Assessment of coffee leaves nutritive value via portable X-ray fluorescence spectrometry and machine learning algorithms

IF 3.2 2区 化学 Q1 SPECTROSCOPY Spectrochimica Acta Part B: Atomic Spectroscopy Pub Date : 2024-07-18 DOI:10.1016/j.sab.2024.106996
Marcela Vieira da Costa , Enio Tarso de Souza Costa , João Paulo Dianin de Oliveira , Geraldo Jânio de Oliveira Lima , Luiz Roberto Guimarães Guilherme , Geila Santos Carvalho , Mariene Helena Duarte , Jernimo Juvêncio Chivale , David C. Weindorf , Somsubhra Chakraborty , Bruno Teixeira Ribeiro
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Abstract

Portable X-ray fluorescence (pXRF) spectrometry is a non-destructive technique that has been successfully used to analyze different matrices. Foliar analysis is challenging because some plant nutrients cannot be detected by pXRF. Even so, the uptake interactions among nutrients which reflect different concentrations of macro- and micronutrients can be accessed via pXRF, constituting a basis to obtain predictive models of plant nutrients. The objective of this work was to compare and assess the accuracy of linear regression and non-linear models (support vector machine – SVM; and random forest – RF) to predict the actual concentration of macro- (N, P, K, Ca, Mg, and S) and micronutrients (B, Cu, Fe, Zn, and Mn) in coffee leaves. A greenhouse experiment was conducted using Hoagland and Arnon solution to obtain leaves with contrasting elemental composition. Ground and oven-dried leaf samples were analyzed via pXRF using: i) a manufactured pXRF calibration developed for general earth-materials (Geoexploration mode); ii) the Spectrometer mode with varying voltage and current (15 kV and 25 μA; 10 kV and 10 μA). The same samples were also analyzed via conventional acid digestion and quantified via inductively coupled plasma-optical emission spectroscopy (ICP-OES). The best predictions were obtained using SVM and RF algorithms, with high R2 (0.82 to 0.99) and high residual prediction deviation (RPD) (2.35 to 9.34) values. However, some elements (e.g., K, Ca, Cu, Mn) were successfully predicted using linear models (LR and MLR). Even elements not detected (N and B) by pXRF were accurately predicted using the RF model. The pXRF operational conditions influenced the performance of the models. However, by parsimony, Geoexploration mode provided data for accurate prediction of macro- and micronutrients. This comprehensive study can potentially spark further investigations into examining coffee leaves from plants cultivated under various environmental and management conditions. Additionally, the methodological framework outlined here holds promise for ongoing experimentation across diverse crops, offering a streamlined, non-invasive, eco-friendly, and rapid approach for foliar analysis.

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通过便携式 X 射线荧光光谱仪和机器学习算法评估咖啡叶营养价值
便携式 X 射线荧光光谱仪(pXRF)是一种非破坏性技术,已成功用于分析不同的基质。叶面分析具有挑战性,因为 pXRF 无法检测到某些植物养分。即便如此,仍可通过 pXRF 获取养分之间的吸收相互作用,这些相互作用反映了宏观和微观养分的不同浓度,为获得植物养分预测模型奠定了基础。这项工作的目的是比较和评估线性回归模型和非线性模型(支持向量机 - SVM 和随机森林 - RF)预测咖啡叶片中宏量营养元素(氮、磷、钾、钙、镁和硫)和微量营养元素(硼、铜、铁、锌和锰)实际浓度的准确性。使用 Hoagland 和 Arnon 溶液进行了温室实验,以获得元素组成对比鲜明的叶片。研磨和烘干的叶片样本通过 pXRF 进行分析,使用的方法包括:i) 为一般地球材料开发的人造 pXRF 校准(地质勘探模式);ii) 不同电压和电流的光谱仪模式(15 kV 和 25 μA;10 kV 和 10 μA)。同样的样品也通过传统的酸消化法进行了分析,并通过电感耦合等离子体-光发射光谱(ICP-OES)进行了定量。使用 SVM 和 RF 算法获得了最佳预测结果,R2(0.82 至 0.99)和残差预测偏差(RPD)(2.35 至 9.34)值都很高。不过,一些元素(如 K、Ca、Cu、Mn)通过线性模型(LR 和 MLR)也能成功预测。即使是 pXRF 未检测到的元素(N 和 B),也能通过 RF 模型准确预测。pXRF 的运行条件影响了模型的性能。不过,通过解析,Geoexploration 模式提供的数据可以准确预测宏量和微量营养元素。这项综合研究有可能引发进一步的研究,对在不同环境和管理条件下种植的咖啡叶片进行检测。此外,本文所概述的方法框架还为正在进行的各种作物实验带来了希望,为叶片分析提供了一种简化、非侵入性、环保和快速的方法。
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来源期刊
CiteScore
6.10
自引率
12.10%
发文量
173
审稿时长
81 days
期刊介绍: Spectrochimica Acta Part B: Atomic Spectroscopy, is intended for the rapid publication of both original work and reviews in the following fields: Atomic Emission (AES), Atomic Absorption (AAS) and Atomic Fluorescence (AFS) spectroscopy; Mass Spectrometry (MS) for inorganic analysis covering Spark Source (SS-MS), Inductively Coupled Plasma (ICP-MS), Glow Discharge (GD-MS), and Secondary Ion Mass Spectrometry (SIMS). Laser induced atomic spectroscopy for inorganic analysis, including non-linear optical laser spectroscopy, covering Laser Enhanced Ionization (LEI), Laser Induced Fluorescence (LIF), Resonance Ionization Spectroscopy (RIS) and Resonance Ionization Mass Spectrometry (RIMS); Laser Induced Breakdown Spectroscopy (LIBS); Cavity Ringdown Spectroscopy (CRDS), Laser Ablation Inductively Coupled Plasma Atomic Emission Spectroscopy (LA-ICP-AES) and Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS). X-ray spectrometry, X-ray Optics and Microanalysis, including X-ray fluorescence spectrometry (XRF) and related techniques, in particular Total-reflection X-ray Fluorescence Spectrometry (TXRF), and Synchrotron Radiation-excited Total reflection XRF (SR-TXRF). Manuscripts dealing with (i) fundamentals, (ii) methodology development, (iii)instrumentation, and (iv) applications, can be submitted for publication.
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