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dc.contributor.authorYee, Nigel
dc.contributor.authorPotgieter, Paul
dc.contributor.authorLiggett, Stephen
dc.date.accessioned2015-04-29T02:49:08Z
dc.date.available2015-04-29T02:49:08Z
dc.date.issued2013-06
dc.identifier.issn2287-1934
dc.identifier.issn2287-1942
dc.identifier.urihttps://hdl.handle.net/10652/2781
dc.description.abstractNear infrared analysis is a tool used for non-destructive determination of material properties and the potato crisp production sector has been using the technique for determination of moisture content however near infrared spectral models suffer from problems associated with light scatter. Light scatter results from geometric irregularities in the samples geometry and this reduces the accuracy of near infrared calibration models without preprocessing for scatter removal. Quantitative calibration models have benefited from the development of artificial intelligence methods and the neural network is now a popular tool for quantitative calibration model formation. In this paper we compare the performance of a back propagation neural network calibration model using 3 forms of preprocessed data, orthogonal signal correction, standard normal variate and data with no scatter preprocessing prior. The correlation coefficient was used to determine the neural networks methods performance and it was found that a neural network using data with no scatter preprocessing yielded the best results.en_NZ
dc.language.isoenen_NZ
dc.publisherAdvanced Institute of Convergence Information Technology (AICIT)en_NZ
dc.subjectpotato crispsen_NZ
dc.subjectneural networksen_NZ
dc.subjectstandard normal variateen_NZ
dc.subjectorthogonal signal correctionen_NZ
dc.titlePotato Crisp moisture determination using NIR data and a Back Propagation Neural Networken_NZ
dc.typeJournal Articleen_NZ
dc.rights.holderAdvanced Institute of Convergence Information Technology (AICIT)en_NZ
dc.identifier.doidoi:10.4156/rnis.vol14.135en_NZ
dc.subject.marsden030399 Macromolecular and Materials Chemistry not elsewhere classifieden_NZ
dc.subject.marsden080108 Neural, Evolutionary and Fuzzy Computationen_NZ
dc.identifier.bibliographicCitationYee, N., Potgieter, P., and Liggett, S. (2013). Potato Crisp moisture determination using NIR data and a Back Propagation Neural Network. Research Notes in Information Science, 14, 750-755.en_NZ
unitec.institutionUnitec Institute of Technologyen_NZ
unitec.publication.spage750en_NZ
unitec.publication.lpage755en_NZ
unitec.publication.volume14en_NZ
unitec.publication.titleResearch Notes in Information Scienceen_NZ
unitec.peerreviewedyesen_NZ
unitec.identifier.roms54723en_NZ


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