Optimization of rice amylose determination by NIR-spectroscopy using PLS chemometrics algorithms

Area of Science:

• Agricultural Science
• Analytical Chemistry
• Food Science

Background:

• Accurate determination of rice amylose content is crucial for the rice industry.
• Current methods are time-consuming, prone to errors, and not suitable for on-line analysis.
• There is a need for a low-cost, non-destructive, and accurate method for real-time amylose determination.

Purpose of the Study:

• To evaluate the feasibility of Near-Infrared (NIR) spectroscopy for determining rice amylose content.
• To develop and optimize a multivariate regression model for accurate amylose quantification.
• To enable Process Analytical Technology (PAT) applications for critical quality factors in rice.

Main Methods:

• Utilized Near-Infrared (NIR) spectroscopy for non-destructive analysis of rice samples.
• Applied various multivariate regression techniques, including Partial Least Squares (PLS), interval-PLS, synergy interval-PLS (siPLS), and moving windows-PLS.
• Evaluated model performance using the root mean square error of prediction (RMSEP) and correlation coefficient (R).

Main Results:

• The synergy interval-PLS (siPLS) method demonstrated high performance for amylose determination.
• Achieved a high correlation coefficient (R=0.94) and a low RMSEP (1.938).
• Specific spectral regions (8941-8194cm⁻¹, 5592-5045cm⁻¹, and 4683-4335cm⁻¹) were identified as optimal for the model.

Conclusions:

• NIR spectroscopy, combined with appropriate chemometrics, is a feasible and highly accurate method for determining rice amylose content.
• The developed siPLS model provides a reliable basis for on-line, non-destructive amylose analysis.
• This technology can significantly benefit the rice industry by enabling efficient quality control and process optimization.