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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]

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]

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$$ {R}_{\mathrm{c}}^2 $$ and Rp2$$ {R}_{\mathrm{p}}^2 $$ 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]

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]

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.

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.

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]

One challenge in predicting soil parameters using in situ visible and near infrared spectroscopy is the distortion of the spectra due to soil moisture. External parameter orthogonalization (EPO) is a mathematical method to remove unwanted variability from spectra. We created two different EPO correction matrices based on the difference between spectra collected in situ and, respectively, spectra collected from the same soil samples after drying and sieving and after drying, sieving and finely grinding. Spectra from 134 soil samples recorded with two different spectrometers were split into calibration and validation sets and the two EPO corrections were applied. Clay, organic carbon and total nitrogen content were predicted by partial least squares regression for uncorrected and EPO-corrected spectra using models based on the same type of spectra ("within domain") as well as using laboratory-based models to predict in situ collected spectra ("cross-domain"). Our results show that the within-domain prediction of clay is improved with EPO corrections only for the research grade spectrometer, with no improvement for the other parameters. For the cross-domain predictions, there was a positive effect from both EPO corrections on all parameters. Overall, we also found that in situ collected spectra provided an equally successful prediction as laboratory-based spectra. [ABSTRACT FROM AUTHOR]

To evaluate the suitability of an analytical instrument, essential figures of merit such as the limit of detection (LOD) and the limit of quantification (LOQ) can be employed. However, as the definitions k nown in the literature are mostly applicable to one signal per sample, estimating the LOD for substances with instruments yielding multidimensional results like electronic noses (eNoses) is still challenging. In this paper, we will compare and present different approaches to estimate the LOD for eNoses by employing commonly used multivariate data analysis and regression techniques, including principal component analysis (PCA), principal component regression (PCR), as well as partial least squares regression (PLSR). These methods could subsequently be used to assess the suitability of eNoses to help control and steer processes where volatiles are key process parameters. As a use case, we determined the LODs for key compounds involved in beer maturation, namely acetaldehyde, diacetyl, dimethyl sulfide, ethyl acetate, isobutanol, and 2-phenylethanol, and discussed the suitability of our eNose for that dertermination process. The results of the methods performed demonstrated differences of up to a factor of eight. For diacetyl, the LOD and the LOQ were sufficiently low to suggest potential for monitoring via eNose. [ABSTRACT FROM AUTHOR]

In this study, we investigated the plankton, water, and macroinvertebrates in Nanwan Reservoir, a subtropical eutrophic reservoir with thermal stratification. We evaluated the status of macroinvertebrate communities and explored the relationships between macroinvertebrate community status and both abiotic and biotic factors. This reservoir exhibited the worst water quality and a high risk of algal blooms in the autumn, whereas it had the best water quality in the spring. The presence of thermal stratification adversely affected macroinvertebrate communities, leading to decreases in species richness, abundance, and diversity. The water characteristics and dominant macroinvertebrate species confirmed that the reservoir was experiencing progressive eutrophication. During the thermal stratification period, the reservoir exhibited a worse status of macroinvertebrate communities, accompanied by an abundance of plankton. The results of PLSR and the Spearman rank test revealed that several abiotic factors, such as water temperature, chlorophyll-a, soluble phosphate, pH, and transparency, as well as biotic factors including the abundance of protozoans, Cyanobacteria, phytoplankton, Bacillariophyta, and Euglenophyta, were the most important factors affecting macroinvertebrate communities. The results of PLS-SEM revealed that both abiotic factors and biotic factors had non-negligible effects on the status of macroinvertebrate communities, with higher effect from abiotic factors compared to biotic factors. [ABSTRACT FROM AUTHOR]

The efficiency of lignocellulosic biomass conversion to fuels and chemicals is highly dependent on the structure and chemical composition of the biomass. So, rapid estimation of the chemical composition of biomass will be highly beneficial for biorefineries to fine-tune the feedstock and other processing parameters like enzymatic loading for a better yield of 2G ethanol. The conventional wet chemistry method for composition analysis is time-consuming, costly and laborious. This study aims to develop rapid, non-laborious, low-cost, and industrially applicable chemometric models like principal component regression (PCR) and partial least squares regression (PLSR) based on FTIR spectroscopy to determine the chemical composition of one such typical lignocellulosic biomass, viz., rice straw (RS). However, the results suggest that PCR and PLSR models constructed using the unprocessed FTIR spectra show poor performance in prediction. So, the spectra were processed, and an exploratory spectral analysis helped identify a specific region from 750 to 1800 cm−1 (SR-1) that accounts for significant variation between the RS samples. Moreover, 58 critical peaks in SR-1 were identified using a novel peak identification method proposed in this study. Further, results suggest that PLSR models developed using SR-1 of the processed spectra and the peaks as excellent prediction models ( R cv 2 > 95% and RPD > 4) and successful prediction models (90% < R cv 2 < 95% and 3 < RPD < 4). Hence this study demonstrates that fine-tuned PLSR models based on processed FTIR spectra can be used as a tool for high-throughput screening of RS samples in biorefineries to improve the yield. [ABSTRACT FROM AUTHOR]

