Partial Least Squares Model Research Articles

Analysis Of The Influence Of Financial, Environmental Performance And Corporate Governance On Performance Of Corporate Social Responsibility Of Energy And Mineral Companies Listed In Indonesian Stock Exchange

The purpose of this study is to determine how company governance, financial performance, and environmental performance affect the disclosure of corporate social responsibility. With secondary data from all energy businesses that have released annual reports, this study employs quantitative methodologies. All companies in the energy sector listed on the Indonesian Stock Exchange comprise the population considered in this study. Purposive sampling was used as the technique for selecting the sample. The partial least squares (PLS) approach is the data analysis technique employed in this study. Microsoft Excel was utilized by researchers to do descriptive statistical computations. The evaluation of the PLS model, which consists of two models, the outer model and the inner model, comes next once descriptive statistics have been completed. Disclosure of corporate social responsibility is positively and significantly impacted by corporate governance using KP, DKI, and KA indices. This demonstrates that a company's corporate social responsibility disclosure is more widely disclosed the better its governance. Disclosure of corporate social responsibility is positively and significantly impacted by environmental performance as measured by the PROPER indicator. This demonstrates that greater corporate social responsibility disclosure corresponds with improved environmental performance. The relationship between financial performance as measured by NPM, ROA, and ROE and corporate social responsibility disclosure is nonexistent. This demonstrates that a company's level of corporate social responsibility disclosure is not always influenced by its financial performance.

In situ process analytical technology for real time viable cell density and cell viability during live-virus vaccine production

Viable cell density (VCD) and cell viability (CV) are key performance indicators of cell culture processes in biopharmaceutical production of biologics and vaccines. Traditional methods for monitoring VCD and CV involve offline cell counting assays that are both labor intensive and prone to high variability, resulting in sparse sampling and uncertainty in the obtained data. Process analytical technology (PAT) approaches offer a means to address these challenges. Specifically, in situ probe-based measurements of dielectric spectroscopy (also commonly known as capacitance) can characterize VCD and CV continuously in real time throughout an entire process, enabling robust process characterization. In this work, we propose in situ dielectric spectroscopy as a PAT tool for real time analysis of live-virus vaccine (LVV) production. Dielectric spectroscopy was collected across 25 discreet frequencies, offering a thorough evaluation of the proposed technology. Correlation of this PAT methodology to traditional offline cell counting assays was performed, in which VCD and CV were both successfully predicted using dielectric spectroscopy. Both univariate and multivariate data analysis approaches were evaluated for their potential to establish correlation between the in situ dielectric spectroscopy and offline measurements. Univariate analysis strategies are presented for optimal single frequency selection. Multivariate analysis, in the form of partial least squares (PLS) regression, produced significantly higher correlations between dielectric spectroscopy and offline VCD and CV data, as compared to univariate analysis. Specifically, by leveraging multivariate analysis of dielectric information from all 25 spectroscopic frequencies measured, PLS models performed significantly better than univariate models. This is particularly evident during cell death, where tracking VCD and CV have historically presented the greatest challenge. The results of this work demonstrate the potential of both single and multiple frequency dielectric spectroscopy measurements for enabling robust LVV process characterization, suggesting that broader application of in situ dielectric spectroscopy as a PAT tool in LVV processes can provide significantly improved process understanding. To the best of our knowledge, this is the first report of in situ dielectric spectroscopy with multivariate analysis to successfully predict VCD and CV in real time during live virus-based vaccine production.

