Agricultural and Biosystems Engineering Publications

Document Type

Article

Publication Date

2004

Journal or Book Title

Transactions of the ASAE

Volume

47

Issue

10

First Page

311

Last Page

320

Research Focus Area(s)

Biological and Process Engineering and Technology

Abstract

The objectives of this research were: (1) to develop a technique for creating calibrations to predict the constituent concentrations of single maize kernels from near-infrared (NIR) hyperspectral image data, and (2) to evaluate the feasibility of an NIR hyperspectral imaging spectrometer as a tool for the quality analysis of single maize kernels. Single kernels of maize were analyzed by hyperspectral transmittance in the range of 750 to 1090 nm. The transmittance data were standardized using an opal glass transmission standard and converted to optical absorbance units. Partial least squares (PLS) regression and principal components regression (PCR) were used to develop predictive calibrations for moisture and oil content using the standardized absorbance spectra. Standard normal variate, detrending, multiplicative scatter correction, wavelength selection by genetic algorithm, and no preprocessing were compared for their effect on model predictive performance. The moisture calibration achieved a best standard error of cross-validation (SECV) of 1.20%, with relative performance determinant (RPD) of 2.74. The best oil calibration achieved an SECV of 1.38%, with an RPD of only 1.45. The performance and subsequent analysis of the oil calibration reveal the need for improved methods of single-seed reference analysis.

Comments

This article is from Transactions of the ASAE 47 (2004): 311–320. Posted with permission.

Copyright Owner

American Society of Agricultural and Biological Engineers

Language

en

Date Available

2013-10-16

File Format

application/pdf

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