and they offer effective forecasting accuracy (Öztunç Kaymak and Kaymak, 2022). However, machine learning models are commonly perceived as enigmatic black boxes, as they present challenges in providing users with a comprehensive understanding of their predictive mechanisms.

The advent of explainable machine learning methods has offered a valuable tool for elucidating how factors drive predictive outcomes, and their application has expanded into research fields such as forecasting bitcoin prices (Goodell et al., 2023) and energy consumption (Aras and Hanifi Van, 2022). Nevertheless, these explainable methods have not yet been applied in crude oil price forecasting research.

Considering the aforementioned limitations, this research makes three significant contributions to the current literature. First, we develop an extremely high-temperature weather index (HTI) based on daily meteorological data specific to the crude oil production and storage sites of the China International Energy Exchange (INE). Unlike previous indices that broadly describe the frequency of extreme weather events on a global or national scale (Guo et al., 2023a) or those derived from textual reports on extreme weather attention (Xu et al., 2023), our HTI provides a finer scale to present extreme high-temperature weather information for INE crude oil price prediction.

Second, our study confirms that including the HTI as a predictive factor enhances the accuracy of INE crude oil futures price prediction in terms of errors and trend changes. In fact, the out-of-sample predictive contribution of the HTI even surpasses several common indicators, such as the stock market index, in most instances. As the HTI value increases, a corresponding rise in the predicted INE crude oil futures price is observed. Third, we introduce explainable methods to improve the credibility of machine learning models in the crude oil prediction process, overcoming the inherent deficiencies of previous black box models. This enables us to gain deeper insights into the correlation between varying degrees of extreme heat and crude oil price dynamics. The remainder of the paper is organized as follows: Section 2 introduces the methodology. Section 3 describes the data. Section 4 presents our results. Finally, Section 5 provides concluding remarks.

See the article:

Can extremely high-temperature weather forecast oil prices?

https://doi.org/10.1007/s42524-025-4075-5

Journal

Frontiers of Engineering Management

DOI

10.1007/s42524-025-4075-5

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Can extremely high-temperature weather forecast oil prices?

Study finds traffic noise linked to depression and anxiety in young adults

University of Oulu, Finland

A new LongITools study, published in Environmental Research, has linked noise levels to depression and anxiety diagnoses. It is the first study of its kind to investigate long-term exposure to traffic noise and mental health in children, adolescents, and young adults.

The study, led by the University of Oulu, found that the risk of mental health disorders increases sharply when traffic noise at the home address is over 53 decibels (dB), the safe level recommended by the World Health Organization. This paper supports policy and planning actions designed to reduce traffic noise exposure, including placing bedrooms on quiet sides of buildings and lowering speed limits.

Background to the study

Environmental noise caused by various sources, including road, rail, airport traffic and construction, is the second biggest environmental health concern in Europe. Noise may affect human health in a variety of ways, including damage to the auditory system, sleep disturbance and additional cognitive and emotional responses, contributing to cardiovascular and neurological diseases. Recent evidence shows associations with mental disorders. However, the quality of available research evidence remains low.

Study methodology

We analysed data from 114,353 individuals born in Finland between 1987 and 1998, living in the Helsinki metropolitan area in 2007, using information from available Finnish registers. Researchers followed the individuals’ data for up to ten years from age 8 to 21, tracking how their health evolved.

To work out their exposure to high noise levels, annual average road and railway traffic noise was modelled at their residential address. This was then cross-referenced with those who had a diagnosis of depression and /or anxiety. By combining this data, researchers were able to build a picture of both the levels of noise an individual was exposed to and their evolving mental health.

Study findings

The World Health Organization sets 53 dB ‘Lden’ (the average sound level over a 24-hour period) as the recommended upper limit for traffic noise. This study confirmed that, at this level or above, there is a distinct increase in the risk of developing depression and anxiety in a young population.

“Our analysis showed that anxiety risk is lowest when traffic noise is around 45 to 50 dB at the quieter side of dwellings but increases significantly after 53 to 55 dB. Above 53 dB, noise becomes a significant psychological stressor for young people regardless of whether an individual sleeps on the quieter or noisier side of a dwelling," says Dr Anna Pulakka, senior author of the study.

