Partial Least Squares Research Articles

A study on hyperspectral soil total nitrogen inversion using a hybrid deep learning model CBiResNet-BiLSTM

Rapid, accurate and non-destructive acquisition of soil total nitrogen (TN) content in the black soil zone is significant for achieving precise fertilization. In this study, the soil types of corn and soybean fields in Jilin Agricultural University, China, were selected as the study area. A total of 162 soil samples were collected using a five-point mixed sampling method. Then, spectral data were obtained and the noisy edge were initially eliminated. Subsequently, the denoised spectral data underwent smoothing by using the Savitzky–Golay (SG) method. After performing the first-order difference (FD) and second-order difference (SD) transformations on the data, it was input to the model. In this study, a hybrid deep learning model, CBiResNet-BiLSTM, was designed for precise prediction of soil TN content. This model was optimized based on ResNet34, and its capabilities were enhanced by incorporating CBAM in the residual module to facilitate additional eigenvalue extraction. Also, Bidirectional Long Short-Term Memory (BiLSTM) was integrated to enhance model accuracy. Besides, partial least squares regression (PLSR), random forest regression (RFR), support vector machine regression (SVR), and back propagation neural network (BP), as well as ResNet(18, 34, 50, 101, 152) models were taken for comparative experiments. The results indicated that the traditional machine learning model PLSR achieved good performance, with R2 of 0.883, and the hybrid deep learning model CBiResNet-BiLSTM had the best inversion capability with R2 of 0.937, with the R2 being improved by 5.4%, compared with the PLSR model. On this basis, we present the LUCAS dataset to demonstrate the generalisability of the model. Therefore, the CBiResNet-BiLSTM model is a fast and feasible hyperspectral estimation method for soil TN content.Graphical abstract

Catalysing sustainable development through water conservation: Insights from Malaysia

This study aims to investigate the attitudinal predictors (attitude, subjective norms, and perceived behavioural control) of consumers’ water conservation intentions. The average daily water usage among consumers in Malaysia is 201 litres higher than the recommended 165 litres by the United Nations. Furthermore, based on the National Water Resource Study (2000-2050), water consumption is expected to rise by 103% by 2050. The water issues require decisive action to ensure sufficient clean water resources for future generations. Thus, educating consumers regarding water saving is of paramount importance in ensuring sufficient water resources and achieving SDGs. Data were collected via 320 questionnaires, and Partial Least Squares (PLS) analysis was employed to test the hypotheses. The findings indicate a positively significant association between attitudinal predictors and Water Conservation Intention among Malaysian consumers. Interestingly, income level exhibits a clear negative and significant relationship with Water Conservation Intention. This implies that income level does not influence consumer’s attitudes. The findings in this paper contribute to the essential component of holistic and integrated governance frameworks aimed at achieving sustainable and inclusive development for water management in Malaysia. This study contributes to the scarce yet crucial body of evidence regarding water-conservation efforts to ensure sustainable water management practices.

The impact of mindfulness on academic achievement of students with the mediating role of adaptability: a structural equation modeling approach

IntroductionExamining the factors influencing students’ academic achievement can lead to improved educational planning and enhanced teaching and learning outcomes. The aim of this study was to investigate the impact of mindfulness on academic achievement with the mediating role of adaptability among students at Jiroft University of Medical Sciences in southern Iran in 2024.MethodsThis descriptive-analytical cross-sectional study was conducted on 290 students from various fields at Jiroft University of Medical Sciences in southern Iran from January to April 2024. To select these 290 individuals, a stratified sampling proportional to size was first carried out based on the year of university entry and field of study. After the sample size was determined for each entry year and field of study, students were randomly selected using their student numbers and a random number table. The data collection instruments were the standard mindfulness, academic achievement, and adaptability questionnaires. Descriptive tests and structural equation modeling using the Partial Least Squares (PLS) method were employed for data analysis, utilizing Smart PLS3 and SPSS26 software.ResultsThe findings indicate that students’ mindfulness positively affects their academic achievement (p < 0.001, β = 0.59). Additionally, mindfulness positively influences students’ adaptability (p < 0.001, β = 0.65). The results also show adaptability positively impacts students’ academic achievement (p = 0.001, β = 0.57). Finally, students’ mindfulness has a significant positive effect on their academic achievement through the mediating role of adaptability (p < 0.001).DiscussionAccording to the results, a favorable state of mindfulness and adaptability among students can improve academic achievement. Therefore, it is recommended that university administrators facilitate the enhancement of students’ mindfulness and adaptability by organizing relevant courses and workshops. This approach can contribute to their academic progress.

