Rapid detection of colored and colorless macro- and micro-plastics in complex environment via near-infrared spectroscopy and machine learning.

To better understand the migration behavior of plastic fragments in the environment, development of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary. However, most of the studies had focused only on colored plastic fragments, ignoring colorless plastic fragments and the effects of different environmental media (backgrounds), thus underestimating their abundance. To address this issue, the present study used near-infrared spectroscopy to compare the identification of colored and colorless plastic fragments based on partial least squares-discriminant analysis (PLS-DA), extreme gradient boost, support vector machine and random forest classifier. The effects of polymer color, type, thickness, and background on the plastic fragments classification were evaluated. PLS-DA presented the best and most stable outcome, with higher robustness and lower misclassification rate. All models frequently misinterpreted colorless plastic fragments and its background when the fragment thickness was less than 0.1mm. A two-stage modeling method, which first distinguishes the plastic types and then identifies colorless plastic fragments that had been misclassified as background, was proposed. The method presented an accuracy higher than 99% in different backgrounds. In summary, this study developed a novel method for rapid and synchronous identification of colored and colorless plastic fragments under complex environmental backgrounds.

Implementing multiblock techniques in a full-scale plant scenario: On-line prediction of quality parameters in a continuous process for different acrylonitrile butadiene styrene (ABS) products.

BACKGROUND: The study explores the challenges of handling multiblock data of different natures (process and NIR sensors) for on-line quality prediction in a full-scale plant scenario, namely a plant operating in continuous on an industrial scale and producing different grade Acrylonitrile Butadiene Styrene (ABS) products. This environment is an ideal scenario to evaluate the use of multiblock data analysis methods, which can enhance data interpretation, visualization, and predictive performances. In particular, a novel multiblock extension of Locally Weighted PLS has been proposed by the authors, namely Locally Weighted Multiblock Partial Least Squares (LW-MB-PLS). Response-Oriented Sequential Alternation (ROSA) has also been employed to evaluate the diverse block relevance for the prediction of two quality parameters associated with the polymer. Data are split in blocks both according to sensor type and different plant sections, and different models have been built by incremental addition of data blocks to evaluate if early estimation of product quality is feasible. RESULTS: ROSA method showed promising predictive performance for both quality parameters, highlighting the most influential plant sections through the selection of data blocks. The results suggested that both early and late-stage sensors play crucial roles in predicting product quality. A reasonable estimation of quality parameters before production completion has been achieved. On the other hand, the proposed LW-MB-PLS, while comparable in predictive performances, allowed reducing systematic prediction errors for specific products. SIGNIFICANCE: This study contributes valuable insights for continuous production processes, aiding plant operators and paving the way for advancements in online quality prediction and control. Furthermore, it is implemented as a locally weighted extension of MB-PLS.

Breaking down barriers: rationalisations and motivation to stop among Chinese male smokers under cigarette dependence.

BACKGROUND: Smoking rationalisation beliefs are a huge barrier to quitting smoking. What types of rationalisations should be emphasised in smoking cessation interventions? Although past literature has confirmed the negative relationship between those beliefs and motivation to stop smoking, little is known regarding the importance and performance of those beliefs on motivation with varying cigarette dependence. The study aimed to ascertain rationalisations that are highly important for motivation yet perform poorly in different cigarette dependence groups. METHODS: The cross-sectional study was conducted from November 19 to December 9, 2023 in Guiyang City, China. Adult male current smokers were enrolled. Partial least squares structural equation modelling was used to test the hypothesis. The multi-group analysis was used to determine the moderating effect of cigarette dependence, and the importance-performance map analysis was utilised to assess the importance and performance of rationalisations. RESULTS: A total of 616 adult male current smokers were analysed, and they were divided into the low cigarette dependence group (n = 297) and the high cigarette dependence group (n = 319). Except for risk generalisation beliefs, smoking functional beliefs (H1: -ß = 0.131, P < 0.01), social acceptability beliefs (H3: ß = -0.258, P < 0.001), safe smoking beliefs (H4: ß = -0.078, P < 0.05), self-exempting beliefs (H5: ß = -0.244, P < 0.001), and quitting is harmful beliefs (H6: ß = -0.148, P < 0.01) all had a significant positive influence on motivation. Cigarette dependence moderated the correlation between rationalisations and motivation. In the high-dependence group, the social acceptability beliefs and smoking functional beliefs were located in the "Concentrate Here" area. In the low-dependence group, the social acceptability beliefs were also situated in there. CONCLUSIONS: Social acceptability beliefs and smoking functional beliefs showed great potential and value for improvement among high-dependence smokers, while only social acceptability beliefs had great potential and value for improvement among low-dependence smokers. Addressing these beliefs will be helpful for smoking cessation. The multi-group analysis and the importance-performance map analysis technique have practical implications and can be expanded to other domains of health education and intervention practice.