In this study, a regression model of paddy soil properties using diffuse reflectance spectroscopy was developed to replace chemical soil analysis as a more efficient alternative. Soil samples were collected and analyzed from saltwater paddy fields located in Jeongnam-myeon, Hwaseong-si, Gyeonggi-do in the Republic of Korea, and the spectral data of wet and dry soil were collected. The regression models were compared and analyzed using partial least squares regression (PLSR) with Savitzky–Golay smoothing (SG smoothing) and Standard Normal Variate (SNV) preprocessing to predict the soil properties. Analysis showed that the predictive regression model of wet soil with SG smoothing and an SNV did not meet the evaluation criteria of a fair model. However, the regression model of dry soil with SG smoothing was fair for clay, pH, EC, and TN at RPD = 1.90, 1.87, 1.60, and 1.43 and R2 = 0.79, 0.81, 0.64, and 0.64, respectively, while the regression model of dry soil with an SNV was good for clay, pH, EC, and TN at RPD = 2.21, 1.96, 1.70, and 1.44 and R2 = 0.84, 0.81, 0.76, 0.69, respectively. When developing predictive regression models of soil properties, the accuracy for dry soil was higher than that for wet soil, and when applying a single round of preprocessing, the regression model with SNV preprocessing was more accurate than that with SG smoothing. [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.

Visible and near‐infrared (vis–NIR) spectroscopy is a promising technology for the analysis of different soil quality parameters. In this study, we used in‐situ vis–NIR spectroscopy in association with partial least squares regression to predict the total and the mineral (nitrate + ammonium) nitrogen content, the permanganate oxidizable carbon (POXC), as well as the ratio of soil organic carbon‐to‐clay content in different agricultural soils in Switzerland. These parameters can indeed be used as indicators of soil quality in response to agronomic practices. To this goal, a total number of 134 soil samples were used for carbon‐, total nitrogen‐ and clay‐related parameters, whereas 69 soil samples were used for the mineral nitrogen‐related parameters. We found that the partial least squares regression model can successfully predict the total nitrogen and the POXC content as well as the ratio of soil organic carbon‐to‐clay content (ratio of performance to interquartile range, RPIQ > 2.62, R2 > 0.73, Lin's concordance correlation coefficient > 0.83). As concerns the mineral nitrogen, it was not possible to successfully predict this parameter by vis–NIR spectroscopy. By demonstrating the possibility to reliably predict POXC content and the soil organic carbon‐to‐clay ratio, we show that vis–NIR can be also used to analyse soil parameters associated with both the quality of organic carbon and the structural quality of agricultural soils. [ABSTRACT FROM AUTHOR]

Conventional methods of quantifying the chemical content of potatoes at different storage temperatures are time-consuming and expensive. This research studied the Visible and Near Infrared (Vis-NIR) spectroscopy for possible rapid and nondestructive methods. In this study, healthy and Fusarium spp. Potato seeds of Granola L varieties were infected artificially through the process of inoculation of fungi, and healthy potatoes were stored in various post-harvest storage conditions, namely temperatures 12°C, 25°C and a combination of temperatures 12°C and 25°C. VIS-NIR spectral data from seeds are observed periodically during the storage period. The study results showed that Vis-NIR predicted sucrose content in potatoes. The best-developed PLSR calibration model for potatoes stored at 25°C and a combination of 12°C and 25°C show R2c of 0.87 and 0.83 and RMSEC of 0.26 and 0.28. The models also successfully predicted the sugar content of potato stored at 25°C and a combination of temperatures 12°C and 25°C with R²p 0.75 and 0.78, RMSEP of 0.36 and 0.32, and RPD of 1.99 and 2.81 for sucrose. The developed model of sucrose content or potato storage temperatures of 12°C is not recommended for monitoring and detection due to the low RPD < 1.9 even though the R²c values are 0.65 - 0.9. the results of this investigation indicate that VIS-NIR spectroscopy could potentially serve as a tool for quantifying the chemical composition of potatoes during post-harvest storage. [ABSTRACT FROM AUTHOR]

Climate change inevitably affects vegetation growth in the Tibetan Plateau (TP). Understanding the dynamics of vegetation phenology and the responses of vegetation phenology to climate change are crucial for evaluating the impacts of climate change on terrestrial ecosystems. Despite many relevant studies conducted in the past, there still remain research gaps concerning the dominant factors that induce changes in the start date of the vegetation growing season (SOS). In this study, the spatial and temporal variations of the SOS were investigated by using a long-term series of the Normalized Difference Vegetation Index (NDVI) spanning from 2001 to 2020, and the response of the SOS to climate change and the predominant climatic factors (air temperature, LST or precipitation) affecting the SOS were explored. The main findings were as follows: the annual mean SOS concentrated on 100 DOY–170 DOY (day of a year), with a delay from east to west. Although the SOS across the entire region exhibited an advancing trend at a rate of 0.261 days/year, there were notable differences in the advancement trends of SOS among different vegetation types. In contrast to the current advancing SOS, the trend of future SOS changes shows a delayed trend. For the impacts of climate change on the SOS, winter Tmax (maximum temperature) played the dominant role in the temporal shifting of spring phenology across the TP, and its effect on SOS was negative, meaning that an increase in winter Tmax led to an earlier SOS. Considering the different conditions required for the growth of various types of vegetation, the leading factor was different for the four vegetation types. This study contributes to the understanding of the mechanism of SOS variation in the TP. [ABSTRACT FROM AUTHOR]

