Near-infrared spectroscopy to quantify overall thermal process intensity during high-moisture extrusion of soy protein concentrate.

High-moisture extrusion (HME) is widely used to produce meat analogues. During HME the plant-based materials experience thermal and mechanical stresses. It is complicated to separate their effects on the final products because these effects are interrelated. In this study we hypothesize that the intensity of the thermal treatment can explain a large part of the physicochemical changes that occur during extrusion. For this reason, near-infrared (NIR) spectroscopy was used as a novel method to quantify the thermal process intensity during HME. High-temperature shear cell (HTSC) processing was used to create a partial least squares (PLS) regression curve for processing temperature under controlled processing conditions (root mean standard error of cross-validation (RMSECV) = 4.00 °C, coefficient of determination of cross-validation (R2CV) = 0.97). This PLS regression model was then applied to HME extrudates produced at different screw speeds (200-1200 rpm) and barrel temperatures (100-160 °C) with two different screw profiles to calculate the equivalent shear cell temperature as a measure for thermal process intensity. This equivalent shear cell temperature reflects the effects of changes in local temperature conditions, residence time and thermal stresses. Furthermore, it can be related to the degree of texturization of the extrudates. This information can be used to gain new insights into the effect of various process parameters during HME on the thermal process intensity and extrudate quality.

Distracted on duty: A theory-based exploration of influences leading to mobile phone distracted riding among food delivery workers.

Using mobile phones while riding is a form of distracted riding that significantly elevates crash risk. Regrettably, the factors contributing to mobile phone use while riding (MPUWR) among food delivery riders remain under-researched. Addressing this literature gap, the current study employs the Job Demands-Resources (JD-R) model and various socio-economic factors to examine the determinants of MPUWR. The research incorporates data from 558 delivery workers in Hanoi and Ho Chi Minh City, Vietnam. The study utilizes two analytical methods to empirically test the hypotheses, considering non-linear relationships between variables: Partial Least Square Structural Equation Modelling (PLS-SEM) and Artificial Neural Network (ANN). The results reveal mixed impacts of factors connected to job resources. Although social support appears to deter MPUWR, work autonomy and rewards seemingly encourage it. Furthermore, a predisposition towards risk-taking behaviour significantly impacts the frequency of mobile phone usage among delivery riders. Interestingly, riders with higher incomes and those who have previously been fined by the police exhibit more frequent mobile phone use. The findings of this study present valuable insights into the crucial factors to be addressed when designing interventions aimed at reducing phone use among food delivery riders.

Irish Amyotrophic Lateral Sclerosis Incidence: Age, Period, and Cohort Effects Using a Partial Least Squares Regression Model.

BACKGROUND AND OBJECTIVES: To investigate the underlying reasons for variability in the incidence rate of amyotrophic lateral sclerosis (ALS) within the Irish population between the years 1996 and 2021. METHODS: The Irish ALS register was used to calculate the incidence and to subsequently extract age at diagnosis (age), year of diagnosis (period), and date of birth (cohort) for all incident patients within the study period (n = 2,771). An age-period-cohort (APC) model using partial least squares regression was constructed to examine each component separately and their respective contribution to the incidence while minimizing the well-known identifiability problem of APC effects. A dummy regression model consisting of 5 periods, 19 cohorts, and 16 age groups was used to examine nonlinear relationships within the data over time. The CIs for each of these were estimated using the jackknife method. RESULTS: The nonlinear model achieved R2 of 99.43% with 2-component extraction. Age variation was evident with those in the ages 65-79 years contributing significantly to the incidence (ßmax = 0.0746, SE = 0.000410, CI 0.00665-0.00826). However, those aged 25-60 years contributed significantly less (ßmin = -0.00393, SE = 0.000291, CI -0.00454 to -0.00340). Each successive period showed an increase in the regression model coefficient suggesting an increasing incidence over time, independent of the other factors examined-an increase of ß from -0.00489 (SE = 0.000264, CI -0.00541 to -0.00437) to 0.00973 (SE = 0.000418, CI 0.0105-0.00891). A cohort effect was demonstrated showing that the contribution of those born between 1927 and 1951 contributed to a significantly greater degree than the other birth cohorts (ßmax = 0.00577, SE = 0.000432, CI 0.00493-0.00662). DISCUSSION: Using the Irish population-based ALS Register, robust age, period, and cohort effects can be identified. The age effect may be accounted for by demographic shifts within the population. Changes in disease categorization, competing risks of death, and improved surveillance may account for period effects. The cohort effect may reflect lifestyle and environmental factors associated with the challenging economic circumstances in Ireland between 1927 and 1951. Age-period-cohort studies can help to account for changes in disease incidence and prevalence, providing additional insights into likely demographic and environmental factors that influence population-based disease risk.

