Image processing and impact analyses of terminal heat stress on yield of lentil.

Abiotic factors, including heat stress, significantly impact the growth and development of lentil across the globe. Although these stresses impact the plant's phenotypic, genotypic, metabolic, and yield development, predicting those traits in lentil is challenging. This study aimed to construct a machine learning-based yield prediction model for lentil using various yield attributes under two different sowing conditions. Twelve genotypes were planted in open-field conditions, and images were captured 45 days after sowing (DAS) and 60 DAS to make predictions for agro-morphological traits with the assessment for the influence of high-temperature stress on lentil growth. Greening techniques like Excess Green, Modified Excess Green (ME × G), and Color Index of Plant Extraction (CIVE) were used to extract 35 vegetative indices from the crop image. Random forest (RF) regression and artificial neural network (ANN) models were developed for both the normal-sown and late-sown lentils. The ME × G-CIVE method with Otsu's thresholding provided superior performance in image segmentation, while the RF model showed the highest level of model generalization. This study demonstrated that yield per plant and number of pods per plant were the most significant attributes for early prediction of lentil production in both conditions using the RF models. After harvesting, various yield parameters of the selected genotypes were measured, showing significant reductions in most traits for the late-sown plants. Heat-tolerant genotypes like RLG-05, Kota Masoor-1, and Kota Masoor-2 depicted decreased yield and harvest index (HI) reduction than the heat-sensitive HUL-57. These findings warrant further study to correlate the data with more stress-modulating attributes. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-024-04031-5.

Prediction of compost organic matter via color sensor.

Composted materials serve as an effective soil nutrient amendment. Organic matter in compost plays an important role in quantifying composted materials overall quality and nutrient content. Measuring organic matter content traditionally takes considerable time, resources, and various laboratory equipment (e.g., oven, muffle furnace, crucibles, precision balance). Much like the quantitative color indices (e.g., sRGB R, sRGB G, sRGB B, CIEL*a* b*) derived from the low-cost NixPro2 color sensor have proven adept at predicting soil organic matter in-situ, the NixPro2 color sensor has the potential to be effective for predicting organic matter in composted materials without the need for traditional laboratory methods. In this study, a total of 200 compost samples (13 different compost types) were measured for organic matter content via traditional loss-on-ignition (LOI) and via the NixPro2 color sensor. The NixPro2 color sensor showed promising results with an LOI-prediction model utilizing the CIEL*a* b* color model through the application of the Generalized Additive Model (GAM) algorithm yielding an excellent prediction accuracy (validation R2 = 0.87, validation RMSE = 4.66 %). Moreover, the PCA scoreplot differentiated the three lowest organic matter compost types from the remaining 10 compost types. These results have valuable practical significance for the compost industry by predicting compost organic matter in real time without the need for laborious, time-consuming methods.

Innovative utilization of harvested mushroom substrate for green synthesis of silver nanoparticles: A multi-response optimization approach.

In this work, harvested mushroom substrate (HMS) has been explored for the first time through a comprehensive optimization study for the green synthesis of silver nanoparticles (AgNPs). A multiple response central composite design with three parameters: pH of the reaction mixture, temperature, and incubation period at three distinct levels was employed in the optimization study. The particle size of AgNPs, UV absorbance, and the percentage of Ag/Cl elemental ratio were considered as the response parameters. For each response variable examined the model used was found to be significant (P < 0.05). The ideal conditions were: pH 8.9, a temperature of 59.4 °C, and an incubation period of 48.5 h. The UV-visible spectra of AgNPs indicated that the absorption maxima for AgNP-3 were 414 nm, 420 for AgNPs-2, and 457 for AgNPs-1. The XRD analysis of AgNPs-3 and AgNPs-2 show a large diffraction peak at ∼38.2°, ∼44.2°, ∼64.4°, and ∼77.4°, respectively, which relate to the planes of polycrystalline face-centered cubic (fcc) silver. Additionally, the XRD result of AgNPs-1, reveals diffraction characteristics of AgCl planes (111, 200, 220, 311, 222, and 400). The TEM investigations indicated that the smallest particles were synthesized at pH 9 with average diameters of 35 ± 6 nm (AgNPs-3). The zeta potentials of the AgNPs are -36 (AgNPs-3), -28 (AgNPs-2), and -19 (AgNPs-1) mV, respectively. The distinct IR peak at 3400, 1634, and 1383 cm-1 indicated the typical vibration of phenols, proteins, and alkaloids, respectively. The AgNPs were further evaluated against gram (+) strain Bacillus subtilis (MTCC 736) and gram (-) strain Escherichia coli (MTCC 68). All of the NPs tested positive for antibacterial activity against both bacterial strains. The study makes a sustainable alternative to disposing of HMS to achieve the Sustainable Development Goals (SDGs).