Quantitative detection of zearalenone in wheat using intervals selection coupled to near-infrared spectroscopy

Wheat is a globally cultivated cereal crop. However, it is susceptible to Fusarium head blight, which leads to the production of zearalenone (ZEN), significantly impacting the value of wheat. This paper proposed a novel technique for quantitatively detecting ZEN in wheat using near-infrared spectroscopy. The absorbance of wheat flour samples with varying degrees of mildew were collected using a spectral detection system based on the spectrometer. And this paper introduced two methods for feature intervals selection, interval combination optimization (ICO) and interval variable iterative space shrinkage approach (iVISSA), combined with partial least squares (PLS) and support vector machine (SVM) established a regression model based on feature intervals selection for quantitatively detecting ZEN in wheat, and then compared the performance of them. The research results showed that the SVM model outperformed the PLS model in terms of detection performance. Additionally, the SVM model detecting the level of ZEN in wheat based on the feature intervals selected by ICO achieved the best generalization performance, with a root mean square error of prediction (RMSEP) of 31.4148 μg·kg−1, a coefficient of determination of prediction (RP2) of 0.9434, and a relative percent deviation (RPD) of 4.2768. These results demonstrated that the SVM regression model, established by using ICO for selecting the near-infrared spectral intervals, can accurately detect ZEN in wheat. Near-infrared spectroscopy combined with appropriate data analysis methods, which can be used as a simple and fast tool for quantitative detection of mycotoxins of wheat and other grains.

Quantification of Lactobacillus reuteri ProTectis in MRS Broth Using Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) Spectroscopy and Chemometrics.

There is an increasing global demand for probiotics because of their numerous health benefits. However, a significant percentage of commercially available probiotic products have microbial quantities that are not in accordance with their product labels. In quantifying bacteria, the viable plate count is the standard method but is considered laborious and time-consuming. We demonstrate the use of an attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy coupled with chemometrics to quantify a pure culture of Lactobacillus reuteri (L. reuteri) ProTectis grown in deMan, Rogosa, and Sharpe broth. The chemometric partial least-squares model generated was able to accurately quantify viable plate count (VPC) (root-mean-square error of cross-validation (RMSECV) = 0.115 log CFU mL-1, root-mean-square error of prediction (RMSEP) = 0.145 log CFU mL-1, R2 = 0.982). These results provide proof of concept for this quantification technique and can potentially be developed and applied for the quantification of L. reuteri ProTectis in food products.

Cluster-Partial Least Squares (c-PLS) regression analysis: Application to miRNA and metabolomic data

BackgroundBiomedicine and biological research frequently involve analyzing large datasets generated by high-throughput technologies like genomics, transcriptomics, miRNomics, and metabolomics. Pathway analysis is a common computational approach used to understand the impact of experimental conditions, phenotypes, or interventions on biological pathways and networks. This involves statistical analysis of omic data to identify differentially expressed variables and mapping them onto predefined pathways. Analyzing such datasets often requires multivariate techniques to extract meaningful insights such as Partial Least Squares (PLS). Variable selection strategies like interval-PLS (iPLS) help improve understanding and predictive performance by identifying informative variables or intervals. However, iPLSis suboptimal to treat omic data such as metabolic or miRNA profiles, where features cannot be distributed along a continuous dimension describing their relationships as in e.g., vibrational or nuclear magnetic resonance spectroscopy. ResultsThis study introduces a novel variable selection approach called cluster PLS (c-PLS) that aims to assess the joint impact of variable groups selected based on biological characteristics (such as miRNA-regulated metabolic pathway or lipid classes) on the predictive performance of a multivariate model. The usefulness of c-PLS is shown using miRNomic and metabolomic datasets obtained from the analysis of 24 liver tissue biopsies collected in the frame of a clinical study of steatosis. Significance and noveltyResults obtained show that c-PLS enables analyzing the effect of biologically relevant variable clusters, facilitating the identification of biological processes associated with the independent variable, and the prioritization of the biological factors influencing model performance, thereby improving the understanding of the biological factors driving model predictions. While the strategy is tested for the evaluation of PLS models, it could be extended to other linear and non-linear multivariate models.