Species discrimination from hair using ATR-FTIR spectroscopy: Application in wildlife forensics.

Hair is a commonly encountered trace evidence in wildlife crimes involving mammals and can be used for species identification which is essential for subsequent judicial proceedings. This proof of concept study aims, to distinguish the black guard hair of three wild cat species belonging to the genus Panthera i.e. Royal Bengal Tiger (Panthera tigris tigris), Indian Leopard (Panthera pardus fusca), and Snow Leopard (Panthera uncia) using a rapid and non-destructive ATR-FTIR spectroscopic technique in combination with chemometrics. A training dataset including 72 black guard hair samples of three species (24 samples from each species) was used to construct chemometric models. A PLS2-DA model successfully classified these three species into distinct classes with R-Square values of 0.9985 (calibration) and 0.8989 (validation). VIP score was also computed, and a new PLS2DA-V model was constructed using variables with a VIP score ≥ 1. External validation was performed using a validation dataset including 18 black guard hair samples (6 samples per species) to validate the constructed PLS2-DA model. It was observed that PLS2-DA model provides greater accuracy and precision compared to the PLS2DA-V model during cross-validation and external validation. The developed PLS2-DA model was also successful in differentiating human and non-human hair with R-Square values of 0.99 and 0.91 for calibration and validation, respectively. Apart from this, a blind test was also carried out using 10 unknown hair samples which were correctly classified into their respective classes providing 100 % accuracy. This study highlights the advantages of ATR-FTIR spectroscopy associated with PLS-DA for differentiation and identification of the Royal Bengal Tiger, Indian Leopard, and Snow Leopard hairs in a rapid, accurate, eco-friendly, and non-destructive way.

Lipidomic studies revealing serological markers associated with the occurrence of retinopathy in type 2 diabetes.

PURPOSE: The duration of type 2 diabetes mellitus (T2DM) and blood glucose levels have a significant impact on the development of T2DM complications. However, currently known risk factors are not good predictors of the onset or progression of diabetic retinopathy (DR). Therefore, we aimed to investigate the differences in the serum lipid composition in patients with T2DM, without and with DR, and search for potential serological indicators associated with the development of DR. METHODS: A total of 622 patients with T2DM hospitalized in the Department of Endocrinology of the First Affiliated Hospital of Xi'an JiaoTong University were selected as the discovery set. One-to-one case-control matching was performed according to the traditional risk factors for DR (i.e., age, duration of diabetes, HbA1c level, and hypertension). All cases with comorbid chronic kidney disease were excluded to eliminate confounding factors. A total of 42 pairs were successfully matched. T2DM patients with DR (DR group) were the case group, and T2DM patients without DR (NDR group) served as control subjects. Ultra-performance liquid chromatography-mass spectrometry (LC-MS/MS) was used for untargeted lipidomics analysis on serum, and a partial least squares discriminant analysis (PLS-DA) model was established to screen differential lipid molecules based on variable importance in the projection (VIP) > 1. An additional 531 T2DM patients were selected as the validation set. Next, 1:1 propensity score matching (PSM) was performed for the traditional risk factors for DR, and a combined 95 pairings in the NDR and DR groups were successfully matched. The screened differential lipid molecules were validated by multiple reaction monitoring (MRM) quantification based on mass spectrometry. RESULTS: The discovery set showed no differences in traditional risk factors associated with the development of DR (i.e., age, disease duration, HbA1c, blood pressure, and glomerular filtration rate). In the DR group compared with the NDR group, the levels of three ceramides (Cer) and seven sphingomyelins (SM) were significantly lower, and one phosphatidylcholine (PC), two lysophosphatidylcholines (LPC), and two SMs were significantly higher. Furthermore, evaluation of these 15 differential lipid molecules in the validation sample set showed that three Cer and SM(d18:1/24:1) molecules were substantially lower in the DR group. After excluding other confounding factors (e.g., sex, BMI, lipid-lowering drug therapy, and lipid levels), multifactorial logistic regression analysis revealed that a lower abundance of two ceramides, i.e., Cer(d18:0/22:0) and Cer(d18:0/24:0), was an independent risk factor for the occurrence of DR in T2DM patients. CONCLUSION: Disturbances in lipid metabolism are closely associated with the occurrence of DR in patients with T2DM, especially in ceramides. Our study revealed for the first time that Cer(d18:0/22:0) and Cer(d18:0/24:0) might be potential serological markers for the diagnosis of DR occurrence in T2DM patients, providing new ideas for the early diagnosis of DR.

