Seeds as Potential Sources of Phenolic Compounds and Minerals for the Indian Population.

Seeds are major sources of nutrients and bioactive compounds for human beings. In this work, the chemical composition and physicochemical properties of 155 Indian seeds (belonging to 49 families) are reported. Moisture and ash were measured with reference protocols from AOAC; total polyphenols and flavonoids were measured with spectrophotometric methods after extraction with organic solvents, and mineral elements were determined by X-ray fluorescence spectrophotometry. Total phenolic compounds, flavonoids and mineral contents (Al, Ba, Ca, Cl, Co, Cr, Cu, Fe, K, Mg, Mn, Mo, Na, P, Rb, S, Sr, Ti, V and Zn) were found to vary in the ranges 182−5000, 110−4465 and 687−7904 mg/100 g (DW), respectively. Noticeably, polyphenol contents higher than 2750 mg/100 g were observed in 18 seeds. In addition, mineral contents >5000 mg/100 g were detected in the seeds from Cuminum cyminum, Foeniculum vulgare, Commiphora wightii, Parkia javanica, Putranjiva roxburghii, Santalum album and Strychnos potatorum. Botanical and taxonomical variations in the proximate characteristics of the examined seeds are also discussed.

Mineral-nutrient relationships in African soils assessed using cluster analysis of X-ray powder diffraction patterns and compositional methods.

Soil mineral compositions are often complex and spatially diverse, with each mineral exhibiting characteristic chemical properties that determine the intrinsic total concentration of soil nutrients and their phyto-availability. Defining soil mineral-nutrient relationships is therefore important for understanding the inherent fertility of soils for sustainable nutrient management, and data-driven approaches such as cluster analysis allow for these relations to be assessed in new detail. Here the fuzzy-c-means clustering algorithm was applied to an X-ray powder diffraction (XRPD) dataset of 935 soils from sub-Saharan Africa, with each diffractogram representing a digital signature of a soil's mineralogy. Nine mineralogically distinct clusters were objectively selected from the soil mineralogy continuum by retaining samples exceeding the 75 % quantile of the membership coefficients in each cluster, yielding a dataset of 239 soils. As such, samples within each cluster represented mineralogically similar soils from different agro-ecological environments of sub-Saharan Africa. Mineral quantification based on the mean diffractogram of each cluster illustrated substantial mineralogical diversity between the nine groups with respect to quartz, K-feldspar, plagioclase, Fe/Al/Ti-(hydr)oxides, phyllosilicates (1:1 and 2:1), ferromagnesians, and calcite. Mineral-nutrient relationships were defined using the clustered XRPD patterns and corresponding measurements of total and/or extractable (Mehlich-3) nutrient concentrations (B, Mg, K, Ca, Mn, Fe, Ni, Cu and Zn) in combination with log-ratio compositional data analysis. Fe/Al/Ti/Mn-(hydr)oxides and feldspars were found to be the primary control of total nutrient concentrations, whereas 2:1 phyllosilicates were the main source of all extractable nutrients except for Fe and Zn. Kaolin minerals were the most abundant phyllosilicate group within the dataset but did not represent a nutrient source, which reflects the lack of nutrients within their chemical composition and their low cation exchange capacity. Results highlight how the mineral composition controls the total nutrient reserves and their phyto-availability in soils of sub-Saharan Africa. The typical characterisation of soils and their parent material based on the clay particle size fraction (i.e. texture) and/or the overall silica component (i.e. acid and basic rock types) alone may therefore mask the intricacies of mineral contributions to soil nutrient concentrations.

Portable X-ray fluorescence (pXRF) calibration for analysis of nutrient concentrations and trace element contaminants in fertilisers.

With the increasing popularity of local blending of fertilisers, the fertiliser industry faces issues regarding quality control and fertiliser adulteration. Another problem is the contamination of fertilisers with trace elements that have been shown to subsequently accumulate in the soil and be taken up by plants, posing a danger to the environment and human health. Conventional characterisation methods necessary to ensure the quality of fertilisers and to comply with local regulations are costly, time consuming and sometimes not even accessible. Alternatively, using a wide range of unamended and intentionally amended fertilisers this study developed empirical calibrations for a portable handheld X-ray fluorescence (pXRF) spectrometer, determined the reliability for estimating the macro and micro nutrients and evaluated the use of the pXRF for the high-throughput detection of trace element contaminants in fertilisers. The models developed using pXRF for Mg, P, S, K, Ca, Mn, Fe, Zn and Mo had R2 values greater or equal to 0.97. These models also performed well on validation, with R2 values greater or equal to 0.97 (except for Fe, R2val = 0.55) and slope values ranging from 0.81 to 1.44. A second set of models were developed with a focus on trace elements in amended fertilisers. The R2 values of calibration for Co, Ni, As, Se, Cd and Pb were greater than or equal to 0.80. At concentrations up to 1000 mg kg-1, good validation statistics were also obtained; R2 values ranged from 0.97-0.99, except in one instance. The regression coefficients of the validation also had good prediction in the range of 0-100 mg kg-1 (R2 values were from 0.78-0.99), but not as well at lower concentrations up to 20 mg kg-1 (R2 values ranged from 0.10-0.99), especially for Cd. This study has demonstrated that pXRF can measure several major (P, Ca) and micro (Mn, Fe, Cu) nutrients, as well as trace elements and potential contaminants (Cr, Ni, As) in fertilisers with high accuracy and precision. The results obtained in this study is good, especially considering that loose powders were scanned for a maximum of 90 seconds without the use of a vacuum pump.