Effect Of Earnings Management, Leverage, And CSR On Tax Avoidance With GCG As Moderating Variable

This study aims to test and analyze the effect of Earning management, leverage, and CSR on Tax avoidance with moderated by independent commissioners for GCG. Property and real estate companies are the population used in this study, where the processed companies are companies listed on the Indonesia Stock Exchange (IDX) from 2018 to 2022. Quantitative approach is used in research”using secondary data in the form of financial statements”for 2018-2022. The purposive sampling method is one of the methods used for research, from this method researchers get 15 real estate companies as research samples. This study uses partial least squares (PLS) and SmartPLS 3. The results of this study indicate that earnings management and leverage have no significant impact on tax avoidance. GCG is unable to moderate Earning Management, Leverage, and CSR on Tax Avoidance because the P-Values are less than 0.5%. While Corporate Social Responsibility has a significant effect on Tax Avoidance because the P-Values are greater than 0.5%. The results of this study can be good input to the government or companies in the best possible taxation practices, so that practices that utilize tax avoidance can be used.

Spectrophotometric determination of olanzapine, fluoxetine HCL and its impurity using univariate and chemometrics methods reinforced by latin hypercube sampling: Validation and eco-friendliness assessments

Novel univariate and chemometrics-aided UV spectrophotometric methods were tailored to undergo the fundamentals of green and white analytical chemistry for the simultaneous estimation of a ternary mixture of olanzapine (OLA), fluoxetine HCL (FLU), and its toxic impurity 4-(Trifluoromethyl) phenol (FMP) without any prior separation. The dual-wavelength ratio spectrum univariate method was used to determine OLA and FLU in the presence of FMP in the range of (4–20) and (5–50) μg/ml, respectively. In compliance with the International Conference on Harmonization (ICH) standards, the technique was validated and established Remarkable accuracy (98–102%) and precision (< 2%) with limits of quantification (LOQs) of 0.432 and 2.002 μg/ml, respectively. Partial least squares (PLS) and artificial neural networks (ANNs) are chemometric methodologies that have advantages over the univariate method and use significant innovations employing Latin hypercube sampling (LHS), allowing the generation of a reliable validation set to guarantee the effectiveness and sustainability of these models. The concentration ranges used were (2–20), (2–20), and (5–50) μg/ml; for PLS, the LOQs were 0.602, 0.508, and 1.429 μg/ml, and the root mean square errors of prediction (RMSEPs) were 0.087, 0.048, and 0.159 for OLA, FMP, and FLU, respectively; and for ANNs, the LOQs were 0.551, 0.465, and 0.965 μg/ml, with RMSEPs of 0.056, 0.047, and 0.087 for OLA, FMP, and FLU, respectively. The developed methods yield a greener National Environmental Methods Index (NEMI) with an eco-scale assessment (ESA) score of 90 and a complementary Green Analytical Procedure Index (complex GAPI) in quadrants with an analytical greenness metric (AGREE) score of 0.8. The Red‒Green–Blue 12 algorithm (RGB 12) scored 88.9, outperforming on reported methods and demonstrating widespread practical and environmental approval. Statistical analysis revealed no noteworthy differences (P > 0.05) among the proposed and published techniques. Both pure powders and pharmaceutical capsules were analyzed via these methods.

Insights into Tetravalent Np Speciation in HNO3 through Spectroelectrochemistry and Multivariate Analysis.

