FTIR-DRIFTS-based prediction of β-carotene, α-tocopherol and L-ascorbic acid in mango (Mangifera indica L.) fruit pulp

Mango fruits contain substantial vitamins and dietary fibre. Vitamins vary among and within fruits depending on cultivar type and ripening stage. Conventional techniques of vitamins analysis are based on High Pressure Liquid Chromatography, which are costly and laborious. This study evaluated the potential of Fourier transform infrared-diffuse reflectance spectroscopy (FTIR-DRIFTS) technique in predicting β-carotene, α-tocopherol and L-ascorbic acid in pulps of four mango cultivar types (‘Apple’, ‘Kent’, ‘Ngowe’, and ‘Tommy Atkins’). Combination of ran dom forest (RF) and first derivative spectra developed the predictive models. Factorial ANOVA examined the interaction effect of cultivar type, site (‘Thika’, ‘Embu’ and ‘Machakos), and fruit canopy position (sun exposed/within crown) on β-carotene, α-tocopherol and L-ascorbic acid contents. RF Models gave R2 = 0.97, RMSE = 2.27, RPD = 0.72 for β-carotene; R2 = 0.98, RMSE = 0.26, RPD = 0.30 for α-tocopherol and R2 = 0.96, RMSE = 0.51, RPD = 1.96 for L-ascorbic acid. Generally cultivar type affected vitamin C, F (3, 282) = 7.812, p < 0.05. Apple and Tommy Atkins had higher mean vitamins than Ngowe and Kent. In Machakos, within canopy fruits had higher β-carotene than sun-exposed fruits, F (5, 257) = 2.328, p = 0.043. However, interactions between fruit position, site and cultivar did not affect α-tocopherol and vitamin C. In Thika, Tommy Atkins at fully ripe stage had higher vitamin C than at intermediate maturity stage, F (2, 143) = 7.328, p = 0.01. These results show that FTIR-DRIFTS spectroscopy is a high-throughput method that can be used to predict mango fruit vitamins of in a large data set

A simple field based method for rapid wood density estimation for selected tree species in Western Kenya

Wood density is an important variable for accurate quantification of woody biomass and carbon stocks. Conventional destructive methods for wood density estimation are resource intensive, prohibiting their use, limiting the application of approaches that would minimize uncertainties in tree biomass estimates. We tested an alternative method involving tree coring with a carpenter's auger to estimate wood density of seven tropical tree species in Western Kenya. We used conventional water immersion method to validate results from the auger core method. The mean densities (and 95% confidence intervals) ranged from 0.36 g cm−3 (0.25–0.47) to 0.67 g cm−3 (0.61–0.73) for the auger core method, and 0.46 g cm−3 (0.42–0.50) to 0.67 g cm−3 (0.61–0.73) for the water immersion method. The auger core and water immersion methods were not significantly different for four out of seven tree species namely; Acacia mearnsii, Mangifera indica, Eucalyptus grandis and Grevillea robusta. However, wood densities estimated from the auger core method were lower (t (61) = 7.992, P = <0.001). The ease of the auger core method application, as a non-destructive method in acquiring wood density data, is a worthy alternative in biomass and carbon stocks quantification. This method could protect trees outside forests found in most parts of Africa

The soils lab in a suitcase

Keith Shepherd describes how infrared spectroscopy and satellite imagery are helping African farmers boost crop production

Un labo d'étude des sols dans une valise

Keith Shepherd nous décrit l'aide que la spectroscopie infrarouge et les images satellites peuvent apporter aux paysans africains pour accroître les rendements

Total elemental composition of soils in Sub-Saharan Africa and relationship with soil forming factors

