Claim · model-written summary
The PLS-developed calibration equation was more accurate than the MLR-developed equation for rice plant nitrogen.
- Taxon
- rice plant
Oryza sativae0 - Outcome
- calibration equatione3
- Direction
- more accuratee3
- Qualifier
- Partial least squares (PLS) methode3
- Comparator
- Multiple linear regression (MLR) methode3
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSettingValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“to update the calibration that is used for the measurement of the total nitrogen content in the rice plant samples”
e3“the calibration equation developed by Partial least squares (PLS) method was more accurate than the Multiple linear regression (MLR) method”
Title
Young-Rip Kwon; Young-Eun Song; Dong-Chil Choi; et al. http://ocean.kisti.re.kr/downfile/volume/kscs/JMHHBK/2009/v54n1/JMHHBK_2009_v54n1_29.pdf 2009
Other claims from this paper (15)
- Method / resource Before merging, the leaf calibration correlation coefficient for nitrogen content in rice was 0.945.
- Method / resource Before merging, the stem calibration correlation coefficient for nitrogen content in rice was 0.928.
- Method / resource Before merging, the whole-plant calibration correlation coefficient for nitrogen content in rice was 0.864.
- Method / resource The leaf calibration model was recorded with relatively high accuracy compared with the models for each part of the rice plant.
- Association The calibration equation was sensitive to variety and location variations in rice.
- Method / resource After merging samples, the leaf R2 value of the calibration was 0.950.
- Method / resource After merging samples, the stem R2 value of the calibration was 0.974.
- Method / resource After merging samples, the whole-plant R2 value of the calibration was 0.940.
- Method / resource After removal of outliers, R2 values increased to 0.998, 0.995 and 0.997 for the calibration models.
- Association In the stem and whole plant, the standard error of prediction (SEP) was reduced.
- Association Biases of the calibration were reduced in the leaf, stem and whole plant.
- Association Slopes of the calibration were high in the stem.
- Association Standard deviation was reduced in the stem.
- Association R2 was high in the stem and whole plant.
- Recommendation The authors conclude that a robust calibration equation can be updated by the merge function and multivariate calibration.