Integration of Vis–NIR Spectroscopy and Machine Learning Techniques to Predict Eight Soil Parameters in Alpine Regions

Visible and near-infrared spectroscopy (Vis–NIR, 350–1100 nm) has great potential for predicting soil properties. However, current research on the hyperspectral prediction of soil parameters in agricultural areas of alpine regions and the types of parameters included is limited, and optimal spectral treatments and predictive models applicable to different parameters have not been sufficiently investigated. Therefore, we evaluated the accuracy of predicting total nitrogen (TN), phosphorus pentoxide (TP2O5), total potassium oxide (TK2O), alkali-hydrolyzable nitrogen (AHN), effective phosphorus (AP), effective potassium (AK), soil organic matter (SOM), and pH in the Qinghai–Tibet Plateau using the Vis–NIR technique in combination with spectral transformations, correlation analysis, feature selection, and machine learning. The results show that spectral transformations improve the correlation between spectra and parameters but are dependent on the parameter type and the method used. Continuum removal (CR), logarithmic first-order differential (FDL), and inverse first-order differential (FDR) had the most significant effects. The feature bands were extracted using the SPA and modeled using partial least squares (PLSR), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and backpropagation neural networks (BPNNs). The accuracy was evaluated based on R2, RMSE, RPD, and RPIQ. We found that the PLSR model only enables the prediction of SOM and pH with lower accuracy than the remaining models. XGBoost can predict all of the parameters but only for AHN; the prediction performance is better than other methods (R2 = 0.776, RMSE = 0.043 g/kg, and RPIQ = 2.88). The RF, SVM, and BPNN models cannot predict AK, AP, and AHN, respectively. In addition, TP2O5, AP, and pH are best suited for modeling using RF (RPIQ = 2.776, 3.011, and 3.198); TN, AK, and SOM are best suited for modeling using BPNN (RPIQ = 2.851, 2.394, and 3.085); and AHN and TK2O are best suited for XGBoost and SVM, respectively (RPIQ = 2.880 and 3.217). Therefore, this study can provide technical and data support for the accurate and efficient acquisition of soil parameters in alpine agriculture.

Development of a PAT platform for the prediction of granule tableting properties

In this work, the feasibility of implementing a process analytical technology (PAT) platform consisting of Near Infrared Spectroscopy (NIR) and particle size distribution (PSD) analysis was evaluated for the prediction of granule downstream processability. A Design of Experiments-based calibration set was prepared using a fluid bed melt granulation process by varying the binder content, granulation time, and granulation temperature. The granule samples were characterized using PAT tools and a compaction simulator in the 100-500kg load range. Comparing the systematic variability in NIR and PSD data, their complementarity was demonstrated by identifying joint and unique sources of variation. These particularities of the data explained some differences in the performance of individual models. Regarding the fusion of data sources, the input data structure for partial least squares (PLS) based models did not significantly impact the predictive performance, as the root mean squared error of prediction (RMSEP) values were similar. Comparing PLS and artificial neural network (ANN) models, it was observed that the ANNs systematically provided superior model performance. For example, the best tensile strength, ejection stress, and detachment stress prediction with ANN resulted in an RMSEP of 0.119, 0.256, and 0.293 as opposed to the 0.180, 0.395, and 0.430 RMSEPs of the PLS models, respectively. Finally, the robustness of the developed models was assessed.

Enhanced harvest performance predictability through advanced multivariate data analysis of mammalian cell culture particle size distribution.

The industry's pursuit for higher antibody production has led to increased cell density cultures that impact the performance of subsequent product recovery steps. This increase in cell concentration has highlighted the critical role of solids concentration in centrifugation yield, while recent product degradation cases have shed light on the impact of cell lysis on product quality. Current methods for measuring solids concentration and cell lysis are not suited for early-stage high-throughput experimentation, which means that these cell culture outputs are not well characterized in early process development. This article describes a novel approach that leveraged the data from a widely-used automated cell counter (Vi-CELL™ XR) to accurately predict solids concentration and a common cell lysis indicator represented as lactate dehydrogenase (LDH) release. For this purpose, partial least squares (PLS) models were derived with k-fold cross-validation from the particle size distribution data generated by the cell counter. The PLS models showed good predictive potential for both LDH release and solids concentration. This novel approach reduced the time required for evaluating the solids concentration and LDH for a typical high-throughput cell culture system (with 48 bioreactors in parallel) from around 7 h down to a few minutes.

Deep PLS: A Lightweight Deep Learning Model for Interpretable and Efficient Data Analytics.