A Smartphone-Enabled Imaging Device for Chromotropic Acid-Based Measurement of Nitrate in Soil Samples.

In this study, a novel chromotropic acid-based color development method was proposed for quick estimation of soil nitrate (NO3-). The method utilized a 3D printed device integrated with the rear-end camera of a smartphone and a stand-alone application called SMART NP. By analyzing the mean Value (V) component of the sample's image, the SMART NP provides instant predictions of soil NO3- levels. The limit of detection was calculated as 0.1 mg L-1 with a sensitivity of 0.26 mg L-1. The device showed a % bias of 0.9% and a precision of 1.95%, indicating its reliability. Additionally, the device-predicted soil NO3- data, combined with kriging interpolation, showcased spatial variability in soil NO3- levels at the regional level. The study employed a Gaussian model of variogram for kriging, and the high Nugget/Sill ratio indicated low spatial autocorrelation, emphasizing the impact of management factors on the spatial distribution of soil NO3- content in the study area. Overall, the imaging device, along with geostatistical interpolation, provided a comprehensive solution for the rapid assessment of spatial variability in soil NO3-content.

Detailed characterization of iron-rich tailings after the Fundão dam failure, Brazil, with inclusion of proximal sensors data, as a secure basis for environmental and agricultural restoration.

Following the Fundão dam failure in Brazil, 60 million m3 of iron-rich tailings were released impacting an extensive area. After this catastrophe, a detailed characterization and monitoring of iron-rich tailings is required for agronomic and environmental purposes. This can be facilitated by using proximal sensors which have been an efficient, fast, and cost-effective tool for eco-friendly analysis of soils and sediments. This work hypothesized that portable X-ray fluorescence (pXRF) spectrometry combined with a pocket-sized (Nix™ Pro) color sensor and benchtop magnetic susceptibilimeter can produce substantial data for fast and clean characterization of iron-rich tailings. The objectives were to differentiate impacted and non-impacted areas (soils and sediments) based on proximal sensors data, and to predict attributes of agronomic and environmental importance. A total of 148 composite samples were collected on totally impacted, partially impacted, and non-impacted areas (natural soils). The samples were analyzed via pXRF to obtain the total elemental composition; via Nix™ Pro color sensor to obtain the red (R), green (G), and blue (B) parameters; and assessed for magnetic susceptibility (MS). The same samples used for analyses via the aforementioned sensors were wet-digested (USEPA 3051a method) followed by ICP-OES quantification of potentially toxic elements. Principal component analysis was performed to differentiate impacted and non-impacted areas. The pXRF data alone or combined with other sensors were used to predict soil agronomic properties and semi-total concentration of potentially toxic elements via random forest regression. For that, samples were randomly separated into modeling (70%) and validation (30%) datasets. The pXRF proved to be an efficient method for rapid and eco-friendly characterization of iron-rich tailings, allowing a clear differentiation of impacted and non-impacted areas. Also, important soil agronomic properties (clay, cation exchange capacity, soil organic carbon, pH and macronutrients availability) and semi-total concentrations of Ba, Pb, Cr, V, Cu, Co, Ni, Mn, Ti, and Li were accurately predicted (based upon the lowest RMSE and highest R2 and RPD values). Sensor data fusion (pXRF + Nix Pro + MS) slightly improved the accuracy of predictions. This work highlights iron-rich tailings from the Fundão dam failure can be in detail characterized via pXRF ex situ, providing a secure basis for complementary studies in situ aiming at identify contaminated hot spots, digital mapping of soil and properties variability, and embasing pedological, agricultural and environmental purposes.

Soil quality under different land uses in eastern India: Evaluation by using soil indicators and quality index.

Indian soils are inherently poor in quality due to the warm climate and erosion. Conversion of land uses like forests to croplands and faulty management practices in croplands further cause soil degradation. This study aimed to understand the extent of these impacts in a small representative part of eastern India, covering Himalayan terai and nearing alluvial plains. Soils were collected from (i) forests, (ii) croplands (under agricultural practices for more than 50-60 years) and (iii) converted lands (converted from forests to croplands or tea gardens over the past 15-20 years). Different soil quality indicators were assessed and soil quality index (SQI) was generated to integrate, scale and allot a single value per soil. Results indicated that continuous organic matter deposition and no disturbances consequence the highest presence of soil carbon pools, greater aggregation and maximum microbial dynamics in forest soils whereas high application of straight fertilizers caused the highest available nitrogen and phosphorus in cropland soils. The SQI scorebook indicated the best soil quality under forests ([Formula: see text] 0.532), followed by soils of converted land ([Formula: see text] 0.432) and cropland ([Formula: see text] 0.301). Comparison of the SQI spatial distribution with land use and land cover confirmed the outcome. Possibly practices like excessive tillage, high cropping intensity, no legume in crop rotations, cultivation of heavy feeder crops caused degraded soil quality in croplands. This study presented an example of soil quality degradation in India due to land use change and faulty management practices. Such soil degradation on a larger scale may affect future food security.