Lake sediments offer valuable information about the geographical, climatic and environmental variations. This work was carried out to examine the chemical elements of eleven sediment samples from the old bottom (0-5m; 0.5m increments) of the dried Lake Gavur in Turkiye by applying XRF along with PCA and PLSR data analyses methods. The highest and negative correlations were observed for Sr (-0.89), S+SO3 (-0.74) and Zr+ZrO2 (-0.61) with sampling depths meaning that their concentrations were lower in deeper locations (4.0-5.0 m). In contrast, Ti+TiO2 (0.60), Al+Al2O3 (0.53) and Cu+CuO (0.51) had high and positive correlations and their quantities increased as the depth increased. The age of the two samples (at 2.5 m and 5.0 m) was determined as 4752±33 BP years (2.5 m) and 5470±35 BP (5.0 m). Ca+CaO content was found as lower and Ti+TiO was higher in the deeper sediment (5.0 m) indicating rainy periods. PCA clustered the samples into two groups as deeper samples (4.0-5.0 m) and other samples (0.0-3.5m) while PLSR grouped them into three clusters as deeper (4.0-5.0 m), medium-depth (1.5-3.5 m) and shallower (0.0-1.0m) samples. PLSR model had a good performance to estimate the sampling depth from the chemical elements (R²cal=0.95; R²val=0.67) which means that the variations of the chemicals were highly dependent on the sampling depth. A general comment that can be obtained for the lake from the geochemical proxies is that: After a rainy period between 5.0m and 4.0m, there was a gradual drought up to 2.5m and a maximum dry period at 2.5m. It was a wet period between 2.5m and 1.0m and the second period with maximum precipitation was not as effective as seen in the first one. [ABSTRACT FROM AUTHOR]

The present work illustrates the potential application of laser-induced breakdown spectroscopy (LIBS) for quick in situ authentication and evaluation of the minerals in milk powder. LIBS spectra of milk powders confirm the presence of the spectral lines of organic elements like Carbon (C), Hydrogen (H), Nitrogen (N), and inorganic elements/minerals like Calcium (Ca), Potassium (K), Magnesium (Mg), and Sodium (Na), along with the electronic bands of the CN molecule using National Institute of Standards and Technology (NIST) database. With the help of integrated intensity of the spectral lines of the various elements present in the LIBS spectra of the samples, we have compared the concentrations of minerals in different brands of milk samples. Rapid differentiation/discrimination/classification of various milk samples based on major and minor elements present in milk powder was performed using a Chemometric technique such as Principal component analysis (PCA). We have applied partial least square regression (PLSR) of the different vibronic bands of the CN molecule to estimate the concentration of protein level in milk samples. Further, the presence of the organic molecules/compounds in milk samples was confirmed by their functional group present in the Fourier-transform infrared (FTIR) spectra and by the absorption peaks/bands present in UV–visible spectra of the milk samples. Biochemical analysis was used to estimate and compare the protein level in the milk samples. The results of the present work reveal the potential of LIBS for quick, real time, cost effective, easy-to-operate, and high sampling rates analysis of any edible products. [ABSTRACT FROM AUTHOR]

BACKGROUND: Aroma is one of the most important quality criterion of different honeys and even defines their merchant value. The composition of volatile compounds, especially the characteristic odor‐active compounds, contributes significantly to the aroma of honey. Evodia rutaecarpa (Juss) Benth honey (ERBH) is a special honey in China with unique flavor characteristics. However, no work in the literature has investigated the volatile compounds and characteristic odor‐active compounds of ERBHs. Therefore, it is imperative to conduct systematic investigation into the volatile profile, odor‐active compounds and odor properties of ERBHs. RESULTS: The characteristic fingerprint of ERBHs was successfully constructed with 12 characteristic peaks and a similarity range of 0.785–0.975. In total, 297 volatile compounds were identified and relatively quantified by headspace solid‐phase microextraction coupled with gas chromatography quadrupole time‐of‐flight mass spectrometry, of which 61 and 31 were identified as odor‐active compounds by relative odor activity values and GC‐olfactometry analysis, respectively, especially the common 22 odor‐active compounds (E)‐β‐damascenone, phenethyl acetate, linalool, cis‐linalool oxide (furanoid), octanal, hotrienol, trans‐linalool oxide (furanoid), 4‐oxoisophorone and eugenol, etc., contributed significantly to the aroma of ERBHs. The primary odor properties of ERBHs were floral, followed by fruity, herbaceous and woody aromas. The partial least‐squares regression results showed that the odor‐active compounds had good correlations with the odor properties. CONCLUSION: Identifying the aroma differences of different honeys is of great importance. The present study provides a reliable theoretical basis for the quality and authenticity of ERBHs. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is a crucial factor influencing soil quality and fertility. In this particular investigation, we aimed to explore the possibility of using diffuse reflectance infrared fourier transform spectroscopy (DRIFT-FTIR) in conjunction with machine-learning models, such as partial least squares regression (PLSR), artificial neural networks (ANN), support vector regression (SVR) and random forest (RF), to estimate SOC in Sohag, Egypt. To achieve this, we collected a total of ninety surface soil samples from various locations in Sohag and estimated the total organic carbon content using both the Walkley-Black method and DRIFT-FTIR spectroscopy. Subsequently, we used the spectral data to develop regression models using PLSR, ANN, SVR, and RF. To evaluate the performance of these models, we used several evaluation parameters, including root mean square error (RMSE), coefficient of determination (R2), and ratio of performance deviation (RPD). Our survey results revealed that the PLSR model had the most favorable performance, yielding an R2 value of 0.82 and an RMSE of 0.006%. In contrast, the ANN, SVR, and RF models demonstrated moderate to poor performance, with R2 values of 0.53, 0.27, and 0.18, respectively. Overall, our study highlights the potential of combining DRIFT-FTIR spectroscopy with multivariate analysis techniques to predict SOC in Sohag, Egypt. However, additional studies and research are needed to improve the accuracy or predictability of machine-learning models incorporated into DRIFT-FTIR analysis and to compare DRIFT-FTIR analysis techniques with conventional soil chemical measurements. [ABSTRACT FROM AUTHOR]

