Physicochemical Profiling and Geographical Origin Discrimination of Nine Virgin Sacha Inchi Oils from Java, Indonesia
Youstiana Dwi Rusita(1), David Fernando(2), Marlyn Dian Laksitorini(3), Purwanto Ratmoyo(4), Abdul Rohman(5*)
(1) Doctoral Program in Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Gadjah Mada, Sekip Utara, Yogyakarta 55281, Indonesia; Department of Pharmaceutical and Food Analysis, Poltekkes Surakarta, Jl. Letjend Sutoyo, Mojosongo, Surakarta 57127, Indonesia
(2) Doctoral Program in Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Gadjah Mada, Sekip Utara, Yogyakarta 55281, Indonesia
(3) Department of Pharmaceutics, Faculty of Pharmacy, Universitas Gadjah Mada, Sekip Utara, Yogyakarta 55281, Indonesia
(4) Department of Pharmaceutical Biology, Faculty of Pharmacy, Universitas Gadjah Mada, Sekip Utara, Yogyakarta 55281, Indonesia
(5) Doctoral Program in Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Gadjah Mada, Sekip Utara, Yogyakarta 55281, Indonesia; Center of Excellence Institute of Halal Industry and Systems, Universitas Gadjah Mada, Sekip Utara, Yogyakarta 55281, Indonesia
(*) Corresponding Author
Abstract
Sacha inchi (Plukenetia volubilis L.) oil (SIO) is valued for its nutritional properties, yet regional variability is underexplored. This study combines physicochemical characterization with discrimination of the geographical origin of virgin SIO samples collected from multiple provinces across Java Island, Indonesia. Conventional quality parameters, including viscosity, density, moisture content, acid value, peroxide value, saponification index, and iodine value, were measured to assess compositional differences. Complementing this, ATR-FTIR spectroscopy was employed to capture spectral fingerprints, which were analyzed using chemometric approaches including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) with three independent replicates per sample. Models successfully classified samples by region, with wavenumber at 2966 cm−1 consistently emerging as a key discriminative marker, highlighting variability in methyl group (C–H stretching) in unsaturated chains. The PLS-DA models achieved classification accuracies of 80–100%, with predictive ability (Q2) scores up to 0.959, confirming strong performance under leave-one-out cross-validation (LOOCV). By integrating compositional profiling with spectral-based classification, this study offers a robust framework for quality assurance, traceability, and regulatory alignment of SIO. Moreover, regional variability should be taken into account when setting quality standards and authentication protocols to ensure accurate labeling and reliable traceability.
Keywords
References
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