Geographical provenance of palm oil by fatty acid and volatile compound fingerprinting techniques.

Analytical methods are required in addition to administrative controls to verify the geographical origin of vegetable oils such as palm oil in an objective manner. In this study the application of fatty acid and volatile organic compound fingerprinting in combination with chemometrics have been applied to verify the geographical origin of crude palm oil (continental scale). For this purpose 94 crude palm oil samples were collected from South East Asia (55), South America (11) and Africa (28). Partial least squares discriminant analysis (PLS-DA) was used to develop a hierarchical classification model by combining two consecutive binary PLS-DA models. First, a PLS-DA model was built to distinguish South East Asian from non-South East Asian palm oil samples. Then a second model was developed, only for the non-Asian samples, to discriminate African from South American crude palm oil. Models were externally validated by using them to predict the identity of new authentic samples. The fatty acid fingerprinting model revealed three misclassified samples. The volatile compound fingerprinting models showed an 88%, 100% and 100% accuracy for the South East Asian, African and American class, respectively. The verification of the geographical origin of crude palm oil is feasible by fatty acid and volatile compound fingerprinting. Further research is required to further validate the approach and to increase its spatial specificity to country/province scale.

Authentication of organic feed by near-infrared spectroscopy combined with chemometrics: a feasibility study.

Organic products tend to retail at a higher price than their conventional counterparts, which makes them susceptible to fraud. In this study we evaluate the application of near-infrared spectroscopy (NIRS) as a rapid, cost-effective method to verify the organic identity of feed for laying hens. For this purpose a total of 36 organic and 60 conventional feed samples from The Netherlands were measured by NIRS. A binary classification model (organic vs conventional feed) was developed using partial least squares discriminant analysis. Models were developed using five different data preprocessing techniques, which were externally validated by a stratified random resampling strategy using 1000 realizations. Spectral regions related to the protein and fat content were among the most important ones for the classification model. The models based on data preprocessed using direct orthogonal signal correction (DOSC), standard normal variate (SNV), and first and second derivatives provided the most successful results in terms of median sensitivity (0.91 in external validation) and median specificity (1.00 for external validation of SNV models and 0.94 for DOSC and first and second derivative models). A previously developed model, which was based on fatty acid fingerprinting of the same set of feed samples, provided a higher sensitivity (1.00). This shows that the NIRS-based approach provides a rapid and low-cost screening tool, whereas the fatty acid fingerprinting model can be used for further confirmation of the organic identity of feed samples for laying hens. These methods provide additional assurance to the administrative controls currently conducted in the organic feed sector.