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Though soil nutrients play important roles in maintaining soil fertility and crop growth, their estimation requires direct soil sampling followed by laboratory analysis incurring huge cost and time. In this research work, soil nutrients were predicted using VIs-NIR reflectance spectroscopy (range 350–2500 nm) with Partial Least Squares Regression (PLSR) and Support Vector Machine Regression Model (SVMR) model through principal component analysis. Two hundred soil samples were collected from Tarekswar, Hooghly, West Bengal, India to predict eight selected soil nutrients, such as soil organic carbon (OC), pH, available nitrogen (N), available phosphorus (P), available potassium(K), electric conductivity (EC), zinc (Zn) and soil texture (sand, silt, and clay) levels. The OC content was predicted with sound accuracy (R2: 0.82, RPD: 2.28, RMSE: 0.13, RPIQ: 4.15 FD-SG), followed by P (R2: 0.71, RPD: 1.83, RMSE: 4575, RPIQ: 3.44 1st derivative). The soil parameters sensitive to the particular band of visible spectrum were also identified viz. wavelengths of 409, 444, 591 and 592 nm for OC, 430 and 505 nm for P, 464 nm for K; 580 nm for Zn, 492,511,596 and 698 nm for N; 493, 569 and 665 nm for EC; 492,567 and 652 nm for pH; 457 nm for sand and 515 nm for clay.The soil nutrient levels were predicted by PLSR and SVMR models through PCA and Sentinel 2 imagery and soil suitability map were also generated for seven soil parameters such as OC, pH, EC, N, P, K and clay content. Through map query tool in ArcGIS software environment the PLSR and SVMR model successfully identify the suitability class with level of accuracy of 87.2% and 88.9%, respectively, against the direct soil analysis based suitability mapping.The machine learning technique based soil nutrient and soil suitability prediction can be easily adopted in different regions. This will reduce the cost of laboratory soil analysis and optimize the total time requirement.

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.

Measuring differences between an individual's age and biological age with biological information from the brain have the potential to provide biomarkers of clinically relevant neurological syndromes that arise later in human life. To explore the effect of multimodal brain magnetic resonance imaging (MRI) features on the prediction of brain age, we investigated how multimodal brain imaging data improved age prediction from more imaging features of structural or functional MRI data by using partial least squares regression (PLSR) and longevity data sets (age 6–85 years). First, we found that the age-predicted values for each of these ten features ranged from high to low: cortical thickness (R = 0.866, MAE = 7.904), all seven MRI features (R = 0.8594, MAE = 8.24), four features in structural MRI (R = 0.8591, MAE = 8.24), fALFF (R = 0.853, MAE = 8.1918), gray matter volume (R = 0.8324, MAE = 8.931), three rs-fMRI feature (R = 0.7959, MAE = 9.744), mean curvature (R = 0.7784, MAE = 10.232), ReHo (R = 0.7833, MAE = 10.122), ALFF (R = 0.7517, MAE = 10.844), and surface area (R = 0.719, MAE = 11.33). In addition, the significance of the volume and size of brain MRI data in predicting age was also studied. Second, our results suggest that all multimodal imaging features, except cortical thickness, improve brain-based age prediction. Third, we found that the left hemisphere contributed more to the age prediction, that is, the left hemisphere showed a greater weight in the age prediction than the right hemisphere. Finally, we found a nonlinear relationship between the predicted age and the amount of MRI data. Combined with multimodal and lifespan brain data, our approach provides a new perspective for chronological age prediction and contributes to a better understanding of the relationship between brain disorders and aging. [ABSTRACT FROM AUTHOR]