In situ optical spectroscopy, spectropotentiometry, and multivariate analysis were applied to the Np(IV) nitrate system to better understand speciation and quantify HNO3 concentration. Thin-layer spectropotentiometry, or spectroelectrochemistry, was leveraged to isolate and stabilize Np(IV) without compromising the solution conditions and generate representative Vis-NIR absorption spectra from 0.5 to 10 M HNO3 and benchmark the corresponding Np(IV) molar absorptivity coefficients. Spectra were described with principal component analysis (PCA) to identify the purest Np(IV) absorbance spectra among other oxidation states [e.g., Np(V/VI)] at each acid concentration and then to identify the primary sources of variance within each Np(IV) spectrum with respect to Np(IV) nitrate complexes. Then, partial least-squares regression (PLSR) and support vector regression (SVR) models were built to predict HNO3 concentration from the Np(IV) spectral data. The nonlinear SVR model outperformed the linear PLSR model for the HNO3 concentration predictions. Finally, the inclusion of spectra collected in edge and center point HNO3 concentrations in the calibration set was determined to be crucial for producing models with strong predictive capabilities. The multivariate approach used in this study makes it possible to quantify HNO3 concentration solely based on Np(IV) absorption spectra, which is essential to quantifying processing streams in various online monitoring applications.

Raman spectroscopy for monitoring free sulfhydryl formation during monoclonal antibody manufacturing

During production, harvested cell culture fluid (HCCF) can degrade due to reductases breaking interchain disulfide bonds, forming low molecular weight (LMW) impurities that contain free sulfhydryl and high molecular weight (HMW) impurities through disulfide shuffling. Thus, detecting and quantifying the free sulfhydryl increase in HCCF is critical. Herein, Raman spectroscopy is implemented as a process analytical technology, and multivariate data analysis is applied to characterize and quantify sulfhydryl formation in HCCF with disulfide-containing indicator molecules. Raman spectra qualitatively probe the presence or absence of disulfide bond breakage in antibodies, consistent with offline non-reduced capillary electrophoresis sodium dodecyl sulfate results. Between two antibodies studied, mAb A was identified for a higher risk of antibody reduction where sulfhydryl formation was observed within 16 h, while mAb B did not show similar concerns even after 1 week. The offline measurement of redox potential is below –100 mV in HCCF for mAb A, while the stable mAb B HCCF shows redox potentials above +20 mV. A multivariate partial least squares (PLS) model for quantification is developed using an offline free sulfhydryl assay, applying Raman spectra to predict free sulfhydryl concentration with high accuracy (R2 > 0.98) and expected mean error of 0.677 mM from the offline Ellman’s Assay. This work confirms the use of Raman PAT to monitor real-time disulfide reduction, enabling improvements to process understanding and product quality.

Determinants affecting the medical students adoption of mobile learning: extended UTAUT

BackgroundThis research investigates the fundamental factors which may stymie or simplify the adoption of m-learning by medical college educators in Sudan. We made use of the Extended Unified Theory of Acceptance and Use Technology (UTAUT) with COVID-19 fear (CF) and social isolation (SIS) as exogenous components.ObjectiveThis research aims to find out factors impacting medical students’ behavioural intention to use mobile learning.MethodsData was gathered from 499 undergraduate learners from different universities in Sudan. Partial Least Square (PLS) and structural equation modeling (SEM) were used to examine the data.The resultsShowed that expected performance, effort expectancy (EE), social influence (SI), facilitation condition (FC), COVID-19 fear, and social isolation (SIS) are predictors of behavioural intentions (BI). In addition, our findings show that COVID-19 fear and social isolation increase student adoption of mobile learning during the COVID-19 epidemics in developing countries.ConclusionsThe study unveiled that the suggested framework, derived from UTAUT, effectively discerns the factors impacting students’ intentions in mobile learning during pandemics. Sudanese medical students’ can strategize and execute effective classroom environments based on these findings.