The salient progress of deep learning is accompanied by nonnegligible deficiencies, such as: 1) interpretability problem; 2) requirement for large data amounts; 3) hard to design and tune parameters; and 4) heavy computation complexity. Despite the remarkable achievements of neural networks-based deep models in many fields, the practical applications of deep learning are still limited by these shortcomings. This article proposes a new concept called the lightweight deep model (LDM). LDM absorbs the useful ideas of deep learning and overcomes their shortcomings to a certain extent. We explore the idea of LDM from the perspective of partial least squares (PLS) by constructing a deep PLS (DPLS) model. The feasibility and merits of DPLS are proved theoretically, after that, DPLS is further generalized to a more common form (GDPLS) by adding a nonlinear mapping layer between two cascaded PLS layers in the model structure. The superiority of DPLS and GDPLS is demonstrated through four practical cases involving two regression problems and two classification tasks, in which our model not only achieves competitive performance compared with existing neural networks-based deep models but also is proven to be a more interpretable and efficient method, and we know exactly how it improves performance, how it gives correct results. Note that our proposed model can only be regarded as an alternative to fully connected neural networks at present and cannot completely replace the mature deep vision or language models.

Comparison of semiquantitative methodologies using Raman mapping for corrosion products on iron artifacts

The quantitative determination of corrosion products is essential for evaluating the corrosion state of archeological iron artifacts. This study explored two semiquantitative approaches using Raman mapping technique combined with chemometrics to quantify a binary mixture of magnetite and goethite. The first approach involved establishing quantification models using Raman spectra treated with Principal Components Regression (PCR) and Partial Least Squares (PLS) algorithms. Both the PCR and PLS model showed good predictive ability as indicated by correlation coefficients and root mean square errors (PLS: Rc2=0.9979, Rp2=0.9970, RMSEC=1.93, RMSEP =2.68, RMSECV=3.25). The second approach was based on spectral fitting using non-negative least squares (NNLS) algorithm. This method demonstrated a fair accuracy between the calculated and actual compositions. The absolute value of relative errors was 0.99%∼10.08% for Fe3O4 and 0.81%∼9.71% for α-FeOOH respectively, for compositions greater than 20%. These methods were then applied to quantify the corrosion products on an iron bar excavated from the Nanhai (South China Sea) No. I shipwreck. Compared with XRD quantitative results, Raman results showed that the spectral fitting method was superior to the established PCA and PLS quantification models in both qualitative determination and quantitative accuracy. Compositional maps depicting the distribution of different phases were also generated using spectral fitting method. It was concluded that Raman mapping has significant potential as an accurate quantitative method for the detection of iron corrosion products, and that the spectral fitting method is more suitable for determining iron corrosion compared to the PCR and PLS quantification models in this study.

FT-IR analysis of tilapia fillets: Developing PLS models for the prediction of storage days, aerobic plate count, and lactobacilli

Chemometric models using FT-IR and Partial Least Squares (PLS) regression were developed for the rapid determination of aerobic plate count (APC), lactobacilli count, and storage time of tilapia fillets. A comprehensive data set of 137 spectra was obtained from sampled tilapia over a 10-month period. IR absorption bands of methyl and methylene stretching vibrations, amide I and II, and 1500–1025 cm−1 regions provided the greatest variance for the models. Successful discrimination of stored tilapia within a 2-day range and accurate prediction of APC and lactobacilli counts with RMSE values lower than ± 1 Log10 CFU·g−1 were achieved.

