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[Building artificial neural networks model on portable NIR integrity wheat component measuring apparatus]
Hai-yan Ji1, Ming Wen, Bin Hao
1College of Information and Electrical Engineering, China Agricultural University, Beijing 100094, China.
Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
|July 11, 2006
Summary
A new artificial neural network model accurately quantifies protein in whole wheat using portable near-infrared spectroscopy. This method offers a reliable approach for analyzing wheat composition.
Area of Science:
- Agricultural Science
- Analytical Chemistry
- Biotechnology
Background:
- Accurate protein analysis is crucial for wheat quality assessment.
- Near-infrared (NIR) spectroscopy offers a rapid, non-destructive method for chemical analysis.
- Developing portable devices enhances on-site analysis capabilities.
Purpose of the Study:
- To develop a quantitative analysis model for protein content in whole wheat.
- To utilize a portable near-infrared (NIR) apparatus for component analysis.
- To apply artificial neural networks (ANNs) for improved accuracy in protein quantification.
Main Methods:
- Construction of a three-layer backpropagation artificial neural network (ANN).
- Utilizing a portable near-infrared (NIR) spectroscopy apparatus for spectral data acquisition.
- Analysis of spectral parameters and chemical values, considering nonlinear relationships.
Main Results:
- The ANN model achieved high correlation coefficients (0.90 for calibration, 0.96 for prediction).
- Relative standard deviations were low (3.77% for calibration, 4.46% for prediction).
- The nonlinear ANN model demonstrated superior performance compared to linear models.
Conclusions:
- Artificial neural networks provide an effective tool for quantitative protein analysis in whole wheat via NIR.
- Portable NIR spectroscopy coupled with ANNs enables accurate and efficient on-site wheat component analysis.
- The developed model addresses nonlinearities inherent in spectral measurements for robust protein quantification.