Quantitative analysis of solid dosage forms of Atenolol by Raman spectroscopy.

OBJECTIVE: To develop a Raman spectroscopy-based analytical model for quantification of solid dosage forms of active pharmaceutical ingredient (API) of Atenolol.Significance: For the quantitative analysis of pharmaceutical drugs, Raman Spectroscopy is a reliable and fast detection method. As part of this study, Raman Spectroscopy is explored for the quantitative analysis of different concentrations of Atenolol. METHODS: Various solid-dosage forms of Atenolol were prepared by mixing API with excipients to form different solid-dosage formulations of Atenolol. Multivariate data analysis techniques, such as Principal Component Analysis (PCA) and Partial least square regression (PLSR) were used for the qualitative and quantitative analysis, respectively. RESULTS: As the concentration of the drug increased in formulation, the peak intensities of the distinctive Raman spectral characteristics associated with the API (Atenolol) gradually increased. Raman spectral data sets were classified using PCA due to their distinctive spectral characteristics. Additionally, a prediction model was built using PLSR analysis to assess the quantitative relationship between various API (Atenolol) concentrations and spectral features. With a goodness of fit value of 0.99, the root mean square errors of calibration (RMSEC) and prediction (RMSEP) were determined to be 1.0036 and 2.83 mg, respectively. The API content in the blind/unknown Atenolol formulation was determined as well using the PLSR model. CONCLUSIONS: Based on these results, Raman spectroscopy may be used to quickly and accurately analyze pharmaceutical samples and for their quantitative determination.

Fatty acid analysis in microalgal mono- and polycultures using diffuse reflectance infrared Fourier transform spectroscopy coupled with partial least squares analysis.

Fatty acids are of particular interest for industrial applications of microalgal feedstock, as these have a wide array of different uses such as pharmaceuticals and biofuels. Fourier transform infrared (FTIR) spectroscopic techniques used in combination with multivariate prediction modeling are showing great potential as analytical methods for characterizing microalgal biomass. The present study investigated the use of diffuse reflectance Fourier transform infrared spectroscopy (DRIFTS) coupled with partial least squares regression (PLSR) to estimate fatty acid contents in microalgae. A prediction model for microalgal samples was developed using algae cultivated in both Bold's basal medium (BBM) and sterilized municipal wastewater under axenic conditions, as well as algal polycultures cultivated in open raceway ponds using untreated municipal wastewater influent. This universal prediction model was able to accurately predict microalgal samples of either type with high accuracy (RMSEP = 1.38, relative error = 0.14) and reliability (R2 > 0.92). DRIFTS in combination with PLSR is a rapid method for determining fatty acid contents in a wide variety of different microalgal samples with high accuracy. The use of spectral characterization techniques offers a reliable and environmentally friendly alternative to traditional labor intensive techniques based on the use of toxic chemicals.

Utilizing Raman spectroscopy for urinalysis to diagnose acute kidney injury stages in cardiac surgery patients.

BACKGROUND: The successful treatment and improvement of acute kidney injury (AKI) depend on early-stage diagnosis. However, no study has differentiated between the three stages of AKI and non-AKI patients following heart surgery. This study will fill this gap in the literature and help to improve kidney disease management in the future. METHODS: In this study, we applied Raman spectroscopy (RS) to uncover unique urine biomarkers distinguishing heart surgery patients with and without AKI. Given the amplified risk of renal complications post-cardiac surgery, this approach is of paramount importance. Further, we employed the partial least squares-support vector machine (PLS-SVM) model to distinguish between all three stages of AKI and non-AKI patients. RESULTS: We noted significant metabolic disparities among the groups. Each AKI stage presented a distinct metabolic profile: stage 1 had elevated uric acid and reduced creatinine levels; stage 2 demonstrated increased tryptophan and nitrogenous compounds with diminished uric acid; stage 3 displayed the highest neopterin and the lowest creatinine levels. We utilized the PLS-SVM model for discriminant analysis, achieving over 90% identification rate in distinguishing AKI patients, encompassing all stages, from non-AKI subjects. CONCLUSIONS: This study characterizes the incidence and risk factors for AKI after cardiac surgery. The unique spectral information garnered from this study can also pave the way for developing an in vivo RS method to detect and monitor AKI effectively.