Pocket-sized sensor for controlled, quantitative and instantaneous color acquisition of plant leaves.

The color of plant leaves can be assessed qualitatively by color charts or after processing of digital images. This pilot study employed a novel pocket-sized sensor to obtain the color of plant leaves. In order to assess its performance, a color-dependent parameter (SPAD index) was used as the dependent variable, since there is a strong correlation between SPAD index and greenness of plant leaves. A total of 1,872 fresh and intact leaves from 13 crops were analyzed using a SPAD-502 meter and scanned using the Nix™ Pro color sensor. The color was assessed via RGB and CIELab systems. The full dataset was divided into calibration (70% of data) and validation (30% of data). For each crop and color pattern, multiple linear regression (MLR) analysis and multivariate modeling [least absolute shrinkage and selection operator (LASSO), and elastic net (ENET) regression] were employed and compared. The obtained MLR equations and multivariate models were then tested using the validation dataset based on r, R2, root mean squared error (RMSE), and mean absolute error (MAE). In both RGB and CIELab color systems, the Nix™ Pro color sensor was able to differentiate crops, and the SPAD indices were successfully predicted, mainly for mango, quinoa, peach, pear, and rice crops. Validation results indicated that ENET performed best in most crops (e.g., coffee, corn, mango, pear, rice, and soy) and very close to MLR in bean, grape, peach, and quinoa. The correlation between SPAD and greenness is crop-dependent. Overall, the Nix™ Pro color sensor was a fast, sensible and an easy way to obtain leaf color directly in the field, constituting a reliable alternative to digital camera imagery and associated image processing.

Heavy Metal-Resistant Filamentous Fungi as Potential Mercury Bioremediators.

Filamentous fungi native to heavy metals (HMs) contaminated sites have great potential for bioremediation, yet are still often underexploited. This research aimed to assess the HMs resistance and Hg remediation capacity of fungi isolated from the rhizosphere of plants resident on highly Hg-contaminated substrate. Analysis of Hg, Pb, Cu, Zn, and Cd concentrations by X-ray spectrometry generated the ecological risk of the rhizosphere soil. A total of 32 HM-resistant fungal isolates were molecularly identified. Their resistance spectrum for the investigated elements was characterized by tolerance indices (TIs) and minimum inhibitory concentrations (MICs). Clustering analysis of TIs was coupled with isolates' phylogeny to evaluate HMs resistance patterns. The bioremediation potential of five isolates' live biomasses, in 100 mg/L Hg2+ aqueous solution over 48 h at 120 r/min, was quantified by atomic absorption spectrometry. New species or genera that were previously unrelated to Hg-contaminated substrates were identified. Ascomycota representatives were common, diverse, and exhibited varied HMs resistance spectra, especially towards the elements with ecological risk, in contrast to Mucoromycota-recovered isolates. HMs resistance patterns were similar within phylogenetically related clades, although isolate specific resistance occurred. Cladosporium sp., Didymella glomerata, Fusarium oxysporum, Phoma costaricensis, and Sarocladium kiliense isolates displayed very high MIC (mg/L) for Hg (140-200), in addition to Pb (1568), Cu (381), Zn (2092-2353), or Cd (337). The Hg biosorption capacity of these highly Hg-resistant species ranged from 33.8 to 54.9 mg/g dry weight, with a removal capacity from 47% to 97%. Thus, the fungi identified herein showed great potential as bioremediators for highly Hg-contaminated aqueous substrates.

Assessment of iron-rich tailings via portable X-ray fluorescence spectrometry: the Mariana dam disaster, southeast Brazil.