Application of machine learning algorithms for predicting the life-long physiological effects of zinc oxide Micro/Nano particles on Carum copticum

Nanoparticles impose multidimensional effects on living cells that significantly vary among different studies. Machine learning (ML) methods are recommended to elucidate more consistence and predictable relations among the affected parameters. In this study, nine ML algorithms [Support-Vector Regression (SVR), Linear, Bagging, Stochastic Gradient Descent (SGD), Gaussian Process, Random Sample Consensus (RANSAC), Partial Least Squares (PLS), Kernel Ridge, and Random Forest] were applied to evaluate their efficiency in predicting the effects of zinc oxide nanoparticles (ZnO NPs: 0.5, 1, 5, 25, and 125 µM) and microparticles (ZnO MPs: 1, 5, 25, and 125 µM) on Carum copticum. The plant root/shoot biomass; number of leaves, branches, umbellates, and flowers; protein content; reducing sugars; phenolic compounds; chlorophylls (a, b, Total); carotenoids; anthocyanins; H2O2; proline; malondialdehyde (MDA); tissue zinc content; superoxide dismutase (SOD) activity; and media ΔpH were measured and considered input variables. All levels of ZnO MPs treatments increased growth parameters compared to the control (ZnSO4). The highest shoot/root fresh and dry mass were recorded at 5 µM ZnO MPs compared with the control. The root fresh/dry mass under ZnO NPs treatments was more sensitive than shoot parameters. The number of flowers increased by 134 and 79% in MPs and NPs treatments compared to the control, respectively. ZnO NPs reduced protein content by up to 81% in 125 µM NPs compared to ZnSO4. Reducing sugar content increased to 25, 40 and 36% in 5, 25, 125 µM MPs and 67, 68, 26, 26 and 21% in 0.5, 1, 5, 25 and 125 µM NPs treatments, respectively. The pH alteration was more significant under NPs and affected zinc uptake. All levels of ZnO NPs treatments increased growth parameters compared to the control. All ML algorithms showed varied efficiencies in predicting the nonlinear relationships among parameters, with higher efficiency in predicting the behavior of root and shoot dry mass, root fresh weight and number of flowers according to R2 index. The model obtained from SVR with the radial basis function (RBF) kernel was selected as a comprehensive model for predicting and determining the efficacy of the results.

Strategy to Improve Business Performance of Palm Oil Sector in Merangkai Village, Dayun District, Siak Regency: Impact of Digital Marketing and Marketing Strategy

This study aims to measure the influence of digital marketing and marketing strategies in improving the business performance strategy of the palm oil sector in Merangkai Village, Dayun District, Siak Regency. The study used a quantitative approach where primary data was collected through a survey of 35 respondents, and the Partial Least Square (PLS) statistical technique was used to process the data. The study's results confirmed that digital marketing had no significant influence on the business performance of the palm oil sector. The marketing strategy variable impacts the business performance of the palm oil sector. Still, some other factors or variables influence the business performance variable of the palm oil sector in Merangkai Village, Dayun District, Siak Regency.

The tea buds detection and yield estimation method based on optimized YOLOv8

The existing methodology for detecting and estimating tea bud yields heavily relies on cultivation experience, resulting in a delay in obtaining accurate information regarding tea bud yields. This delay poses challenges in providing timely data support for tea leaf growth management during the early stages of tea production. To address this issue, we propose a novel approach for tea bud detection and yield estimation. Initially, image data is gathered from the tea plantation area to train an optimized YOLOv8 model, enabling precise detection and enumeration of tea buds within the specified region. Ablation experiment is conducted to validate the effectiveness of the model improvements. Subsequently, data associating tea bud quantities with yields are collected, and a yield estimation mathematical model is derived through the partial least squares (PLS) method. Finally, the target detection model is integrated with the yield estimation model. By leveraging data such as the tea bud count within the target area, estimated area, and total planting area, the system accomplishes the estimation of tea bud yields. The outcomes of research are as follows: 1) The optimized YOLOv8 model achieves a recognition accuracy of 95.9 % for tea buds, boasting a recall rate of 85.5 % and an average precision of 91.7 %. 2) The relative error between the estimated yield and actual yield, as determined by the proposed method in this study, stands at 10.28 %. The tea bud detection and yield estimation methodology introduced in this research offer a convenient and expeditious means to estimate tea bud yields. It provides valuable data support to tea growers, facilitating pertinent production planning within the tea industry.

Rapid Classification of Milk Using a Cost-Effective Near Infrared Spectroscopy Device and Variable Cluster-Support Vector Machine (VC-SVM) Hybrid Models.

