Authenticity analysis of oregano: development, validation and fitness for use of several food fingerprinting techniques

Abstract

Several analytical techniques, i.e. spectroscopic techniques as Near Infrared (NIR) and Mid-Infrared (MIR), Hyper Spectral Imaging (HSI), Gas Chromatography coupled to Mass Spectrometry (GC–MS) and Proton-transfer Reaction Time-of-Flight Mass spectrometry (PTR-TOF-MS), combined with chemometrics, are examined to evaluate their potential to solve different food authenticity questions on the case of oregano. In total, 102 oregano samples from one harvest season were analyzed for origin and variety assessment, 159 samples for adulteration-assessment and 72 samples for batch-to-batch control. The Gaussian Process Latent Variable Model (GP-LVM) was applied as technique to obtain a reduced two-dimensional space. A Random Forest Regression algorithm was used as regression model for the adulteration assessment. Prediction rates of more than 89% could be achieved for origin assessment. For variety assessment, prediction rates of more than 78% could be obtained. Batch-to-batch control could be successfully performed with NIR and PTR-TOF-MS. Detection of adulteration could be successfully performed from 10% on with HSI, NIR and PTR-TOF-MS.