Partial Least Squares Research Articles

Another pipeline in local Partial Least Squares Regression (LPLS) methods: Assessing the impact of wavelet transform integration

This study evaluates the performance of four Partial Least Squares Regression (PLS) methods, focusing on a new Local Partial Least Squares Regression (LPLS) variant integrating wavelet transformation, named WLPLS, for analyzing feed using Near-Infrared (NIR) spectroscopy. While traditional PLS methods are effective for many spectroscopic applications, their global modeling approach often reduces predictive accuracy in large, heterogeneous datasets. In contrast, LPLS adapts models to local data characteristics, which can enhance prediction but also increase computational demands (power and time). WLPLS seeks to mitigate these demands by incorporating wavelet transformation to reduce data dimensionality while effectively managing spectral variances. This research conducts a comparative analysis of WLPLS against traditional PLS, LPLS, and another LPLS pipeline reducing data dimensionality: the LPLS on global PLS scores (LPLS-S). The performances of these methods were evaluated using a large feed database containing 24,644 samples, analyzing five key constituents: ash, crude fibers, fat, moisture, and proteins. The results demonstrate that local approaches outperformed the global PLS method for this dataset and that the performances of the local methods were relatively similar to each other. The selection of the optimal method therefore depends on the specific requirements of the application, such as dataset characteristics and the required prediction speed. Future studies should broaden this comparative framework to additional datasets and contexts to ensure they are adapted for diverse applications.

Spatial-spectral feature mining in hyperspectral corn leaf venation structure and its application in nitrogen content estimation

The growth of corn plants requires a significant amount of nitrogen nutrient supply to ensure the yield. Meanwhile, overfertilization could result in adverse environmental impacts, making it crucial to monitor the nitrogen supply condition in corn plants precisely. Hyperspectral imaging (HSI) could be the solution to this challenge, as HSI plant phenotyping technology has been widely utilized to nondestructively identify plant traits, such as mineral nutrient deficiencies in corn plants. However, limitations persist where the conventional HSI nitrogen prediction models mainly relied on the overall colors of the plant tissue extracted from the spectral domain but rarely included the spatially distributed information on the leaf level. This study aimed to examine if including new information from the spatial domain could potentially improve the performance of HSI models. Because the venation structure plays a crucial role in the distribution of various nutrients across the leaf, a series of physiological responses caused by nitrogen deficiency could result in non-uniform color patterns related to the veins. The recent advancement of HSI techniques such as LeafSpec, whose imaging quality and spatial-spectral resolution have been significantly improved, made it possible to extract the venation structure together with high-resolution spatial-spectral features. In this study, the hypothesis was that utilizing high-resolution spatial-spectral features extracted based on the venation structure could yield a better-performed nitrogen content prediction model compared to the averaged spectrum of the corn leaf. First, a venation structure segmentation algorithm was developed using the Local Binary Patterns (LBP) method for hyperspectral corn leaf images scanned with LeafSpec. Second, for every hyperspectral leaf image, 131 spectral index heatmaps were generated, and 30 leaf regions were picked based on the venation structure, resulting in 3930 spatial-spectral features. Third, the features were picked using the Least Absolute Shrinkage and Selection Operator (LASSO) regression. As the results tested with a field assay involving two different corn genotypes and two nitrogen treatments, the Partial Least Square Regression (PLS-R) model built with selected spatial-spectral features outperformed the model built with the averaged leaf spectra in terms of the Root Mean Squared Error (RMSE) of predictions. The results provided encouraging proof and guidance for the potential benefit of using high-resolution spatial-spectral features derived from hyperspectral leaf images to improve the accuracy and robustness of the nitrogen content prediction models for corn plants.

Powder dosing within continuous manufacturing: A lean approach for gravimetric dosing configuration and equipment selection

This work presents a novel approach to select gravimetric dosing configurations for continuous unit operations in pharmaceutical processing. It optimizes material, time, and personnel use, and allows selections for configurations of materials not included in the model by robust material attribute-based interpolation. The approach does not apply Principal Component Analysis (PCA) and Partial Least Squares (PLS). Instead, a reduced set of material attributes that require measurement was determined from existing material attribute libraries found in literature. Only those identified 17 attributes were measured for 13 materials and employed in this study. The established model proved to be predictive for an even further reduced attribute set of 12 attributes. The model introduces a simplified method for selecting feeding equipment combining precision metrics (relative mean intra-bin variability) and model parameters (fmax, fmin, beta). The reduction of Material Attributes (MAs) in this short-cut method enables fast evaluation and enhances resource efficiency by incorporating only a few selected MAs into the model and therefore introduces a different line of thinking. It was also possible to compare feeding of different hopper geometries, showing that flat bottom-hoppers have higher dosing precision than round bottom-hoppers. In conclusion, this work introduces a smart and innovative model that simplifies the equipment selection process and brings objectivity through a comprehensive numerical description of the discharge characteristics, and provides new options for continuous dosing in pharma through combining precision and curve fitting parameters.