Benchtop NMR Coupled with Chemometrics: A Workflow for Unveiling Hidden Drug Ingredients in Honey-Based Supplements.

Recently, benchtop nuclear magnetic resonance (NMR) spectrometers utilizing permanent magnets have emerged as versatile tools with applications across various fields, including food and pharmaceuticals. Their efficacy is further enhanced when coupled with chemometric methods. This study presents an innovative approach to leveraging a compact benchtop NMR spectrometer coupled with chemometrics for screening honey-based food supplements adulterated with active pharmaceutical ingredients. Initially, fifty samples seized by French customs were analyzed using a 60 MHz benchtop spectrometer. The investigation unveiled the presence of tadalafil in 37 samples, sildenafil in 5 samples, and a combination of flibanserin with tadalafil in 1 sample. After conducting comprehensive qualitative and quantitative characterization of the samples, we propose a chemometric workflow to provide an efficient screening of honey samples using the NMR dataset. This pipeline, utilizing partial least squares discriminant analysis (PLS-DA) models, enables the classification of samples as either adulterated or non-adulterated, as well as the identification of the presence of tadalafil or sildenafil. Additionally, PLS regression models are employed to predict the quantitative content of these adulterants. Through blind analysis, this workflow allows for the detection and quantification of adulterants in these honey supplements.

Machine learning-assisted chromium speciation using a single-well ratiometric fluorescent nanoprobe.

Chromium is widely recognized as a significant pollutant discharged into the environment by various industrial activities. The toxicity of this element is dependent on its oxidation state, making speciation analysis crucial for monitoring the quality of environmental water and assessing the potential risks associated with industrial waste. This study introduces a single-well fluorometric sensor that utilizes orange emissive thioglycolic acid stabilized CdTe quantum dots (TGA-QDs) and blue emissive carbon dots (CDs) to detect and differentiate between various chromium species, such as Cr (III) and Cr (VI) (i.e., CrO42- and Cr2O72-). The variations of fluorescence spectra of the proposed probe upon chromium species addition were analyzed using machine learning techniques such as linear discriminant analysis and partial least squares regression as a classification and multivariate calibration technique, respectively. Linear discriminant analysis (LDA) demonstrated exceptional accuracy in differentiating single-component and bicomponent samples. Additionally, the findings from the partial least squares regression (PLSR) showed that the sensor created has strong linearity within the 1.0-100.0, 1.0-100.0, and 0.1-15 µM range for Cr2O72-, CrO42-, and Cr3+, respectively. Furthermore, appropriate detection limits were successfully achieved, which were 2.6, 2.9, and 0.7 µM for Cr2O72-, CrO42-, and Cr3+, respectively. Ultimately, the successful capability of the sensing platform in the identification and quantification of chromium species in environmental water samples provides innovative insights into general speciation analytics.

Determination of seawater COD spectra using double-loop contraction and sorted frog optimization.