Analysis of Models for Increasing Consumer Loyalty of Beauty MSMEs Through Brand Equity Management, Service Quality, and Customer Bonding

In an era of increasingly massive disruption, beauty MSMEs need to be able to optimize the resources they have. In the beauty industry, business competition is very tight, so consumer loyalty is very important to maintain a business to survive in a competitive market. The purpose of this study was to analyze the effect of brand equity, service quality, and customer bonding on consumer loyalty. This study used a sample size of 170 consisting of MSME beauty actors. Data analysis techniques using the SEM method with Partial Least Squares Modeling (PLS-SEM). The structural Equation Model (SEM) approach based on Partial Least Square is used for hypothesis testing. The results of the data analysis confirm brand equity, service quality, and customer bonding have a positive and significant effect on consumer loyalty. The implications of this research conclude that it is very important for beauty MSMEs to measure and evaluate the level of brand equity, service quality, and customer bonding regularly. MSME owners can use consumer surveys, online reviews, and data analysis to understand the extent to which these aspects influence consumer loyalty. This data can be used to make necessary changes and improvements. and data analysis to understand the extent to which these aspects influence consumer loyalty. This data can be used to make necessary changes and improvements. and data analysis to understand the extent to which these aspects influence consumer loyalty. This data can be used to make necessary changes and improvements.

Detection of saffron adulteration with Crocus sativus style using NIR-hyperspectral imaging and chemometrics

Saffron is a valuable spice that is often adulterated. This study proposes using near-infrared hyperspectral imaging (NIR-HSI) and chemometrics as a fast and cost-effective method for detecting and quantifying adulteration in saffron stigmas. Adulterated saffron samples were prepared by adding Crocus sativus style to pure saffron stigmas in varying concentrations (20–90%). The spectral data were pre-treated using standard normal variate (SNV), and multiplicative scatter correction (MSC), while variable reduction was performed by Principal Component Analysis (PCA) and Partial Least Squares (PLS). Classification was done using Linear Discriminant Analysis (LDA), PLS-DA, Support Vector Machine (SVM), and Multi-layer Perceptron (MLP) models, while quantification was achieved by PLS, PCA, SVM, and MLP-based regression models. The HSI technique achieved correct classification rates of 95.6%–100% in discriminating authentic saffron from plant adulterants and adulterated saffron across all the models. Regression models to quantify the percentage style adulteration in saffron demonstrated excellent prediction abilities with almost all models achieving RPD (Residual Predictive Deviation) values of 3.0–5.4. The MLP model (1 hidden layer with 3 neurons) built from SNV pre-processed and PLS reduced data (15 LVs), showed exceptional predictive capabilities, with an R2p of 0.97, a Root Mean Squared Error of Prediction (RMSEP) of 4.3%, and an RPD of 5.4. The results demonstrate the potential of NIR-HSI and chemometrics for rapid and nondestructive detection and quantification of style in saffron stigmas.

Positive rate and quantification of amyloid pathology with [18F]florbetapir in the urban Chinese population.

Amyloid deposition is considered the initial pathology in Alzheimer's disease (AD). Personalized management requires investigation of amyloid pathology and the risk factors for both amyloid pathology and cognitive decline in the Chinese population. We aimed to investigate amyloid positivity and deposition in AD patients, as well as factors related to amyloid pathology in Chinese cities. This cross-sectional multicenter study was conducted in Shanghai and Zhengzhou, China. All participants were recruited from urban communities and memory clinics. Amyloid positivity and deposition were analyzed based on amyloid positron emission tomography (PET). We used partial least squares (PLS) models to investigate how related factors contributed to amyloid deposition and cognitive decline. In total, 1026 participants were included: 768 participants from the community-based cohort (COMC) and 258 participants from the clinic-based cohort (CLIC). The overall amyloid-positive rates in individuals with clinically diagnosed AD, mild cognitive impairment (MCI), and normal cognition (NC) were 85.8%, 44.5%, and 26.9%, respectively. The global amyloid deposition standardized uptake value ratios (SUVr) (reference: cerebellar crus) were 1.44 ± 0.24, 1.30 ± 0.22, and 1.24 ± 0.14, respectively. CLIC status, apolipoprotein E (ApoE) ε4, and older age were strongly associated with amyloid pathology by PLS modeling. The overall amyloid-positive rates accompanying AD, MCI, and NC in the Chinese population were similar to those in published cohorts of other populations. ApoE ε4 and CLIC status were risk factors for amyloid pathology across the AD continuum. Education was a risk factor for amyloid pathology in MCI. Female sex and age were risk factors for amyloid pathology in NC. This study provides new details about amyloid pathology in the Chinese population. Factors related to amyloid deposition and cognitive decline can help to assess patients' AD risk. • We studied amyloid pathology and related risk factors in the Chinese population. •·The overall amyloid-positive rates in individuals with clinically diagnosed AD, MCI, and NC were 85.8%, 44.5%, and 26.9%, respectively. • These overall amyloid-positive rates were in close agreement with the corresponding prevalence for other populations.

