A prototype sensor system for the early detection of microbially linked spoilage in stored wheat grain

Sensors based on composites of metal oxides were fabricated and tested extensively under high-humidity and high-flow conditions with exposure to vapours reported to increase in the headspace of wheat grain (Triticum aestivum cv Hereward) colonized by fungi. The sensors that exhibited high sensitivity to target vapours combined with high stability were selected for inclusion into a four-sensor array prototype system. A sampling protocol aligned to parallel GC-MS (gas chromatography-mass spectrometry) and human olfactory assessment studies was established for use with the sensor system. The sensor system was utilized to assess irradiated wheat samples that had been conditioned to 25% moisture content and inoculated with pathogens known to cause spoilage of grain in storage. These included the fungi Penicillium aurantiogriseum, Penicillium vulpinum, Penicillium verrucosum, Fusarium culmorum, Aspergillus niger, and Aspergillus flavus and the actinomycete, Streptomyces griseus. The sensor system successfully tracked the progress of the infections from a very early stage and the results were compared with human olfactory assessment panels run concurrently. A series of dilution studies were undertaken using previously infected grain mixed with sound grain, to improve the sensitivity and maximize the differentiation of the sensor system. An optimum set of conditions including incubation temperature, incubation time, sampling time, and flow rate were ascertained utilizing this method. The sensor system differentiated samples of sound grain from samples of sound grain with 1% (w/w) fungus infected grain added. Following laboratory trials, the prototype sensor system was evaluated in a commercial wheat grain intake facility. Thresholds calculated from laboratory tests were used to differentiate between sound and infected samples (classified by intake laboratory technicians) collected routinely from trucks delivering grain for use in food manufacture. All samples identified as having an odour-related problem by the intake laboratory gave a total system output above the set threshold and were therefore rejected by the prototype system. A number of samples passed by the intake laboratory were rejected by the prototype system, resulting in what appeared to be false positive results. However, the thresholds were selected on the basis of a limited number of samples and may need to be adjusted to minimize false positives. The output from the sensor system was also compared with moisture content values for the wheat (where available) to demonstrate that the system was not simply measuring differences in moisture. A separate study (carried out at the intake facility) assessed 37 newly harvested wheat samples of different varieties and from different geographic locations within the UK. These samples were analysed by the sensor system, using the same thresholds as before. Six samples rejected by the system were then assessed by the wheat intake laboratory, where only one sample was rejected. This rejected sample had given the highest output when exposed to the sensor system. The commercial trial highlighted the promise of this prototype for the detection of spoilage in wheat grain and a larger trial should ascertain the reliability and long-term stability of the device and therefore confirm its usefulness to the industry.

[1]  Carmen García,et al.  Rapid discrimination of meat products and bacterial strains using semiconductor gas sensors , 1996 .

[2]  Antonella Macagnano,et al.  Electronic nose and sensorial analysis: comparison of performances in selected cases , 1998 .

[3]  Julian W. Gardner,et al.  A brief history of electronic noses , 1994 .

[4]  N. Magan,et al.  Volatiles as an indicator of fungal activity and differentiation between species, and the potential use of electronic nose technology for early detection of grain spoilage. , 2000, Journal of stored products research.

[5]  Richard J. Ewen,et al.  The development of a sensor system for the early detection of soft rot in stored potato tubers , 2000 .

[6]  Hans Sundgren,et al.  Electronic nose for odor classification of grains , 1996 .

[7]  Joseph R. Stetter,et al.  Quality classification of grain using a sensor array and pattern recognition , 1993 .

[8]  B. Costello,et al.  A study of the catalytic and vapour-sensing properties of zinc oxide and tin dioxide in relation to 1-butanol and dimethyldisulphide , 1999 .

[9]  E. Schaller,et al.  ‘Electronic Noses’ and Their Application to Food , 1998 .

[10]  Naresh Magan,et al.  Evaluation of a radial basis function neural network for the determination of wheat quality from electronic nose data , 2000 .

[11]  Hao Gong,et al.  Interaction between thin-film tin oxide gas sensor and five organic vapors , 1999 .

[12]  Tim C. Pearce,et al.  Electronic nose for monitoring the flavour of beers , 1993 .

[13]  J. Schnürer,et al.  Volatiles for mycological quality grading of barley grains: determinations using gas chromatography-mass spectrometry and electronic nose. , 2000, International journal of food microbiology.

[14]  M. K. Andrews,et al.  High sensitivity conducting polymer sensors , 1996 .