On November 5, 2015, the Fundão dam collapsed and released > 60 million m3 of iron-rich mining sediments into the Doce river basin, covering >1000 ha of floodplain soils across ~80 km from the rupture. The characterization of alluvial mud covering and/or mixed with native soil is a priority for successful environmental rehabilitation. Portable X-ray fluorescence (pXRF) spectrometry was used to (1) assess the elemental composition of native soils and alluvial mud across impacted riparian areas; and 2) predict fertility properties of the mud and soils that are crucial for environmental rehabilitation and vegetation establishment (e.g., pH, available macro and micronutrients, cation exchange capacity, organic matter). Native soils and alluvial mud were sampled across impacted areas and analyzed via pXRF and conventional laboratory methods. Random forest (RF) regression was used to predict fertility properties using pXRF data for pooled soil and alluvial mud samples. Mud and native surrounding soils were clearly differentiated based on chemical properties determined via pXRF (mainly SiO2, Al2O3, Fe2O3, TiO2, and MnO). The pXRF data and RF models successfully predicted pH for pooled samples (R2 = 0.80). Moderate predictions were obtained for soil organic matter (R2 = 0.53) and cation exchange capacity (R2 = 0.54). Considering the extent of impacted area and efforts required for successful environmental rehabilitation, the pXRF spectrometer showed great potential for screening impacted areas. It can assess total elemental composition, differentiate alluvial mud from native soils, and reasonably predict related fertility properties in pooled heterogeneous substrates (native soil + mud + river sediments).

Agricultural soils of the Animas River watershed after the Gold King Mine spill: An elemental spatiotemporal analysis via portable X-ray fluorescence spectroscopy.

In August 2015, 11.3 million L of heavy metal-contaminated water spilled into the Animas River from the Gold King Mine (Colorado, USA). National attention focused on water quality and agricultural production in areas affected by the spill. In response to local concerns, surface soil elemental concentrations were analyzed in three New Mexico agricultural fields to determine potential threats to agronomic production. Irrigated fields in the Animas watershed were scanned using portable X-ray fluorescence (PXRF) spectrometry to monitor the spatiotemporal variability of Pb, As, Cu, and Cr. A total of 175 locations were scanned using PXRF before and after the growing season for 3 yr. The geostatistical model with the lowest RMSE was chosen as the optimal model. The lowest RMSE for the elements ranged from to 0.10 to 0.44 m for As, from 0.50 to 0.98 m for Cr, from 0.15 to 0.91 m for Cu, and from 0.14 to 0.44 m for Pb across the models selected. The spatial dependence between the measured values exhibited strong to moderate autocorrelation for all metals except for As, for which spatial dependence was strong to weak. Some areas in each field exceeded the New Mexico Environment Department soil screening limit of 7.07 mg As kg-1 . All sampling locations were below the screening limit at last sampling time in 2019. Mixed models used for temporal analysis showed a significant decrease only in As below the screening value at the end of the study. Results indicate that the agricultural soils were below the soil screening guideline values.

Maize yield in smallholder agriculture system-An approach integrating socio-economic and crop management factors.

Yield gaps of maize (Zea mays L.) in the smallholder farms of eastern India are outcomes of a complex interplay of climatic variations, soil fertility gradients, socio-economic factors, and differential management intensities. Several machine learning approaches were used in this study to investigate the relative influences of multiple biophysical, socio-economic, and crop management features in determining maize yield variability using several machine learning approaches. Soil fertility status was assessed in 180 farms and paired with the surveyed data on maize yield, socio-economic conditions, and agronomic management. The C&RT relative variable importance plot identified farm size, total labor, soil factors, seed rate, fertilizer, and organic manure as influential factors. Among the three approaches compared for classifying maize yield, the artificial neural network (ANN) yielded the least (25%) misclassification on validation samples. The random forest partial dependence plots revealed a positive association between farm size and maize productivity. Nonlinear support vector machine boundary analysis for the eight top important variables revealed complex interactions underpinning maize yield response. Notably, farm size and total labor synergistically increased maize yield. Future research integrating these algorithms with empirical crop growth models and crop simulation models for ex-ante yield estimations could result in further improvement.

Elemental assessment of vegetation via portable X-ray fluorescence (PXRF) spectrometry.

