Project Description

Background and rationale: Oil palm represents nearly 40% of all global vegetable oil production. Its demand has been increasing dramatically worldwide. However, concerns exist surrounding the environmental and socio-economic sustainability of oil palm production, arising from widespread deforestation across Indonesia and Malaysia to satisfy consumer demand from western countries driven by its capacity to replace costly alternatives in processed foods and cosmetics. The social, environmental and biodiversity concerns of oil palm production are exacerbated by the expansion of new plantations on tropical peatlands, which play a vital role as carbon sinks and defence against floods and droughts. The main challenges for sustainability of oil palm cultivation are to identify and prevent planting on newly deforested land, especially peatlands.

The conversion of newly deforested tropical forests and any forested peatlands to oil palm plantations contravenes the criteria and principles of the Roundtable on Sustainable Palm Oil (RSPO). Compensation liabilities have emerged for RSPO members that breach those principles, which has spurred a demand for new methods of authenticating sustainable oil palm. However, there is no current analytical framework capable of distinguishing oil palm based on its geographical origin, despite the prevalence of such methods for other food and drink products such as wine, olive oil and tea. These applications are based on the empirical observation that chemical concentrations in plant tissues display a signal indicative of geographic origin, but the underlying mechanisms linking soil and plant tissue chemistry have not been verified at scales suitable for widespread adoption of these methods. The aim of this project is to develop understanding of the mechanistic linkage between soil and plant tissue chemistry across environmental gradients that contribute to soil formation. This understanding will be applied to develop a novel method that could determine the geographical origin of processed palm oil to differentiate end-products derived from newly deforested lands vs second rotation or older plantations, and between end-products from oil grown on peat vs mineral soils. The guiding hypothesis is that the origin of a plant-derived product can be detected as a unique chemical signature in the plant material in terms of elemental or stable isotope composition, and addition to its metabolomics profile, including both polar metabolome and lipidome. This will be tested through precise analyses of dried leaves, oil and processed end-products. Working with a network of established oil palm growers, this project will aim to develop a method for precise authentication of the geographical origin of their products and verification of claims to conformity to RSPO sustainability principles.

Aims and objectives: To enhance the protection of biodiversity in commercial agroecosystems by developing an authentication and tracking system for oil palm grown under the sustainability principles of the RSPO. The specific objectives are as follows.

  1. To develop a statistically robust sampling design for soil, leaf tissue, crude palm and kernel oil derived from plantations of commercial oil palm growers linked to the RSPO across Southeast Asia.
  2. To derive fine-scale maps of multi-elemental soil chemistry in relation to underlying parent material and hydrological drivers within representative oil palm landscapes.
  3. To measure stable isotope ratios, multi-elemental concentrations and metabolomic profiles of oil palm tissues across boundaries defined by underlying geology and hydrology to build mechanistic understanding of the relationship between soil and plant tissue chemistry at scales relevant to determination of geographical origin.
  4. To develop and validate a multivariate analytical framework to test the relationships among plant chemical fingerprint data, soil chemistry and geographic origin and identify appropriate chemical markers that can distinguish specific RSPO sustainability criteria in crude oil palm and processed end-products.

Eligibility: Candidates should have (or expect to achieve) a minimum of a 2.1 Honours and/or MSc degree in a relevant Science subject.

Desirable skills: Interest in earth and agricultural sciences, lab experience including instrumental analysis and method development, background in statistical analysis, fluency or willingness to learn a programming language.

Training: As well as training required for the Quadrat DTP, the student will gain a range of critical skills, including (i) an advanced conceptual understanding of soil science, environmental chemistry, climate change and conservation sciences, (ii) experience in liaising with overseas stakeholders of global importance, and (iii) expertise in lipid analysis and omics, stable isotopes, and trace elemental analysis in tandem with multivariate modelling, including untargeted analysis and big data processing.