BACKGROUND: The present study aimed to determine the components related to sensory properties in soy sauce and to characterize the differences between low‐salt solid‐state fermented soy sauce (LSFSS) and high‐salt diluted‐state fermented soy sauce (HDFSS). The taste and aroma active components of 18 commercially available soy sauces (eight types of LSFSS and 10 types of HDFSS) were characterized. The relationship between these compounds, soy sauce samples, and sensory properties was modeled by partial least squares regression. RESULTS: The analysis showed that the 11 taste‐active components, including glutamic acid, glycine, alanine, threonine, malic acid, citric acid, tartaric acid, acetic acid, lactic acid, reducing sugar and salt, contributed greatly to the taste of soy sauce. In addition, umami, saltiness and sweetness are the characteristic tastes of HDFSS, whereas sourness and bitterness were the characteristic tastes of LSFSS. At the same time, seven aroma‐active compounds, namely 4‐ethyl‐2‐methoxyphenol, ethanol, 3‐methyl‐1‐butanol, ethyl acetate, 2‐phenethyl alcohol, 3‐methyl thiopropanol and 2‐ethyl‐4‐hydroxy‐5‐methylfuran‐3‐one, played a decisive role in the flavor of soy sauce. In addition, HDFSS presented the aroma attributes of smoky, alcoholic, floral, fruity and caramel‐like, whereas LSFSS mainly presented sour and malty aroma attributes. CONCLUSION: The present study reveals new insight into the relationship between the chemical composition and sensory characteristics of soy sauce, which is of great significance for developing an objective measurement system and providing a theoretical basis to improve the sensory quality of soy sauce. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Mid-infrared (MIR) analysis of wet sediments or soils usually requires freezing and drying, adding considerable analysis time and cost, and promoting changes in chemical properties. A novel attenuated total reflectance (ATR) mid-infrared (MIR) method was developed to enable analysis of non-processed, aqueous sediments. Wet sediment samples from the Coorong Lagoon, South Australia, were subjected to laboratory analysis for physical and chemical properties, our new filter-press ATR method, and conventional ATR and diffuse reflectance (DRIFT) analysis of dried samples. For ATR on wet sediments, samples were pressed onto the ATR crystal with a filter-paper backing and scanned. The spectra were analyzed by principal components analysis (PCA) and partial least squares regression (PLSR) to develop multivariate models for total organic carbon (TOC). ATR spectra of as-received wet sediments were poor due to water absorption features, but filter-pressing reduced these and greatly enhanced the solids spectral component. The filter-pressed PLSR cross-validation for TOC resulted in an R2 = 0.89 and RMSECV = 0.78%. Prediction accuracies were similar to freeze-dried ATR samples and far more accurate than testing wet samples without filter-pressing. Our findings confirmed the filter-press ATR method as proof-of-concept and showed that it has the potential to remove one of the major barriers to in-field application of MIR techniques, the high and variable amounts of moisture levels commonly present in many environmental samples. Extension of the filter-press ATR method to a wider range of analytes may enable it to rapidly acquire moist or wet sediment and soil property data while in the field. [ABSTRACT FROM AUTHOR]

Many customers prefer goji berry pulp, well-known for its high nutritional content, over fresh goji berries. However, there is limited research on its sensory lexicon and distinctive flavor compounds. This study focused on developing a sensory lexicon for goji berry pulp and characterizing its aroma by sensory and instrumental analysis. Sensory characteristics of goji berry pulp were evaluated by our established lexicon. A total of 83 aromatic compounds in goji berry pulp were quantified using HS-SPME-GC-Orbitrap-MS. By employing OAV in combination, we identified 17 aroma-active compounds as the key ingredients in goji berry pulp. Then, we identified the potentially significant contributors to the aroma of goji berry pulp by combining principal component analysis and partial least squares regression (PLSR) models of aroma compounds and sensory attributes, which included 3-ethylphenol, methyl caprylate, 2-hydroxy-4-methyl ethyl valerate, benzeneacetic acid, ethyl ester, hexanal, (E,Z)-2,6-nonadienal, acetylpyrazine, butyric acid, 2-ethylhexanoic acid, 2-methyl-1-propanol, 1-pentanol, phenylethyl alcohol, and 2-nonanone. This study provides a theoretical basis for improving the quality control and processing technology of goji berry pulp. [ABSTRACT FROM AUTHOR]

Genotype by environment interaction (GEI) is a complex problem that complicates the barley selection and breeding process. The knowledge of the relationship between cereal phenology and climatic data is important for understanding GEI and the physiological pathways responsible for the interaction effect. The grain yield of twenty winter barley genotypes in six environments was observed. Factors influencing the variability were analyzed using a linear mixed model. The partial least squares regression (PLSR) model was applied to determine the most relevant environmental variables in certain stages of development that explained GEI effects. Biplot with environmental variables explained 43.7% of the GEI. The barley was generally the most sensitive to the environmental conditions (relative humidity, maximum temperature and its variation, sun hours, and precipitation) during the anthesis and filling stage (May) which caused GEI. Temperature variables did not show significance only in the vegetative phase. Different genotypes responded differently to environmental factors. Genotypes NS-525, NS-589, and J-103 were highlighted as widely adaptable, and Zaječar was a suitable and reliable location for yield testing. The GEI information presented in this paper can be useful in traditional plant breeding and future breeding programs through molecular research of crop developmental genes and examination of physiological processes in two-row barley. [ABSTRACT FROM AUTHOR]

