Mishra Puneet, Herrmann Ittai, Angileri Mariagiovanna
Wageningen Food and Biobased Research, Bornse Weilanden 9, P.O. Box 17, Wageningen, 6700AA, the Netherlands.
The Robert H. Smith Institute for Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, P.O. Box 12, Rehovot 7610001, Israel.
Talanta. 2021 Apr 1;225:121971. doi: 10.1016/j.talanta.2020.121971. Epub 2020 Dec 4.
Wet chemistry analysis of agricultural plant materials such as leaves is widely performed to quantify key chemical components to understand plant physiological status. Visible and near-infrared (Vis-NIR) spectroscopy is an interesting tool to replace the wet chemistry analysis, often labour intensive and time-consuming. Hence, this study accesses the potential of Vis-NIR spectroscopy to predict nitrogen (N) and potassium (K) concentration in bell pepper leaves. In the chemometrics perspective, the study aims to identify key Vis-NIR wavelengths that are most correlated to the N and K, and hence, improves the predictive performance for N and K in bell pepper leaves. For wavelengths selection, six different wavelength selection techniques were used. The performances of several wavelength selection techniques were compared to identify the best technique. As a baseline comparison, the partial least-square (PLS) regression analysis was used. The results showed that the Vis-NIR spectroscopy has the potential to predict N and K in pepper leaves with root mean squared error of prediction (RMSEP) of 0.28 and 0.44%, respectively. The wavelength selection in general improved the predictive performance of models for both K and N compared to the PLS regression. With wavelength selection, the RMSEP's were decreased by 19% and 15% for N and K, respectively, compared to the PLS regression. The results from the study can support the development of protocols for non-destructive prediction of key plant chemical components such as K and N without wet chemistry analysis.
对叶片等农业植物材料进行湿化学分析以量化关键化学成分，从而了解植物生理状态，这一做法被广泛应用。可见近红外（Vis-NIR）光谱法是一种很有吸引力的工具，可用于取代通常既耗费人力又耗时的湿化学分析。因此，本研究探讨了Vis-NIR光谱法预测甜椒叶片中氮（N）和钾（K）浓度的潜力。从化学计量学角度来看，该研究旨在确定与N和K相关性最强的关键Vis-NIR波长，从而提高对甜椒叶片中N和K的预测性能。在波长选择方面，使用了六种不同的波长选择技术。比较了几种波长选择技术的性能，以确定最佳技术。作为基线比较，使用了偏最小二乘（PLS）回归分析。结果表明，Vis-NIR光谱法有潜力预测辣椒叶片中的N和K，预测均方根误差（RMSEP）分别为0.28%和0.44%。与PLS回归相比，一般来说波长选择提高了模型对K和N的预测性能。通过波长选择，与PLS回归相比，N和K的RMSEP分别降低了19%和15%。该研究结果可为无需湿化学分析即可对K和N等关键植物化学成分进行无损预测的方案制定提供支持。