The association with anxiety was stronger in males and individuals whose parents did not have mental health disorders.

Study recommendations

Yiyan He, the lead author of the study suggests: "Our findings support further actions towards reducing traffic noise exposure. For policymakers and urban planners, this should include measures such as ensuring bedrooms are on the quieter side of the dwelling and ensuring there is green space nearby. For transport, quieter tyres or reduced speed limits should also be considered."

This study demonstrates one of the many ways our exposome and the environment can affect our health.

Paper: Yiyan He, Sylvain Sebert, Anna Pulakka et al. Residential exposure to traffic noise and incidence of depression and anxiety from childhood through adulthood: a Finnish register study Environmental Research, Volume 285, Part 2, 2025.

Traffic noise linked to depression and anxiety in young adults - LongITools LongITools

Journal

Environmental Research

DOI

10.1016/j.envres.2025.122443

Article Title

Residential exposure to traffic noise and incidence of depression and anxiety from childhood through adulthood: a Finnish register study

Geological risk prediction under uncertainty in tunnel excavation using online learning and hidden Markov model

Higher Education Press

Geological Risk Forecasting Using an Online Hidden Markov Model — image:
Sparse early-stage data limits accurate geological risk assessment, increasing the chance of undetected hazards ahead of the TBM. By integrating borehole-derived information through an observation extension mechanism, the OHMM mitigates prediction uncertainty. Continuous online updating improves forward accuracy, enabling earlier hazard alerts. The trade-off lies in maximizing early-stage predictive resolution while minimizing dependence on extensive historical data.
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Credit: Limao ZHANG1 , Ying WANG2 , Xianlei FU2 , Xieqing SONG1 , Penghui LIN2

Geological hazards such as collapses, water ingress, and landslides pose serious threats to tunnel construction, potentially leading to delays, cost overruns, and safety incidents. The challenge is compounded in the early stages of excavation, when detailed subsurface information is limited and conventional geological survey methods—whether invasive drilling or non-invasive geophysical techniques—offer only partial, sometimes inconsistent, insights.

A research team from Huazhong University of Science and Technology and Nanyang Technological University has developed a novel online hidden Markov model (OHMM) to tackle this uncertainty. By merging online learning capabilities with the probabilistic framework of hidden Markov models, the OHMM can continuously update its geological risk predictions as new in situ observations arrive, without waiting for lengthy data collection cycles.

One of the major innovations of the approach is an observation extension mechanism designed for the data-scarce early excavation stage. This mechanism integrates pre-construction borehole samples—often the only reliable geological data available before tunneling begins—into the OHMM framework. By intelligently extending short observation sequences to the length of a complete excavation dataset, the model preserves predictive accuracy even when historical data is minimal.

The researchers validated the method in a tunnel excavation project in Singapore, where geological conditions vary along the alignment. The OHMM produced high-resolution, ring-by-ring forecasts of geological risks ahead of the tunnel boring machine (TBM). Compared with traditional approaches—including standard hidden Markov models, neural networks, long short-term memory networks, and support vector machines—the OHMM consistently delivered superior accuracy, especially for forward predictions into yet-to-be-excavated regions.

The study revealed key operational benefits. First, the continuous model updating allowed for timely adaptation to changing geological patterns, enabling earlier hazard alerts. Second, by maximizing the utility of sparse borehole data, the method bridged the information gap between pre-construction investigations and ongoing excavation. Finally, the framework’s ability to operate effectively with minimal historical data made it particularly valuable for early-stage risk prediction, where proactive measures can prevent costly and dangerous incidents.

Beyond immediate tunneling applications, the researchers note that the OHMM framework could be extended to other infrastructure projects where geological uncertainty is high and observational data arrives incrementally—such as mining operations, slope stability monitoring, and underground storage development. The integration of machine learning with probabilistic risk modeling also opens the door for future enhancements, including coupling with real-time sensor networks and incorporating physical process models for improved interpretability.

By providing a dynamic, data-efficient, and forward-looking tool for geological risk prediction, the OHMM offers engineers and project managers a new way to safeguard tunneling operations, optimize construction schedules, and improve safety outcomes in complex subsurface environments.