AbstractA thorough understanding of the variation in total soil element concentrations is important especially in the Sub-Saharan Africa (SSA) soil contexts for agricultural and environmental management at large scale. Fingerprinting of soil elemental composition may form a useful basis for evaluating soils in a way that relates to soil-forming factors and inherent soil functional properties. The objectives of this paper are to quantify the proportion of variability in total elemental composition by total X-ray fluorescence (TXRF) method of 1074 soil samples from the Africa Soil Information Service (AfSIS) Project baseline and to determine the relationships with soil forming factors. The samples were from 34 sentinel sites measuring 10×10km, randomized within major climate zones in SSA. Within each sentinel site there were sixteen spatially stratified 1km2 clusters, within which there were ten 100m2 plots. The within and between site patterns of variation in total element composition of 17 elements; Al, P, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Ga, Sr, Y, Ta, and Pb, were explored. Total element concentration values were within the range reported globally for soil Cr, Mn, Zn, Ni, V, Sr, and Y and higher than reported range for Al, Cu, Ta, Pb, and Ga. There were significant variations (P<0.05) in total element composition within and between the sites for all the elements analyzed with the greatest proportion of total variance and number of significant variance components occurring at the site (55–88%) followed by the cluster nested within site (10–40%) levels. The explorations of the relationships between element composition data and site factors using Random Forest regression demonstrated that soil-forming factors have important influence on total elemental composition in the soil. The fact that the soil-forming factors are related to the concentration of naturally occurring elements in the soil gives rise to the notion that they might be predicted from the soils' element composition. Results implied that >70% of variation in soil element composition patterns can be predicted using information in existing databases or readily observable features. Successful use of TXRF technique would open up possibilities for using total soil elemental composition fingerprints as a useful basis for characterizing soils in a way that relates to soil-forming factors and inherent soil functional properties

Improved algebraic cryptanalysis of QUAD, Bivium and Trivium via graph partitioning on equation systems

We present a novel approach for preprocessing systems of polynomial equations via graph partitioning. The variable-sharing graph of a system of polynomial equations is defined. If such graph is disconnected, then the corresponding system of equations can be split into smaller ones that can be solved individually. This can provide a tremendous speed-up in computing the solution to the system, but is unlikely to occur either randomly or in applications. However, by deleting certain vertices on the graph, the variable-sharing graph could be disconnected in a balanced fashion, and in turn the system of polynomial equations would be separated into smaller systems of near-equal sizes. In graph theory terms, this process is equivalent to finding balanced vertex partitions with minimum-weight vertex separators. The techniques of finding these vertex partitions are discussed, and experiments are performed to evaluate its practicality for general graphs and systems of polynomial equations. Applications of this approach in algebraic cryptanalysis on symmetric ciphers are presented: For the QUAD family of stream ciphers, we show how a malicious party can manufacture conforming systems that can be easily broken. For the stream ciphers Bivium and Trivium, we nachieve significant speedups in algebraic attacks against them, mainly in a partial key guess scenario. In each of these cases, the systems of polynomial equations involved are well-suited to our graph partitioning method. These results may open a new avenue for evaluating the security of symmetric ciphers against algebraic attacks

Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning

Spatial predictions of soil macro and micro-nutrient content across Sub-Saharan Africa at 250 m spatial resolution and for 0–30 cm depth interval are presented. Predictions were produced for 15 target nutrients: organic carbon (C) and total (organic) nitrogen (N), total phosphorus (P), and extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), aluminum (Al) and boron (B). Model training was performed using soil samples from ca. 59,000 locations (a compilation of soil samples from the AfSIS, EthioSIS, One Acre Fund, VitalSigns and legacy soil data) and an extensive stack of remote sensing covariates in addition to landform, lithologic and land cover maps. An ensemble model was then created for each nutrient from two machine learning algorithms

Changes in organic carbon to clay ratios in different soils and land uses in England and Wales over time

Realistic targets for soil organic carbon (SOC) concentrations are needed, accounting for differences between soils and land uses. We assess the use of SOC/clay ratio for this purpose by comparing changes over time in (a) the National Soil Inventory of England and Wales, first sampled in 1978–1983 and resampled in 1994–2003, and (b) two long-term experiments under ley-arable rotations on contrasting soils in the East of England. The results showed that normalising for clay concentration provides a more meaningful separation between land uses than changes in SOC alone. Almost half of arable soils in the NSI had degraded SOC/clay ratios ( 1/8, respectively. Given the wide range of soils and land uses across England and Wales in the datasets used to test these targets, they should apply across similar temperate regions globally, and at national to sub-regional scales.Biotechnology and Biological Sciences Research Council (BBSRC): BBS/E/C/000I0310 and BBS/E/C/000 J0300. Lawes Agricultural Trus