Monitoring of the Homogeneity of Primer Layers for Ink Jet Printing on Polyester Fabrics by Hyperspectral Imaging.

Untreated polyester films and fibers can be hardly printed or coated, in particular if aqueous inks or lacquers have to be applied. Therefore, an adequate primer layer has to be applied first. A cationic polymer formulation based on poly(dimethylamine-co-epichlorohydrin-co-ethylenediamine) (PDEHED) was used as primer layer for digital printing on polyester fabrics. Because of the exceedingly high requirements on the homogeneity of such layers, hyperspectral imaging was used for qualitative and quantitative monitoring of the distribution of the primer layer on the textiles. Multivariate data analysis methods based on the PLS algorithm were applied for quantification of the NIR reflection spectra using gravimetry as a reference method. Optimization of the calibration method resulted in various models with prediction errors of about 1.2 g/m2. The prediction performance of the models was proven in external validations using independent samples. Moreover, a special ink jet printing technology was tested for application of the aqueous primer formulation itself. Since possible clogging of jet nozzles in the print head might lead to inhomogeneity in the coatings such as missing tracks, the potential of hyperspectral imaging to detect such defects was investigated. It was demonstrated that simulated missing tracks can be clearly detected. Consequently, hyperspectral imaging has been proven to be a powerful analytical tool for in-line monitoring of the quality of printability improvement layers and similar systems.

Hybrid modeling of T-shaped partial least squares regression and transfer learning for formulation and manufacturing process development of new drug products.

T-shaped partial least squares regression (T-PLSR) is a valuable machine learning technique for the formulation and manufacturing process development of new drug products. An accurate T-PLSR model requires experimental data with multiple formulations and process conditions. However, it is usually challenging to collect comprehensive experimental data using large-scale manufacturing equipment because of the cost, time, and large consumption of raw materials. This study proposes a hybrid modeling of T-PLSR and transfer learning (TL) to enhance the prediction performance of a T-PLSR model for large-scale manufacturing data by exploiting a large amount of small-scale manufacturing data for model building. The proposed method of T-PLSR+TL was applied to a practical case study focusing on scaling up the tableting process from an experienced compaction simulator to a less-experienced rotary tablet press. The T-PLSR+TL models achieved significantly better prediction performance for tablet quality attributes of new drug products than T-PLSR models without using large-scale manufacturing data with new drug products. The results demonstrated that T-PLSR+TL is more capable of addressing new drug products than T-PLSR by using small-scale manufacturing data to cover a scarcity of large-scale manufacturing data. Furthermore, T-PLSR+TL holds the potential to streamline formulation and manufacturing process development activities for new drug products using an extensive database.

Detection of Anthocyanins in Potatoes Using Micro-Hyperspectral Images Based on Convolutional Neural Networks.

The color potato has the function of both a food and vegetable. The color potato not only contains various amino acids and trace elements needed by the human body but also contains anthocyanins. Anthocyanins have many functions, such as antioxidation, inflammation inhibition, vision improvement, and cancer prevention, so colored potatoes are deeply loved by consumers and have good market prospects. However, at present, the detection of anthocyanin content in color potatoes mainly depends on chemical methods, which are time-consuming and laborious, so it is necessary to study a fast and accurate detection method. In this study, microscopic hyperspectral equipment was used to collect the spectral information of the outer skin and inner skin of potatoes. The original spectrum, pretreatment spectrum, and characteristic spectrum variables of the outer skin and inner skin were predicted by the convolution neural network (CNN) algorithm and partial least squares regression (PLS) algorithm, respectively, and the performance of the model was evaluated by the prediction set correlation coefficient (Rp), prediction set root mean square error (RMSEP), correction set correlation coefficient (Rc), correction set root mean square error (RMSEC), and residual prediction deviation (RPD). The results revealed that the inner skin Raw + CNN model constructed under raw spectral data is optimal with Rc = 0.9508, RMSEC = 0.0374%, Rp = 0.9461, RMSEP = 0.2361% and RPD = 4.4933. The inner skin Savitzky-Golay (SG) + Detrend (DET) + CNN model constructed from pre-processed spectral data is optimal with Rc = 0.9499, RMSEC = 0.0359%, Rp = 0.9439, RMSEP = 0.2384%, RPD = 4.6516. The inner skin DET + competitive adaptive reweighted sampling (CARS) +CNN model constructed from the feature-based spectral data was optimal with Rc = 0.9527, RMSEC = 0.0708%, Rp = 0.9457, RMSEP = 0.2711%, and RPD = 4.1623. It can be seen that the Rp, RMSEP, Rc, RMSEC, and RPD values for modeling the spectral information of the inner skin were higher than those of the outer skin under the three different spectral data. The prediction accuracy of the model built by the CNN algorithm was better than the conventional algorithm PLS, the application of the CNN algorithm in inner skin can achieve accurate prediction of anthocyanin content in potato.

