Please use this identifier to cite or link to this item:
https://hdl.handle.net/10321/885
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ijabadeniyi, Oluwatosin Ademola | en_US |
dc.contributor.author | Olugbara, Oludayo O. | en_US |
dc.date.accessioned | 2013-08-08T13:57:20Z | - |
dc.date.available | 2013-08-08T13:57:20Z | - |
dc.date.issued | 2013-06-11 | - |
dc.identifier.citation | 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. | en_US |
dc.identifier.uri | http://hdl.handle.net/10321/885 | - |
dc.description.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. | en_US |
dc.format.extent | 4 p | en_US |
dc.language.iso | en | en_US |
dc.subject | Partial least squares | en_US |
dc.subject | Nanosensing | en_US |
dc.subject.lcsh | Listeria monocytogenes | en_US |
dc.subject.lcsh | Neural networks (Computer science) | en_US |
dc.subject.lcsh | Food--Safety measures | en_US |
dc.title | Predicting the microbial safety of irrigation water and fresh produce : a collaborative approach | en_US |
dc.type | Article | en_US |
dc.publisher.uri | http://www.academicjournals.org/SRE/PDF/pdf2013/11Jun/Ijabadeniyi%20and%20Olugbara.pdf | en_US |
dc.dut-rims.pubnum | DUT-002775 | en_US |
dc.identifier.doi | 10.5897/SRE11.1843 | - |
local.sdg | SDG06 | - |
local.sdg | SDG02 | - |
item.grantfulltext | open | - |
item.cerifentitytype | Publications | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.languageiso639-1 | en | - |
item.openairetype | Article | - |
Appears in Collections: | Research Publications (Accounting and Informatics) |
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File | Description | Size | Format | |
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ijabadeniyi_and_olugbara_2013_output.pdf | 106.29 kB | Adobe PDF | View/Open |
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