Predictive Modeling of Lignocellulosic Content in Crop Straws Using NIR Spectroscopy

This study employs near-infrared spectroscopy (NIRS) combined with chemometrics to explore the feasibility and methodology for the rapid analysis of lignocellulosic content in straw. As the demand for biofuels and bioproducts increases, the efficient utilization of agricultural waste, such as straw, has become particularly important. Rapid analysis of lignocellulosic content helps improve the resource utilization efficiency of agricultural waste, providing significant support for biofuel production, agricultural waste valorization, and environmental protection. A total of 148 straw samples were used in this study, collected from Zhejiang, Jiangsu, and Heilongjiang provinces in China, covering rice straw (<i>Oryza sativa</i> L.), corn straw (<i>Zea mays</i> L.), wheat straw (<i>Triticum aestivum</i> L.), soybean straw (<i>Glycine max</i> L.), sorghum straw (<i>Sorghum bicolor</i> L.), rapeseed straw (<i>Brassica napus</i> L.), and peanut straw (<i>Arachis hypogaea</i> L.). After collection, the samples were first air-dried until surface moisture evaporated and then ground and sifted before being numbered and sealed for storage. To ensure the accuracy of the experimental results, all samples were subjected to a 6 h drying treatment at 60 °C before the experiment to ensure uniform moisture content. Partial least squares (PLS) and support vector machine (SVM) regression methods were employed for modeling analysis. The results showed that NIRS in combination with PLS modeling outperformed SVM in the calibration and prediction of lignocellulosic content. Specifically, the cellulose PLS model achieved a prediction set coefficient of determination (R²_P) of 0.8983, root mean square error of prediction (RMSEP) of 0.6299, and residual predictive deviation (RPD) of 3.49. The hemicellulose PLS model had an R²_P of 0.7639, RMSEP of 1.5800, and RPD of 2.11, while the lignin PLS model achieved an R²_P of 0.7635, RMSEP of 0.6193, and RPD of 2.17. The results suggest that NIRS methods have broad prospects in the analysis of agricultural waste, particularly in applications related to biofuel production and the valorization of agricultural by-products.