Diversity of chemical compounds in lily fragrance and identification of key sensory markers

Lilies are economically significant crops, and their fragrance is a crucial trait for cut flowers. Different lily varieties exhibit a rich diversity of fragrance profiles. To identify chemical markers associated with sensory attributes, this study screened 36 popular cut lily varieties with varying fragrances. Sensory analysis and HS–SPME–GC–MS were used to evaluate their fragrance characteristics. The aroma intensity evaluation showed that OT and O varieties had stronger fragrances, while LA and L varieties had lower fragrance intensities. LO, L, and LA varieties were preferred by evaluators over O, OA, and OT varieties. However, there was no significant correlation between aroma intensity and hedonic tone. A total of 73 VOCs were detected. Methyl benzoate, β-myrcene, (E)-β-ocimene, allo-ocimene, ethyl benzoate, and (Z)-caryophyllene were present in the fragrances of most varieties. PCA and HCA analyses indicated that the relative emission of volatile organic compounds (VOCs) could effectively distinguish the fragrance characteristics of LO, O, and OT varieties, while the proportion of VOCs emissions could differentiate LA, LO, and OT varieties. PLS regression and jackknife analyses identified that the relative emission and proportion of terpinolene, eugenol, and (E)-isoeugenol were positively correlated with aroma intensity. Linalool (proportion) and cyclopentene, 3-isopropenyl-5,5-dimethyl- were positively correlated with hedonic tone, while methyl salicylate (proportion) was negatively correlated with hedonic tone. These findings provide potential chemical markers for the efficient evaluation of cut lily fragrances and offer references for selecting raw materials for lily fragrance oils and hydrosols.

Study on bionics-based swarm intelligence optimization algorithms for wavelength selection in near-infrared spectroscopy

Wavelength selection is one of the most important steps in the modeling of near-infrared spectroscopy (NIRS), which is of great significance to reduce model complexity and improve model performance. In this paper, a total of ten bionics-based swarm intelligence optimization algorithms (BSIOAs) inspired by natural creatures, such as Harris Hawks Optimization (HHO), Butterfly Optimization Algorithm (BOA), Whale Optimization Algorithm (WOA), Monarch Butterfly Optimization (MBO), Grey Wolf Optimization (GWO), Fruit Fly Optimization Algorithm (FOA), Bat Algorithm (BA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) were studied on application to wavelength selection in the NIRS modeling. Three benchmark NIRS datasets were used to evaluate the algorithms by calculating the indicators, including coefficients of determination, root mean square error, and residual predictive deviation in calibration and prediction. The results obtained showed that these BSIOAs can significantly reduce the number of wavelengths (retaining half or fewer). Compared with the full-spectrum models, the present models not only simplified the model structures but improved the model performances. The performances were generally better than the ones by some popular and classic wavelength selection algorithms, such as competitive adaptive reweighted sampling, Monte Carlo uninformative variable elimination, variable importance in projection, interval partial least-squares, and successive projections algorithm.

Analysis of the application of financial management to the profitability of MSMES through debt management of MSME Players in Kwala Serapuh Village, Langkat

This study aims to analyze the effect of financial management implementation on the profitability of Micro, Small and Medium Enterprises (MSMEs) through debt management. The case study was conducted on MSME players in Kwala Serapuh Village. Good financial management is key to the sustainability and success of a business, especially MSMEs that have limitations in terms of capital and resources. This research uses quantitative methods by distributing questionnaires to MSME actors in Kwala Serapuh Village. The number of samples in this study were 50 respondents of MSME actors. Data analysis was carried out using the Partial Least Square (PLS) method using SmartPLS version 4 software. PLS is one of the methods of solving Structural Equation Modeling (SEM). The results showed that the variable of financial management implementation has a positive and significant influence on debt management. The variable of financial management implementation has a positive and significant influence on the profitability of MSMEs. The debt management variable has a positive and significant influence on the profitability of MSMEs. The variable of financial management implementation on MSME profitability through debt management has a positive and significant influence on MSME profitability. The debt management variable can be influenced by the application of financial management by 76.1% and the remaining 23.9% is influenced by other variables outside those studied. While the MSME profitability variable can be influenced by the application of financial management and debt management by 50.9% and the rest is influenced by other variables outside the study.

The role of attitude mediates the effect of brand awareness and brand image on product purchase intention

This study aims to analyze the role of attitude in mediating brand awareness and brand image on purchase intention. The population in this study were consumers who had never used the Carasun brand sunscreen in Denpasar City. The number of samples used in this study were 160 samples. The method used in sample selection is purposive sampling, which is a sampling technique with certain considerations. The analysis technique used is Structural Equation Modeling (SEM) analysis based on Partial Least Square (PLS). The results showed that brand awareness and brand image have a positive and significant effect on purchase intention, brand awareness and brand image have a positive and significant effect on attitude, attitude has a positive and significant effect on purchase intention, and attitude has a role in partially mediating brand awareness and brand image on purchase intention. The results of the study can be concluded that the higher the brand awareness and the creation of a positive brand image in the minds of consumers, the positive consumer attitudes will emerge which encourage making purchases. The higher the brand awareness and the creation of a positive brand image in the minds of consumers, the higher the consumer's purchase intention. The creation of a positive attitude from consumers will create purchase intentions. The higher the brand awareness and the creation of a positive brand image will create a positive attitude from consumers, so that in the end the consumer's purchase intention will increase.

