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Objectives: Pteris vittata L. is one of the hyperaccumulator plants that can grow in heavy metal-contaminated soil. This plant may tolerate metal stress through different mechanisms, eg, by producing a wide range of secondary metabolites. These compounds play an essential role in physiological function and can also serve as rich sources of chemicals for health benefit purposes. This study aims to determine the phytochemicals of plant extracts and the relationship between these profiles with pharmacological properties to predict active agents and their interaction. Methods: The total phenolic and total flavonoid contents were measured by colorimetric assays, while the widely targeted metabolomics was applied to reveal leaf and root P. vittata metabolome. In parallel, the antioxidant and antibacterial effects of these extracts were determined by ABTS scavenging and micro-broth dilution methods. Multivariate analysis, ie, Partial Least Square Regression, calculated the correlation between metabolites and bioactivities. Results: The results showed the significant content of phenolics and flavonoids in plants collected from mining soils of Thai Nguyen province, Vietnam, and their potential ABTS scavenging activity. Additionally, the plant's metabolome characterized 82 compounds, mainly secondary metabolites. The multivariate analysis indicated the positive correlation among quercetin derivatives and bioactivity, suggesting the contribution of their combination, not individually, to the antibacterial effect of P. vittata. Conclusion: Hyperaccumulators in metalliferous soils are potential reservoirs for exploring the antibacterial agents against multi-drug resistant strains, and this effect may be exerted by additive or synergistic mechanisms of action of multiple compounds. [ABSTRACT FROM AUTHOR]

To bridge the data gap of carbon reservoirs in the Arunachal Himalaya, estimation of carbon stock is of utmost importance. Despite being a major carbon reservoir, the high-altitude forests are lacking in such data mainly due to inaccessibility and rough terrain. The present study aimed to model the total soil carbon along the altitudinal gradient in the Eastern Himalayan region to understand the variation in soil carbon and other important soil variables in the ecosystem. The model offers predicting the readily available soil physico-chemical properties to predict total soil carbon in undisturbed forest ecosystems of Eaglenest Wildlife Sanctuary, Arunachal Pradesh, India. Soil samples were collected using stratified random sampling and analysed following standard methodologies. The XLSTAT application was used for partial least squares regression modelling. The results indicated an average annual total soil carbon content of 4.79±0.36% in tropical, 4.32±0.42% in subtropical and 3.88±0.35% in temperate zone. Notably, there was a significant decrease in microbial biomass carbon and soil inorganic carbon with increasing altitude. Highly accurate partial least squares regression prediction models were developed, with R2 values ranging from 0.87 to 0.96, root mean squares error values from 0.29% to 0.59% and mean squared error values from 0.08% to 0.35%. These models will serve as valuable tools for assessing soil carbon stocks across different elevations, particularly in inaccessible areas. The study highlights the effectiveness of partial least squares regression models in predicting total soil carbon along altitudinal gradients and underscores the need to better understand ecosystem responses to environmental change. This information can be utilized by policymakers to gain insights into the important implications for REDD + + reporting, policy making and other relevant applications. [ABSTRACT FROM AUTHOR]

Surface water depletion in Odisha tract poses significant challenges for sustainable water management. An important part of the effort to satisfy the growing demand for water is surface water quality control. For that purpose, this study's primary goal is to assess the surface water quality for drinking and irrigation at nine different locations, via the use of the innovative techniques. In this regard, Drinking Water Quality Index (DWQI), Partial Least Square Regression (PLSR) and Spatial reflectance (SR) Indices (I), were considered to determine water suitability for different people's activities. The samples were collected in the study area during the pre-monsoon season of period 2023–2024. The parameters analyzed: pH, DO, Alkalinity, Conductivity, Nitrate, Phosphate and Hardness. The results were subsequently contrasted with the water quality requirements, as instructed by World Health Organizations (WHO). The major anionic trend is expressed in the subsequent order: NO3− > PO43−. Finally, the analytical results were collected in order to produce the parameters' numerical geographic distribution using the geographical information system (GIS) environment. According to the results of pH, the obtained average value is recorded as 8.0. This implies that the water is slight alkaline in nature. The results of the DWQI showed that 44.44% shared investigated locations, were classified as excellent to good, and 11% as poor, 22.22% as very poor and, 22.22% is indicated as unsuitable for drinking purpose classes. In addition, the new SRIs that were taken out of the VIS and NIR regions demonstrated a substantial correlation with DWQI, according to the results. The new SRIs and DWQI had R2 correlations with values ranging from 0.65 to 0.82. The results from DWQI and SRI depicts that Nitrate and Phosphate concentration were higher and exceeds the WHO standards. At five sites, which confers as poor water quality, these parameters were recorded very high. Additionally, the main factors causing variations in water quality were fertilizer, organic waste, and soil leaching. Based on the values of R2, the PLSR model generated an evaluation of DWQI that was more accurate. Furthermore, the PLSR model generated accurate predictions for DWQI, with an R2 of 0.82 and 0.85, in the validation and calibration dataset. Hence, PLSR is efficient and provides us with a clear image for evaluating surface water's fitness for drinking and its regulating elements. This study provides a quantitative framework for assessing surface water suitable potential zones in the chosen region. By identifying the hidden variables influencing water quality, the three approaches work together to maintain their advantages while also offering crucial information for water management. The results allow for the monitoring of restoration measures to be prioritized, the identification of the anthropogenic impact on the five locations (S-(1), (2), (3), (4), and (5)) and the type of anthropogenic pressure associated with each location, as well as the optimization of monitoring programs to reflect significant anthropogenic pressures. The resulting maps and data offer valuable insights for policy makers and water resource managers to develop targeted surface water management strategies. These findings have significant implications for sustainable water resource management in the region, particularly in addressing challenges related to drinking and agricultural water demand and climate change adaptation. A more thorough assessment of the surface water quality would result from the addition of more water quality indicators, such as hydrological, biological, and particular pollutants, to the straightforward and trustworthy assessment scheme that has been suggested. [ABSTRACT FROM AUTHOR]

