The document presents an abstract on an electronic nose (e-nose) device called the Pico-1 that was used to analyze groups of coffees. The e-nose was able to classify the different coffee types with over 90% accuracy and also predict sensory descriptors assessed by coffee judges. E-noses can discriminate between similar food products like different coffee blends and roasting levels, as well as correlate to sensory data from human panels, making them a valid tool for routine food analysis compared to more expensive techniques like gas chromatography–mass spectrometry.

1. AN ABSTRACT ON E-NOSEHere I would like to present the Pico-1 Electronic Nose (EN) based on thin film semiconductor sensors and an application to the analysis of two groups of seven coffees each. Cups of coffee were also analyzed by two panels of trained judges who assessed quantitative descriptors and a global index (called Hedonic Index, HI) characterizing the sensorial appeal of the coffee. Two tasks are performed by Pico-1. First, for each group, we Analysis performed the classification of the seven different coffee types using Principal Component and Multilayer Perceptrons for the data analysis. Classification rates were above 90%. Secondly, the panel test descriptors were predicted starting from the measurements performed with Pico-1. The standard deviation for the prediction of the HI are comparable to the uncertainty of the HI itself. 1. INTRODUCTIONELECTRONIC Noses (EN), in the broadest meaning, are instruments that analyze gaseous mixtures for discriminating between different (but similar) mixtures and, in the case of simple mixtures, quantify the concentration of the constituents. ENs consists of a sampling system (for a reproducible collection of the mixture), an array of chemical sensors, Electronic circuitry and data analysis software. Chemical sensors, which are the heart of the system, can be divided into three categories according to the type of sensitive material used: inorganic crystalline materials (e.g. semiconductors, as in MOSFET structures, and metal oxides); organic materials and polymers; biologically derived materials.The use of ENs for food quality analysis tasks is twofold. ENs is normally used to discriminate different classes of similar odour-emitting products. In particular ENs already served to distinguish between different coffee blends and between different coffee roasting levels. On the other hand, ENs can also be used to predict sensorial descriptors of food quality as determined by a panel (often one generically speaks of correlating EN and sensory data). ENs can therefore represent a valid help for routine food analysis. The combination of gas chromatography and mass spectroscopy (GC-MS) is by far the most popular technique for the identification of volatile compounds in foods and beverages. This is because the separation achieved by the gas chromatographic technique is complemented by the high sensitivity of mass spectroscopy and its ability to identify the molecules eluting from the column on the basis of their fragmentation patterns. Detection limits as low as 1 ppb (parts per billion) are frequently reached. The main drawbacks of the approach are, however, the cost and complexity of the instrumentation and the time required to fully analyze each sample (around one hour for a complete chromatogram). Comparatively, ENs are simpler, cheaper devices. They recognize a fingerprint, that is global information, of the samples to be classified. For food products, the sensory characteristics determined by a panel are important for quality assessment. While man still is the most efficient instrument for sensorial evaluation, the formation of a panel of trained judges involves considerable expenses. Commercial coffees are blends, which, for economic reasons, contain (monovarietal) coffees of various origins. For the producers the availability of analysis and control techniques is of great importance. There exists a rich literature on the characterization of coffee using the chemical profile of one of its fractions, such as the headspace of green or roasted beans or the phenolic fraction. In the literature up to 700 diverse molecules have been identified in the headspace. Their relative abundance depends on the type, provenance and manufacturing of the coffee. It is to be noticed that none of these molecules can alone be identified as a marker. On the contrary one has to consider the whole spectrum, as for instance the gas chromatographic profile. 2. REFERENCES1 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENTS, 20022. IEEE SPECTRUM, 19963. http://WWW.IIT.EDU 4. http://www.kolkatacdac.in/html/imgtea.PDF