Quantitative Prediction of Protein Content in Corn Kernel Based on Near-Infrared Spectroscopy.
Chenlong Fan1, Ying Liu1, Tao Cui2
1College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Foods (Basel, Switzerland)
|January 8, 2025
Summary
This study developed a rapid near-infrared spectroscopy (NIR) method for accurate protein detection in maize grain powder. The optimized Partial Least Squares Regression (PLSR) model achieved high prediction accuracy, improving maize quality control.
Area of Science:
- Agricultural Science
- Analytical Chemistry
- Spectroscopy
Background:
- Accurate protein content detection is crucial for maize quality assurance.
- Traditional near-infrared spectroscopy (NIR) methods for whole maize grains are limited by surface effects and sample homogeneity.
- Analyzing maize grain powder offers improved data quality and prediction accuracy for protein content.
Purpose of the Study:
- To develop a rapid and accurate method for determining protein content in maize grain powder using near-infrared spectroscopy (NIR).
- To evaluate the performance of different spectral preprocessing techniques and chemometric models for protein prediction.
- To identify optimal feature wavelengths for enhancing model accuracy.
Main Methods:
- Collected near-infrared (NIR) reflectance spectra (940-1660 nm) from various maize grain powder varieties.
- Applied spectral preprocessing techniques: Savitzky-Golay (S-G), multiplicative scatter correction (MSC), standard normal variate (SNV), and first derivative (1D).
- Utilized chemometric models including Partial Least Squares Regression (PLSR), Support Vector Machine (SVM), and Extreme Learning Machine (ELM).
- Employed feature selection algorithms: Successive Projections Algorithm (SPA) and Uninformative Variable Elimination (UVE).
Main Results:
- The Partial Least Squares Regression (PLSR) model, preprocessed with 1D + MSC, demonstrated superior performance.
- Achieved a root mean square error of prediction (RMSEP) of 0.3 g/kg, a correlation coefficient (Rp) of 0.93, and a residual predictive deviation (RPD) of 3.
- The selected feature wavelengths significantly enhanced the predictive capability of the models.
Conclusions:
- The developed NIR spectroscopy method provides a rapid and accurate approach for quantifying protein content in maize grain powder.
- The findings offer a robust scientific basis for improving maize quality control and processing.
- This technique overcomes limitations associated with whole grain analysis, enabling more reliable protein assessment.
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