Authentication of the Origin, Variety and Roasting Degree of Coffee Samples by Non-Targeted HPLC-UV Fingerprinting and Chemometrics. Application to the Detection and Quantitation of Adulterated Coffee Samples.
Nerea NúñezXavi ColladoClara MartínezJavier SaurinaOscar NuñezPublished in: Foods (Basel, Switzerland) (2020)
In this work, non-targeted approaches relying on HPLC-UV chromatographic fingerprints were evaluated to address coffee characterization, classification, and authentication by chemometrics. In general, high-performance liquid chromatography with ultraviolet detection (HPLC-UV) fingerprints were good chemical descriptors for the classification of coffee samples by partial least squares regression-discriminant analysis (PLS-DA) according to their country of origin, even for nearby countries such as Vietnam and Cambodia. Good classification was also observed according to the coffee variety (Arabica vs. Robusta) and the coffee roasting degree. Sample classification rates higher than 89.3% and 91.7% were obtained in all the evaluated cases for the PLS-DA calibrations and predictions, respectively. Besides, the coffee adulteration studies carried out by partial least squares regression (PLSR), and based on coffees adulterated with other production regions or variety, demonstrated the good capability of the proposed methodology for the detection and quantitation of the adulterant levels down to 15%. Calibration, cross-validation, and prediction errors below 2.9%, 6.5%, and 8.9%, respectively, were obtained for most of the evaluated cases.
Keyphrases
- high performance liquid chromatography
- simultaneous determination
- ms ms
- tandem mass spectrometry
- solid phase extraction
- mass spectrometry
- machine learning
- liquid chromatography tandem mass spectrometry
- deep learning
- liquid chromatography
- loop mediated isothermal amplification
- label free
- gas chromatography mass spectrometry
- gas chromatography
- real time pcr
- emergency department
- patient safety
- drug delivery
- data analysis