Remote sensing technologies continue to expand their role in environmental monitoring, providing invaluable advances in soil assessing and mapping. This study aimed to prove the need to apply spatial statistical models for processing data in remote sensing (RS), which appears to be an important source of spatial data at multiple scales. A crucial problem facing us is the fusion of multi-source spatial data of different natures and characteristics, among which there is the support size of measurement that unfortunately is little considered in RS. A data fusion approach of both sample (point) and grid (areal) data is proposed that explicitly takes into account spatial correlation and change of support in both increasing support (upscaling) and decreasing support (downscaling). The techniques of block cokriging and kriging downscaling were employed for the implementation of such an approach, respectively. The method is applied to soil sample data, jointly analysed with hyperspectral data measured in the laboratory, UAV, and satellite data (Planet and Sentinel 2) of an olive grove after filtering soil pixels. Each data type had its own support that was transformed to the same support as the soil sample data so that the data fusion approach could be applied. To demonstrate the statistical, as well as practical, effectiveness of such a method, it was compared by a cross-validation test with a univariate approach for predicting each soil property. The positive results obtained should stimulate advanced statistical techniques to be applied more and more widely to RS data. [ABSTRACT FROM AUTHOR]

The study demonstrated the efficacy of integrating time-series uncrewed aerial system (UAS) multispectral imaging with data-driven deep learning methodologies to systematically and precisely predict field-scale crop yield throughout the growing seasons. A UAS equipped with a micasense rededge MX+ sensor was used for data acquisition at the Hermiston Agricultural Research and Extension Center, Oregon State University. The data were collected throughout the potato (Solanum tuberosum L.) growing seasons under varied nitrogen (N)-rates ranging from 0 to 639 kg/ha. The raw data were preprocessed using Pix4Dmapper and the quantum geographic information system. A linear unmixing model followed by Otsu-based adaptive autosegmentation was implemented to generate soil-masked spatio-spectral fusion maps for accurate vegetation feature extraction. The proposed feature engineering and prediction model followed a two-fold approach: first, adoption of partial least squares regression (PLSR) algorithm to extract features relevant to yield, and second, a novel context-aware attention and residual connection convolution-bidirectional gated recurrent unit bidirectional long short-term memory-network (CAR Conv1D-BiGRU-BiLSTM-Net) to exploit time-series multifeatures information to predict final yield. On integrating the PLSR-derived robust features, the proposed model demonstrated an increase in predictive capability from emergence (T1) to bulking (T4) growth stage by effectively capturing the temporal dynamics of physiological and biological traits. Overall, using multifeatures such as simple ratio, Chlorophyll Green, modified anthocyanin reflectance index, vegetation fraction ($V_{f}$), and N-rate from T1–T4 growth stage resulted in predictive accuracy with high $\text{R}^{2}$ = 0.775 and low root mean square error of 16.4%, outperforming other deep learning models.

Abstract Surface water depletion in Odisha tract poses significant challenges for sustainable water management. An important part of the effort to satisfy the growing demand for water is surface water quality control. For that purpose, this study’s primary goal is to assess the surface water quality for drinking and irrigation at nine different locations, via the use of the innovative techniques. In this regard, Drinking Water Quality Index (DWQI), Partial Least Square Regression (PLSR) and Spatial reflectance (SR) Indices (I), were considered to determine water suitability for different people’s activities. The samples were collected in the study area during the pre-monsoon season of period 2023–2024. The parameters analyzed: pH, DO, Alkalinity, Conductivity, Nitrate, Phosphate and Hardness. The results were subsequently contrasted with the water quality requirements, as instructed by World Health Organizations (WHO). The major anionic trend is expressed in the subsequent order: NO3 − > PO4 3−. Finally, the analytical results were collected in order to produce the parameters’ numerical geographic distribution using the geographical information system (GIS) environment. According to the results of pH, the obtained average value is recorded as 8.0. This implies that the water is slight alkaline in nature. The results of the DWQI showed that 44.44% shared investigated locations, were classified as excellent to good, and 11% as poor, 22.22% as very poor and, 22.22% is indicated as unsuitable for drinking purpose classes. In addition, the new SRIs that were taken out of the VIS and NIR regions demonstrated a substantial correlation with DWQI, according to the results. The new SRIs and DWQI had R2 correlations with values ranging from 0.65 to 0.82. The results from DWQI and SRI depicts that Nitrate and Phosphate concentration were higher and exceeds the WHO standards. At five sites, which confers as poor water quality, these parameters were recorded very high. Additionally, the main factors causing variations in water quality were fertilizer, organic waste, and soil leaching. Based on the values of R2, the PLSR model generated an evaluation of DWQI that was more accurate. Furthermore, the PLSR model generated accurate predictions for DWQI, with an R2 of 0.82 and 0.85, in the validation and calibration dataset. Hence, PLSR is efficient and provides us with a clear image for evaluating surface water’s fitness for drinking and its regulating elements. This study provides a quantitative framework for assessing surface water suitable potential zones in the chosen region. By identifying the hidden variables influencing water quality, the three approaches work together to maintain their advantages while also offering crucial information for water management. The results allow for the monitoring of restoration measures to be prioritized, the identification of the anthropogenic impact on the five locations (S-(1), (2), (3), (4), and (5)) and the type of anthropogenic pressure associated with each location, as well as the optimization of monitoring programs to reflect significant anthropogenic pressures. The resulting maps and data offer valuable insights for policy makers and water resource managers to develop targeted surface water management strategies. These findings have significant implications for sustainable water resource management in the region, particularly in addressing challenges related to drinking and agricultural water demand and climate change adaptation. A more thorough assessment of the surface water quality would result from the addition of more water quality indicators, such as hydrological, biological, and particular pollutants, to the straightforward and trustworthy assessment scheme that has been suggested.