Elemental concentrations in vegetation are of critical importance, whether establishing plant essential element concentrations (toxicity vs. deficiency) or investigating deleterious elements (e.g., heavy metals) differentially extracted from the soil by plants. Traditionally, elemental analysis of vegetation has been facilitated by acid digestion followed by quantification via inductively coupled plasma (ICP) or atomic absorption (AA) spectroscopy. Previous studies have utilized portable X-ray fluorescence (PXRF) spectroscopy to quantify elements in soils, but few have evaluated the vegetation. In this study, a PXRF spectrometer was employed to scan 228 organic material samples (thatch, deciduous leaves, grasses, tree bark, and herbaceous plants) from smelter-impacted areas of Romania, as well as National Institute of Standards and Technology (NIST) certified reference materials, to demonstrate the application of PXRF for elemental determination in vegetation. Samples were scanned in three conditions: as received from the field (moist), oven dry (70 °C), and dried and powdered to pass a 2 mm sieve. Performance metrics of PXRF models relative to ICP atomic emission spectroscopy were developed to asses optimal scanning conditions. Thatch and bark samples showed the highest mean PXRF and ICP concentrations (e.g., Zn, Pb, Cd, Fe), with the exceptions of K and Cl. Validation statistics indicate that the stable validation predictive capacity of PXRF increased in the following order: oven dry intact < field moist < oven dried and powdered. Even under field moist conditions, PXRF could reasonably be used for the determination of Zn (coefficient of determination, R2val 0.86; residual prediction deviation, RPD 2.72) and Cu (R2val 0.77; RPD 2.12), while dried and powdered samples allowed for stable validation prediction of Pb (R2val 0.90; RPD 3.29), Fe (R2val 0.80; RPD 2.29), Cd (R2val 0.75; RPD 2.07) and Cu (R2val 0.98; RPD of 8.53). Summarily, PXRF was shown to be a useful approach for quickly assessing the elemental concentration in vegetation. Future PXRF/vegetation research should explore additional elements and investigate its usefulness in evaluating phytoremediation effectiveness.

Compost salinity assessment via portable X-ray fluorescence (PXRF) spectrometry.

Compost salinity is an ongoing concern for compost producers, especially with certain feedstocks and in arid or semiarid regions. Current testing protocols call for sampling and testing ex-situ via 1:5 (w/v) slurries via electrical conductance. For this research an alternate approach has been proposed, the use of portable X-ray fluorescence (PXRF) spectrometry. Adapting methods developed for soil and water salinity analysis via PXRF, elemental data was used as a proxy for the prediction of compost salinity. In total, 74 compost samples were scanned with PXRF followed by traditional laboratory analysis. Results indicated a strong correlation between the datasets (R2 0.80; RMSE 1.04 dS m-1), similar to findings for soil and water salinity. Furthermore, using the same elemental dataset, compost pH was reasonably predicted (R2 0.63; RMSE 0.35). PXRF has the benefit of being able to be conducted in-situ or in the laboratory. And, multiple chemical parameters of interest can potentially be predicted from the same dataset. In conclusion, PXRF shows promise for rapid, in-situ salinity determination of composted products.

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.

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

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 (RPD's 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.

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.

Screening of rice landraces for salinity tolerance at seedling stage through morphological and molecular markers.

The present investigation was carried out to evaluate 33 rice landrace genotypes for assessment of their salt tolerance at seedling stage. Growth parameters like root length, shoot length and plant biomass were measured after 12 days of exposure to six different levels of saline solution (with electrical conductivity of 4, 6, 8, 10, 12 or 14 dS m (-1)). Genotypes showing significant interaction and differential response towards salinity were assessed at molecular level using 11 simple sequence repeats (SSR) markers, linked with salt tolerance quantitative trait loci. Shoot length, root length and plant biomass at seedling stage decreased with increasing salinity. However, relative salt tolerance in terms of these three parameters varied among genotypes. Out of the 11 SSR markers RM8094, RM336 and RM8046, the most competent descriptors to screen the salt tolerant genotypes with higher polymorphic information content coupled with higher marker index value, significantly distinguished the salt tolerant genotypes. Combining morphological and molecular assessment, four lanraces viz. Gheus, Ghunsi, Kuthiahara and Sholerpona were considered as true salt tolerant genotypes which may contribute in greater way in the development of salt tolerant genotypes in rice.

Analysis of petroleum contaminated soils by spectral modeling and pure response profile recovery of n-hexane.

This pilot study compared penalized spline regression (PSR) and random forest (RF) regression using visible and near-infrared diffuse reflectance spectroscopy (VisNIR DRS) derived spectra of 164 petroleum contaminated soils after two different spectral pretreatments [first derivative (FD) and standard normal variate (SNV) followed by detrending] for rapid quantification of soil petroleum contamination. Additionally, a new analytical approach was proposed for the recovery of the pure spectral and concentration profiles of n-hexane present in the unresolved mixture of petroleum contaminated soils using multivariate curve resolution alternating least squares (MCR-ALS). The PSR model using FD spectra (r(2) = 0.87, RMSE = 0.580 log10 mg kg(-1), and residual prediction deviation = 2.78) outperformed all other models tested. Quantitative results obtained by MCR-ALS for n-hexane in presence of interferences (r(2) = 0.65 and RMSE 0.261 log10 mg kg(-1)) were comparable to those obtained using FD (PSR) model. Furthermore, MCR ALS was able to recover pure spectra of n-hexane.

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.

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.