Supervision: The strength of the supervisory team is its interdisciplinary expertise. The principal supervisor Dr Koidis (QUB) has extensive experience in vegetable oil analysis, especially developing analytical methods for palm oil detection in complex food systems and multivariate chemometric modelling using machine learning algorithms. Prof Burslem (Aberdeen) is a tropical plant scientist who has worked in Southeast Asia for 30 years, including extensive experience in transitional agro-ecological landscapes and recent work on authentication of commercial plant products (second supervisor). Additional specialist supervision in soil science relevant to mapping soil chemistry is available in the School of Biological Sciences at Aberdeen. Dr Williams (QUB) is an analytical chemist, specialising in trace metal analysis. Prior to joining QUB he was an Associate Professor at the University of Nottingham/Crops for the Future Research Centre (Malaysia). The project includes external collaboration with Prof Yit-Arn Teh (Newcastle), the Southeast Asia Rainforest Research Partnership (SEARRP) and the RSPO.

Partner organisations and field locations: This project will be conducted in partnership with the SEARRP and the RSPO using field sites within oil palm plantations across Southeast Asia. The RSPO is a non-profit membership organisation of commercial oil palm growers supporting the move towards greater sustainability of oil palm through the development of an industry-wide certification standard. In order to be certified, growers have to commit to a set of environmental and social principles and criteria, which includes avoidance of new planting on recently deforested land and on organic soils. Currently the verification of conformity is conducted using a records-based system of supply chain checks (PalmTrace). However, this system currently lacks any mechanism of external authentication of the origin of the oil palm in certified products. The student will develop these techniques for oil palm with the support of RSPO and its member companies.

Funding and eligibility information available here.

Supervisors

Tassos Koidis

Primary Supervisor:

Profile: Tassos Koidis
Email: t.koidis@qub.ac.uk
Institution: Queen's University, Belfast
Department/School: School of Biological Sciences

David Burslem

Secondary Supervisor:

Profile: David Burslem
Email: d.burslem@abdn.ac.uk
Institution: University of Aberdeen
Department/School: School of Biological Sciences

Paul N. Williams

Additional Supervisor:

Profile: Paul N. Williams
Email: p.williams@qub.ac.uk
Institution: Queen's University, Belfast
Department/School: School of Biological Sciences

Additional Supervisor:

Professor Yit Arn Teh, Professor in Soil Science

School of Natural and Environmental Sciences, Newcastle University

yitarn.teh@ncl.ac.uk

References

Absalome, M. A., Massara, C. C., Alexandre, A. A., Gervais, K., Chantal, G., Ferdinand, D.., … & Marion, M. (2020). Biochemical properties, nutritional values, health benefits and sustanaibility of palm oil. Biochimie. 2020.

Afriyanti, D., Kroeze, C., & Saad, A. (2016). Indonesia palm oil production without deforestation and peat conversion by 2050. Science of the Total Environment, 557, 562-570.
Alaswad, F., Mohamat-Yusuff, F., Ismail, A., Kusin, F. M., Zulkifli, S. Z., & Awang, M. Tracing carbon and nitrogen fluxes in soil of log-over forest and highly degraded area of oil palm plantations using stable isotope analysis.

Danezis, G. P., Tsagkaris, A. S., Camin, F., Brusic, V., & Georgiou, C. A. (2016). Food authentication: techniques, trends and emerging approaches. Trends in Analytical Chemistry, 85, 123–132. doi: 10.1016/j.trac.2016.02.026.

Diaz-Chito, K., Georgouli, K., Koidis, A., & del Rincon, J. M. (2017). Incremental model learning for spectroscopy-based food analysis. Chemometrics and Intelligent Laboratory Systems, 167, 123-131.

Georgouli, K., Del Rincon, J. M., & Koidis, A. (2017). Continuous statistical modelling for rapid detection of adulteration of extra virgin olive oil using mid infrared and Raman spectroscopic data. Food Chemistry, 217, 735-742.

