Tag Archives: Multivariate analysis

xxx A. Brangule, M. Bērtiņš, A. Vīksna and D. Bandere
Potential of multivariate analyses of X-ray fluorescence spectra for characterisation of the microchemical composition of plant materials
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Potential of multivariate analyses of X-ray fluorescence spectra for characterisation of the microchemical composition of plant materials

A. Brangule¹³*, M. Bērtiņš², A. Vīksna² and D. Bandere¹

¹Riga Stradins University, Department of Pharmaceutical Chemistry, Dzirciema 16,
LV-1007 Riga, Latvia
²University of Latvia, Faculty of Chemistry, Jelgavas 1, LV-1004 Riga, Latvia
³Baltic Biomaterials Centre of Excellence, Headquarters at Riga Technical University, Kalku street 1, LV-1658 Riga, Latvia
*Correspondence: agnese.brangule@rsu.lv

Abstract:

This work describes a method for the rapid element analysis of plant material using ED-XRF in conjunction with chemometrics. An effective analysis method is developed by measuring certified reference materials (CRM) of plant materials (algae, cabbage, lichen) covering major chemical elements with ED-XRF, to overcome the matrix effect. All samples have been measured additionally by ICP-MS. The ICP-MS analysis was used for missing information on the concentration of some elements in certificated standards. In addition, ICP-MS with CRM has been used to determine sample related element sensitivity for microelements for ED-XRF analyses.

The ED-XRF spectral patterns were used for multivariate principal component analyses by SIMCA strategy instead of each element concentration calculation. The model allows quickly analyse samples for similarity and differentiate them based on a little difference in spectral pattern, which corresponds to a minor difference in element concentration pattern. Samples with specific chemical composition could be easily spotted for in-depth analysis.

The proposed strategy for plant material sample chemical composition screening allows the quick method to improve laboratory work efficiency, reduce unnecessary analysis and rapid method for control reliability of results of more complex chemical methods, such as ICP-MS.

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418–429 G.A.S. Ferraz, P.F.P. Ferraz, F.B. Martins, F.M. Silva, F.A. Damasceno and M. Barbari
Principal components in the study of soil and plant properties in precision coffee farming
Abstract |

Principal components in the study of soil and plant properties in precision coffee farming

G.A.S. Ferraz¹*, P.F.P. Ferraz¹, F.B. Martins², F.M. Silva¹, F.A. Damasceno¹ and M. Barbari³

¹Federal University of Lavras – UFLA, Departament of Agricultural Engineering, University Campus, BR37200-000 Lavras-MG, Brazil
²Rural Federal University of Rio de Janeiro – UFRRJ, BR-465, Km 7, BR 23.897-000 Seropédica- RJ, Brazil
³Department of Agriculture, Food, Environment and Forestry (DAGRI), Università degli Studi di Firenze, Via San Bonaventura, 13, IT50145 Firenze, Itália
*Correspondence: gabriel.ferraz@ufla.br

Abstract:

In this work, a principal component analysis was performed to evaluate the possibility of discarding obsolete soil and plant variables in a coffee field to eliminate redundant and difficult-to-measure information in precision coffee farming. This work was conducted at Brejão Farm in Três Pontas, Minas Gerais, Brazil, in a coffee field planted with 22 ha of Topázio cultivar. The evaluated variables were the yield, plant height, crown diameter, fruit maturation index, degree of fruit maturation, leafing, soil pH, available phosphorus (P), remaining phosphorus (Prem), available potassium (K), exchangeable calcium (Ca2+), exchangeable magnesium (Mg2+), exchangeable acidity (Al3+), potential acidity (H + Al), aluminium saturation (N(Al)), potential CEC (CECp), actual CEC (CECa), sum of bases (SB), base saturation (BS) and organic matter (OM). The data were evaluated by a principal component analysis, which generated 20 components. Of these, 7 representing 88.98% of the data variation were chosen. The variables were discarded based on the preservation of the variables with the greatest coefficients in absolute values corresponding to the first component, followed by the variable with the second highest absolute value corresponding to the second principal component. Based on the results, the variables V, OM, fruit maturity index, plant height, yield, leafing and P were selected. The other variables were discarded.

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