Recycled polypropylene (rPP) often contains a small amount of polyethylene (PE). Since polypropylene (PP) and PE are incompatible, the presence of PE compromises the performance of rPP materials and needs to be closely monitored. In our previous work, Raman and near-infrared (NIR) spectrometries were evaluated to monitor PE content in rPP with partial least square regression (PLSR) modeling. The NIR spectrometry exhibited a wider application range, but the accuracy of the prediction models might be further improved. In the current work, a different modeling method, principal component regression (PCR) was employed to analyze PE content in rPP with NIR spectrometry. Spectrum pretreatment methods, including multivariate scatter correction (MSC), standard normal variate transformation (SNV), smoothing, and first derivative, were investigated to improve the NIR spectrum quality. Forward and backward interval methods were used to optimize spectral range selection. The outcomes were compared with our previous PLSR modeling results. The highest accuracy in independent validation was achieved by a PCR model with an R2 of 0.9991 and a root-mean-square error of prediction (RMSEP) of 0.1596 PE%. On the other hand, a PLSR model achieved the lowest RMSEP of 0.9712 PE% for a non-colored post-consumer rPP sample. The PCR models might be sensitive to interference and more suitable for post-industrial materials, which have a simpler chemical composition. The PLSR models might have better stability and be more suitable for complicated post-consumer samples. Both the PCR and PLSR models were successfully applied to a gray commercial rPP sample. [ABSTRACT FROM AUTHOR]

The accuracy, speed, and feasibility of studying soil properties depend largely on the techniques used and their efficiency to correlate soil attributes. This study aimed to investigate the capability of the diffuse reflectance spectroscopy (DRS) and magnetic susceptibility (MS) methods in predicting and mapping soil physicochemical properties and monitoring their spatial distribution in an arid and semi-arid region, southern Iran. A total of 100 surface soil samples were collected from the depth of 0–20 cm and analyzed for OC, CCE, gypsum, clay, EC, Fed, χlf, and VIS–NIR–SWIR (350–2500 nm) spectroscopy using standard laboratory methods. The DRS and MS techniques were then used to estimate soil properties using the partial least squares regression (PLSR) and the linear regression methods, respectively. Results showed an accurate estimation of gypsum (RPD = 2.3 and R2 = 0.81), and reliable estimations for clay, Fed, OC, CCE, and EC by the PLSR method. However, the MS method presented reliable results only for the Fed (RPD = 1.5, R2 = 0.55). The DRS- and MS-derived predictive maps showed different spatial distribution patterns from the interpolated maps which were derived based on the observed data. The Fed parameter showed the most similarity with its observed data in both DRS- and MS-derived maps; whereas in the case of other soil properties, the DRS method presented a better prediction performance compared to the MS one. The findings of the present study highlighted the significant capability of the DRS technique as an efficient predictor for soil properties in the studied area. [ABSTRACT FROM AUTHOR]

Though soil nutrients play important roles in maintaining soil fertility and crop growth, their estimation requires direct soil sampling followed by laboratory analysis incurring huge cost and time. In this research work, soil nutrients were predicted using VIs-NIR reflectance spectroscopy (range 350–2500 nm) with Partial Least Squares Regression (PLSR) and Support Vector Machine Regression Model (SVMR) model through principal component analysis. Two hundred soil samples were collected from Tarekswar, Hooghly, West Bengal, India to predict eight selected soil nutrients, such as soil organic carbon (OC), pH, available nitrogen (N), available phosphorus (P), available potassium(K), electric conductivity (EC), zinc (Zn) and soil texture (sand, silt, and clay) levels. The OC content was predicted with sound accuracy (R2: 0.82, RPD: 2.28, RMSE: 0.13, RPIQ: 4.15 FD-SG), followed by P (R2: 0.71, RPD: 1.83, RMSE: 4575, RPIQ: 3.44 1st derivative). The soil parameters sensitive to the particular band of visible spectrum were also identified viz. wavelengths of 409, 444, 591 and 592 nm for OC, 430 and 505 nm for P, 464 nm for K; 580 nm for Zn, 492,511,596 and 698 nm for N; 493, 569 and 665 nm for EC; 492,567 and 652 nm for pH; 457 nm for sand and 515 nm for clay. The soil nutrient levels were predicted by PLSR and SVMR models through PCA and Sentinel 2 imagery and soil suitability map were also generated for seven soil parameters such as OC, pH, EC, N, P, K and clay content. Through map query tool in ArcGIS software environment the PLSR and SVMR model successfully identify the suitability class with level of accuracy of 87.2% and 88.9%, respectively, against the direct soil analysis based suitability mapping. The machine learning technique based soil nutrient and soil suitability prediction can be easily adopted in different regions. This will reduce the cost of laboratory soil analysis and optimize the total time requirement. [ABSTRACT FROM AUTHOR]

