Fully Automated Food Quality and Authenticity - NMR FoodScreener

The Bruker FoodScreener is a completely automated, push-button, nuclear magnetic resonance (NMR) solution that includes analysis and reporting based on 400MHz. It is capable of simultaneous identification and complete quantification of compounds with minimum sample preparation at a reduced cost per sample. It is a dependable screening technique for targeted and non-targeted multi-marker analyses.

The FoodScreener enables the detection of unpredicted and even unknown fraud. Complex statistical models allow the testing of origin authenticity, production process control, false labeling, sample similarity, and species purity. The results are lab-, user- and system independent. Different NMR solutions for juice, wine and honey profiling are detailed below:

Juice Profiling

SGF Profiling™ is an NMR-based screening technique for fruit juices. This method was a joint venture by Bruker BioSpin GmbH and SGF International e.V. Numerous parameters relating to quality and authenticity are used to analyze each fruit juice at the same time from a single data set obtained within a few minutes.

Key Features

The main features of the SGF Profiling™ are:

  • Dependable screening technique providing targeted and non-targeted multi-marker evaluation
  • Statistical analysis is based on a wide-ranging NMR spectroscopic database consisting over 16.000 reference juices, which was acquired from production units across the globe and is frequently updated.
  • Targeted evaluation - Total quantification of relevant organic compounds at the same time with reference to A.I.J.N. and NMR distribution
  • Classification testing e.g. determination of fruit origin
  • Determination of fruit content e.g., detection of addition of amino acids, water, or sugar)
  • Non-targeted analysis - NMR-profile is compared with a related group of reference spectra. Deviating concentrations including unidentified compounds are detected automatically.

Wine Profiling

Bruker, along with some experts in the field, have developed an innovative solution for the analysis of wine using NMR spectroscopy. The principle of the technique depends upon acquiring a spectroscopic fingerprint specific to each individual sample. Using a multivariate statistical approach, these profiles are compared to a large database of authentic wine samples. Wine-profiling by NMR takes into account quality control as well as testing of authenticity and safety issues in a unique way.

Key Features

The main features of the NMR spectroscopy for wine profiling are:

  • Consistent screening method providing targeted and non-targeted analyses from a single measurement
  • Quantification of 56 parameters
  • Automatic generation of analysis reports
  • Quantification values are compared to official reference values and for all compounds to the concentration distribution of authentic samples, thereby enabling interpretation of the results
  • Predicts authenticity parameters such as geographical origin, grape variety, and vintage using classification analyses
  • Non-targeted verification of sample conformity by comparison to authentic reference data using verification models

Honey Profiling

Bruker BioSpin, Famille Michaud Apiculteurs, Alnumed, and QSI have worked together to develop an NMR-based screening technique for honey profiling. Numerous parameters are used to test the quality and authenticity of each honey simultaneously from a single data set acquired within a few minutes.

Key Features

The main features of the NMR spectroscopy for honey profiling are:

  • Simultaneous identification and total quantification of numerous related parameters such as acids, amino acids and sugars with reference to NMR distribution
  • Development of statistical models for the evaluation of authenticity according to geographical origin, and honey variety such as blossom, linden, pine, lavender, chestnut, and honeydew
  • Detection of frauds such as addition of rice syrup or other types of sugar
  • Detection of unpredicted and even unknown frauds

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