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Classification models for food authenticity tests can - in principle -  be based on any analytical technique that collects multi-variate data.  In the case of spectrometric data (such as NIR or multi-spectral imaging) the equipment can be relatively cheap.  For collecting chemical data, researchers often use high-end equipment such as advanced LC-MS or GC-MS

This proof-of-concept study (purchase required) is a rare example of building a classification model using a cheaper test (HPLC with fluorescence detection) to measure a chemical parameter.  The authors prepared cold-pressed walnut and pumpkin seed oils adulterated with 0 – 50% of sunflower oil.  They developed a classification model based on the concentrations of the four tocopherols (α-, β-, γ-, and δ-).  They report that the model was capable of discriminating sunflower oil adulteration down to 2-3%.

High performance liquid chromatography and high-resolution mass spectrometry (HPLC-MS/MS) was used to identify gelatin from seven commercial cyprinid fishes;, black carp, grass carp, silver carp, bighead carp, common carp, crucian carp, and Wuchang bream.

By comparison with theoretical mammalian collagen (bovine and porcine collagen), the common and unique theoretical peptides were found in the collagen of grass carp, silver carp, and crucian carp, respectively.  Seven common characteristic peptides were obtained from the fish gelatins. Moreover, 44, 36, and 42 unique characteristic peptides were detected in the gelatins of grass carp, silver carp, and crucian carp, respectively.

The researchers concluded that the combined use of common and unique characteristic peptides could verify fish gelatin in comparison with mammalian gelatin.

Read abstract.