Removing fat from whole milk and adding water to milk to increase its volume are among the most common food fraud practices that alter the characteristics of milk. Usually, deviations from the expected fat content can indicate adulteration. Infrared spectroscopy is a commonly used technique for distinguishing pure milk from adulterated milk, even when it comes from different animal species. More recently, portable spectrometers have enabled in situ analysis with analytical performance comparable to that of benchtop instruments. Partial Least Square (PLS) analysis is the most popular tool for developing calibration models, although the increasing availability of portable near infrared spectroscopy (NIRS) has led to the use of alternative supervised techniques, including support vector machine (SVM). The aim of this study was to develop and implement a method based on the combination of a compact and low-cost Fourier Transform near infrared (FT-NIR) spectrometer and variable cluster-support vector machine (VC-SVM) hybrid model for the rapid classification of milk in accordance with EU Regulation EC No. 1308/2013 without any pre-treatment. The results obtained from the external validation of the VC-SVM hybrid model showed a perfect classification capacity (100% sensitivity, 100% specificity, MCC = 1) for the radial basis function (RBF) kernel when used to classify whole vs. not-whole and skimmed vs. not-skimmed milk samples. A strong classification capacity (94.4% sensitivity, 100% specificity, MCC = 0.95) was also achieved in discriminating semi-skimmed vs. not-semi-skimmed milk samples. This approach provides the dairy industry with a practical, simple and efficient solution to quickly identify skimmed, semi-skimmed and whole milk and detect potential fraud.

Purchase Intention of Halal Toiletries: The Role of Mediating and Moderating Effects

Research aims: This study analyzes consumers’ purchase intentions regarding halal toiletries by examining the independent variables of halal awareness, attitude, subjective norms and behavioral control. Design/Methodology/Approach: Data were collected from 260 consumers throughout questionnaires. The respondents had all purchased and used halal toiletries at least once. Hypothesis testing of the independent variable on the dependent variable was conducted using Partial Least Square (PLS) analysis, with the Smart PLS 3.0 software.Research findings: The findings indicate that halal awareness significantly influences the purchase intention of halal toiletries and has a strong impact on customer attitudes. The results also show that subjective norms influence the purchase intention of halal toiletries. Additionally, behavioral control contributes to shaping the purchase intentions. Theoretical Contribution/Originality: The study demonstrates that well-developed halal awareness significantly influences the intention to purchase halal toiletries and is a key factor in shaping consumer attitudes. The findings also highlight the role of subjective norms in forming purchase intentions, while behavioral control plays a crucial role in supporting these intentions. Practitioners/Policy Implications: The results of this study are expected to support the development of a sustainable halal industry, especially within the toiletry sector. Research Limitations/Implications: This study is limited by the indicators tested and offers opportunities for future research to explore additional variables. Future studies could incorporate a gender perspective to provide insights into differences in purchase intentions between men and women.

Evaluation of Soumbara (Parkia biglobosa) Quality During Storage Using Mid-Infrared Spectroscopy and Physicochemical Methods

The fermented seed of néré (Parkia biglobosa), known as Soumbara, is a condiment widely consumed by the population in West Africa and Guinea in particular. The present study aims to use physicochemical, colorimetric methods and mid-infrared (MIR) spectroscopy to evaluate the evolution of the quality of Soumbara during storage (30 days). Thus, 5 kg of Soumbara were taken, subdivided into 7 batches of samples, and analysed every 5 days for 30 days. Physicochemical analyses (water activity, moisture, proteins, and total ash) would show that the storage duration induced a variation during storage; for example, the total ash contents on days 1, 5, 10, 15, 20, 25 and 30 are respectively 3.96 ± 0.25, 3.89 ± 0.43, 3.97 ± 0, 23, 3.99 ± 0.12, 4.54 ± 0.14, 4.57 ± 0.2 and 4.8 ± 0.21%. During the first 15 days of storage, no significant differences (p &lt; 0.05) were observed between samples. The colorimetric measurements showed that the storage duration did not significantly affect the values of a* and b*, except the L* value which decreased significantly (p&lt;0.05) (42.16 ± 0.015 on day 1 to 40.02 ± 0.015 on day 30). Applying statistical methods to mid-infrared spectroscopy allowed a clear distinction between the Soumbara samples during storage for 30 days. The partial least squares regression (PLSR) applied to MIR spectra made it possible to predict water, moisture and protein activity levels, since R2 greater than 0.99 were obtained. The results demonstrated that mid-infrared spectroscopy coupled with chemometric tools could be used as a rapid screening tool to assess and monitor the quality of Soumbara during storage.

