Lincoln Agritech, Lincoln University, Lincoln 7647, New Zealand.
AgResearch, Lincoln 7608, New Zealand.
Sensors (Basel). 2024 Jul 30;24(15):4944. doi: 10.3390/s24154944.
Biological nitrogen fixation (BNF) by symbiotic bacteria plays a vital role in sustainable agriculture. However, current quantification methods are often expensive and impractical. This study explores the potential of Raman spectroscopy, a non-invasive technique, for rapid assessment of BNF activity in soybeans. Raman spectra were obtained from soybean plants grown with and without rhizobia bacteria to identify spectral signatures associated with BNF. δN isotope ratio mass spectrometry (IRMS) was used to determine actual BNF percentages. Partial least squares regression (PLSR) was employed to develop a model for BNF quantification based on Raman spectra. The model explained 80% of the variation in BNF activity. To enhance the model's specificity for BNF detection regardless of nitrogen availability, a subsequent elastic net (Enet) regularisation strategy was implemented. This approach provided insights into key wavenumbers and biochemicals associated with BNF in soybeans.
共生细菌的生物固氮(BNF)在可持续农业中起着至关重要的作用。然而,目前的定量方法通常既昂贵又不切实际。本研究探讨了拉曼光谱(一种非侵入性技术)在快速评估大豆中 BNF 活性方面的潜力。对种植有和没有根瘤菌的大豆植株进行拉曼光谱采集,以确定与 BNF 相关的光谱特征。利用 δN 同位素比质谱(IRMS)测定实际的 BNF 百分比。采用偏最小二乘回归(PLSR)基于拉曼光谱建立 BNF 定量模型。该模型解释了 BNF 活性变化的 80%。为了提高模型对 BNF 检测的特异性,无论氮素供应情况如何,我们都采用了后续的弹性网络(Enet)正则化策略。该方法提供了有关大豆中 BNF 相关关键波数和生物化学物质的见解。