The Electronic Nose

It seems like science fiction to think that it was possible to mimic either the tongue or nose in terms of being able to distinguish one food from another. In recent years though, the technology to develop electronic devices which copy the biological senses has gathered ground considerably. It is now increasingly reliable and used in conjunction with sensory evaluation to analyse food aroma (Wardencki et al., 2013). It’s quite simply an instrument that detects odours or taste but when coupled to the sophisticated software now available can help the food scientist for example distinguish the presence of a taint within in a complex mixture, the difference in age of a cheese, or state whether a food has the umami flavour.

What Is A Nose In The First Place?

To mimic a nose, an electronic form of it must replicate what is termed the olfactory system in humans which is composed of many specific and non-specific receptors. These receptors have a broad range of responses and taken together allow the brain to form a pattern about the food being sniffed or tasted. A human nose contains around 50 million such receptors which all send their signals to the olfactory bulb. This is composed of secondary cells that help order and segregate information from these ‘primary’ receptors in the nose. Each secondary cell is fed by responses from up to 20 000 primary receptor cells each, the minimum number being 1000. The primary receptors are non-specific and so it’s the cells in the olfactory bulb which convert this information into meaningful data for the brain to assess.  The electronic nose (and tongue) is thus trying to copy the nose by using non-specific chemical sensors coupled to data acquisition and software processing.

What is at the heart of an electronic nose?

The electronic nose is an array of chemical gas sensors. Each of these has broad and overlapping selectivity for the measurement of volatile compounds. These types of compounds exist in the headspace above a food or any other sample. Computer based multivariate statistical analysis helps process this information into something meaningful. 

The range of applications is vast, a few mentioned already, and I noticed it’s possible to find great examples on the web. Wine (see the guest post for example) contains a vast number of compounds, all with different sensory thresholds and many of which interact with each other. Only a trained sensory panel might distinguish the subtle differences between one vintage and another. It is possible to use GC-MS (gas chromatography coupled to mass spectrometry) to identify a pattern of compounds in the wine but it takes time and has proved enormously difficult to relate to a quality parameter.

Beer is a similar example, characterised by over 700 volatile and non-volatile compounds but a device using polymeric chemoresistors as the primary receptor coupled to pattern recognition was developed 20 years ago to identify different beer flavour and even off-flavour (Pearce et al., 1993) building on work at the University of Warwick a decade earlier (Persaud and Dodd, 1982).

I’ve been looking at a number of reviews which help outline the development of the electronic tongue and its worth following these through to understand how the technology has developed (Gardner and Bartlett, 1993; Schaller et al., 1998; Rock et al., 2008; Peris et al., 2009; Wilson and Baietto, 2009; Baldwin et al., 2011). Most notably the improvements in processing power such as pattern recognition (Berrueta et al., 2007) and the performance of the solid state gas sensors (Arshak et al., 2004) which serve as the receptors.

The range of applications in food quality control as well as beer covers beverages such as apple juice (Bleibaum et al., 2002),  alcoholic beverages (Ragazzo-Sanchez et al., 2009) and wines (López de Lerma et al., 2013). The shelf-life of milk has been assessed (Labreche et al., 2005).

One critical application for electronic noses is tea discrimination (Yu et al., 2008). One study has shown the technology to be as effective as sensory panels and colourimetric analysis in predicting the optimum time for black tea fermentation (Bhattacharyya et al., 2007) and estimating the theaflavin content (Ghosh et al., 2012).

Another area of application is the use of electronic noses to detect obnoxious smells, especially where inspectors of meat and fish quality have been seriously challenged! When a human is stressed by a bad smell, the judgements are prone to error and it is difficult to quantify such a measure. The electronic nose has proved to be an invaluable tool for checking the spoilage of fish, molluscs, prawns (Luzuriaga et al., 1997) and meat generally.

Monitoring Fermentations

Electronic noses have shown potential in monitoring fermentations where gas and aroma are important measures of growth. This technology has been reviewed in this matter by Mandenius (2001). Being non-invasive, the chemical gas sensor array measures gas taken from a bioreactor where it is used to monitor progress and completion.

The technology works effectively in combination with artificial neural networks for predicting both biomass and metabolite concentrations in fermentation broths. Some authors suggest these measures can be used as prediction methods for the fermentability of a medium before starting fermentation. One example was predicting the fermentability of lignocellulose hydrolyzates from various species of tree. The volatile emission from the hydrolyzates before fermentation was measured, and the sensor array response pattern was compared with the observed fermentability of the hydrolyzates, such as the final ethanol concentration after fermentation and the maximum specific ethanol production rate (Mandenius et al., 1999) . 