Hori, K., Koh, F. H., & Tsumura, K. (2019). A metabolomics approach using LC TOF-MS to evaluate oxidation levels of edible oils. Food Analytical Methods, 12(8), 1799-1804.

Ma, G., Zhang, Y., Zhang, J., Wang, G., Chen, L., Zhang, M., Liu, T., Liu, X., & Lu, C. (2016) Determining the geographical origin of Chinese green tea by linear discriminant analysis of trace metals and rare earth elements: taking Dongting Biluochun as an example Food Control, 59, 714-720.

Osorio, M. T., Haughey, S. A., Elliott, C. T., & Koidis, A. (2014). Evaluation of methodologies to determine vegetable oil species present in oil mixtures: Proposition of an approach to meet the EU legislation demands for correct vegetable oils labelling. Food Research International, 60, 66-75.

Othman, R., & Ameer, R. (2010). Environmental disclosures of palm oil plantation companies in Malaysia: a tool for stakeholder engagement. Corporate Social Responsibility and Environmental Management, 17(1), 52-62.

Ramli, U. S., Tahir, N. I., Rozali, N. L., Othman, A., Muhammad, N. H., Muhammad, S. A., … & Manaf, M. A. A. (2020). Sustainable Palm Oil—The Role of Screening and Advanced Analytical Techniques for Geographical Traceability and Authenticity Verification. Molecules, 25(12), 2927.

Tan, K. T., Lee, K. T., Mohamed, A. R., & Bhatia, S. (2009). Palm oil: addressing issues and towards sustainable development. Renewable and sustainable energy reviews, 13(2), 420-427.

Vargas, L. H. G., Neto, J. C. R., de Aquino Ribeiro, J. A., Ricci-Silva, M. E., Souza, M. T., Rodrigues, C. M., … & Abdelnur, P. V. (2016). Metabolomics analysis of oil palm (Elaeis guineensis) leaf: evaluation of sample preparation steps using UHPLC–MS/MS. Metabolomics, 12(10), 153.

Research Methods

There are three types of research methods that are likely to be used in this PhD project:

  1. Sample acquisition for fine-scale mapping: Devising a statistically robust sampling design (with detailed geographic data, reliable plant growth history and authentic correctly sampled plant and soil material) and effectively liaising with the external organisations is fundamental for this project. Soil sampling will be conducted across geological and hydrological gradients that contribute to soil formation within representative landscapes where oil palm is grown.
  2. Laboratory Analysis: The three types of material (soil, leaf, crude/refined oil) will be analysed. Conventional soil analysis (pH, cation exchange capacity, organic matter) and mineral and trace metal analysis will be performed using wet chemistry and Inductively coupled plasma mass spectrometry (ICP-MS) respectively. Leaf analysis will focus on mineral and trace elements. Crude and refined oil samples will be conventionally analysed for the determination of minor (e.g. tocopherols) and major components (Fatty Acids) as well as in untargeted mode scanning the polar and non-polar metabolome using Liquid Chromatography Time-of-Flight Mass Spectrometry (LC-QTOF-MS). The acquisition of high resolution full scanned fingerprints permits the combination of target analysis with screening of non-target compounds, novel compound identification, and retrospective data analysis. ICP-MS will also be performed on the oil samples. In all types of samples, stable isotope ratio analysis (SIRA) will be employed to determine the isotope ratios of 2H/1H, 13C/12C and 18O/16O, using mass spectrometry (IR-MS).
  3. Multivariate data Analysis: Chemometrics provide a wealth of techniques for both exploratory analysis of multivariate data as well as classification strategies to predict quantitative and qualitative responses based on the experimental profiles of the samples. Exploratory dimensionality reduction techniques (Principal Component Analysis), regression (Partial Least Squares regression), discriminant methods (Linear Discriminant Analysis, Partial Least Squares Discriminant Analysis and Class-modelling methods will be explored.