This study develops a novel double-loop contraction and C value sorting selection-based shrinkage frog-leaping algorithm (double-contractive cognitive random field [DC-CRF]) to mitigate the interference of complex salts and ions in seawater on the ultraviolet-visible (UV-Vis) absorbance spectra for chemical oxygen demand (COD) quantification. The key innovations of DC-CRF are introducing variable importance evaluation via C value to guide wavelength selection and accelerate convergence; a double-loop structure integrating random frog (RF) leaping and contraction attenuation to dynamically balance convergence speed and efficiency. Utilizing seawater samples from Jiaozhou Bay, DC-CRF-partial least squares regression (PLSR) reduced the input variables by 97.5% after 1,600 iterations relative to full-spectrum PLSR, RF-PLSR, and CRF-PLSR. It achieved a test R2 of 0.943 and root mean square error of 1.603, markedly improving prediction accuracy and efficiency. This work demonstrates the efficacy of DC-CRF-PLSR in enhancing UV-Vis spectroscopy for rapid COD analysis in intricate seawater matrices, providing an efficient solution for optimizing seawater spectra.

Comparing visible and near infrared 'point' spectroscopy and hyperspectral imaging techniques to visualize the variability of apple firmness.

In this work, visible and near-infrared 'point' (Vis-NIR) spectroscopy and hyperspectral imaging (Vis-NIR-HSI) techniques were applied on three different apple cultivars to compare their firmness prediction performances based on a large intra-variability of individual fruit, and develop rapid and simple models to visualize the variability of apple firmness on three apple cultivars. Apples with high degree of intra-variability can strongly affect the prediction model performances. The apple firmness prediction accuracy can be improved based on the large intra-variability samples with the coefficient variation (CV) values over 10%. The least squares-support vector machine (LS-SVM) models based on Vis-NIR-HSI spectra had better performances for firmness prediction than that of Vis-NIR spectroscopy, with the with the Rc2 over 0.84. Finally, The Vis-NIR-HSI technique combined with least squares-support vector machine (LS-SVM) models were successfully applied to visualize the spatial the variability of apple firmness.

Assessing the performance of physician's prescribing preference as an instrumental variable in comparative effectiveness research with moderate and small sample sizes: a simulation study.

Aim: This simulation study is to assess the utility of physician's prescribing preference (PPP) as an instrumental variable for moderate and smaller sample sizes. Materials & methods: We designed a simulation study to imitate a comparative effectiveness research under different sample sizes. We compare the performance of instrumental variable (IV) and non-IV approaches using two-stage least squares (2SLS) and ordinary least squares (OLS) methods, respectively. Further, we test the performance of different forms of proxies for PPP as an IV. Results: The percent bias of 2SLS is around approximately 20%, while the percent bias of OLS is close to 60%. The sample size is not associated with the level of bias for the PPP IV approach. Conclusion: Irrespective of sample size, the PPP IV approach leads to less biased estimates of treatment effectiveness than OLS adjusting for known confounding only. Particularly for smaller sample sizes, we recommend constructing PPP from long prescribing histories to improve statistical power.

Moderating Effect of Coping Strategies on the Association Between the Infodemic-Driven Overuse of Health Care Services and Cyberchondria and Anxiety: Partial Least Squares Structural Equation Modeling Study.