Estimation of biophysical vegetation variables is of interest for diverse applications, such as monitoring of crop growth and health or yield prediction. However, remote estimation of these variables remains challenging due to the inherent complexity of plant architecture, biology and surrounding environment, and the need for features engineering. Recent advancements in deep learning, particularly convolutional neural networks (CNN), offer promising solutions to address this challenge. Unfortunately, the limited availability of labeled data has hindered the exploration of CNNs for regression tasks, especially in the frame of crop phenotyping. In this study, the effectiveness of various CNN models in predicting wheat dry matter, nitrogen uptake, and nitrogen concentration from RGB and multispectral images taken from tillering to maturity was examined. To overcome the scarcity of labeled data, a training pipeline was devised. This pipeline involves transfer learning, pseudo-labeling of unlabeled data and temporal relationship correction. The results demonstrated that CNN models significantly benefit from the pseudolabeling method, while the machine learning approach employing a PLSr did not show comparable performance. Among the models evaluated, EfficientNetB4 achieved the highest accuracy for predicting above-ground biomass, with an R² value of 0.92. In contrast, Resnet50 demonstrated superior performance in predicting LAI, nitrogen uptake, and nitrogen concentration, with R² values of 0.82, 0.73, and 0.80, respectively. Moreover, the study explored multi-output models to predict the distribution of dry matter and nitrogen uptake between stem, inferior leaves, flag leaf, and ear. The findings indicate that CNNs hold promise as accessible and promising tools for phenotyping quantitative biophysical variables of crops. However, further research is required to harness their full potential. [ABSTRACT FROM AUTHOR]

Quantitative studies of mangrove leaf functional traits will help us understand the adaptive evolutionary strategies of mangrove plants and the relationship between mangrove biodiversity and ecosystem functions. Because of the special environment of the intertidal zone where mangroves are located, it is very difficult to obtain the functional traits of mangrove canopies from the ground, and relevant studies are lacking. The maturity of Unmanned Aerial Vehicle (UAV) hyperspectral remote sensing technology provides a new means of conducting such research. This study considered mangroves on Qi'ao Island, Zhuhai, as the research object. Based on UAV hyperspectral data, two UAV hyperspectral data processing methods, which combined Partial Least Squares Regression with Normalized Difference Vegetation Index (PLSR+NDVI) and Partial Least Squares Regression with Continuous Wavelet Transform (PLSR+CWT), were used to estimate the 10 canopy leaf functional traits of mangroves on Qi'ao Island. The results showed that the PLSR + NDVI method was more suitable for the inversion of mangrove canopy-specific leaf weight (LMA), phosphorus content per unit mass (Pmass), and nitrogen content per unit area (Narea), whereas the PLSR + CWT method was more suitable for the estimation of the nitrogen/phosphorus ratio (N/P), chlorophyll content (Cab), and carotenoid content (Cxc). However, the results of the above two methods for retrieving the nitrogen content per unit mass (Nmass), potassium content per unit mass (Kmass), phosphorus content per unit area (Parea), and potassium content per unit area (Karea) were not ideal (R2

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

This study reported an application of Au nanogap substrates for surface-enhanced Raman scattering (SERS) measurements to quantitatively analyze melamine and its derivative products at trace levels in pet liquid food (milk) combined with a waveband selection approach, namely variable importance in projection (VIP). Six different concentrations of melamine, cyanuric acid, and melamine combined with cyanuric acid were created, and SERS spectra were acquired from 550 to 1620cm−1. Detection was possible up to 200 pM for melamine-contaminated samples, and 400 pM concentration detection for other two groups. The VIP-PLSR models obtained correlation coefficient (R2) values of 0.997, 0.985, and 0.981, with root mean square error of prediction (RMSEP) values of 18.492 pM, 19.777 pM, and 15.124 pM for prediction datasets. Additionally, partial least square discriminant analysis (PLS-DA) was used to classify both pure and different concentrations of spiked samples. The results showed that the maximum classification accuracy for melamine was 100%, for cyanuric acid it was 96%, and for melamine coupled with cyanuric acid it was 95%. The results obtained clearly demonstrated that the Au nanogap substrate offers low-concentration, rapid, and efficient detection of hazardous additive chemicals in pet consuming liquid food.

On the basis of linear multiple regression methods such as PLSR and ICR, this paper introduces the kernel function to kernelize these linear multiple statistical regression methods, derives the KPLSR and KICR algorithms, and gives the specific implementation process. Then kernel-based multiple regression methods are applied to analyze the English teaching mode using a cognitive coupling network. According to the students’ learning data under the English teaching mode of cognitive coupling network, then the students’ learning situation and other precise calculations and statistics, so as to truly reflect the student’s learning status, presenting the students’ learning process in need of an urgent solution. Finally, the correlation between students’ multi-sensory learning styles and English performance is analyzed, and the results show that visual, auditory, kinesthetic, experiential, and independent styles all show a significant positive correlation to the total English performance, with correlation coefficients of 0.416, 0.158, 0.349, 0.339, and 0.327, respectively, among which the correlation coefficient of the visual type of perception is the largest.