The technology also works well with other systems – machine vision, infra-red detectors and the like.

As the technology develops further, we should see many more applications and a greater degree of sophistication.

References

Arshak, K.,  Moore, E., Lyons, G.,  Harris, J.,  Clifford, S. (2004)  Sens. Rev. 24 pp. 181–191.
Baldwin, E.A.; Bai, J.; Plotto, A.; Dea, S. (2011) Electronic Noses and Tongues: Applications for the Food and Pharmaceutical Industries. Sensors 11, pp. 4744-4766.

Berrueta, L.A., Alonso-Salces, R.M., Héberger, K. (2007) Supervised pattern recognition in food analysis.  J. Chromatography A 1158 pp. 196–214.

Bhattacharyya, N., Seth, S., Tudu, B. et al. (2007). Detection of optimum fermentation time for black tea manufacturing using electronic nose. Sensors and Actuators B: Chemical, 122, pp. 627–634.

Bleibaum, R.N., Stone, H., Tan, T., Labreche, S., Saint-Martin, E., Isz, S. (2002) Comparison of sensory and consumer results with electronic nose and tongue sensors for apple juices. Food Qual. Prefer. 13(6) pp. 409–22.

Gardner, J.W. and  Bartlett, P. N. (1993) A Brief History of Electronic Noses. Sensors and Actuators B., 18, pp. 211–220

Ghosh, A., Tamuly, P., Bhattacharyya, N., Tudu, B., Gogoi, N. & Bandyopadhyay, R. (2012). Estimation of theaflavin content in black tea using electronic tongue. J. Food Engineering, 110, pp. 71–79.

López de Lerma, Md.l.N., Bellincontro, A., García-Martínez, T., Mencarelli, F., Moreno, J.J. (2013) Feasibility of an electronic nose to differentiate commercial Spanish wines elaborated from the same grape variety. Food Res. Int. 51(2) pp. 790–6.

Luzuriaga, D.A., Balaban, M.O. (1999). Evaluation of the odor of decomposition in raw and cooked shrimp: correlation of electronic nose readings, odor sensory evaluation and ammonia levels. In: Hurst W.J., editor. Electronic Noses and Sensor Array Based Systems Design and Applications. Lancaster, PA: Technomic. p 177-184.

Mandenius, CF. (1999). Electronic Noses for Bioreactor Monitoring. In: Sonnleitner, B. (eds) Bioanalysis and Biosensors for Bioprocess Monitoring. Advances in Biochemical Engineering/Biotechnology, vol. 66. Springer, Berlin, Heidelberg. (Article) 

Mandenius, C. F., Liden, H., Eklöv, T., Taherzadeh, M. J., & Liden, G. (1999). Predicting fermentability of wood hydrolyzates with responses from electronic noses. Biotechnology Progress, 15(4), pp. 617-621.

Pearce, T. C., Gardner, J. W., Friel, S., Bartlett, P. N. & Blair, N. (1993) Analyst 118, pp. 371-377. 

Peris, M., & Escuder-Gilabert, L. (2009). A 21st century technique for food control: Electronic noses. Analytica Chimica Acta, 638(1), pp. 1-15.

Persaud, K. AND Dodd, G. H. (1982)  Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose. Nature, 299, pp. 352–355

Ragazzo-Sanchez, J.A., Chalier, P., Chevalier-Lucia, D., Calderon-Santoyo, M., Ghommidh, C. (2009) Off-flavours detection in alcoholic beverages by electronic nose coupled to GC. Sensor Actuat. B-Chem. 140(1) pp. 29–34.

Rock, F., Barsan, N., Weimar, U. (2008) Electronic Nose: Current Status and Future Trends. Chem. Rev. 108 pp. 705-725

Schaller, E.,   Bosset, J.O.,  Escher, F. (1998)  Electronic noses and their application to food: a review. Lebensm. Wiss. u. Technol. 31, pp. 305–316.

Yu, H.C.,Wang, J., Yao, C., Zhang, H.M., Yu, Y. (2008) Quality grade identification of green tea using E-nose by CA and ANN.  LWT—Food Sci. Technol. 41(7) pp. 1268–73.

Wardencki, W., Chmiel, T., Dymerski, T. (2013) 7 – Gas chromatography-olfactometry (GC-O), electronic noses (e-noses) and electronic tongues (e-tongues) for in vivo food flavour measurement. In: Kilcast D, editor. Cambridge, British. Instrumental Assessment of Food Sensory Quality: Woodhead Publishing. p. 195–229.