Soil profile organic carbon (OC) and total nitrogen (TN) are influenced by topographic attributes, and land use. The visible and near-infrared (Vis-NIR) spectroscopy method can be used for the prediction of OC and TN because it is reliable, nondestructive, fast, and cost-effective. VIS-NIR soil spectral and environmental data were combined with the Partial least squares regression (PLSR) model to examine the effect of topography attributes and land use on topsoil and subsoil OC and TN stocks. After this, based on the soil depth, 114 soil samples were collected from 0 to 20 cm (topsoil) and 20–50 cm (subsoil) under three land uses, as well as OC and TN, along with several soil properties including soil particles (sand, silt, clay), pH, and bulk density in both topsoil and subsoil samples were measured. A DEM with a resolution of 30 m was used to derive the topography factors and remote sensing data was used to calculate the vegetation index. Soils (0–50 cm) under orchard land use had the highest stock of SOC (7.4 kg m−2) as well as TN (2.4 kg m−2). There was a significant increase in the organic matter stock of soils located on the south aspect (8.3 kg m−2) compared to soils located on other aspects, particularly on the north aspect (3.9% increase). Soils on the south aspect contain higher soil-water contents and lower temperatures, resulting in a decrease in the decomposition of soil organic matter. A strong positive correlation was demonstrated between topography wetness index (0.57–0.63) and topography TN stocks (0.54–0.66) as well as the highest loading score among terrain attributes, suggesting that topography is the primary factor controlling SOC stocks, particularly subsoil stocks. Additionally, we found that soils on the south-facing aspects (N aspects) had the highest spectra. Additionally, the PLSR, which showed an R2 of 0.82, a RMSE of 0.15 %, and a RPD of 0.39 indicated excellent prediction capabilities for the OC content. We concluded that the PLSR model coupled with Vis-NIR spectroscopy is able to predict topsoil and subsoil OC and N content under different aspect slopes.

Abstract The utilization of visible-near infrared (Vis–NIR) spectroscopy presents a nondestructive, fast, reliable and cost-effective approach to predicting total nitrogen (TN) and organic carbon (OC) levels. This study employed a combination of Vis–NIR spectroscopy, partial least-squares regression (PLSR), and support vector machine (SVM) models to investigate the effects of mining on TN and OC stocks in both the topsoil (0–10 cm) and subsoil (10–40 cm). 105 soil samples were collected from agricultural areas near an iron mine, polluted, moderately-polluted, and non-polluted sites. Results indicated that soils at the non-polluted site had the highest of soil OC stocks (7.5 kg m–2) and total nitrogen (2.5 kg m–2), followed by the moderately-polluted site. Furthermore, it was observed that soils from the polluted site displayed the highest spectral reflectance. The spectral bands in the range of 500–700 nm showed the strongest correlation with soil organic carbon content. Notably, the SVM method utilizing Vis–NIR spectroscopy provided superior predictions for both subsoil and topsoil organic carbon and total nitrogen compared to the PLSR methods. Additionally, SVM demonstrated better performance in predicting topsoil soil organic carbon (R2 = 0.87, RMSE = 0.13%, and RPD = 2.8) and total nitrogen (R2 = 0.91, RMSE = 0.13%, and RPD = 2.4) compared to the subsoil, owing to the larger OM content in the topsoils.

Summary: Fresh‐cut apples are susceptible to enzymatic browning and spoilage. The objective of this study was to evaluate the effect of different coatings on fresh‐cut apples and develop a predictive model for their shelf life. The apples were treated with antioxidant peptide from Candida utilis (CUH), carboxymethyl chitosan (CMCS), and composite coatings, and their physicochemical properties were subsequently evaluated. Key factors identified through correlation analysis of shelf life were used as input parameters for a partial least squares regression (PLSR) model to predict the shelf life of fresh‐cut apples treated with different coatings. The results indicate that CUH, CMCS, and CUH‐CMCS coatings effectively delay the deterioration of fresh‐cut apples. Notably, the CUH‐CMCS composite coating demonstrated superior performance, showing only a slight 3.50% increase in the browning index (BI) during storage. Minimal changes were observed in weight loss and firmness, while overall total acidity (TA) and pH exhibited slight decreases. Moreover, the levels of ascorbic acid (Vc), polyphenol oxidase (PPO), and peroxidase (POD) were significantly lower compared to the control group, and the total bacterial count increased by no more than 0.7 log CFU g−1. The PLSR model accurately predicted the shelf life of fresh‐cut apples treated with different coatings, with Rc2 and Rp2 values both exceeding 0.90. The research results indicate that the coating and model developed in this study offer a novel approach for preserving and managing fresh‐cut apples. [ABSTRACT FROM AUTHOR]

Indomethacin (INDO) is a synthetic non-steroidal antipyretic, analgesic, and anti-inflammatory drug that commonly exists in both amorphous and crystalline states. Its amorphous state (A-INDO) is utilized by pharmaceutical companies as an active pharmaceutical ingredient (API) in the production of INDO drugs due to its higher apparent solubility and bioavailability. The crystal state also encompasses various crystal forms such as the α-crystal form (α-INDO) and γ-crystal form (γ-INDO), with the highly crystalline and insoluble γ-INDO being commercially available. A-INDO, existing in a thermodynamically high-energy state, is susceptible to several factors during the preparation, storage, and transportation of API leading to its conversion into γ-INDO, thus impacting the bioavailability and efficacy of INDO drugs. Therefore, quantitative analysis of the A-INDO/γ-INDO content in INDO API becomes essential for controlling the production quality of INDO. The primary objective of this study is to investigate the feasibility of NIR for the quantitative analysis of A-INDO in INDO API, and to further elucidate its quantitative analysis mechanism. The NIR spectral data were collected for A-INDO and γ-INDO binary mixture samples with different resolutions, and these spectra were then selected and reconstructed using the interval partial least square (iPLS) method. Different pretreatment methods were employed to enhance the reconstructed spectra by highlighting relevant eigen information while eliminating invalid information caused by environmental factors or physical characteristics of samples. The most suitable PLSR model for quantitative analysis of A-INDO within the range of 0.0000–100.0000% w/w% was established, screened, and validated. From various perspectives, including distribution of spectral effective information, impact of resolution on PLSR model performance, variance contribution/cumulative variance contribution of PLSR model principal components (PCs), PCI loadings, relationship between spectral scores, and A-INDO content, feasibility assessment was conducted for the quantitative analysis of A-INDO in INDO using NIR spectroscopy. Additionally, a detailed investigation on the quantitative analysis mechanism of the optimal PLSR model was undertaken including the correlation between the characteristic peaks of spectra and information regarding hydrogen groups or hydrogen bonds in A-INDO or γ-INDO molecules. This study aims to provide theoretical support for the quantitative analysis of A-INDO in INDO API as well as serve as a reliable reference method for API quantification and quality control in similar drugs. [ABSTRACT FROM AUTHOR]