Expected Training Provision

The training for this PhD studentship has four main components:

  1. Sampling design and fieldwork for mapping multi-elemental profiles across the soil-plant system: Training in the theoretical principles and practical techniques required for field sampling of plant and soil material for fine-scale mapping of elemental concentrations. This will require balancing considerations such as sample size, quantity and accessibility of sampling locations, and engagement with logistical issues such as research access, export and import permits, and maintenance of correct biosecurity protocols including storage and disposal.
  1. Laboratory analyses: The student will obtain training in all the laboratory protocols required for the analytical component of this project, including extraction and conventional lipid analysis, stable isotope analyses using isotope ratio mass spectrometry (IR-MS), multi-elemental analysis using ICP-MS, analysis of the polar metabolome and the lipidome using LC-TOF-MS and MS-MS platforms for all the types of samples. The student will also gain familiarity with the process of method validation against international standards such as ISO.
  1. Data analysis and chemometrics (“big data” analysis): This project will generate large multivariate data-sets with high dimensionality, because the resolution of geographical tracking of plant material improves with the number of elements, metabolites and other chemical parameters analysed. These multivariate data-sets require analysis using specialised techniques commonly known as chemometrics. In particular, various linear and non-linear exploratory, regression and classification techniques will be performed in order to identify the set of chemical markers responsible for the geographical origin and plant growth information.
  1. Complementary training in transferable skills: Training in core scientific skills will be provided through the Quadrat DTP, including presentation skills, time management, team-working, communication skills and paper writing. Since this studentship involves direct interaction with commercial oil palm growers, and an industry body (the Roundtable on Sustainable Palm Oil), there will be opportunities for the student to obtain commercial and business awareness training through an internship with one of the organisations engaged with the project.

Impact

The evidence underpinning the development of chemical methods for food product authentication is very descriptive, lacking a clear basis in the mechanisms that determine the response of plant tissue chemistry to underlying geographic variation in soil properties. Although this project is focussed on a single plant species, the detailed insights gained from fine-scale mapping of elemental concentrations across the soil-plant system will have important implications for understanding of abiotic controls on the distribution of resources important for plant productivity and their animal consumers. Moreover, palm oil is the most significant vegetable oil in global trade. Intense demand has driven widespread deforestation and inappropriate planting on fragile peatlands across the Southeast Asia region. A majority of the large multi-national growers are members of the RSPO, which has established a set of principles and criteria underpinning a certification system for sustainable palm oil. The aim is to provide consumers with confidence that labelled products have been sourced from sustainably managed plantations, and not from plantations grown on deforested lands or peatlands. Certification is conducted at the level of the mill/ refinery. Since 2014, the largest refiners have adopted stricter policies over their supply chain. However, unregistered and non-certified product continues to leak into the market from non-cooperating refiners during the blending and processing stages. To overcome this problem the industry, is attempting greater segregation of certified oil palm across the supply chain. This will only work effectively if there is an effective mechanism to authenticate the origin of the certified product. No other parties are currently working on chemical methods for authentication of oil palm and industry contacts suggest that such a test would be “game-changing”. A validated test would substantially enhance industry-led objectives to reduce tropical deforestation and protect biodiversity, whilst balancing the need to sustain livelihoods and food security.

Proposed Supervision

This project will be jointly supervised by Dr Koidis (principal supervisor) from the Queen’s University Belfast and Prof Burslem (second supervisor) from Aberdeen. Dr Paul Williams will act as additional supervisor from Queen’s and will the support the student and the project accordingly.

Collaborative partners will be engaged to provide additional advice and logistic support.  For both the student and the members of staff involved, the roles and responsibilities of the respective supervisors will be clarified at the start. Dr Koidis will lead a supervisory team but all members of the team are responsible for working together in providing supervision. The team is carefully assembled in order to exploit complementary skills (wet analysis, method development, validation, modelling), knowledge (chemistry/geochemistry, biology/ecology etc) and external networks (SEARRP, DEFRA, RSPO etc.). Especially important is that the supervisory team has key contacts within all the major stakeholders of the research and they will be involved in an advisory role in the project. This will also facilitate the specific arrangements with regards to the sample plan and fieldwork, which is key to project’s success.