Mongolian pasture plays an essential role in the national economy. Reliable pasture biomass estimation is indispensable to support the agricultural sector and also sustainable livelihood in the country. The aim of this study is to determine an appropriate method to estimate and map pasture biomass in a forest-steppe area of Mongolia. For this purpose, machine learning methods such as random forest (RF), support vector machine (SVM), and partial least squares regression (PLSR) were compared. As data sources, spectral indices derived from Sentinel-2B image of 2019 and field-measured biomass sample datasets were used. To determine the optimal spectral predictor variables, initially, 20 spectral indices were evaluated using the PLSR. Of these, five indices (i.e. ATSAVI2, EVI, GRVI, IPVI and MSR) with the highest correlation coefficients (r ≥ 0.94) were considered for further analysis. These indices were also examined and validated by a variable importance analysis. Then, the RF, SVM, and PLSR models were applied to predict and map pasture biomass using the selected five indices. The PLSR method demonstrated the highest accuracy with coefficient of determination (R2) =0.899 and root mean square error (RMSE)=10.560 g/m2. The SVM technique showed the second highest accuracy with R2=0.837 and RMSE = 12.881 g/m2. The RF model gave the lowest accuracy with R2=0.823 and RMSE = 13.430g/m2. Our research showed that different machine learning models might be applied (because in all cases R2>0.82) for a pasture biomass estimation and mapping in the selected test site, but the best result could be achieved by the use of the PLSR.

Near-infrared (NIR) spectroscopy is actually a well-established technique that demonstrates its performance in the frame of detection of poor-quality medicines. The use of low-cost handheld NIR spectrophotometers in low-resource contexts can allow an inexpensive and more rapid detection compared to laboratory methods. Considering these points, it was decided to develop, validate, and transfer methods for the quantification of ciprofloxacin and metronidazole tablet samples using a NIR handheld spectrophotometer in transmission mode (NIR-M-T1) coupled to chemometrics such as partial least squares regression (PLSR) algorithm. All of the models were validated with the total error approach using an accuracy profile as a decision tool, with ±10% specifications and a risk α set at 5%. Quantitative PLSR models were first validated in Belgium, which is a temperate oceanic climate zone. Second, they were transferred to Cameroon, a tropical climate zone, where issues regarding the prediction of new validation series with the initial models were highlighted. Two augmentation strategies were then envisaged to make the predictive models robust to environmental conditions, incorporating the potential variability linked to environmental effects in the initial calibration sets. The resulting models were then used for in-field analysis of ciprofloxacin and metronidazole tablet samples collected in three cities in Cameroon. The contents results obtained for each sample with the two strategies were close and not statistically different. Nevertheless, the first one is easier to implement and the second is the best regarding model diagnostic measures and accuracy profiles. Two samples were found to be noncompliant in terms of content, and these results were confirmed using high-performance liquid chromatography taken as the reference method. Graphical Abstract This is a visual representation of the abstract. [ABSTRACT FROM AUTHOR]

Quantitative studies of mangrove leaf functional traits will help us understand the adaptive evolutionary strategies of mangrove plants and the relationship between mangrove biodiversity and ecosystem functions. Because of the special environment of the intertidal zone where mangroves are located, it is very difficult to obtain the functional traits of mangrove canopies from the ground, and relevant studies are lacking. The maturity of Unmanned Aerial Vehicle (UAV) hyperspectral remote sensing technology provides a new means of conducting such research. This study considered mangroves on Qi'ao Island, Zhuhai, as the research object. Based on UAV hyperspectral data, two UAV hyperspectral data processing methods, which combined Partial Least Squares Regression with Normalized Difference Vegetation Index (PLSR+NDVI) and Partial Least Squares Regression with Continuous Wavelet Transform (PLSR+CWT), were used to estimate the 10 canopy leaf functional traits of mangroves on Qi'ao Island. The results showed that the PLSR + NDVI method was more suitable for the inversion of mangrove canopy-specific leaf weight (LMA), phosphorus content per unit mass (Pmass), and nitrogen content per unit area (Narea), whereas the PLSR + CWT method was more suitable for the estimation of the nitrogen/phosphorus ratio (N/P), chlorophyll content (Cab), and carotenoid content (Cxc). However, the results of the above two methods for retrieving the nitrogen content per unit mass (Nmass), potassium content per unit mass (Kmass), phosphorus content per unit area (Parea), and potassium content per unit area (Karea) were not ideal (R²<0.3). The optimal method established in this study was used to estimate the contents of LMA, Pmass, Narea, N/P, Cab, and Cxc of the mangrove canopy leaves in the study area and map their spatial distribution. Mangrove canopy leaf functional traits obtained using UAV hyperspectral data inversion better reflect the horizontal structure and function of the mangrove community. Regarding the spatial distribution patterns of canopy leaf functional traits, the spatial distribution patterns of Narea, Cab, Cxc, and N/P were relatively consistent with higher values in the middle region and lower values in the edge region. The spatial distribution patterns of the LMA and Pmass were similar, and the distribution was relatively uniform throughout the study area. Combined with ground survey data, the internal relationship between species composition and spatial patterns of functional traits, as well as ecosystem functions and processes, can be deeply explored, and rapid investigation and assessment of mangrove forests can be realized at the community and ecosystem scales. The spatial distribution pattern of functional traits was closely related to the spatial distribution pattern of canopy structure and species. The inversion model of hyperspectral functional traits was constructed by separating mangrove species with different life types, which is expected to further improve the inversion accuracy of the model. Constructing a specific functional trait inversion model for each mangrove species, combined with the species identification results of visible-light images, will effectively improve the inversion accuracy of mangrove canopy leaf functional traits. [ABSTRACT FROM AUTHOR]