The utilization of visible-near infrared (Vis–NIR) spectroscopy presents a nondestructive, fast, reliable and cost-effective approach to predicting total nitrogen (TN) and organic carbon (OC) levels. This study employed a combination of Vis–NIR spectroscopy, partial least-squares regression (PLSR), and support vector machine (SVM) models to investigate the effects of mining on TN and OC stocks in both the topsoil (0–10 cm) and subsoil (10–40 cm). 105 soil samples were collected from agricultural areas near an iron mine, polluted, moderately-polluted, and non-polluted sites. Results indicated that soils at the non-polluted site had the highest of soil OC stocks (7.5 kg m–2) and total nitrogen (2.5 kg m–2), followed by the moderately-polluted site. Furthermore, it was observed that soils from the polluted site displayed the highest spectral reflectance. The spectral bands in the range of 500–700 nm showed the strongest correlation with soil organic carbon content. Notably, the SVM method utilizing Vis–NIR spectroscopy provided superior predictions for both subsoil and topsoil organic carbon and total nitrogen compared to the PLSR methods. Additionally, SVM demonstrated better performance in predicting topsoil soil organic carbon (R2 = 0.87, RMSE = 0.13%, and RPD = 2.8) and total nitrogen (R2 = 0.91, RMSE = 0.13%, and RPD = 2.4) compared to the subsoil, owing to the larger OM content in the topsoils. [ABSTRACT FROM AUTHOR]

This study explores the use of leaf-level visible-to-shortwave infrared (VSWIR) reflectance observations and partial least squares regression (PLSR) to predict foliar concentrations of macronutrients (nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur), micronutrients (boron, copper, iron, manganese, zinc, molybdenum, aluminum, and sodium), and moisture content in winter wheat. A total of 360 fresh wheat leaf samples were collected from a wheat breeding population over two growing seasons. These leaf samples were used to collect VSWIR reflectance observations across a spectral range spanning 350 to 2,500 nm. These samples were then processed for nutrient composition to allow for the examination of the ability of reflectance to accurately model diverse chemical components in wheat foliage. Models for each nutrient were developed using a rigorous cross-validation methodology in conjunction with three distinct component selection methods to explore the trade-offs between model complexity and performance in the final models. We examined absolute minimum predicted residual error sum of squares (PRESS), backward iteration over PRESS, and Van der Voet's randomized t -test as component selection methods. In addition to contrasting component selection methods for each leaf trait, the importance of spectral regions through variable importance in projection scores was also examined. In general, the backward iteration method provided strong model performance while reducing model complexity relative to the other selection methods, yielding R 2 [relative percent difference (RPD), root mean squared error (RMSE)] values in the validation dataset of 0.84 (2.45, 6.91), 0.75 (1.97, 18.67), 0.78 (2.13, 16.49), 0.66 (1.71, 17.13), 0.68 (1.75, 14.51), 0.66 (1.72, 12.29), and 0.84 (2.46, 2.20) for nitrogen, calcium, magnesium, sulfur, iron, zinc, and moisture content on a wet basis, respectively. These model results demonstrate that VSWIR reflectance in combination with modern statistical modeling techniques provides a powerful high throughput method for the quantification of a wide range of foliar nutrient contents in wheat crops. This work has the potential to advance rapid, precise, and nondestructive field assessments of nutrient contents and deficiencies for precision agricultural management and to advance breeding program assessments. [ABSTRACT FROM AUTHOR]

The effects of co-fermentation with Torulaspora delbrueckii on the composition of volatile compounds and sensory characteristics of strawberry fruit wines were investigated by fermenting strawberry pulp with T. delbrueckii in pure fermentation, as well as in simultaneous and sequential inoculations with Saccharomyces cerevisiae. E-nose analysis results showed that these strawberry fruit wines with different fermentation patterns exhibited distinct aromatic characteristics. The sensory attributes intensities of 'fruit', 'strawberry', 'sweet' and 'floral' were significantly enhanced in sequential fermentation compared to pure S. cerevisiae fermentation. Altogether, 87 volatile compounds were detected with HS-SPME-GC/MS in strawberry products, with 32 of them identified as key contributors to the overall aroma due to their high odor activity values. The sequential fermentation increased the concentration of desirable aroma-active compounds, including isoamyl alcohol, phenylethanol, isoamyl acetate, and some ethyl esters in strawberry fruit wines. The partial least squares regression analysis revealed that the fruity, floral and sweet characteristics were primarily attributed to specific aroma-active compounds, including esters such as ethyl caproate, ethyl caprylate, ethyl cinnamate, isoamyl acetate, ethyl cinnamate, ethyl 3-phenylpropionate and ethyl butyrate, as well as terpenes such as limonene, α-terpineol and nerolidol. [ABSTRACT FROM AUTHOR]