BACKGROUND: The COVID-19 pandemic has led to a substantial increase in health information, which has, in turn, caused a significant rise in cyberchondria and anxiety among individuals who search for web-based medical information. To cope with this information overload and safeguard their mental well-being, individuals may adopt various strategies. However, the effectiveness of these strategies in mitigating the negative effects of information overload and promoting overall well-being remains uncertain. OBJECTIVE: This study aimed to investigate the moderating effect of coping strategies on the relationship between the infodemic-driven misuse of health care and depression and cyberchondria. The findings could add a new dimension to our understanding of the psychological impacts of the infodemic, especially in the context of a global health crisis, and the moderating effect of different coping strategies on the relationship between the overuse of health care and cyberchondria and anxiety. METHODS: The data used in this study were obtained from a cross-sectional web-based survey. A professional survey company was contracted to collect the data using its web-based panel. The survey was completed by Chinese individuals aged 18 years or older without cognitive problems. Model parameters of the relationships between infodemic-driven overuse of health care, cyberchondria, and anxiety were analyzed using bootstrapped partial least squares structural equation modeling. Additionally, the moderating effects of coping strategies on the aforementioned relationships were also examined. RESULTS: A total of 986 respondents completed the web-based survey. The mean scores of the Generalized Anxiety Disorder-7 and Cyberchondria Severity Scale-12 were 8.4 (SD 3.8) and 39.7 (SD 7.5), respectively. The mean score of problem-focused coping was higher than those of emotion- and avoidant-focused coping. There was a significantly positive relationship between a high level of infodemic and increased overuse of health care (bootstrapped mean 0.21, SD 0.03; 95% CI 0.1581-0.271). The overuse of health care resulted in more severe cyberchondria (bootstrapped mean 0.107, SD 0.032) and higher anxiety levels (bootstrapped mean 0.282, SD 0.032) in all the models. Emotion (bootstrapped mean 0.02, SD 0.008 and 0.037, SD 0.015)- and avoidant (bootstrapped mean 0.026, SD 0.009 and 0.049, SD 0.016)-focused coping strategies significantly moderated the relationship between the overuse of health care and cyberchondria and that between the overuse of health care and anxiety, respectively. Regarding the problem-based model, the moderating effect was significant for the relationship between the overuse of health care and anxiety (bootstrapped mean 0.007, SD 0.011; 95% CI 0.005-0.027). CONCLUSIONS: This study provides empirical evidence about the impact of coping strategies on the relationship between infodemic-related overuse of health care services and cyberchondria and anxiety. Future research can build on the findings of this study to further explore these relationships and develop and test interventions aimed at mitigating the negative impact of the infodemic on mental health.

PLS-based gene subset augmentation and tumor-specific gene identification.

In the study of tumor disease pathogenesis, the identification of genes specifically expressed in disease states is pivotal, yet challenges arise from high-dimensional datasets with limited samples. Conventional gene (feature) selection methods often fall short of capturing the complexity of gene-phenotype and gene-gene interactions, necessitating a more robust analysis method. To address these challenges, a gene subset augmentation strategy is proposed in this paper. Our approach introduces diverse perturbation mechanisms to generate distinct gene subsets. The partial least squares-based multiple gene measurement algorithm considers gene-phenotype and gene-gene correlations, identifying differentially expressed genes, including those with weak signals. The constructed gene networks derived from the augmented subsets unveil regulatory patterns, enabling association analysis to explore gene associations comprehensively. Our algorithm excels in identifying small-sized gene subsets with strong discriminative power, surpassing traditional methods that yield a single gene subset. Unlike conventional approaches, our algorithm reveals a spectrum of different gene subsets and their weakly differentially expressed genes. This nuanced perspective aids in unraveling the molecular characteristics and specific expression patterns of tumor genes. The versatility of our approach not only contributes to the advancement of tumor-specific gene identification but also holds promise for addressing challenges in various fields characterized by high-dimensional datasets and limited samples. The Python implementation is available at http://github.com/wenjieyou/PLSGSA.

Comparing the performance of two-stage residual inclusion methods when using physician's prescribing preference as an instrumental variable: unmeasured confounding and noncollapsibility.

Aim: The first objective is to compare the performance of two-stage residual inclusion (2SRI), two-stage least square (2SLS) with the multivariable generalized linear model (GLM) in terms of the reducing unmeasured confounding bias. The second objective is to demonstrate the ability of 2SRI and 2SPS in alleviating unmeasured confounding when noncollapsibility exists. Materials & methods: This study comprises a simulation study and an empirical example from a real-world UK population health dataset (Clinical Practice Research Datalink). The instrumental variable (IV) used is based on physicians' prescribing preferences (defined by prescribing history). Results: The percent bias of 2SRI in terms of treatment effect estimates to be lower than GLM and 2SPS and was less than 15% in most scenarios. Further, 2SRI was found to be robust to mild noncollapsibility with the percent bias less than 50%. As the level of unmeasured confounding increased, the ability to alleviate the noncollapsibility decreased. Strong IVs tended to be more robust to noncollapsibility than weak IVs. Conclusion: 2SRI tends to be less biased than GLM and 2SPS in terms of estimating treatment effect. It can be robust to noncollapsibility in the case of the mild unmeasured confounding effect.

