Please use this identifier to cite or link to this item: https://hdl.handle.net/10321/885
Title: Predicting the microbial safety of irrigation water and fresh produce : a collaborative approach
Authors: Ijabadeniyi, Oluwatosin Ademola 
Olugbara, Oludayo O. 
Keywords: Partial least squares;Nanosensing
Issue Date: 11-Jun-2013
Source: Ijabadeniyi, O.A. and Olugbara, O.O. Predicting the microbial safety of irrigation water and fresh produce : a collaborative approach 2013. Scientific Research and Essays 8(22) 965-968.
Abstract: 
Outbreak of food borne illnesses as a result of consumption of fresh vegetables and fruits is occurring
regularly. In USA for example, it has become business as usual to hear and read about fresh produce
recalls. Although, the increase has been attributed to many factors it is however more important to find
solution to the problem. An effective solution will be a proactive approach such as prediction and
forecasting which are not new in the field of meteorology. For many years now, meteorologists have
been predicting the weather. It is indeed high time that food scientists in collaboration with other
professionals found out dependable and realistic methods to predict the presence of pathogens in
irrigation water and fresh produce. In this review, several prediction tools such as factor analysis,
artificial neural network, support vector machine, logistic regression analysis, partial least square and
‘nanosensing’ were discussed. The problem of produce safety may in fact be solved when food
scientist collaborate with I.T professionals, biotechnologists and others.
URI: http://hdl.handle.net/10321/885
DOI: 10.5897/SRE11.1843
Appears in Collections:Research Publications (Accounting and Informatics)

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