The scale factor of thermal sensitivity serves as a crucial performance metric for micro-electromechanical system (MEMS) gyroscopes, and is commonly employed to assess the temperature stability of inertial sensors. To improve the temperature stability of the scale factor of MEMS gyroscopes, a self-compensation method is proposed. This is achieved by integrating the primary and secondary relevant parameters of the scale factor using the partial least squares regression (PLSR) algorithm. In this paper, a scale factor prediction model is presented. The model indicates that the resonant frequency and demodulation phase angle are the primary correlation terms of the scale factor, while the drive control voltage and quadrature feedback voltage are the secondary correlation terms of the scale factor. By employing a weighted fusion of correlated terms through PLSR, the scale factor for temperature sensitivity is markedly enhanced by leveraging the predicted results to compensate for the output. The results indicate that the maximum error of the predicted scale factor is 0.124% within the temperature range of −40 °C to 60 °C, and the temperature sensitivity of the scale factor decreases from 6180 ppm/°C to 9.39 ppm/°C. [ABSTRACT FROM AUTHOR]

In the current investigation, a novel methodology was employed to assess iron powder as a recyclable and sustainable energy carrier. Concurrently, an examination of the modeling of iron powder ignition and the ensuing heat output from the burner was undertaken. The flame temperature was determined by examining the light intensity emitted by the particles as they melted, which is directly related to the particle's cross-sectional area. An account of the characterization of the experimental procedure, validation, and calibration is presented. Through measurements, distinct one-to-one correlations have been established between the scales of flame combustion and the temperatures of particles of varying sizes of iron. Additionally, a theoretical model for the combustion of expanding particles, particularly iron, within the diffusion-limited regime has been rigorously developed. This model delves into the spectra acquired from particle flames within the burner, utilizing Partial Least Squares Regression (PLSR) and Principal Component Analysis (PCA). This study investigates the use of optical fiber spectroscopy to predict flame temperature and assess iron powder size. The aim was to investigate how different sizes of iron powder affect flame temperature and to create calibration models for non-destructive prediction. The study shows that smaller particles had an average temperature of 1381 °C while larger particles reach up to 1842 °C, demonstrating the significant impact of particle size on combustion efficiency. The results were confirmed using advanced statistical methods, including PLSR and PCA, with PCA effectively differentiating between particle sizes and PLSR achieving an R2 value of 0.90 for the 30 µm particles. [ABSTRACT FROM AUTHOR]

Designed experiments were conducted to investigate the influence of ohmic heating (OH) at varying electric field strength (EFS) and holding time on the recovery of oil from mustard (Brassica juncea) seeds during mechanical expression. Hyperspectral imaging (HSI) in the visible-near infrared (Vis–NIR, 399–1003 nm) and short-wave infrared (SWIR, 895–1712 nm) ranges was used to visualize the change in oil distribution induced by OH on the mustard seeds. OH treatment led to an increase in expression of oil content by 25% as compared to control samples. Chemometric techniques, including partial least squares discriminant analysis (PLS-DA) and partial least squares regression (PLSR), were employed to analyze spectral data and develop models for predicting the enhancement in expressible oil due to OH treatment and its quality in terms of free fatty acids thereof. PLS-DA differentiated OH treated seeds from the control sample for by Vis–NIR and SWIR HSI at 93.0 and 95.8% accuracy, respectively. The variable selection method (iPLS) identified crucial wavelengths with minimal performance loss for accurate prediction. The PLSR model using SWIR HSI data accurately predicted oil content and fatty acid composition (R2 > 0.92), while Vis–NIR predictions exhibited a lower accuracy (R2 > 0.73). [ABSTRACT FROM AUTHOR]

In 2020, China pledged carbon reduction targets at the United Nations: peaking emissions by 2030 and achieving carbon neutrality by 2060. Research and prediction of regional carbon emissions are crucial for achieving these dual carbon targets across China. This study aims to construct an indicator system for regional carbon emissions and utilize it for forecasting. Analyzing carbon emission data from a specific area in Hainan Province from 2010 to 2020, we established an indicator system. Using the interpretable SHAP model, we assessed indicator importance and trends. Employing an improved STIRPAT model with partial least squares regression to address multicollinearity among influencing factors, we developed a carbon emission prediction model. Based on this, we forecasted carbon emissions from 2021 to 2060 in the specified area under three scenarios: natural, baseline, and ambitious. The results show that the growth of resident population and per capita GDP has the most significant promoting effect on carbon emissions in the region while optimizing industrial structure, energy consumption structure, and reducing energy intensity will inhibit carbon emissions. The prediction results indicate that in the natural scenario, regional carbon emissions will peak in 2035, and achieving carbon neutrality by 2060 is not feasible, while the baseline scenario and ambitious scenario can achieve the dual carbon targets on time or even earlier. The research results of this article provide a reference method for predicting carbon emissions in other regions and a guide for future regional emission reduction. [ABSTRACT FROM AUTHOR]

To rapidly acquire fiber phenotypic data for wood quality assessment, we used a portable NIR spectrometer to collect spectral data in 100 individuals of Schima superba at 18-year-old of 20 different provenances, and simultaneously collected wood cores. Wood basic density and the anatomical structure of wood fiber were measured. The standard normal variate (SNV), orthogonal signal correction (OSC ), and multiplicative scatter correction (MSC) methods were used for spectral preprocessing, the competitive adaptive reweighted sampling (CARS) method were used for wavelength selection, and the partial least squares regression (PLSR) model were established. The results showed a significant difference for the absolute reflectance data between forest and indoor environments, and the spectral data of which were relatively independent. SNV, OSC and MSC showed significant differences for predictive performance of the model. OSC had the excellent preprocessing capability in multiple characteristics of wood fiber ether in forest and indoor environments. The predictive accuracy of the models with R was 0.47-0.78 in forest (average = 0.63), and R² was 0.54-0.82 in indoor environment (average = 0.71). However, the SNV and MSC methods could not establish the models, except the fiber wall-cavity ratio from forest data. After wavelength selection through the CARS method, the predictive accuracy of the models was significantly improved using both forest and indoor data (R²=0.58 and 0.72, respectively). When performed OSC before and after CARS, the predictive accuracy of the models was improved to 0.68 and 0.84 respectively using forest and indoor data. The OSC and CARS could significantly improve the accuracy of the models for wood fiber anatomical structures. First OSC, then CARS, and finally OSC methods could be used to establish the PLSR model for fiber length, fiber cell wall thickness, fiber lumen diameter, wood basic density, fiber cavity-width ratio, and fiber wall-cavity ratio, and the R² ranged from 0.80 to 0.95. These models had effective predictive ability and accuracy to assess the physical properties of wood fibers of S. superba. [ABSTRACT FROM AUTHOR]