Research on the chemical oxygen demand spectral inversion model in water based on IPLS-GAN-SVM hybrid algorithm.

Spectral collinearity and limited spectral datasets are the problems influencing Chemical Oxygen Demand (COD) modeling. To address the first problem and obtain optimal modeling range, the spectra are preprocessed using six methods including Standard Normal Variate, Savitzky-Golay Smoothing Filtering (SG) etc. Subsequently, the 190-350 nm spectral range is divided into 10 subintervals, and Interval Partial Least Squares (IPLS) is used to perform PLS modeling on each interval. The results indicate that it is best modeled in the 7th range (238~253 nm). The values of Mean Square Error (MSE), Mean Absolute Error (MAE) and R2score of the model without pretreatment are 1.6489, 1.0661, and 0.9942. After pretreatment, the SG is better than others, with MSE and MAE decreasing to 1.4727, 1.0318 and R2score improving to 0.9944. Using the optimal model, the predicted COD for three samples are 10.87 mg/L, 14.88 mg/L, and 19.29 mg/L. To address the problem of the small dataset, using Generative Adversarial Networks for data augmentation, three datasets are obtained for Support Vector Machine (SVM) modeling. The results indicate that, compared to the original dataset, the SVM's MSE and MAE have decreased, while its accuracy has improved by 2.88%, 11.53%, and 11.53%, and the R2score has improved by 18.07%, 17.40%, and 18.74%.

Application of Fourier-Transform Infrared Spectroscopy for the Assessment of Wine Spoilage Indicators: A Feasibility Study.

Wine aroma is one of the most frequently used and explored quality indicators. Typically, its assessment involves estimating the volatile composition of wine or highly trained assessors conducting sensory analysis. However, current methodologies rely on slow, expensive and complicated analytical procedures. Additionally, sensory evaluation is inherently subjective in nature. Therefore, the aim of this work is to verify the feasibility of using FTIR spectroscopy as a fast and easy methodology for the early detection of some of the most common off-odors in wines. FTIR spectroscopy was combined with partial least squares (PLS) regression for the simultaneous measurement of isoamyl alcohol, isobutanol, 1-hexanol, butyric acid, isobutyric acid, decanoic acid, ethyl acetate, furfural and acetoin. The precision and accuracy of developed calibration models (R2P > 0.90, range error ratio > 12.1 and RPD > 3.1) proved the ability of the proposed methodology to quantify the aforementioned compounds.

Remote Sensing Techniques: Hyperspectral Imaging and Data Analysis.

Hyperspectral imaging is a remote sensing technique that enables remote, noninvasive measurement of plant traits. Here, we outline the procedures for camera setup, scanning, and calibration, along with the acquisition of black and white reference materials, which are the key steps in collecting hyperspectral imagery. We also discuss the development of predictive models such as partial least-squares regression, using both large and small datasets, which are used to predict plant traits from hyperspectral data. To ensure practical applicability, we provide code examples that allow readers to immediately implement these techniques in real-world scenarios. We introduce these topics to beginners in an accessible and understandable manner.

Malaria risk mapping among children under five in Togo.

Malaria is a major health threat in sub-Sahara Africa, especially for children under five. However, there is considerable heterogeneity between areas in malaria risk reported, associated with environmental and climatic. We used data from Togo to explore spatial patterns of malaria incidence. Geospatial covariate datasets, including climatic and environmental variables from the 2017 Malaria Indicator Survey in Togo, were used for this study. The association between malaria incidence and ecological predictors was assessed using three regression techniques, namely the Ordinary Least Squares (OLS), spatial lag model (SLM), and spatial error model (SEM). A total of 171 clusters were included in the survey and provided data on environmental and climate variables. Spatial autocorrelation showed that the distribution of malaria incidence was not random and revealed significant spatial clustering. Mean temperature, precipitation, aridity and proximity to water bodies showed a significant and direct association with malaria incidence rate in the SLM model, which best fitted the data according to AIC. Five malaria incidence hotspots were identified. Malaria incidence is spatially clustered in Togo associated with climatic and environmental factors. The results can contribute to the development of specific malaria control plans taking geographical variation into consideration and targeting transmission hotspots.