Comprehensive nutrient analysis in agricultural organic amendments through non-destructive assays using machine learning.

Portable X-ray fluorescence (pXRF) and Diffuse Reflectance Fourier Transformed Mid-Infrared (DRIFT-MIR) spectroscopy are rapid and cost-effective analytical tools for material characterization. Here, we provide an assessment of these methods for the analysis of total Carbon, Nitrogen and total elemental composition of multiple elements in organic amendments. We developed machine learning methods to rapidly quantify the concentrations of macro- and micronutrient elements present in the samples and propose a novel system for the quality assessment of organic amendments. Two types of machine learning methods, forest regression and extreme gradient boosting, were used with data from both pXRF and DRIFT-MIR spectroscopy. Cross-validation trials were run to evaluate generalizability of models produced on each instrument. Both methods demonstrated similar broad capabilities in estimating nutrients using machine learning, with pXRF being suitable for nutrients and contaminants. The results make portable spectrometry in combination with machine learning a scalable solution to provide comprehensive nutrient analysis for organic amendments.

Quantification of total element concentrations in soils using total X-ray fluorescence spectroscopy (TXRF).

Total X-ray fluorescence spectroscopy (TXRF) determines concentrations of major and trace elements in multiple media. We developed and tested a method for the use of TXRF for direct quantification of total element concentrations in soils using an S2 PICOFOX™ spectrometer (Bruker AXS Microanalysis GmbH, Germany). We selected 15 contrasting soil samples from across sub-Saharan Africa for element analysis to calibrate the instrument against concentrations determined using the inductively coupled plasma-mass spectroscopy (ICP-MS) standard method. A consistent underestimation of element concentrations using TXRF compared to ICP-MS reference analysis occurred, indicating that spectrometer recalibration was required. Single-element recalibration improved the TXRF spectrometer's sensitivity curve. Subsequent analysis revealed that TXRF determined total element concentrations of Al, K, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, and Ga accurately (model efficacy/slope close to 1:1 line, and R(2)>0.80) over a wide range of soil samples. Other elements that could be estimated with an acceptable precision (R(2)>0.60) compared with ICP-MS although generally somewhat under- or overestimated were P, Ca, As, Rb, Sr, Y, Pr, Ta and Pb. Even after recalibration, compared to ICP-MS the TXRF spectrometer produced underestimations for elements Na, Mg, Ba, Ce, Hf, La, Nd, W and Sm and overestimations for elements Bi, Tl and Zr. We validated the degree of accuracy of the TXRF analytical method after recalibration using an independent set of 20 soil samples. We also tested the accuracy of the analysis using 2 multi-element standards as well as the method repeatability on replicate samples. The resulting total element concentration repeatability for all elements analyzed were within 10% coefficient of variability after the instrument recalibration except for Cd and Tl. Our findings demonstrate that TXRF could be used as a rapid screening tool for total element concentrations in soils assuming that sufficient calibration measures are followed.

Applicability of near-infrared reflectance spectroscopy (NIRS) for determination of crude protein content in cowpea (Vigna unguiculata) leaves.

There is uncertainty on how generally applicable near-infrared reflectance spectroscopy (NIRS) calibrations are across genotypes and environments, and this study tests how well a single calibration performs across a wide range of conditions. We also address the optimization of NIRS to perform the analysis of crude protein (CP) content in a variety of cowpea accessions (n = 561) representing genotypic variation as well as grown in a wide range of environmental conditions in Tanzania and Uganda. The samples were submitted to NIRS analysis and a predictive calibration model developed. A modified partial least-squares regression with cross-validation was used to evaluate the models and identify possible spectral outliers. Calibration statistics for CP suggests that NIRS can predict this parameter in a wide range of cowpea leaves from different agro-ecological zones of eastern Africa with high accuracy (R (2)cal = 0.93; standard error of cross-validation = 0.74). NIRS analysis improved when a calibration set was developed from samples selected to represent the range of spectral variability. We conclude from the present results that this technique is a good alternative to chemical analysis for the determination of CP contents in leaf samples from cowpea in the African context, as one of the main advantages of NIRS is the large number of compounds that can be measured at once in the same sample, thus substantially reducing the cost per analysis. The current model is applicable in predicting the CP content of young cowpea leaves for human nutrition from different agro-ecological zones and genetic materials, as cowpea leaves are one of the popular vegetables in the region.