Excess nitrogen fertilizer use leads to vegetables with high amounts of nitrate content. Consumption of vegetables with high amounts of nitrate is carcinogenic to human beings. In this study, fenugreek plants were grown under varying nitrogen fertilizer doses (0, 50, 100, 150, 200, 250, 300, 350 and 400 kg N/ha). A Vis-NIR hyperspectral imaging (HIS) camera captured images of fenugreek leaves within the 398–1,003 nm spectral range. The spectral data were pre-processed using different pre-processing techniques before the model development. Partial least-squares regression (PLSR) models were constructed with complete spectral data and selected wavelengths. The performance of the PLSR model decreased with pre-processed spectral data, and there was no significant difference compared to the model constructed with raw spectral data (R 2 CV = 0.915, SECV = 591.933, slope = 0.518 and RPDCV = 1.421). The wavelengths 411, 435, 466, 558, 669, and 720 nm were selected as feature wavelengths for representing nitrate content in fenugreek leaves. The performance of the PLSR model constructed with feature wavelengths (SECV = 648.672; RPDCV = 1.482; R 2 CV = 0.869) was non-significant compared with the model developed with raw complete spectral data (SECV = 591.933; R 2 CV = 0.915 and RPDCV = 1.421). Using the complete raw spectral data, the spatial distribution images of nitrate content in fenugreek leaves indicated that the nitrate content was concentrated near and along the midrib up to the apex. The overall results obtained in the present study suggest that VIS-NIR HSI, along with suitable chemometric techniques, can be used for rapid assessment of nitrate content in fenugreek leaves.

Carbohydrate levels are important regulators of the growth and yield of tree crops. Current methods for measuring foliar carbohydrate concentrations are time consuming and laborious, but rapid imaging technologies have emerged with the potential to improve the effectiveness of tree nutrient management. Carbohydrate concentrations were predicted using hyperspectral imaging (400–1000 nm) of leaves of the evergreen tree crops, avocado, and macadamia. Models were developed using partial least squares regression (PLSR) and artificial neural network (ANN) algorithms to predict carbohydrate concentrations. PLSR models had R2 values of 0.51, 0.82, 0.86, and 0.85, and ANN models had R2 values of 0.83, 0.83, 0.78, and 0.86, in predicting starch, sucrose, glucose, and fructose concentrations, respectively, in avocado leaves. PLSR models had R2 values of 0.60, 0.64, 0.91, and 0.95, and ANN models had R2 values of 0.67, 0.82, 0.98, and 0.98, in predicting the same concentrations, respectively, in macadamia leaves. ANN only outperformed PLSR when predicting starch concentrations in avocado leaves and sucrose concentrations in macadamia leaves. Performance differences were possibly associated with nonlinear relationships between carbohydrate concentrations and reflectance values. This study demonstrates that PLSR and ANN models perform well in predicting carbohydrate concentrations in evergreen tree-crop leaves. [ABSTRACT FROM AUTHOR]

Buriti oil is a vegetable oil extracted from the pulp and seeds of buriti (Mauritia flexuosa L.), a palm commonly found in the Amazon region, and is used both in popular medicine and in the cosmetic and food industries. This work aimed to develop a faster and more accessible procedure to quantify the content of carotenoids, polyphenols, and total flavonoids in buriti oils, where predictive models emphasize figures of merit. The study was carried out with 50 buriti oil samples from the state of Pará, Brazil, which were sampled by combining attenuated total reflection (ATR) spectroscopy with mid-infrared Fourier transform (FT-MIR) together with partial least squares regression (PLSR). The confidence and validation matrix were obtained from ultraviolet–visible spectroscopy. The PLSR model regarding the total carotenoid content presented values ​​between 335.33 and 1557.05 μg/g was validated by the concentration demonstration coefficient (R2cal) equal to 0.9556, prediction demonstration coefficient (R2pred) equal to 0.85642, bias = 5.68.10−13, performance deviation ratio value (RDP) of 2.0135, and range error rate (RER) equal to 4.3747. Concentrations of phenolic compounds were predicted between 96.2964 and 121.857 GAE/100 g, where the model presented R2cal = 0.9762, R2pred = 0.8198, bias = 3.38.10−10, RDP = 5.9028, and RER = 5.7578. The flavonoid prediction model contains concentrations between 86.844 and 133.852 mg EC/100 g that circulate R2cal = 0.9445, R2pred = 0.8536, bias = 6.98.10−8, RDP = 6.7085, and RER = 6.7085. Buriti oil showed high levels of b-carotene. Prediction models are overwhelming and can be used for screening and quality control of natural products. [ABSTRACT FROM AUTHOR]

The objective was to verify the performance of spectral techniques as well as validation models in the prediction of nitrogen, total organic carbon, and humic fractions under different cultivation conditions. Chemical analyses for the determination of nitrate, total nitrogen, total organic carbon, and the chemical fractionation of soil organic matter were performed, as well as spectral analyses by Vis-NIR-SWIR and X-ray fluorescence. The results of the spectroscopy were processed using RStudio v. 4.1.3, and PLSR and support vector machine learning algorithms were applied to validate the models. The Vis-NIR-SWIR and XRF spectroscopic techniques showed high performance and are indicated for the prediction of nitrogen, total organic carbon, and humic fractions in Ferralsols of medium sandy texture. However, it is important to highlight that each technique has its own characteristic mechanism of action: Vis-NIR-SWIR detects the element based on harmonic tones, while XRF is based on the atomic number of the element or elemental association. The PLSR and SVM models showed excellent validation results, allowing them to fit the experimental data, emphasizing that they are different statistical methods. [ABSTRACT FROM AUTHOR]

