Data-fusion for multiplatform characterization of an italian craft beer aimed at its authentication

• Characterization of beer samples by five different fingerprinting techniques.
• Chemometric discriminant and class-modeling techniques used for their authentication.
• Mid-level data fusion allowed correct classification of all samples.

Five different instrumental techniques: thermogravimetry, mid-infrared, near-infrared, ultra-violet and visible spectroscopies, have been used to characterize a high quality beer (Reale) from an Italian craft brewery (Birra del Borgo) and to differentiate it from other competing and lower quality products. Chemometric classification models were built on the separate blocks using soft independent modeling of class analogies (SIMCA) and partial least squares-discriminant analysis (PLS-DA) obtaining good predictive ability on an external test set (75% or higher depending on the technique). The use of data fusion strategies – in particular, the mid-level one – to integrate the data from the different platforms allowed the correct classification of all the training and validation samples.

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