Spectral Profile Partial Least-Squares (SP-PLS): Local multivariate pansharpening on spectral profiles

The compatibility of multispectral (MS) pansharpening algorithms with hyperspectral (HS) data is limited. With the recent development in HS satellites, there is a need for methods that can provide high spatial and spectral fidelity in both HS and MS scenarios.The present article presents a fast pansharpening method, based on the division of similar hyperspectral data in spectral subgroups using k-means clustering and Spectral Angle Mapper (SAM) profiling. Local Partial Least-Square (PLS) models are calibrated for each spectral subgroup against the respective pixels of the panchromatic image. The models are inverted to retrieve high-resolution pansharpened images. The method is tested against different methods that are able to handle both MS and HS pansharpening and assessed using reduced- and full-resolution evaluation methodologies. Based on a statistical multivariate approach, the proposed method is able to render uncertainty maps for spectral or spatial fidelity - functionality not reported in any other pansharpening study.

Partnership Modelling of Entrepreneurial, Digital and Performance Competencies among Coffee SME’s in Bener Meriah Regency

The current initiative on SMEs development suggest that effective partnership programs play a pivotal role in enhancing SME competitiveness. However, current research in the area of partnership programs, specifically their impact on SMEs performance, remains limited. Therefore, partnerships need to be examined with an emphasis on moderator variables to assess their impact on various aspects. This research presents an objective to determine a partnership program impact on SME performance. To fully understand the dynamic of the study, this research propose a Partnership Program model consists of three exogenous variables: Entrepreneurial Competence (KK), Partnership Program (PK), and Digital Competence (KD), and one endogenous variable: SME Business Performance. To analyse the data in the research, this research employs the Partial Least Square (PLS) analysis method. The PLS model is utilized to examine and determine the extent of influence among variables. The research population comprises 88 coffee-based SMEs participating in a partnership program in Bener Meriah Regency. The result of this research indicates that the significance test for the relationships within the Structural Model reveals strong statistical significance among various research parameters. Notably, the impact of digital competence (KD) on SMEs performance (KB) demonstrates the most robust relationship, with a substantial Std Beta value of 0.902. This pattern is consistent with the perspective of partnership programs (PK), where it exhibits significant effects on SMEs performance (KB) (0.429), digital competences (KD) (0.294), and entrepreneurial competences (KK) (0.451).

Analysis of urban wetland park cooling effects and their potential influence factors: Evidence from 477 urban wetland parks in China

Urban wetland parks can lower surrounding temperatures efficiently and play a critical role in mitigating extreme urban high-temperature weather. Understanding the urban wetland park cooling effects and their potential influence factors is necessary for the management plan of urban parks. This study first crawled 477 urban wetland parks from the Baidu Map and classified the urban wetland parks into river-based and lake-based urban wetland parks based on the distance to rivers. Then, this study investigated the urban wetland park cooling effects during the warm (April–September) and cold (January–March, and October–December) seasons based on the largest cooling distance (LCD), the largest cooling intensity (LCI), the random forest (RF) and the partial least squares (PLS) regression models, and Spearman’s correlation coefficient. The results show that: (1) Both river-based and lake-based urban wetland parks exhibit a stronger cooling effect during the warm season. Lake-based urban wetland parks have a higher LCI; however, river-based parks have a higher LCD. (2) The RF model outperforms the PLS model in modeling the relationship between LCI and the potential influence factors, with a 5.68% increase in average RMSE and a 20.64% increase in R2.(3) The water cover fraction inside urban wetland parks is the most influential factor. There is a strong similarity in the relative importance of potential influence factors to river-based and lake-based urban wetland parks. The study indicates a new park should be located with priority in regions with more impervious layers, for example, the high-density building areas. Besides, a new park should limit the proportion of impervious layers to less than 13% and ensure the proportion of water bodies is at 70% at least to obtain a stronger cooling effect. Our study reinforces the understanding of urban wetland park cooling effects and has critical implications for better management plan of urban parks to mitigate the urban heat problem.

