Somsubhra Chakraborty

Use of portable X-ray fluorescence spectrometry for environmental quality assessment of peri-urban agriculture

Urban expansion into traditional agricultural lands has augmented the potential for heavy metal contamination of soils. This study examined the utility of field portable X-ray fluorescence (PXRF) spectrometry for evaluating the environmental quality of sugarcane fields near two industrial complexes in Louisiana, USA. Results indicated that PXRF provided quality results of heavy metal levels comparable to traditional laboratory analysis. When coupled with global positioning system technology, the use of PXRF allows for on-site interpolation of heavy metal levels in a matter of minutes. Field portable XRF was shown to be an effective tool for rapid assessment of heavy metals in soils of peri-urban agricultural areas.

Development of a hybrid proximal sensing method for rapid identification of petroleum contaminated soils.

UNLABELLED Using 108 petroleum contaminated soil samples, this pilot study proposed a new analytical approach of combining visible near-infrared diffuse reflectance spectroscopy (VisNIR DRS) and portable X-ray fluorescence spectrometry (PXRF) for rapid and improved quantification of soil petroleum contamination. Results indicated that an advanced fused model where VisNIR DRS spectra-based penalized spline regression (PSR) was used to predict total petroleum hydrocarbon followed by PXRF elemental data-based random forest regression was used to model the PSR residuals, it outperformed (R(2)=0.78, residual prediction deviation (RPD)=2.19) all other models tested, even producing better generalization than using VisNIR DRS alone (RPDs of 1.64, 1.86, and 1.96 for random forest, penalized spline regression, and partial least squares regression, respectively). Additionally, unsupervised principal component analysis using the PXRF+VisNIR DRS system qualitatively separated contaminated soils from control samples. CAPSULE Fusion of PXRF elemental data and VisNIR derivative spectra produced an optimized model for total petroleum hydrocarbon quantification in soils.

Soil Salinity Measurement Via Portable X-ray Fluorescence Spectrometry

Abstract Saline soils are defined as those containing appreciable salts more soluble than gypsum (e.g., various combinations of Na+, Mg2+, Ca2+, K+, Cl−, SO42-, HCO3−, and CO32-). Saline soils can occur across diverse climates and geological settings. As such, salinity is not germane to specific soil textures or parent materials. Traditional methods of measuring soil salinity (e.g., electrical conductance), although accurate, provide limited data and require laboratory analysis. Given the success of previous studies using portable X-ray fluorescence (PXRF) as a tool for measuring soil characteristics, this study evaluated its applicability for soil salinity determination. Portable X-ray fluorescence offers accurate quantifiable data that can be produced rapidly, in situ, and with minimal sample preparation. For this study, 122 surface soil samples (0–15 cm) were collected from salt-impacted soils of coastal Louisiana. Soil samples were subjected to standard soil characterization, including particle size analysis, loss-on-ignition organic matter, electrical conductivity (EC), and elemental quantification via PXRF. Simple and multiple linear regression models were developed to correlate elemental concentrations and auxiliary input parameters (simple: Cl; multiple: Cl, S, K, Ca, sand, clay, and organic matter) to EC results. In doing so, logarithmic transformation was used to normalize the variables to obtain a normal distribution for the error term (residual, ei). Although both models resulted in similar acceptable r2 between soil EC and elemental data produced by PXRF (0.83 and 0.90, respectively), multiple linear regression is recommended. In summary, PXRF has the ability to predict soil EC with reasonable accuracy from elemental data.

Spectral data mining for rapid measurement of organic matter in unsieved moist compost

Fifty-five compost samples were collected and scanned as received by visible and near-IR (VisNIR, 350-2500 nm) diffuse reflectance spectroscopy. The raw reflectance and first-derivative spectra were used to predict log(10)-transformed organic matter (OM) using partial least squares (PLS) regression, penalized spline regression (PSR), and boosted regression trees (BRTs). Incorporating compost pH, moisture percentage, and electrical conductivity as auxiliary predictors along with reflectance, both PLS and PSR models showed comparable cross-validation r(2) and validation root-mean-square deviation (RMSD). The BRT-reflectance model exhibited best predictability (residual prediction deviation=1.61, cross-validation r(2)=0.65, and RMSD=0.09 log(10)%). These results proved that the VisNIR-BRT model, along with easy-to-measure auxiliary variables, has the potential to quantify compost OM with reasonable accuracy.