Cation exchange capacity is a crucial metric for managing soil fertility and promoting agricultural sustainability. An alternative technique for the non-destructive assessment of important soil parameters is reflectance spectroscopy. The main focus of this paper is on how to analyze and predict the content of various soil cation exchange capacities (CEC) in arid conditions (Sohag governorate, Egypt) at a low cost using laboratory analysis of CEC, visible near-infrared and shortwave infrared (Vis-NIR) spectroscopy, partial least-squares regression (PLSR), and Ordinary Kriging (OK). Utilizing reflectance spectroscopy with a spectral resolution of 10 nm and laboratory studies with a spectral range of 350 to 2500 nm, 104 surface soil samples were collected to a depth of 30 cm in the Sohag governorate, Egypt (which is part of the dry region of North Africa), in order to accomplish this goal. The association between the spectroradiometer and CEC averaged values was modeled using PLSR in order to map the predicted value using Ordinary Kriging (OK). Thirty-one soil samples were selected for validation. The predictive validity of the cross-validated models was evaluated using the coefficient of determination (R2), root mean square error (RMSE), residual prediction deviation (RPD), and ratio of performance to interquartile distance (RPIQ). The results indicate that ten transformation methods yielded calibration models that met the study's requirements, with R2 > 0.6, RPQ > 2.5, and RIQP > 4.05. For evaluating CEC in Vis-NIR spectra, the most efficient transformation and calibration model was the reciprocal of Log R transformation (R2 = 0.98, RMSE = 0.40, RPD = 6.99, and RIQP = 9.22). This implies that combining the reciprocal of Log R with PLSR yields the optimal model for predicting CEC values. The CEC values were best fitted by four models: spherical, exponential, Gaussian, and circular. The methodology used here does offer a "quick", inexpensive tool that can be broadly and quickly used, and it can be readily implemented again in comparable conditions in arid regions. [ABSTRACT FROM AUTHOR]

Spices and other food products have been permanently susceptible to adulteration, affecting safety and acceptability when commercialized. A relevant alternative to detect contaminants in food products is to couple near-infrared spectroscopy (NIR) with chemometrics. Among the most accurate chemometric techniques employed to analyze food products, partial least squares regression (PLSR) combines features from and generalizes principal component analysis (PCA) to create compact and accurate models. Other techniques inspired in the human brain, such as multilayer perceptron, the long short-term memory (LSTM) models, and other approaches based on deep learning, take advantage of the high complexity of weights and neurons to train models based on large amounts of data. In this paper, a methodology is proposed to evaluate chemometric tools to estimate the percentage of adulterants in paprika powder using NIR spectroscopy, and three approaches are proposed and compared showing different performances. According to the methodology, the paprika samples were dried and separated into pericarp, peduncle, and seed cake. The resulting elements were finely milled, sieved, and mixed into 21 different combinations with a different percentage of each. Spectral profiles were used to train PLSR, multilayer perceptron, and regression models based on LSTM networks. The models were compared following a k-fold cross-validation strategy. Results showed that PLSR presented the highest R 2 = 0.978 for peduncle adulterant estimation, and the lowest R M S E = 6.24 . In particular, when seed cake powder was used as an adulterant, the PLSR approach showed the highest R 2 = 0.981 , and the lowest R M S E = 5.806 . The RPD values were higher than 2.000 for all models that use the peduncle as an adulterant and only for models bound to the PLSR in the adulterated samples with pressed seed cake. In summary, the best predictions were obtained using PLSR models, providing evidence of the feasibility of using NIR spectra to estimate the percentage of adulterants in paprika powder. [ABSTRACT FROM AUTHOR]

Flunixin (usually formulated as flunixin meglumine (FLN)), a common non-steroidal anti-inflammatory drug (NSAID) given to cattle, raises human health concerns when it is present in milk. Maximum residue limit (MRL) of 10 µg/kg has been set by Food Safety and Standards Authority of India (FSSAI) for FLN residues in milk. Attenuated Total Reflectance- Fourier-Transform Mid-Infrared (FT-MIR) spectroscopy in conjunction with multivariate techniques was applied to detect the presence of FLN residues in milk. Samples of FLN, pure milk as well as milk samples spiked with FLN at concentrations (1, 5, 10, 20 and 50 µg/kg) below and above the MRL were analysed. ATR-FTIR measurements were conducted in the 4000 –400 cm− 1 wavenumber range, and the wavenumber regions (2942 − 2838, 1748 − 1734 and 1149 –1012 cm− 1) were selected based on the maximum variability in the intensity of peaks and chemometrics techniques such as Principal Component Regression (PCR), Partial Least Square Regression (PLSR), Principal Component Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA) were applied. The study concluded that the presence of parent drug- FLN residues in milk even at 1 µg/kg level (below MRL prescribed by regulatory bodies) can be detected using ATR-FT-MIR coupled with chemometrics. [ABSTRACT FROM AUTHOR]

The expansion of urban areas contributes to the growth of impervious surfaces, leading to increased pollution and altering the configuration, composition, and context of land covers. This study employed machine learning methods (partial least square regressor and the Shapley Additive exPlanations) to explore the intricate relationships between urban expansion, land cover changes, and water quality in a watershed with a park and lake. To address this, we first evaluated the spatio-temporal variation of some physicochemical and microbiological water quality variables, generated yearly land cover maps of the basin adopting several machine learning classifiers, and computed the most suitable landscape metrics that better represent the land cover. The main results highlighted the importance of spatial arrangement and the size of the contributing watershed on water quality. Compact urban forms appeared to mitigate the impact on pollutants. This research provides valuable insights into the intricate relationship between landscape characteristics and water quality dynamics, informing targeted watershed management strategies aimed at mitigating pollution and ensuring the health and resilience of aquatic ecosystems. [ABSTRACT FROM AUTHOR]

