Statistical data mining algorithms for optimising analysis of spectroscopic data from on-line NIR mill systems
Sexton, Justin David (2020) Statistical data mining algorithms for optimising analysis of spectroscopic data from on-line NIR mill systems. PhD thesis, James Cook University.
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Abstract
Justin Sexton investigated techniques to identify atypical sugarcane from spectral data. He found that identifying atypical samples could help remove bias in estimates of CCS. His results can be used to track occurrences of atypical cane or improve quality estimates providing benefits at various stages along the industry value chain.
| Item ID: | 65317 |
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| Item Type: | Thesis (PhD) |
| Keywords: | atypical sugarcane, spectral data, spectroscopic data, Near Infra Red (NIR) spectroscopy, statistical data mining methodologies, Partial Least Squares (PLS) |
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| Copyright Information: | © 2020 Justin David Sexton. |
| Additional Information: | Four publications arising from this thesis are stored in ResearchOnline@JCU, at the time of processing. Please see the Related URLs. The publications are: [Chapter 3] Sexton, J., Everingham, Y., and Donald, D. (2017) A comparison of data mining algorithms for improving NIR models of cane quality measures. In: Proceedings of the 39th Annual Conference of the Australian Society of Sugar Cane Technologists (39) pp. 557-567. From: ASSCT 2017: 39th Annual Conference of the Australian Society of Sugar Cane Technologists, 3-5 May 2017, Cairns, QLD, Australia. [Chapter 4] Sexton, Justin, Everingham, Yvette, Donald, David, Staunton, Steve, and White, Ronald (2018) A comparison of non-linear regression methods for improved on-line near infrared spectroscopic analysis of a sugarcane quality measure. Journal of Near Infrared Spectroscopy, 26 (5). pp. 297-310. [Chapter 5] Sexton, J., Everingham, Y., Donald, D., Staunton, S., and White, R. (2018) A feasibility test for detection of atypical cane samples using near infrared spectroscopy. In: Proceedings of the 40th Annual Conference of the Australian Society of Sugar Cane Technologists. pp. 382-390. From: ASSCT 2018: 40th Annual Conference of the Australian Society of Sugar Cane Technologists, 17-20 April 2018, Mackay, QLD, Australia. [Chapter 6] Sexton, Justin, Everingham, Yvette, Donald, David, Staunton, Steve, and White, Ronald (2020) Investigating the identification of atypical sugarcane using NIR analysis of online mill data. Computers and Electronics in Agriculture, 168. 105111. |
| Funders: | Sugar Research Australia (SRA) |
| Projects and Grants: | SRA Project 2014/019 |
| Date Deposited: | 15 Dec 2020 02:14 |
| FoR Codes: | 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 50% 07 AGRICULTURAL AND VETERINARY SCIENCES > 0799 Other Agricultural and Veterinary Sciences > 079999 Agricultural and Veterinary Sciences not elsewhere classified @ 50% |
| SEO Codes: | 82 PLANT PRODUCTION AND PLANT PRIMARY PRODUCTS > 8203 Industrial Crops > 820304 Sugar @ 100% |
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