Bamboo: an overview on its genetic diversity and characterization.

Genetic diversity represents the heritable variation both within and among populations of organisms, and in the context of this paper, among bamboo species. Bamboo is an economically important member of the grass family Poaceae, under the subfamily Bambusoideae. India has the second largest bamboo reserve in Asia after China. It is commonly known as “poor man’s timber”, keeping in mind the variety of its end use from cradle to coffin. There is a wide genetic diversity of bamboo around the globe and this pool of genetic variation serves as the base for selection as well as for plant improvement. Thus, the identification, characterization and documentation of genetic diversity of bamboo are essential for this purpose. During recent years, multiple endeavors have been undertaken for characterization of bamboo species with the aid of molecular markers for sustainable utilization of genetic diversity, its conservation and future studies. Genetic diversity assessments among the identified bamboo species, carried out based on the DNA fingerprinting profiles, either independently or in combination with morphological traits by several researchers, are documented in the present review. This review will pave the way to prepare the database of prevalent bamboo species based on their molecular characterization.

Rapid estimation of compost enzymatic activity by spectral analysis method combined with machine learning

The aim of this study was to investigate the feasibility of using visible near-infrared (VisNIR) diffuse reflectance spectroscopy (DRS) as an easy, inexpensive, and rapid method to predict compost enzymatic activity, which traditionally measured by fluorescein diacetate hydrolysis (FDA-HR) assay. Compost samples representative of five different compost facilities were scanned by DRS, and the raw reflectance spectra were preprocessed using seven spectral transformations for predicting compost FDA-HR with six multivariate algorithms. Although principal component analysis for all spectral pretreatments satisfactorily identified the clusters by compost types, it could not separate different FDA contents. Furthermore, the artificial neural network multilayer perceptron (residual prediction deviation=3.2, validation r(2)=0.91 and RMSE=13.38 μg g(-1) h(-1)) outperformed other multivariate models to capture the highly non-linear relationships between compost enzymatic activity and VisNIR reflectance spectra after Savitzky-Golay first derivative pretreatment. This work demonstrates the efficiency of VisNIR DRS for predicting compost enzymatic as well as microbial activity.

Visible near infrared diffuse reflectance spectroscopy (VisNIR DRS) for rapid measurement of organic matter in compost

Commercial compost is the inherently variable organic product of a controlled decomposition process. In the USA, assessment of compost’s physicochemical parameters presently relies on standard laboratory analyses set forth in Test Methods for the Examination of Composting and Compost (TMECC). A rapid, field-portable means of assessing the organic matter (OM) content of compost products would be useful to help producers ensure optimal uniformity in their compost products. Visible near infrared diffuse reflectance spectroscopy (VisNIR DRS) is a rapid, proximal-sensing technology proven effective at quantifying organic matter levels in soils. As such, VisNIR DRS was evaluated to assess its applicability to compost. Thirty-six compost samples representing a wide variety of source materials and moisture content were collected and scanned with VisNIR DRS under moist and oven-dry conditions. Partial least squares (PLS) regression and principal component regression (PCR) were used to relate the VisNIR DRS spectra with laboratory-measured OM to build compost OM prediction models. Raw reflectance, and first- and second-derivatives of the reflectance spectra were considered. In general, PLS regression outperformed PCR and the oven-dried first-derivative PLS model produced an r2 value of 0.82 along with a residual prediction deviation value of 1.72. As such, VisNIR DRS shows promise as a suitable technique for the analysis of compost OM content for dried samples.