Raman-based PAT for VLP precipitation: systematic data diversification and preprocessing pipeline identification.

Virus-like particles (VLPs) are a promising class of biopharmaceuticals for vaccines and targeted delivery. Starting from clarified lysate, VLPs are typically captured by selective precipitation. While VLP precipitation is induced by step-wise or continuous precipitant addition, current monitoring approaches do not support the direct product quantification, and analytical methods usually require various, time-consuming processing and sample preparation steps. Here, the application of Raman spectroscopy combined with chemometric methods may allow the simultaneous quantification of the precipitated VLPs and precipitant owing to its demonstrated advantages in analyzing crude, complex mixtures. In this study, we present a Raman spectroscopy-based Process Analytical Technology (PAT) tool developed on batch and fed-batch precipitation experiments of Hepatitis B core Antigen VLPs. We conducted small-scale precipitation experiments providing a diversified data set with varying precipitation dynamics and backgrounds induced by initial dilution or spiking of clarified Escherichia coli-derived lysates. For the Raman spectroscopy data, various preprocessing operations were systematically combined allowing the identification of a preprocessing pipeline, which proved to effectively eliminate initial lysate composition variations as well as most interferences attributed to precipitates and the precipitant present in solution. The calibrated partial least squares models seamlessly predicted the precipitant concentration with R 2 of 0.98 and 0.97 in batch and fed-batch experiments, respectively, and captured the observed precipitation trends with R 2 of 0.74 and 0.64. Although the resolution of fine differences between experiments was limited due to the observed non-linear relationship between spectral data and the VLP concentration, this study provides a foundation for employing Raman spectroscopy as a PAT sensor for monitoring VLP precipitation processes with the potential to extend its applicability to other phase-behavior dependent processes or molecules.

Assessment of Peanut Protein Powder Quality by Near-Infrared Spectroscopy and Generalized Regression Neural Network-Based Approach.

The global demand for protein is on an upward trajectory, and peanut protein powder has emerged as a significant player, owing to its affordability and high quality, with great future market potential. However, the industry currently lacks efficient methods for rapid quality testing. This research paper addressed this gap by introducing a portable device with employed near-infrared spectroscopy (NIR) to quickly assess the quality of peanut protein powder. The principal component analysis (PCA), partial least squares (PLS), and generalized regression neural network (GRNN) methods were used to construct the model to further enhance the accuracy and efficiency of the device. The results demonstrated that the newly established NIR method with PLS and GRNN analysis simultaneously predicted the fat, protein, and moisture of peanut protein powder. The GRNN model showed better predictive performance than the PLS model, the correlation coefficient in calibration (Rcal) of the fat, the protein, and the moisture of peanut protein powder were 0.995, 0.990, and 0.990, respectively, and the residual prediction deviation (RPD) were 10.82, 10.03, and 8.41, respectively. The findings unveiled that the portable NIR spectroscopic equipment combined with the GRNN method achieved rapid quantitative analysis of peanut protein powder. This advancement holds a significant application of this device for the industry, potentially revolutionizing quality testing procedures and ensuring the consistent delivery of high-quality products to fulfil consumer desires.

Predictions of biorelevant solubility change during dispersion and digestion of lipid-based formulations.

