Title:	Predictive modeling of nutrients in Conilon coffee leaves using portable X-ray fluorescence spectrometry.
Authors:	PIRES, I. F. FONTANA, A. MAESTÁ, B. C. MENDONÇA, H. M. PARTELLI, F. L. TEIXEIRA, W. G. BENEDET, L. MANCINI, M. CURI, N.
Affiliation:	IVNE FRANCO PIRES, PREFEITURA DE BOA ESPERANÇA; ADEMIR FONTANA, CNPGC; BEATRIZ CÉSAR MAESTÁ; HIGOR MOREIRA MENDONÇA; FABIO LUIZ PARTELLI, UNIVERSIDADE FEDERAL DO ESPÍRITO SANTO; WENCESLAU GERALDES TEIXEIRA, CNPS; LUCAS BENEDET, UNIVERSIDADE FEDERAL DE LAVRAS; MARCELO MANCINI, UNIVERSIDADE FEDERAL DE LAVRAS; NILTON CURI, UNIVERSIDADE FEDERAL DE LAVRAS.
Date Issued:	2025
Citation:	Coffee Science, v. 20, e202386, 2025.
Description:	Portable X-ray fluorescence (pXRF) can offer accurate assessment of total elemental contents in crop leaves rapidly without generating chemical waste, contributing to sustainable practices. The objective of this work was to create models capable of predicting nutrient contents in coffee leaves determined by ICP-OES from pXRF data. 1520 coffee leaf samples were collected across Conilon coffee farms in the State of Espirito Santo, Brazil, to compose 19 composite samples (80 leaves per sample). Leaf samples were subjected to macro and micronutrient analyses by wet chemistry and pXRF. pXRF analyses of leaf samples used two calibration methods: Mode 1 (“Soil”) and Mode 2 (“Geochem”). Mode 1 was not capable of determining P contents. Nutrient contents determined by pXRF, in both modes, were higher than those determined by wet chemistry, except for Ca. High correlations were found between contents determined by wet chemistry and those determined by pXRF analysis, especially for mode 2 (r between 0.93 and 0.97), except for Zn (r = 0.65). With the calibration of linear equations, it was possible to predict P, K, Ca, Fe, and Mn contents in Conilon coffee leaves from pXRF data using mode 2 (R2between 0.89 and 0.95). Results showed that the prediction of ICP-OES contents in leaves using pXRF is an accurate, fast, and ecofriendly method.
Thesagro:	Coffea Canephora
NAL Thesaurus:	Plant nutrition Nutrition monitoring
Keywords:	PXRF Predictive modeling
DOI:	https://doi.org/10.25186/.v20i.2386
Type of Material:	Artigo de periódico
Access:	openAccess
Appears in Collections:	Artigo em periódico indexado (CNPGC)