Laser-induced breakdown spectroscopy (LIBS) and visible near-infrared spectroscopy (vis-NIRS) are spectroscopic techniques that offer promising alternatives to traditional laboratory methods for the rapid and cost-effective determination of soil properties on a large scale. Despite their individual limitations, combining LIBS and vis-NIRS has been shown to enhance the prediction accuracy for the determination of soil properties compared to single-sensor approaches. In this study, we used a comprehensive Danish national-scale soil dataset encompassing mostly sandy soils collected from various land uses and soil depths to evaluate the performance of LIBS and vis-NIRS, as well as their combined spectra, in predicting soil organic carbon (SOC) and texture. Firstly, partial least squares regression (PLSR) models were developed to correlate both LIBS and vis-NIRS spectra with the reference data. Subsequently, we merged LIBS and vis-NIRS data and developed PLSR models for the combined spectra. Finally, interval partial least squares regression (iPLSR) models were applied to assess the impact of variable selection on prediction accuracy for both LIBS and vis-NIRS. Despite being fundamentally different techniques, LIBS and vis-NIRS displayed comparable prediction performance for the investigated soil properties. LIBS achieved a root mean square error of prediction (RMSEP) of <7% for texture and 0.5% for SOC, while vis-NIRS achieved an RMSEP of <8% for texture and 0.5% for SOC. Combining LIBS and vis-NIRS spectra improved the prediction accuracy by 16% for clay, 6% for silt and sand, and 2% for SOC compared to single-sensor LIBS predictions. On the other hand, vis-NIRS single-sensor predictions were improved by 10% for clay, 17% for silt, 16% for sand, and 4% for SOC. Furthermore, applying iPLSR for variable selection improved prediction accuracy for both LIBS and vis-NIRS. Compared to LIBS PLSR predictions, iPLSR achieved reductions of 27% and 17% in RMSEP for clay and sand prediction, respectively, and an 8% reduction for silt and SOC prediction. Similarly, vis-NIRS iPLSR models demonstrated reductions of 6% and 4% in RMSEP for clay and SOC, respectively, and a 3% reduction for silt and sand. Interestingly, LIBS iPLSR models outperformed combined LIBS-vis-NIRS models in terms of prediction accuracy. Although combining LIBS and vis-NIRS improved the prediction accuracy of texture and SOC, LIBS coupled with variable selection had a greater benefit in terms of prediction accuracy. Future studies should investigate the influence of reference method uncertainty on prediction accuracy. [ABSTRACT FROM AUTHOR]

High soil variability necessitates a large number of samples, which poses disadvantages in terms of labor, time, and economic and environmental impacts. Utilizing spectroradiometers and geostatistical methods can lead to significant savings in chemical inputs and time. In this study, sixty surface soil samples from basaltic parent material areas were analyzed in the laboratory for their physical (clay, silt, sand), chemical (pH, EC, exchangeable cations: Ca, Mg, Na, K, CEC, percent CaCO3) and biological (soil organic matter; OM100mµ, OM2mm) properties. Spectral and geostatistical methods were employed to estimate and map these properties. Spectral reflectance were obtained within the 350 to 2500 nm wavelength range. Modeling the relationships between laboratory measurements and spectral readings were performed using Partial Least Squares Regression (PLSR). Additionally, geostatistical techniques such as Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and Cokriging (COK) were utilized to generate maps illustrating the spatial distribution of soil parameters. The accuracy of the predictions were evaluated using RMSE (Root Mean Square of Estimation) parameter. The predictive success of prediction techniques varied depending on the specific soil property under investigation. The VNIRSPLSR method achieved the highest accuracy and the lowest RMSE values for parameters such as organic matter, sand, clay contents, cation exchange capacity (CEC), and electrical conductivity (EC). Conversely, geostatistical methods yielded the lowest RMSE results for parameters such as lime (CaCO3), pH, silt, exchangeable Ca, exchangeable K, exchangeable Na, and exchangeable Mg. The application of the COK technique using a secondary variable resulted in a 1 % to 19 % increase in prediction success compared to OK and IDW techniques. Overall, each estimation technique has its own advantages and disadvantages, which should be taken into consideration in the selection of the technique for prediction of soil variables. [ABSTRACT FROM AUTHOR]

Vaginal infections, medically termed vaginitis, encompass a spectrum of symptomatic presentations arising from disturbances within the vaginal microflora. The conventional diagnostic approach relies on microscopic examination of wet preparation of vaginal discharge, considered the ‘gold standard’ in clinical practice. Complementary to this, culture-based methodologies are often employed to reinforce diagnostic accuracy. However, challenges such as subjectivity in result interpretation, resource-intensive requirements regarding skilled personnel, and reagent utilization underscore the need for alternative diagnostic strategies.In this article, we demonstrate surface-enhanced Raman spectroscopy (SERS) and partial least squares regression (PLSR) techniques to elucidate the molecular signatures present in vaginal fluids, accounting for various influencing factors, including disruptions in the natural microflora, vaginal irrigation practices, and contraceptive usage. Furthermore, we investigated the spectral manifestations associated with vulvovaginal candidiasis (VVC) relative to control samples. Each clinical specimen underwent meticulous characterization encompassing microbial composition, pH levels, purity, and other pertinent parameters.Our findings unveil significant associations between extraneous inflammatory factors such as vaginal irrigation and diminished sample purity with alterations in SERS signals. Conversely, the day of the menstrual cycle phase exhibits negligible influence on spectral profiles. Notably, VVC samples demonstrated diverse spectral responses correlating with the abundance of pathogenic bacteria. These explorations hold promise in paving the path towards developing a novel intrinsic framework for the diagnosis of vaginitis.