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Assessment of quality defects in macadamia kernels using NIR spectroscopy

journal contribution
posted on 06.12.2017, 00:00 by John GuthrieJohn Guthrie, Colin GreensillColin Greensill, R Bowden, Kerry WalshKerry Walsh
Spectral data were collected of intact and ground kernels using 3 instruments (using Si-pbS, Si, and InGaAs detectors), operating over different areas of the spectrum (between 400 and 2500 nm) and employing transmittance, interactance, and reflectance sample presentation strategies. Kernels were assessed on the basis of oil and water content, and with respect to the defect categories of insect damage, rancidity, discoloration, mould growth, germination, and decomposition. Predictive model performance statistics for oil content models were acceptable on all instruments ( -R2> 0.98; RMSECV <2.5o/ow, hich is similar to reference analysis error), although that for the instrument employing reflectance optics was inferior to models developed for the instruments employing transmission optics. The spectral positions for calibration coefficients were consistent with absorbance due to the third overtones of CH, stretching. Calibration models for moisture content in ground samples were acceptable on all instruments ( R2> 0.97; RMSECV <0.2o/o), whereas calibration models for intact kernels were relatively poor. Calibration coefficients were more highly weighted around 1360, 740, and 840 nm, consistent with absorbance due to overtones of O-H stretching and combination. Intact kernels with brown centres or rancidity could be discriminated from each other and from sound kernels using principal component analysis. Part kernels affected by insect damage, discoloration, mould growth, germination, and decomposition could be discriminated from sound kernels. However, discrimination among these defect categories was not distinct and could not be validated on an independent set. It is concluded that there is good potential for a low cost Si photodiode array instrument to be employed to identify some quality defects of intact macadamia kernels and to quantify oil and moisture content of kernels in the process laboratory and for oil content in-line. Further work is required to examine the robustness of predictive models across different populations, including growing districts, cultivars, and times of harvest.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Volume

55

Start Page

471

End Page

476

Number of Pages

6

ISSN

0004-9409

Location

Australia

Publisher

CSIRO Publishing

Language

en-aus

Peer Reviewed

Yes

Open Access

No

Era Eligible

Yes

Journal

Australian journal of agricultural research.

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