Verification of authenticity is a crucial aspect of food quality control, and also important to regulatory organizations. In this study, two wines of known pure varietal along with some commercial wines were examined using a mass spectrometry based chemical sensor. The fast analysis times obtained using this instrument makes this technology ideal for detection of adulteration.

Multivariate statistics were used to create models that discriminate between wine varieties. Exploratory analysis such as principal component analysis (PCA) and hierarchical cluster analysis (HCA) indicated the viability of the data set for classifi cation models. Soft-independent-modeling-of-class-analogy (SIMCA) and K Nearest Neighbors (KNN) were used to create two classification models.

Both SIMCA and KNN provided a quick identification of unknown samples. Overall, the fast identification of wine varieties demonstrates the usefulness of the MS chemical sensor in detecting samples with close chemical composition.

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GERSTEL Headspace ChemSensor System

GERSTEL Headspace ChemSensor System

Unlike traditional electronic noses that are based on solid-state sensors, the GERSTEL ChemSensor System use proven quadrupole mass-spectrometer technology.

Benefits:
  • The GERSTEL Headspace ChemSensor System is unaffected by moisture in the sample, ambient humidity, or ambient temperature fluctuations. It is also immune to sensor poisoning.
  • Quadrupole technology enables customized screening and classification for multiple applications on the same system.
  • The GERSTEL Headspace ChemSensor System can ignore ions associated with dominant sample components – such as alcohol in wines or acetic acid in salad dressings – and model only the critical factors that differentiate samples.
  • Ions in a suspect sample that are not present in a good sample can be monitored in subsequent analyses using GC/MS. This can reduce troubleshooting time substantially and puts you steps ahead of e-noses that do not have this correlation capability.
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