Rapid detection of lignin content in corn straw based on Laplacian Eigenmaps

Abstract

Lignin is an essential components of corn stalk and has a wide range of application. To realize the rapid detection of lignin content in corn straw and increase the detection accuracy, we initially preprocess the gathered corn straw near-infrared spectral data with Standard Normal Variable transformation (SNV). After that, the nonlinear dimensionality reduction method Laplacian Eigenmaps (LE) and Local Tangent Space Alignment (LTSA) are applied independently to reduce the dimension of spectral data. principal component analysis (PCA), a linear dimensionality reduction method, is also utilized for spectral data dimensionality reduction. Finally, models for Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) are constructed. According to the model findings, LE-SVR model offers the best prediction accuracy and stability. The determination coefficient and root mean square error of the training and tests sets are 97.17%, 0.1875 and 96.25%, 0.2718 respectively. Furthermore, the relative analytical error is 5.0776. In addition, the study findings show that the number of neighbor points k has no discernible effect on the model performance. According to the findings, nonlinear modeling using LE-SVR for NIR spectral data of corn stover can lower model complexity, while improving model prediction accuracy and stability. NIR spectroscopy may be used to determine lignin content in corn straw. At the same time, the technique in this work offers a novel approach for the rapidly detecting lignin content in other crops straw.