Chemometrics in food chemistry
In: Data handling in science and technology 28
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In: Data handling in science and technology 28
This study aimed to present the South African maize industry with an accurate and affordable automated analytical technique for white maize grading using near infrared (NIR) spectral imaging. The 17 categories and sub-categories stipulated in South African maize grading legislation were simultaneously classified (1044 samples; 60 kernels of each class) using 25 partial least squares discriminant analysis (PLS-DA) models. The models were assembled in a hierarchical decision pathway that progressed from the most easily classified classes to the most difficult. The full NIR spectrum (288 wavebands) model performed with an overall accuracy of 93.3% for the main categories. Three waveband selection techniques were employed, namely waveband windows (48 wavebands), variable importance in projection (VIP) (21 wavebands) and covariance selection (CovSel) (13 wavebands). Overall, the VIP set based on only 7.3% of the original spectral variables was recommended as the best trade-off between performance and expected cost of a reduced waveband system. © 2020 Elsevier B.V.
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In: FOODCHEM-D-24-02896
SSRN
In: Environmental science and pollution research: ESPR, Volume 25, Issue 29, p. 28748-28759
ISSN: 1614-7499
In: Environmental science and pollution research: ESPR, Volume 25, Issue 29, p. 28780-28786
ISSN: 1614-7499
In: Environmental science and pollution research: ESPR, Volume 25, Issue 29, p. 28772-28779
ISSN: 1614-7499
In: Environmental science and pollution research: ESPR, Volume 21, Issue 11, p. 6939-6951
ISSN: 1614-7499