The adoption of renewable energy towards environmental sustainability: Evidence from Partial Least Square Structural Equation Modelling (PLS-SEM).

The effect of carbon emissions on the environment has made some of the Sustainable Development Goals difficult to achieve. Despite the efforts of international bodies, there is still a need to address the problem since the transition is not complete. Therefore, this study investigates the effect of globalization, economic growth, financial inclusion, renewable energy, and government institutions on carbon emissions from the period of 1998 to 2021. To be able to assess both the direct and indirect effects of the variables, the Partial Least Square Structural Equation Modelling is employed, where renewable energy serves as the mediator, and the Two-Stage Least Squares is employed as the robustness check. The findings of the study reveal that globalization promotes the use of renewable energy, but financial inclusion has a negative effect on renewable energy use. Renewable energy has a direct positive and significant effect on carbon emissions. Financial inclusion has an indirect negative and significant effect on carbon emissions. The results imply that more enlightenment on financial inclusion will help a smooth transition, and globalization should be embraced when all environmental regulations are enforced.

[Development of Sample Size Formulas for Least Square Regression-Based Consistency Evaluation of Quantitative Indicators].

Objective To develop and verify the sample size formulas for quantitative data consistency evaluation based on the least square regression method. Methods According to the principle of least square regression-based quantitative consistency evaluation,statistical inference,and formula derivation,we developed the formulas for calculating sample size based on regression constant and regression coefficient.Furthermore,the accuracy of the formulas was verified by the data of three examples,and the results were compared with those of the sample size formula established based on the Bland-Altman(BA)method. Results The sample size formulas for regression-based quantitative consistency evaluation were deduced,and the accuracy of the formulas was verified by three examples.In addition,the results obtained with this formula had differences compared with those of the sample size formula established based on the BA method.Furthermore,consistent conclusions could be obtained by regression analysis and BA analysis with the sample size calculated with the regression method.However,with the sample size calculated based on the BA method,the consistency conclusion of regression analysis and BA analysis was sometimes not valid. Conclusion A sample size formula for quantitative consistency evaluation based on the regression method was proposed for the first time,which provided methodological support for the research in this field.

Synchronous spectrofluorimetry and chemometric modeling: A synergistic approach for analyzing simeprevir and daclatasvir, with application to pharmacokinetics evaluation.

Simeprevir and daclatasvir represent a cornerstone in the management of Hepatitis C Virus infection, a global health concern that affects millions of people worldwide. In this study, we propose a synergistic approach combining synchronous spectrofluorimetry and chemometric modeling i.e. Partial Least Squares (PLS-1) for the analysis of simeprevir and daclatasvir in different matrices. Moreover, the study employs firefly algorithms to further optimize the chemometric models via selecting the most informative features thus improving the accuracy and robustness of the calibration models. The firefly algorithm was able to reduce the number of selected wavelengths to 47-44% for simeprevir and daclatasvir, respectively offering a fast and sensitive technique for the determination of simeprevir and daclatasvir. Validation results underscore the models' effectiveness, as evidenced by recovery rates close to 100% with relative root mean square error of prediction (RRMSEP) of 2.253 and 2.1381 for simeprevir and daclatasvir, respectively. Moreover, the proposed models have been applied to determine the pharmacokinetics of simeprevir and daclatasvir, providing valuable insights into their distribution and elimination patterns. Overall, the study demonstrates the effectiveness of synchronous spectrofluorimetry coupled with multivariate calibration optimized by firefly algorithms in accurately determining and quantifying simeprevir and daclatasvir in HCV antiviral treatment, offering potential applications in pharmaceutical formulation analysis and pharmacokinetic studies for these drugs.