Computational approaches are increasingly explored in development of drug products, including the development of lipid-based formulations (LBFs), to assess their feasibility for achieving adequate oral absorption at an early stage. This study investigated the use of computational pharmaceutics approaches to predict solubility changes of poorly soluble drugs during dispersion and digestion in biorelevant media. Concentrations of 30 poorly water-soluble drugs were determined pre- and post-digestion with in-line UV probes using the MicroDISS Profiler™. Generally, cationic drugs displayed higher drug concentrations post-digestion, whereas for non-ionized drugs there was no discernible trend between drug concentration in dispersed and digested phase. In the case of anionic drugs there tended to be a decrease or no change in the drug concentration post-digestion. Partial least squares modelling was used to identify the molecular descriptors and drug properties which predict changes in solubility ratio in long-chain LBF pre-digestion (R2 of calibration = 0.80, Q2 of validation = 0.64) and post-digestion (R2 of calibration = 0.76, Q2 of validation = 0.72). Furthermore, multiple linear regression equations were developed to facilitate prediction of the solubility ratio pre- and post-digestion. Applying three molecular descriptors (melting point, LogD, and number of aromatic rings) these equations showed good predictivity (pre-digestion R2 = 0.70, and post-digestion R2 = 0.68). The model developed will support a computationally guided LBF strategy for emerging poorly water-soluble drugs by predicting biorelevant solubility changes during dispersion and digestion. This facilitates a more data-informed developability decision making and subsequently facilitates a more efficient use of formulation screening resources.

[Effects of Straw Addition on N2O and CO2 Emissions from Red Soil Subjected to Different Long-Term Fertilization].

Straw return, as an important measure for soil fertility improvement in farmland, significantly affects the emissions of greenhouse gases N2O and CO2. Thus, the collected soil samples from five long-term (30-year) fertilization treatments (no fertilization, CK; recommended chemical fertilizer, F; 200 % of recommended chemical fertilizer, 2F; pig manure, M; and chemical fertilizer combined with pig manure, FM) were amended with and without straw and incubated under constant temperature and humidity conditions (25 ℃ and 65 % maximum field water holding capacity) for 20 days so as to investigate the key factors influencing N2O and CO2 emissions in response to straw addition in long-term fertilization treatments. The results showed that fertilization significantly increased N2O emissions. Compared to those under the unfertilized treatment[(22.05 ±2.09) µg·kg-1, calculated as nitrogen, the same as below], cumulative N2O emissions from the chemical fertilizer treatments significantly increased by 119 %-195 %[(48.38 ±20.81) µg·kg-1 and (65.13 ±12.55) µg·kg-1 from the F and 2F treatments, respectively], and those from the manure treatments increased by 275 %-399 %[(82.72 ±12.73) µg·kg-1 and (1 101.99 ±425.71) µg·kg-1 from the M and FM treatments, respectively]. Soil NO3--N, DOC, and DTN were the main factors influencing N2O emissions from fertilized treatments in the absence of straw addition. Straw addition significantly increased cumulative N2O emissions by 345 % and 247 % in the 2F and M treatments, respectively, compared to those in the corresponding fertilized treatments without straw addition, with no significant effect on N2O emissions in the CK, F, and FM treatments. Straw addition increased DOC content and microbial activity and decreased soil NO3--N and DTN contents, thereby increasing N2O emissions. Fertilization also significantly increased CO2 emissions. Compared to those from the unfertilized treatment, cumulative CO2 emissions from the manure treatments significantly increased by 120 %-130 %[(122.11 ±4.3) mg·kg-1 (calculated as carbon, the same as below) and (116.47 ±4.55) mg·kg-1 from the M and FM treatments, respectively], and those in the 2F treatment increased by 28 %[(65.13 ±12.55) mg·kg-1]. In the absence of straw addition, soil MBC, DOC, and DTN were the main factors influencing CO2 emissions. Compared to those in the treatments without straw addition, straw addition significantly increased cumulative CO2 emissions by 660 %-1132 % among fertilization treatments, due to increased DOC and MBC contents and enhanced microbial activity. In conclusion, straw addition significantly increased N2O and CO2 emissions through increased soil DTN consumption and DOC content among fertilization treatments. In soils treated with manure amendment, straw return should be rationally considered for the purpose of balancing the comprehensive trade-offs between fertility improvement and greenhouse gas emissions.

Seabuckthorn (Hippophae rhamnoides L.) plantation degradation aggravates microbial metabolic C and P limitations on the Northern Loess Plateau in China.

