Prediction of coffee sensory quality in cup by Headspace Solid Phase Microextraction-Mass-Spectrometry based electronic nose (HS-SPME-MS)

Aroma is a primary hedonic aspect of a good coffee playing a fundamental role in the coffee choice [1] and can be considered as a signature of the products [2-6]. The cup tasting is nowadays the most important criteria to define the coffee quality, nevertheless it is time-consuming in terms of panel training and alignment. Defining a relationship between the chemical profile and the aroma sensory impact is an important challenge both in analytical and in industrial fields because it can afford food industries to obtain an objective evaluation of their products. The aim of this work is to use diagnostics mass spectral fingerprints in developing an instrumental prediction model which can be exploited as an analytical decision maker in routine controls to define coffee sensory quality in cup. Coffee samples were sensorially evaluated through monadic profiling and analyzed by HS-SPME-MS and data elaboration was carried out with multivariate analysis. HS-SPME is a reliable high-concentration-capacity technique easy to automate, that can directly be combined online with mass spectrometry. Non-separative MS methods, better known as mass spectrometry-based electronic nose or MS-nose, provide a representative, diagnostic, and generalized mass spectrometric fingerprint of the volatile fraction of a sample, analyzed directly without prior chromatographic separation, in which each m/z ratio acts as a “sensor” whose intensity derives from the contribution of each compound producing that fragment. These methods, in combination with sensometrics, can be completely implemented in an automatic TAS technology (Total Analysis System) giving a high throughput solution for quality control.