LTFM: Long-tail few-shot module with loose coupling strategy for mineral spectral identification

In recent years, deep learning methods have exhibited superior performance in mineral identification when especially compared with conventional machine learning methods such as Support Vector Machine (SVM) and Partial Least Squares (PLS). Nevertheless, almost all of these deep learning methods pay more attention to improving and designing network structures, while neglecting the phenomenon of long-tail distribution in spectral data due to the inconsistency of ore distribution and the scarcity of several natural minerals. To alleviate the interference of majority categories on minority categories, we propose Long-Tail Few-shot Module (LTFM) which is inspired by rethinking the fashionable decoupling strategy that conducts primary representation learning and further classifier retrained on mineral spectral data. In particular, LTFM serves as a multi-expert mode, where these experts are respectively specialized in improving feature representation learning, mitigating the long-tail effect, and alleviating the interference of few shots. Additionally, the loose coupling learning strategy is introduced to facilitate primary representation learning and the subsequent additional experts to inherit this knowledge. Experiments on two publicly available spectral datasets show that the proposed LTFM significantly outperforms existing methods. In the end, extensive ablation studies are conducted to investigate the effectiveness, correctness, and robustness of our proposal.

The present scenario of artificial intelligence and machine learning in financial services: An empirical study

The financial services industry is experiencing a swift adoption of artificial intelligence (AI) and machine learning for a variety of applications. These technologies can be employed by both public and private sector entities to ensure adherence to regulatory requirements, monitor activities, evaluate data accuracy, and identify instances of fraudulent behavior. The utilization of artificial intelligence (AI) and machine learning (ML) has the potential to provide novel and unforeseen manifestations of interconnectivity within financial markets and institutions. This can be represented by the adoption of previously disparate data sources by diverse institutions. The researchers employed convenience sampling as the sampling method. The form was filled out over the period spanning from July 2023 to February 2024, and it was designed to be both anonymous and accessible through online and offline platforms. To assess the reliability and validity of the measurement scales and evaluate the structural model, we employed Partial Least Squares (PLS) for model validation. Specifically, we have used the software package Smart-PLS 3 with a bootstrapping of 5000 samples to estimate the significance of the parameters. The results indicate a positive and direct connection between artificial intelligence (AI) and either financial services or financial institutions. On the contrary, machine learning (ML) exhibits a strong and positive association among financial services and financial institutions. Similarly, there exists a positive and direct connection between AI and investors, as well as between ML and investors.

Mangrove Ecotourism in East Java, Indonesia: A Memorable Tourist Experience that Awakens Revisit Intention

As outdoor tourism grows amidst competition in the tourism industry, ecotourism must offer tourists Memorable tourist experiences (MTEs). Memorable experiences shared on social media can be re-shared and have the potential to go viral. This study drew from previous empirical research and explored the mediating role of electronic word of mouth (eWOM) and MTEs on revisit intention in mangrove ecotourism in East Java, Indonesia. Samples were selected using a probabilistic technique among mangrove ecotourism tourists in Trenggalek District and Surabaya City, East Java, Indonesia. 250 questionnaires were distributed to respondents, out of which 160 were returned and deemed complete. The structural equation model (SEM) tested the hypothesis with the Partial Least Squares (PLS) approach. This research concludes that positive experiences left by tourists can directly influence revisit intention, but this influence is more significant if the experience is staged through eWOM. This research has implications for theory in understanding the role of MTEs and eWOM on revisit intention, while managerially it has implications for developing destination marketing strategies to create maximum MTEs and eWOM to generate revisit intention. Keywords: ecotourism, eWOM, memorable tourist experiences, revisit intention

Childhood Vaccination Decisions during the Pandemic: Tradition, Generational Influence, and Spiritual Perspectives

