Investigating the identification of atypical sugarcane using NIR analysis of online mill data

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.

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In any given season thousands of tonnes of sugarcane with atypically low quality can pass undocumented through Australian sugarcane mills. Sugarcane with atypically low quality can negatively impact mill processes and throw off grower payment calculations. Mill laboratory operators often observe a small subset (1 similar to 5%) of cane consignments that have an unusually low juice Pol (Pij; a measure of sucrose content) relative to juice brix (Bij; a measure of dissolved sugars), that can indicate deteriorated or contaminated cane. Many mills only test a small subset of cane in the laboratory, with the majority of consignments analysed using fast near infrared (NIR) spectroscopic techniques. This means the true extent of 'atypical! consignments cannot be identified. To address this limitation, this paper compares five modelling approaches: Linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), random forest (RF), Artificial Neural Networks (ANN) and Support Vector Machines (SVM). Model performance was reported as the correct classification rate (CCR) of typical and atypical samples based on independent test sets. The best performance was achieved by PLS-DA (CCRtypical = 88.65% and CCRtypical = 88.75%), while ANN had the lowest performance (CCRatypical = 85.27% and CCRtypical = 83.66%). The methodology used in this paper could be used to identify atypical consignments allowing mills to track occurrences to farms and if necessary develop process control operations for atypical cane. Furthermore, the use of a relatively simple modelling technique such as PLS-DA means model updates can be made efficiently and with confidence as PLS is already well established within the industry.

Item ID: 62354
Item Type: Article (Research - C1)
ISSN: 0168-1699
Keywords: Chemometric, Classification, Deterioration, Process control, Imbalanced
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Copyright Information: © 2019 Elsevier B.V. All rights reserved.
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A version of this publication was included as Chapter 6 of the following PhD thesis: 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, which is available Open Access in ResearchOnline@JCU. Please see the Related URLs for access.

Funders: Sugar Research Australia (SRA), James Cook University
Projects and Grants: SRA grant no. 2014/109
Date Deposited: 26 Feb 2020 07:37
FoR Codes: 49 MATHEMATICAL SCIENCES > 4905 Statistics > 490501 Applied statistics @ 50%
30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3099 Other agricultural, veterinary and food sciences > 309999 Other agricultural, veterinary and food sciences not elsewhere classified @ 50%
SEO Codes: 82 PLANT PRODUCTION AND PLANT PRIMARY PRODUCTS > 8203 Industrial Crops > 820304 Sugar @ 100%
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