See the article:

Geological risk prediction under uncertainty in tunnel excavation using online learning and hidden Markov model

https://doi.org/10.1007/s42524-024-0082-1

Journal

Frontiers of Engineering Management

DOI

10.1007/s42524-024-0082-1

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Geological risk prediction under uncertainty in tunnel excavation using online learning and hidden Markov model

Don’t throw away those Cannabis leaves – they're packed with rare compounds

Researchers from Stellenbosch University found first evidence of rare phenolic compounds in Cannabis leaves

Stellenbosch University

Chromatographic image of Cannabis compounds — image:
Two-dimensional liquid chromatographic separation of phenolics from *Cannabis* leaf and bud material, with each peak indicating a distinct compound present in the sample. An example structure of the newly discovered flavoalkaloids is shown on the top left.
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Credit: Magriet Muller

Analytical chemists from Stellenbosch University (SU) have provided the first evidence of a rare class of phenolics, called flavoalkaloids, in Cannabis leaves.

Phenolic compounds, especially flavonoids, are well-known and sought after in the pharmaceutical industry because of their antioxidant, anti-inflammatory, and anti-carcinogenic properties.

The researchers identified 79 phenolic compounds in three strains of Cannabis grown commercially in South Africa, of which 25 were reported for the first time in Cannabis. Sixteen of these compounds were tentatively identified as flavoalkaloids. Interestingly, the flavoalkaloids were mainly found in the leaves of only one of the strains. The results were published in the Journal of Chromatography A recently.

Dr Magriet Muller, an analytical chemist in the LC-MS laboratory of the Central Analytical Facility (CAF) at Stellenbosch University and first author on the paper, says the analysis of plant phenolics is challenging due to their low concentration and extreme structural diversity.

“Most plants contain highly complex mixtures of phenolic compounds, and while flavonoids occur widely in the plant kingdom, the flavoalkaloids are very rare in nature,” she explains.

“We know that Cannabis is extremely complex – it contains more than 750 metabolites – but we did not expect such high variation in phenolic profiles between only three strains, nor to detect so many compounds for the first time in the species. Especially the first evidence of flavoalkaloids in Cannabis was very exciting.”

For her postgraduate studies in SU’s Department of Chemistry and Polymer Science, she developed powerful analytical methods combining comprehensive two-dimensional liquid chromatography and high-resolution mass spectrometry for the detailed characterisation of phenolic compounds.

“We were looking for a new application for the methods that I developed, after successfully testing them on rooibos tea, grapes and wine. I then decided to apply the methods to Cannabis because I knew it was a complex sample, and that Cannabis phenolics have not been well characterised,” she explains.

According to Prof. André de Villiers, her study leader and main author on the paper, he was blown away by the chromatographic results that Muller obtained: “The excellent performance of two-dimensional liquid chromatography allowed separation of the flavoalkaloids from the much more abundant flavonoids, which is why we were able to detect these rare compounds for the first time in Cannabis”. He leads the analytical chemistry research group in SU’s Department of Chemistry and Polymer Science.

Prof. De Villiers says it is obvious there is still much to gain from studying Cannabis, as the bulk of research in this field to date has been focused on the pharmacological properties of the mood-effecting cannabinoids.

“Our analysis again highlights the medicinal potential of Cannabis plant material, currently regarded as waste. Cannabis exhibits a rich and unique non-cannabinoid phenolic profile, which could be relevant from a biomedical research perspective,” he concludes.

Dr Magriet Muller in front of a high-resolution mass spectrometer at the LC-MS laboratory in Stellenbosch University’s Central Analytical Facility, where part of the practical work was conducted.

Credit

Wiida Fourie-Basson

Dr Magriet Muller on methods used [AUDIO] |

This is a short audio clip on how Dr Magriet Muller came about to test the methods she developed on Cannabis.

Dr Magriet Muller on finding the compounds in Cannabis [AUDIO

Short audio clip on the significance of finding so many, and such rare, compounds in Cannabis leaves.

Journal

Journal of Chromatography A

DOI

10.1016/j.chroma.2025.466023

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Comprehensive two-dimensional liquid chromatographic analysis of Cannabis phenolics and first evidence of flavoalkaloids in Cannabis.

Article Publication Date

2-Aug-2025