Correlating Electronic Nose and Sensory Panel Data

Abstract

Analytical methods such as gas chromatography-mass spectrometry (GC-MS), or near infrared spectroscopy provide the mainstay for measurement of volatile components in food, agricultural, chemical, or environmental industries. Although data obtained give very precise measurements of individual components in a mixture, they give very poor indication of the sensory quality perceived by the human nose or tongue. The control of odor quality within these industries is associated with problems that are unique, because they also rely on human perception and preference for particular types of odors or tastes. It is difficult to relate the output of traditional analytical instruments to human perception, because the chemosensory systems of smell and taste use information gathered from the interaction of complex chemical mixtures with the biological sensors without separation of individual components. Many such industries therefore rely on human sensory panels that are trained to discriminate subtle nuances of smell and taste in a given product or raw material, or to quantify the odor level in a sample. This in itself presents problems because such panels can only cope with relatively few sample assessments per day, and are very costly to run. Theymay be used for optimization of a new product, periodic sampling of problematic systems, and random quality control. This highlights the need for automated chemical sensing systems that produce data that are easily correlated to human odor perception. The human nose contains a large array of chemical sensors, and patterns of information are processed in the olfactory brain of an animal in order to achieve quantification and discrimination of odors based on previous learning experiences. With instrumental means of odor measurement, the human user interface needs to be considered very carefully, as the results need to be presented in a form that can be easily interpreted by the user. If an electronic nose is applied, the signals produced by an array of sensors consist of measurements of responses to odors producing different patterns that are projected into multidimensional space. In many instances we are dealing with complex mixtures of compounds in which only relatively few components (which may be at very low concentrations relative to other components) are important in the determination of odor quality by a human sensory panel [1, 2]. Olfactory data depend strongly on individual physiological differences, on measurement methods, and on psychological factors. Classifications of odors are necessary to put