Rejection by pigs of mouldy grain containing deoxynivalenol

Weaner pigs on a farm near Beaudesert in south eastern Queensland refused to eat feed comprised largely of wheat and barley. Older pigs consumed small amounts and some prepubertal gilts subsequently displayed enlarged and reddened vulvas. Wheat, barley and triticale were grown on the farm during 1983, which was unusually and persistently wet. The wheat and triticale were harvested and stored for about 3 weeks with moisture contents above 14% before being fed. Samples of the wheat and triticale contained pale pink grains, which can indicate infection by the fungus Fusariurn grarninearurn Schw. On analysis 2 mycotoxins known to be produced by F. graminearurn were detected, deoxynivalenol (vomitoxin) which causes feed refusal and vomiting, and zearalenone which causes oestrogenic effects. Concentrations of deoxynivalenol in the wheat, triticale and barley were 34, 10, and <0.1 mg/kg respectively. Concentrations of zearalenone were 6.2, 2.8 and 0.1 mg/kg respectively. Subsequently, F. grarninearurn was isolated from grains and crop residues. Although the wet weather contributed to F. grarninearurn infection of the crops before harvest, most of the toxins probably developed during storage.

Potential of VIS-NIR Spectroscopy to predict perceived ‘muddy’ taint in australian farmed barramundi

Sensory analysis of food involves the measurement, interpretation and understanding of human responses to the properties of food perceived by the senses such as sight, smell, and taste (Cozzolino et al. 2005). It is important to have a quantitative means for assessing sensory properties in a reasonable way, to enable the food industry to rapidly respond to the changing demands of both consumers and the market. Aroma and flavour are among the most important properties for the consumer, and numerous studies have been performed in attempts to find correlations between sensory qualities and objective instrumental measurements. Rapid instrumental methods such as near infrared spectroscopy (NIR) might be advantageous to predict quality of different foods and agricultural products due to the speed of analysis, minimum sample preparation and low cost. The advantages of such technologies is not only to assess chemical structures but also to build an spectrum, characteristic of the sample, which behaves as a “finger print” of the sample.