Potato Crisp moisture determination using NIR data and a Back Propagation Neural Network
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Other Title
Authors
Yee, Nigel
Potgieter, Paul
Liggett, Stephen
Potgieter, Paul
Liggett, Stephen
Author ORCID Profiles (clickable)
Degree
Grantor
Date
2013-06
Supervisors
Type
Journal Article
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
potato crisps
neural networks
standard normal variate
orthogonal signal correction
neural networks
standard normal variate
orthogonal signal correction
ANZSRC Field of Research Code (2020)
Citation
Yee, 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.
Abstract
Near 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.
Publisher
Advanced Institute of Convergence Information Technology (AICIT)
Permanent link
Link to ePress publication
DOI
doi:10.4156/rnis.vol14.135
Copyright holder
Advanced Institute of Convergence Information Technology (AICIT)
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