The student will be based in Belfast but various research stays and fieldwork are planned in Aberdeen and in external partner organisations and adjacent sampling sites. The main analytical work will be divided between the two main institutions (Queens: oil/soil/leaf analysis using conventional and metabolomic analysis; Aberdeen: mineral and trace metal analysis). Fieldwork in Malaysia and Indonesia will take place in the first two years of the PhD (2022-2023 and 2023-2024) in prior arrangement with our external partners. The sampling plan will be developed and approved by all parties before fieldwork commencement.

The student and supervisors will agree the frequency, duration and format of their formal meetings, the topics to be covered, and keep them under review thereafter. As a guideline, these should normally be scheduled for at least twice a month and supervisor’s time spend on the project will be not less than 2h/week. The meetings will take place either in person or online remotely. It may be necessary to change the frequency depending on progress and performance. The supervisory team will ensure good communication and clear reporting lines, including keeping agreed records of supervisory meetings. The supervisors will regularly review (every 3 months) training needs with the student, including in relation to personal and professional development.

Monitoring of progress will be decided in advance in discussion with the student, and in accordance with the Code of Practice on Research Students in both academic institutions. QUB requires the at least 6 formal meetings/year to be recorded as a requirement for all PG Research students.

Where possible, supervisors with existing funded research in Southeast Asia will accompany the student during overseas fieldwork, and additional support will be provided by the collaborating partners. Arrangements will be discussed among the supervisory team before and during any period away from campus.

Information provided to research students that is of relevance to their supervisors’ academic and pastoral responsibilities must be copied to the main supervisor.

Proposed Timetable

YEAR 1:

  • Induction, set up of meeting schedules
  • Literature review (2-3 months as required). Drafting of research proposal.
  • Training in the analytical methods. Development and adaptation of the analytical protocols in the wet lab using controls. Taking online courses if necessary for additional support.
  • Developing the sampling plan for fieldwork work.
  • Fieldwork A (travelling overseas for 1-2 months). Collection of 50-100 samples of each type of plant and soil material (month 9-12)
  • Training on the principles multivariate data analysis method. Practicals using random datasets. Taking online courses.
  • General DTP training and skills development.
  • Presentation at the PGR student symposium.
  • Initial Review and Differentiation (QUB).

YEAR 2:

  • Training related to fieldwork.
  • Conducting analytical work for Fieldwork A samples.
  • Research stay at Aberdeen (1-2 months) for extra analytical work.
  • Familiarising with the R-programming environments.
  • Specific training as required (scientific writing, etc)
  • Preliminary data analysis for Fieldwork A samples using multivariate techniques
  • Fieldwork B (travelling overseas for 1-2 months). Collection of 50-100 samples of each type of plant and soil material.
  • Presentation at the PGR student symposium and/or participation in a national or international conference.

YEAR 3:

  • Fieldwork C (limited sample procurement if necessary).
  • Conducting analytical work for Fieldwork B, C samples.
  • Research stay at Aberdeen (1-2 months) for extra analytical work.
  • Preliminary data analysis for Fieldwork B,C samples using multivariate techniques.
  • Perform the chemometric and modelling work (using all data produced).
  • Drafting Paper #2 and #3 focusing on identifying origin of oil palm.
  • Presentation in PGR student symposium and/or in national/international conference.

YEAR 4:

  • Conducting limited analytical work for remaining fieldwork samples as required.
  • Short research stay at Aberdeen (<1 month) for extra analytical work.
  • Aim to finish lab work early at Year 4.
  • Continue and conclude chemometric and modelling work.
  • Dissertation writing and corrections including remaining papers.
  • Submission of the thesis

QUADRAT Themes

  • biodiversity
  • earth-systems
  • environmental-management

Partners

Non-CASE collaborators: SEARRP and RSPO

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