Multicomponent prediction of Sudan dye adulteration in crude palm oil using SERS – Based bimetallic nanoflower combined with genetic algorithm

Crude palm oil (CPO) is an important edible vegetable oil used globally, recently subjected to Sudan dye adulteration. This study explored the feasibility of a novel SERS-based Au@Ag substrate to detect four Sudan dyes (I - IV) in CPO. When mixed with spiked CPO, the SERS substrate produced strong signals that increased with increasing concentrations from 0.001 to 4.0 ppm. The genetic algorithm partial least square (GA-PLS) model outperformed the partial least square (PLS) and the ant colony optimization – PLS (ACO-PLS) models with Rc values of 0.9844, 0.9865, 0.9884, and 0.9888 0.9846. The calculated LOD were 0.00088 ppm, 0.00092 ppm, 0.00095 ppm, and 0.00097 and real sample recovery rates of 88.0–113.0%, 92.0–103.0%, 91.2–98.5% and 97.0–109.5% for Sudan I, II, III, and IV, respectively. The findings affirmed the SERS sensor’s considerable potential for rapid and selective detection of Sudan dyes in CPO when combined with chemometrics.

Quantitative analysis of adulterated pear juice based on attenuated total reflectance mid-infrared spectroscopy and bagging PLS

The methods of pear juice adulteration have evolved from simple adding water to blending based on characteristic spectrum of juice, which greatly increases the difficulty of identification. Herein, a rapid and convenient analytical method based on attenuated total reflectance (ATR) mid-infrared (MIR) spectroscopy and bagging partial least square (PLS) was developed. A total of 63 samples consisted of three kind of adulteration were designed to simulate actual counterfeiting of pear juice. All samples are roughly evenly divided into the calibration and test sets. Three kinds of PLS models including full-spectrum PLS, local PLS with pre feature selection by the ReliefF algorithm and bagging PLS were trained and compared. On the independent test set, the bagging PLS model achieved the best performance, followed by the local PLS model. Specifically, the bagging PLS model with 40 members achieved a root-mean squared error of prediction (RMSEP) of 3.24 %, a 20 % decrease compared to the full spectrum PLS model. Moreover, the error of bagging PLS model on the calibration and test sets is closer, and there is no over-fitting phenomenon, indicating that its robustness is better, with good fitting and generalization. In today's world where computing resources are not a problem, using simple model ensemble is highly desirable.

Dynamic fluctuations in plant leaf interception of airborne microplastics

Plant leaves have been demonstrated to be a crucial sink of airborne microplastics (MPs). However, because of the particular shape of MPs and their relatively weak forces with leaves, the traditional accumulation model used for the adsorption of particulate matter and persistent organic pollutants may not be appropriate for describing the interception of MPs by leaves. Here, we performed a 7-day exploration of the interception of MPs by leaves in downtown Nanning. The abundances and characteristics of leaf-intercepted MPs showed dramatic diurnal fluctuations and interspecies differences (conifers > arbors > shrubs). The fluctuation (Coefficient of Variation (CV) = 0.459; abundances 0.003 ± 0.002 to 0.047 ± 0.005 n·cm−2) was even more drastic than that measured across species (CV = 0.353; 0.06 ± 0.01 to 0.40 ± 0.04 n·cm−2). Further analysis using partial least-squares path modeling demonstrated that stomatal variation and divergence largely dominated diurnal fluctuations and interspecies differences in microplastic interception by leaves, respectively. Our results highlight that the leaf-intercepted MPs is characterized by dynamic fluctuations rather than static equilibrium and reveal the important regulatory roles played by leaf micromorphological structures in intercepting MPs, thus enhancing our understanding of the interactions between terrestrial plants and airborne pollution.