In-Situ Differentiation of Acidic and Non-Acidic Tundra via Portable X-ray Fluorescence （PXRF） Spectrometry

Abstract Frozen soils or those with permafrost cover large areas of the earths surface and support unique vegetative ecosystems. Plants growing in such harsh conditions have adapted to small niches, which allow them to survive. In northern Alaska, USA, both moist acidic and non-acidic tundra occur, yet determination of frozen soil pHs currently requires thawing of the soil so that electrometric pH methods can be utilized. Contrariwise, a portable X-ray fluorescence (PXRF) spectrometer was used in this study to assess elemental abundances and relate those characteristics to soil pH through predictive multiple linear regressions. Two operational modes, Soil Mode and Geochem Mode, were utilized to scan frozen soils in-situ and under laboratory conditions, respectively, after soil samples were dried and ground. Results showed that lab scanning produced optimal results with adjusted coefficient of determination (R2) of 0.88 and 0.33 and root mean squared errors (RMSEs) of 0.87 and 0.34 between elemental data and lab-determined pH for Soil Mode and Geochem Mode, respectively. Even though the presence of ice attenuated fluoresced radiation under field conditions, adjusted R2 and RMSEs between the datasets still provided reasonable model generalization (e.g., 0.73 and 0.49 for field Geochem Mode). Principal component analysis qualitatively separated multiple sampling sites based on elemental data provided by PXRF, reflecting differences in the chemical composition of the soils studied. Summarily, PXRF can be used for in-situ determination of soil pH in arctic environments without the need for sample modification and thawing. Furthermore, use of PXRF for determination of soil pH may provide higher sample throughput than traditional eletrometric-based methods, while generating elemental data useful for the prediction of multiple soil parameters.

Selection of Rice Genotypes for Salinity Tolerance Through Morpho-Biochemical Assessment

The present study reported the morpho-biochemical evaluation of 15 selected rice genotypes for salt tolerance at the seedling stage. Growth parameters including shoot length, root length, plant biomass, plant turgid weight, plant dry weight along with relative water content were measured after exposure to saline solution (with electrical conductivity value of 12 dS/m). Genotypes, showing significant differential responses towards salinity in the fields, were assessed through 14 salinity-linked morpho-biochemical attributes, measured at 14 d after exposure of seedling in saline nutrient solution. Relative water content, chlorophyll a/b, peroxidase activity and plant biomass were identified as potential indicators of salt tolerance. Principal component analysis and successive Hierarchical clustering using Euclidean distance revealed that Talmugur, Gheus, Ghunsi, Langalmura, Sabitapalui, and Sholerpona were promising genotypes for further breeding programmes in rice. The maximum Euclidean distance was plotted between Thavallakanan and Talmugur (7.49), followed by Thavallakanan and Langalmura (6.82), indicating these combinations may be exploited as parental lines in hybridization programmes to develop salinity tolerant variety.

Forensic identification of pharmaceuticals via portable X-ray fluorescence and diffuse reflectance spectroscopy

The importance of unknown substance identification in forensic science is vital to implementation or exclusion of criminal charges against an offender. While traditional laboratory measures include the use of gas chromatography/mass spectroscopy, an alternate method has been proposed to efficiently perform presumptive analyses of unknown substances at a crime scene or at airport security points. The use of portable X-ray fluorescence (PXRF) and visible near infrared diffuse reflectance spectroscopy (DRS) to determine elemental composition was applied to pharmaceutical medications (n=83), which were then categorized into 21 classifications based on their active ingredients. Each pharmaceutical was processed by standard laboratory procedures and scanned with both PXRF and DRS. Lastly, the datasets obtained were compared using multivariate statistical analyses. The aforementioned devices indicate that differentiation of unknown substances is clearly demonstrated among the samples with 73.49% DRS classification accuracy. Thus, the approach shows promise for future development as a rapid analytical technique for unknown pharmaceutical substances and/or illicit narcotics.