闲鹤野云
第11楼2009/06/09
具体的内容是:
September/October 2001, Volume 78, Number 5
Pages 572-577
DOI:10.1094/CCHEM.2001.78.5.572
Performance of European Artificial Neural Network (ANN) Calibrations for Moisture and Protein in Cereals Using the Danish Near-Infrared Transmission (NIT) Network
Nils Bo Büchmann,1,2,4 Henrik Josefsson,2 and Ian A. Cowe3
1 DLG (Danish Cooperative Farm Supply), Axelborg, DK 1503, Copenhagen.
2 Foss Tecator AB, Box 70, SE 26321, Höganäs, Sweden.
3 Foss Electric Development (UK) Ltd., Millfield Lane Industrial Estate, Wheldrake, York, YO 19 6NA, UK.
4 Corresponding author. E-mail: bo.buchmann@foss.tecator.se Phone: +46 42 361507. Fax: +46 42 34 0349.
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Three problems need to be addressed in networks of Infratec Grain Analysers: 1) the networks are not interconnected, 2) the partial least squares (PLS) calibrations used so far have to be individually adjusted for bias when transferred to the slave instruments, and 3) the calibrations are not entirely stable over time. Nonlinear artificial neural network (ANN) calibrations based on a large common European data set (≈4,000 samples in the training sets and ≈1,000 samples in the stop sets) were introduced to overcome these constraints. The performance of these ANN calibrations was compared with Danish PLS models for protein and moisture in cereals during the 1998 harvest in Denmark, and subsequently with PLS models based on the same European data set. ANN models were more accurate than PLS and, unlike PLS, were linear and transferable up to 25% moisture. It is suggested that the improved performance of the ANN models is attributable to the modeling technique rather than the size and nature of the European data set. In most cases, ANN models could be applied directly and without bias adjustment to slave instruments. The ANN models were also more stable, they required fewer bias adjustments or remodeling over time compared with Danish PLS models. ANN calibrations using shared data have been adopted for commercial use in several European countries and work is in progress to develop global ANN models for determination of protein in wheat and barley.
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闲鹤野云
第14楼2009/06/09
日本的研究
Title: Factors affecting in determining constituent content of grain by NIR spectroscopy (Part 1). Effects of grain and milling condition on the Factors affecting in determining constituent content of grain by NIR spectroscopy (Part 1). Effects of grain and milling condition on the whole-meal granularity and moisture.
Personal Authors: Natsuga, M., Kawamura, S., Itoh, K.
Author Affiliation: Faculty of Agriculture, Hokkaido University, Sapporo-shi, 060 Japan.
Editors: No editors
Document Title: Journal of the Japanese Society of Agricultural Machinery
Abstract:
The effect of milling conditions on whole-meal granularity and m.c. was investigated for 3 types of milling machine and 3 wheat and 1 brown rice varieties using near-infrared reflectance spectroscopy. Granularity and m.c. were significantly affected by variety, type of milling machine and milling temp.
Publisher:
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