Zhao Yifan, Zhu Yingying, Ren Yumeng, Lu Yu, Yu Chunling, Chen Geng, Hong Yu, Liu Qian
Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China.
Ningbo Key Laboratory of Green Shipping Technology, Ningbo 315211, China.
Plants (Basel). 2025 May 10;14(10):1430. doi: 10.3390/plants14101430.
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 ( L.), corn straw ( L.), wheat straw ( L.), soybean straw ( L.), sorghum straw ( L.), rapeseed straw ( L.), and peanut straw ( 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 (R) 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 R of 0.7639, RMSEP of 1.5800, and RPD of 2.11, while the lignin PLS model achieved an R 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.
本研究采用近红外光谱（NIRS）结合化学计量学方法，探讨快速分析秸秆中木质纤维素含量的可行性和方法。随着对生物燃料和生物产品需求的增加，秸秆等农业废弃物的高效利用变得尤为重要。快速分析木质纤维素含量有助于提高农业废弃物的资源利用效率，为生物燃料生产、农业废弃物增值和环境保护提供重要支持。本研究共使用了148个秸秆样本，采集于中国的浙江、江苏和黑龙江三省，涵盖稻草（L.）、玉米秸秆（L.）、小麦秸秆（L.）、大豆秸秆（L.）、高粱秸秆（L.）、油菜秸秆（L.）和花生秸秆（L.）。采集后，样本先进行风干直至表面水分蒸发，然后研磨并过筛，之后编号并密封储存。为确保实验结果的准确性，所有样本在实验前于60°C下进行6小时干燥处理，以保证水分含量均匀。采用偏最小二乘法（PLS）和支持向量机（SVM）回归方法进行建模分析。结果表明，在木质纤维素含量的校准和预测方面，NIRS结合PLS建模优于SVM。具体而言，纤维素PLS模型的预测集决定系数（R）为0.8983，预测均方根误差（RMSEP）为0.6299，剩余预测偏差（RPD）为3.49。半纤维素PLS模型的R为0.7639，RMSEP为1.5800，RPD为2.11，而木质素PLS模型的R为0.7635，RMSEP为0.6193，RPD为2.17。结果表明，NIRS方法在农业废弃物分析中具有广阔前景，特别是在与生物燃料生产和农业副产品增值相关的应用中。
