Journal
FOOD CHEMISTRY
Volume 293, Issue -, Pages 323-332Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.foodchem.2019.04.073
Keywords
One-class; Random forest; Artificial outliers; Food adulteration; Infrared spectroscopy
Funding
- Instituto Nacional de Ciencia e Tecnologia de Bioanalitica - INCTBio [2014/508673, 465389/2014]
- Conselho Nacional de Desenvolvimento Cientifico e Tecnologico [303994/2017-7]
- Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES, Brazil) [001]
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This paper proposes the use of random forest for adulteration detection purposes, combining the random forest algorithm with the artificial generation of outliers from the authentic samples. This proposal was applied in two food adulteration studies: evening primrose oils using ATR-FTIR spectroscopy and ground nutmeg using NIR diffuse reflectance spectroscopy. The primrose oil was adulterated with soybean, corn and sunflower oils, and the model was validated using these adulterated oils and other different oils, such as rosehip and andiroba, in pure and adulterated forms. The ground nutmeg was adulterated with cumin, commercial monosodium glutamate, soil, roasted coffee husks and wood sawdust. For the primrose oil, the proposed method presented superior performance than PLS-DA and similar performance to SIMCA and for the ground nutmeg, the random forest was superior to PLS-DA and SIMCA. Also, in both applications using the random forest, no sample was excluded from the external validation set.
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