Untargeted LC–HRMS Metabolomics Reveals Metabolic Changes in Processed Rubber Seed Flour (Hevea brasiliensis)
How to cite (AJARCDE) :
Rubber seeds (Hevea brasiliensis) are underutilized plant by-products with potential for food and feed applications, but cyanogenic glycosides remain an important safety concern. This study investigated processing-associated changes in the metabolite profile of rubber-seed flour using untargeted liquid chromatography–high-resolution mass spectrometry (LC–HRMS). We evaluated four treatments: unprocessed whole seeds (A), soaked and boiled seeds without the seed coat (B), soaked and boiled seeds followed by oil pressing (C), and soaked and boiled seeds with the seed coat retained (D), with three biological replicates per treatment. After spectral-quality assessment, contaminant removal, and plausibility review, 39 putative metabolite annotations were retained for multivariate analysis. Principal component analysis separated the unprocessed samples from the processed groups; PC1 and PC2 explained 35.0% and 19.6% of the variance, respectively. A two-component partial least-squares discriminant analysis model showed Q² = 0.630 and R² = 0.918, but leave-one-out cross-validation classified only 50% of samples correctly, indicating limited predictive performance in this small dataset. The processed groups showed substantial overlap, suggesting that their profiles were more similar to one another than to the unprocessed control. VIP-ranked features included glycerolipids, amino-acid-related compounds, and fatty acids; these were treated as putative annotations rather than definitive identifications. Linamarin was assigned in only one sample and lacked MS/MS confirmation, so its presence and any effect of processing on cyanogenic risk remain unresolved. Overall, soaking and boiling were associated with changes in the measured metabolite profile. Targeted quantification of linamarin and hydrogen cyanide, confirmation of key metabolite annotations with authentic standards and MS/MS, and validation in a larger factorial experiment are required before practical safety or nutritional claims can be made.
Contribution to Sustainable Development Goals (SDGs):
SDG 2- Zero Hunger
SDG 12 - Responsible Consumption and Production
SDG 13 - Climate Action
[1] E. Hossain, M. H. Karim, S. Alam, and S. K. Nath, “Nutritive value of rubber seed (Hevea brasiliensis),” Online Journal of Animal and Feed Research, vol. 5, no. 1, pp. 18–21, 2015.
[2] B. M. Abdullah, J. Salimon, E. Yousif, and N. Salih, “Occurrence of cyanogenic glycoside and cyanide in the Malaysian rubber seed oil,” Journal of the Association of Arab Universities for Basic and Applied Sciences, vol. 14, no. 1, pp. 83–86, 2013, doi: 10.1016/j.jaubas.2012.11.004.
[3] J. Salimon, B. M. Abdullah, and N. Salih, “Rubber (Hevea brasiliensis) seed oil toxicity effect and linamarin compound analysis,” Lipids in Health and Disease, vol. 11, art. no. 74, 2012, doi: 10.1186/1476-511X-11-74.
[4] C. M. Agbai, I. A. Olawuni, C. E. Ofoedu, C. J. Ibeabuchi, C. O. R. Okpala, I. Shorstkii, and M. Korzeniowska, “Changes in anti-nutrient, phytochemical, and micronutrient contents of different processed rubber (Hevea brasiliensis) seed meals,” PeerJ, vol. 9, art. no. e11327, 2021, doi: 10.7717/peerj.11327.
[5] S. S. Daulay, Adelina, and I. Suharman, “Detoxification of hydrogen cyanide acids (HCN) from rubber seed (Hevea brasiliensis Mull. Arg) through some physical treatment as fish feed ingredients,” Jurnal Online Mahasiswa (JOM) Bidang Perikanan dan Ilmu Kelautan, Universitas Riau, 2014.
[6] Neswati and S. D. Ismanto, “Hydrogen cyanide (HCN) detoxification of crude rubber oil seeds using ultrasonic waves and neutralization,” IOP Conference Series: Earth and Environmental Science, vol. 515, art. no. 012013, 2020, doi: 10.1088/1755-1315/515/1/012013.
[7] Y. Xiong, Y. Fang, J. Zhu, X. Xiao, Y. Huang, B. Zhou, C. Wang, Y. Qin, J. Wang, and C. Tang, “Exploring metabolic responses upon harvesting via untargeted metabolomics coupled with machine learning algorithm in rubber tree,” Industrial Crops and Products, 2025, doi: 10.1016/j.indcrop.2025.122300.
[8] A. Windarsih, Suratno, H. D. Warmiko, A. W. Indrianingsih, A. Rohman, and Y. I. Ulumuddin, “Untargeted metabolomics and proteomics approach using liquid chromatography–Orbitrap high-resolution mass spectrometry to detect pork adulteration in Pangasius hypopthalmus meat,” Food Chemistry, vol. 386, art. no. 132856, 2022, doi: 10.1016/j.foodchem.2022.132856.
[9] B. Worley and R. Powers, “Multivariate analysis in metabolomics,” Current Metabolomics, vol. 1, no. 1, pp. 92–107, 2013, doi: 10.2174/2213235X11301010092.
[10] K. Pirttilä, D. Balgoma, J. Rainer, C. Pettersson, M. Hedeland, and C. Brunius, “Comprehensive peak characterization (CPC) in untargeted LC–MS analysis,” Metabolites, vol. 12, no. 2, art. no. 137, 2022, doi: 10.3390/metabo12020137.
[11] R. Wei, J. Wang, M. Su, E. Jia, S. Chen, T. Chen, and Y. Ni, “Missing value imputation approach for mass spectrometry-based metabolomics data,” Scientific Reports, vol. 8, art. no. 663, 2018, doi: 10.1038/s41598-017-19120-0.
[12] Z. Pang, G. Zhou, J. Ewald, L. Chang, O. Hacariz, N. Basu, and J. Xia, “Using MetaboAnalyst 5.0 for LC–HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data,” Nature Protocols, vol. 17, pp. 1735–1761, 2022, doi: 10.1038/s41596-022-00710-w.
[13] J. A. Westerhuis, H. C. J. Hoefsloot, S. Smit, D. J. Vis, A. K. Smilde, E. J. J. van Velzen, J. P. M. van Duijnhoven, and F. A. van Dorsten, “Assessment of PLSDA cross validation,” Metabolomics, vol. 4, pp. 81–89, 2008, doi: 10.1007/s11306-007-0099-6.
[14] A. Lex, N. Gehlenborg, H. Strobelt, R. Vuillemot, and H. Pfister, “UpSet: Visualization of intersecting sets,” IEEE Transactions on Visualization and Computer Graphics, vol. 20, no. 12, pp. 1983–1992, 2014, doi: 10.1109/TVCG.2014.2346248.
[15] J. R. Conway, A. Lex, and N. Gehlenborg, “UpSetR: An R package for the visualization of intersecting sets and their properties,” Bioinformatics, vol. 33, no. 18, pp. 2938–2940, 2017, doi: 10.1093/bioinformatics/btx364.
[16] J. Ivanisevic, H. P. Benton, D. Rinehart, A. Epstein, M. E. Kurczy, M. D. Boska, H. E. Gendelman, G. Siuzdak, “An interactive cluster heat map to visualize and explore multidimensional metabolomic data,” Metabolomics, vol. 11, no. 4, pp. 1029–1034, 2015, doi: 10.1007/s11306-014-0759-2.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.