LEAF IONOME TO PREDICT THE PHYSIOLOGICAL STATUS OF NITROGEN, PHOSPHORUS, AND POTASSIUM IN CAMELLIA OLEIFERA

The mineral nutrient and trace element composition of a tissue or organism is known as its ionome. In the present study, a statistical method to predict the physiological status of nitrogen (N), phosphorous (P), and potassium (K) using the leaf ionome in Camellia oleifera was explored. The latter is an important non-wood forest shrub for edible oil production in China. The elements N, P, K, calcium (Ca), magnesium (Mg), iron (Fe), manganese (Mn), boron (B), zinc (Zn), copper (Cu), and molybdenum (Mo) in the leaves of the sand-cultured seedlings with N, P, or K deficiency were investigated using Inductively coupled plasma mass spectrometry (ICP-MS). Significant relationships among the ions were found, and the leaf N, K, B, and Cu ions under N deficiency, the N, P, Fe, and Cu ions under P deficiency, and the N, K, B, Cu, Fe, and Mg ions under K deficiency showed obvious variations. Additionally, the principal component analysis (PCA) was introduced to reduce the dimensions of the multivariables based on the leaf ion concentrations. The PCA established that the leaf ionome contained information that could discriminate between the seedlings based on their N, P, or K status. Thus, statistical models were developed that could be used to classify the C. oleifera seedlings by their response to the N, P, and K deficiency or sufficiency, based on logistic regression. The area under the receiver-operator curves (AUC) showed that the P and K status prediction models built using the leaf ionome performed far better than those using single ions. Additionally, a >85% accuracy was obtained in discriminating between the seedlings under nutrient deficiency. The P prediction model also exhibited excellent specificity in the tests under N, K, Mg, or Mn deficiency.