samples [32]. For spectral measurement, the powder of each leaf was placed on the probe and covered with a black cover. All spectral data ranged from 400 to 2400 nm with 5 nm resolution. The data set is freely available at the official website of ecological spectral information system (EcoSIS) and can be obtained with the link: https://ecosis.org/package /fresh-and-dry-pepper-leaf-spectra-with-associated-potassium-and-nit rogen-measurements. 2.2. Data analysis The data were partitioned to calibration (60%) and test (40%) set using the duplex algorithm [33]. The reflectance data were used directly for the data processing as using chemometric pre-processing methods may remove the
Open resource ↗EcoSIS · pdf-raw-page:2 lines:1-78Paper record
Improved prediction of potassium and nitrogen in dried bell pepper leaves with visible and near-infrared spectroscopy utilising wavelength selection techniques.
Talanta · 4 Dec 2020 · 10.1016/j.talanta.2020.121971
Abstract
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.
Code and data availability