Vegetation degradation in arid and semi-arid regions reduces plant C inputs to the soil, which can impede soil nutrient cycling because of the limited C source for microbial metabolism. However, whether vegetation degradation aggravates microbial nutrient limitation in degraded ecosystems in arid and semi-arid regions is not fully understood. Here, we investigated changes in soil enzyme activity and microbial nutrient limitation along a well-documented gradient of degraded seabuckthorn (Hippophae rhamnoides L.) (slightly degraded, canopy dieback <25 %, moderately degraded, canopy dieback 25 %-75 %, and severely degraded, canopy dieback >75 %) in Liang (long ridge) and gully channel locations in the Pisha Sandstone region of the Loess Plateau, China. We found that as the magnitude of seabuckthorn degradation increased, activities of C-acquiring enzymes and ratios of C:N and C:P enzymes (0.54-0.80 and 0.52-0.77, respectively) increased whereas the N:P enzyme ratio (0.93-0.99) decreased. Stoichiometric modelling further indicated that microorganisms were limited by soil C and P (vector angle >45°) in the seabuckthorn plantation region, and the degradation of seabuckthorn plantation aggravated microbial C and P limitations. Partial least squares path modelling revealed that seabuckthorn degradation (canopy dieback) was the main factor explaining microbial C limitation variations, while soil physicochemical properties (pH and soil moisture content) and understory plant parameters (litter biomass) were the major factors underlying microbial P limitation of long ridge and gully channel formations, respectively. Our findings highlight synergistic changes between aboveground and belowground processes, suggesting an unexpected negative effect of vegetation degradation on soil microbial community and nutrient cycling. These insights offer a direction for the development of plantation nutrients management strategies in semi-arid and arid areas.

A non-invasive, sensor-based approach to exploit the autofluorescence of saffron (Crocus sativus L.) for on-site evaluation of aging.

So far, compliance with ISO 3632 standard specifications for top-quality saffron guarantees good agricultural and post-harvest production practices. Tracking early-stage oxidation remains challenging. Our study aims to address this issue by exploring the visible, fluorescence, and near-infrared spectra of category I saffron. Using a multi-spectral sensor, we tested fresh and artificially aged saffron in powder form. High autofluorescence intensities at 600-700 nm allowed calibration for the 'content of aged saffron'. Samples with minimum coloring strength (200-220 units) were classified as 70% aged, while those exceeding maximum aroma strength (50 units) as 100% aged. Consistent patterns across origin, age, and processing history indicated potential for objectively assessing early-oxidation markers. Further analyses uncovered multiple contributing fluorophores, including cis-apocarotenoids, correlated with FTIR-based aging markers. Our findings underscore that sensing autofluorescence of traded saffron presents an innovative quality diagnostic approach, paving new research pathways for assessing the remaining shelf-life along its supply chain.

Characterization of Near Infrared-Dye Colored Fabrics Using Hyperspectral Imaging.

Near-infrared (NIR) dyes have a unique ability to interact favorably with light in the NIR region, which is particularly interesting where stealth and camouflage are paramount, such as in military uniforms. Characterization of cotton fabric dyed with NIR-absorbing dyes using visible-NIR (Vis-NIR) and short-wave infrared (SWIR) hyperspectral imaging was done. The aim of the study was to discern spectral changes caused by variations in dye concentration and dyeing temperature as these parameters directly influence color- and crocking-fastness of fabrics impacting the camouflage effect. The fabric was dyed at three concentrations (2.5, 5, and 10%) and two dyeing temperatures (55 °C and 85 °C) and principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed on the spectra to discriminate the fabrics based on dye concentrations. The PCA models successfully segregated the fabrics based on the dye concentration and dyeing temperature, while PLS-DA models demonstrated classification accuracies between 75 and 100% in the Vis-NIR range. Spectra in the SWIR region could not be used to detect the differences in the concentrations of the NIR dyes. This finding is promising, as it aligns with the objective of creating NIR-dyed camouflage fabrics that remain indistinguishable under varying dye concentrations. These results open possibilities for further exploration in enhancing the stealth capabilities of textiles in military applications.

Optimizing feature selection with gradient boosting machines in PLS regression for predicting moisture and protein in multi-country corn kernels via NIR spectroscopy.