Background: Vaccination is an attempt to form active immunity in an individual. Vaccination is carried out as a preventive measure against infection, both in adults and children. This study aims to determine the effect of parental attitudes, generational influenced, and spiritual contexts on children's vaccination decisions, in this case, the COVID-19 vaccination. Methods: This study used a cross-sectional online survey method, with parents who have children aged 6–11 and willing to take part in this study as respondents. Data collection was carried out from November 2022 to April 2023. There were 66 participants who met the criteria, and then an analysis was carried out using the Structural Equation Model (SEM) method, which was processed using Partial Least squares (PLS). Results: According to research findings, parents' points of view play an important role in deciding whether to vaccinate their child or not. Up to 58% of respondents said they support vaccinating their children, in this case with the COVID-19 vaccination. Conclussion: Parental attitudes of vaccination in children in this research is strongly influenced by parents' views, so the right approach is needed for parents for the successful implementation of vaccination in children. Other influential factors are spiritual context and generational influence from family regarding COVID-19.

Consumer Purchase Decision and Brand Image Analysis Through Product Quality and Price: Study Case Frozen Pastry in Jakarta - Indonesia

The study investigates the effects of product quality and price on the purchasing decisions of Bonpatis Danish pastry sheets, with brand image acting as a mediator. Indonesia's food and beverage industry, mainly retail, is experiencing significant growth as modern retailers like Hypermart introduce competitive products. A quantitative approach was used, applying Structural Equation Modeling (SEM) with Partial Least Squares (PLS) to data collected from 83 respondents, mainly Hypermart bakery employees in DKI Jakarta. Results indicate that product quality and price positively influence brand image and purchasing decisions. Specifically, product quality boosts brand image and directly affects purchase decisions, whereas price influences purchase decisions directly but not via brand image. The study emphasizes the role of product appearance and packaging in shaping brand perceptions and suggests improving these elements to attract more consumers. Due to a limited sample size, the study recommends future research to explore broader aspects like customer satisfaction and loyalty. Keywords: Danish pastry sheet, product quality, price, brand image, purchase decision.

Based on Spectrum-Effect Relationship, Network Pharmacology, and Molecular Docking, the Material Basis and Mechanism of Antioxidant Effect of Miao Medicine Indigofera stachyoides Lindl Were Studied.

Indigofera stachyoides Lindl (IS.Lindl, Xuerensen in Chinese) is a traditional medicine frequently utilized by ethnic minorities; nevertheless, the chemicals responsible for these effects have not been identified. Ultraperformance liquid chromatography (UPLC) was utilized to establish the fingerprints of various origins. Free radical scavenging in 1,1-diphenyl-2-trinitrophenylhydrazine (DPPH) and 2,2-biazobis(3-ethyl-phenylpropylthiazole-6-sulfonate) diammonium salt (ABTS) assays was used to evaluate the antioxidant activity. Partial least-squares regression analysis (PLSR) and gray correlation analysis (GRA) were utilized to determine the spectral-effect relationship in order to screen the antioxidant pharmacodynamic components. The corresponding targets were obtained from Traditional Chinese Medicine Systems Pharmacology (TCMSP) and Integrated Traditional Chinese Medicine (ITCM). Disease-related targets for antioxidants were collected from GeneCards and Online Mendelian Inheritance in Man (OMIM) databases. The PPI interaction network analysis, GO enrichment analysis, and KEGG pathway analysis were established using the online analysis platform, and Autodock software assisted in aligning the components with important targets. The fingerprint profile revealed 32 common peaks, and eight standards were identified using standard comparison, with similarity ranging from 0.920 to 0.995. The primary antioxidant components include proanthocyanidin B1, epicatechin, and proanthocyanidin B3. Important targets include EGFR, CASP3, IL6, PTGS2, and TNF. Important signaling pathways include the AGE-RAGE signaling pathways in diabetic complications, the MAPK signaling pathway, the IL-17 signaling pathway, and the pathways in cancer. The results of molecular docking technology showed that the main active ingredients of the drug could bind well to the core target. In this study, we successfully established the spectral-effect relationship of IS.Lindl and clarified the effective substances of IS.Lindl. Through network pharmacology and molecular docking methods, it was clear that IS.Lindl plays an antioxidant role through multicomponent, multitarget, and multipathway synergy.