Differences in moisture and protein content impact both nutritional value and processing efficiency of corn kernels. Near-infrared (NIR) spectroscopy can be used to estimate kernel composition, but models trained on a few environments may underestimate error rates and bias. We assembled corn samples from diverse international environments and used NIR with chemometrics and partial least squares regression (PLSR) to determine moisture and protein. The potential of five feature selection methods to improve prediction accuracy was assessed by extracting sensitive wavelengths. Gradient boosting machines (GBMs), particularly CatBoost and LightGBM, were found to effectively select crucial wavelengths for moisture (1409, 1900, 1908, 1932, 1953, 2174 nm) and protein (887, 1212, 1705, 1891, 2097, 2456 nm). SHAP plots highlighted significant wavelength contributions to model prediction. These results illustrate GBMs' effectiveness in feature engineering for agricultural and food sector applications, including developing multi-country global calibration models for moisture and protein in corn kernels.

Raman spectroscopy with an improved support vector machine for discrimination of thyroid and parathyroid tissues.

The objective of this study was to discriminate thyroid and parathyroid tissues using Raman spectroscopy combined with an improved support vector machine (SVM) algorithm. In thyroid surgery, there is a risk of inadvertently removing the parathyroid glands. At present, there is a lack of research on using Raman spectroscopy to discriminate parathyroid and thyroid tissues. In this article, samples were obtained from 43 individuals with thyroid and parathyroid tissues for Raman spectroscopy analysis. This study employed partial least squares (PLS) to reduce dimensions of data, and three optimization algorithms are used to improve the classification accuracy of SVM algorithm model in spectral analysis. The results show that PLS-GA-SVM algorithm has higher diagnostic accuracy and better reliability. The sensitivity of this algorithm is 94.67% and the accuracy is 94.44%. It can be concluded that Raman spectroscopy combined with the PLS-GA-SVM diagnostic algorithm has significant potential for discriminating thyroid and parathyroid tissues.

Chaotic neural network algorithm with competitive learning integrated with partial Least Square models for the prediction of the toxicity of fragrances in sanitizers and disinfectants.

This study addresses the need for accurate structural data regarding the toxicity of fragrances in sanitizers and disinfectants. We compare the predictive and descriptive (model stability) potential of multiple linear regression (MLR) and partial least squares (PLS) models optimized through variable selection (VS). A novel hybrid chaotic neural network algorithm with competitive learning (CCLNNA)-PLS modeling strategy can offer specific optimization with satisfactory results, even for a limited dataset. While also exploring the preliminary comparative analysis, the goal is to introduce an adapted novel CCLNNA optimization strategy for VS, inspired by neural networks, along with exploring the influence of the percentage of significant descriptors in the optimization function to enhance the final model's capabilities. We analyzed an available dataset of 24 molecules, incorporating ADMET and PaDEL descriptors as predictor variables, to explore the relationship between the response/target variable (pLC50) and the meticulously optimized set of descriptors. The suitability of the selected PLS models (cross- and external-validated accuracy combined with percentage of significant descriptors at a level equal to or >80 %) underscores the importance of expanding the dataset to amplify the validation protocols, thus enhancing future model reliability and environmental impact.

Tailings particle size effects on pollution and ecological remediation: A case study of an iron tailings reservoir.

The particle size distribution in tailings notably influences their physical properties and behavior. Despite this, our understanding of how the distribution of tailings particle sizes impacts in situ pollution and ecological remediation in in-situ environment remains limited. In this study, an iron tailings reservoir was sampled along a particle flow path to compare the pollution characteristic and microbial communities across regions with different particle sizes. The results revealed a gradual reduction in tailings particle size along the flow direction. The predominant mineral composition shifts from minerals such as albite and quartz to layered minerals. Total nitrogen, total organic carbon, and total metal concentrations increased, whereas the acid-generating potential decreased. The region with the finest tailings particle size exhibited the highest microbial diversity, featuring metal-resistant microorganisms such as KD4-96, Micrococcaceae, and Acidimicrobiia. Significant discrepancies were observed in tailings pollution and ecological risks across different particle sizes. Consequently, it is necessary to assess tailings reservoirs pollution in the early stages of remediation before determining appropriate remediation methods. These findings underscore that tailings particle distribution is a critical factor in shaping geochemical characteristics. The responsive nature of the microbial community further validated these outcomes and offered novel insights into the ecological remediation of tailings.