Impact of financial and internet support on SME performance: Moderating effect of technology adoption during COVID-19 pandemic

Small and medium-sized enterprises (SMEs) are widely recognised as significant catalysts for economic growth at a worldwide level. The benefits that are frequently highlighted for their promotion, particularly in developing nations like Bangladesh, include their relatively high labor intensity, reliance on local technology and skills, contributions to the growth of entrepreneurialism and innovativeness, and industrial linkage expansion. The objective of this study is to examine the influence of financial and internet support on the performance of small and medium enterprises (SMEs) in Bangladesh during the COVID-19 pandemic while also considering the moderating effect of technology adoption. By going through the literature, the study identified a number of factors namely financial support, ICT support, technological support played a strong role on SME performance during COVID-19. The present study utilises Partial Least Square (PLS) path modelling, a variance-based technique within the framework of Structural Equation Modelling (SEM), to investigate the correlations. A well-structured questionnaire is prepared for collecting primary data from the SME owners or managers and finally 378 data have been collected from the different types of SME entrepreneurs through using purposive sampling technique. Eight hypotheses are tested and found seven positive relationships with moderating interaction of technology adoption between influential factors and SME performance (financial and non-financial).

Effects of Red Vinasse on Physicochemical Qualities of Blue Round Scad (Decapterus maruadsi) During Storage, and Shelf Life Prediction

A fish processed with red vinasse is a type of Fujian cuisine with regional characteristics. In order to monitor the effect of red vinasse on storage quality and shelf life of blue round scad (Decapterus maruadsi) during storage, the changes in fat content, thiobarbituric acid reactive substances (TBARS), composition of polyunsaturated fatty acids (PUFAs), pH value, texture, and sensory quality were studied at different storage temperatures (4 °C, 25 °C, and 37 °C). By analyzing the correlation between changes in sensory qualities and physical and chemical indexes, a first-order kinetic model and the Arrhenius equation were used to build a shelf-life prediction model for blue round scad during storage. The results showed that processing with red vinasse can significantly reduce the malondialdehyde (MDA) production and the decrease in PUFAs, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) (p < 0.05) during storage. Based on partial least squares regression (PLSR), the storage temperature and time have a significant impact on the PUFA composition in blue round scad, which changed less when the samples were stored at 4 °C and 25 °C, and they had better nutritional composition of fatty acids at lower temperatures. Among the PUFAs, DHA (C22:6n-3) and EPA (C20:5n-3) had higher relative contents and significantly decreased during storage (p < 0.05). Additionally, the processing with red vinasse can slow down the increase in total volatile basic nitrogen (TVB-N) value and pH of blue round scad, maintain the appropriate hardness, elasticity, cohesion and chewability, and improve the overall sensory quality of the fish. In addition, according to the results of model prediction based on TBARS value, the storage shelf life of blue round scad with red vinasse added was 55 d, 2.7 d and 28 h at 4 °C, 25 °C and 37 °C, respectively. The accuracy of the forecast model was high, and the relative errors of the measured values and predicted values were less than 10%. Thus, it not only provided a theoretical basis for the processing and application of red vinasse to Chinese traditional food, but also provided innovative ideas for the safe storage and high-value utilization of blue round scad.

Strategic excellence and technological integration: Sustaining competitive advantage for private universities in Indonesia

The competition among private universities in Indonesia is intensifying, requiring institutions to establish a sustainable competitive advantage through the right strategies, strong vision and mission, and effective use of information technology. This study analyzes the influence of strategy, vision and mission, university rankings, and information technology on the sustainable competitive advantage of private universities. It also examines the moderating role of information technology in strengthening these relationships. Using a quantitative approach with a descriptive and causal design, data were collected from 204 respondents across private universities in Indonesia via a questionnaire. Analysis using Partial Least Squares (PLS) and SmartPLS software reveals that strategy, vision and mission, university rankings, and information technology significantly impact sustainable competitive advantage. Furthermore, information technology strengthens the influence of strategy, vision, and mission but does not moderate the relationship between university rankings and competitive advantage. These findings indicate that while information technology supports strategic implementation and the achievement of institutional vision and mission, its direct role in enhancing university rankings is limited. The study emphasizes the importance of formulating innovative, sustainable strategies and integrating information technology effectively to maintain competitiveness. Practical implications include the necessity of resource planning and investment in technology to achieve strategic objectives. This study contributes to the literature by exploring the moderating role of information technology in private universities, providing valuable insights for managers on leveraging technology to navigate competitive challenges and sustain growth.

Nondestructive Detection of Litchi Stem Borers Using Multi-Sensor Data Fusion

To enhance lychee quality assessment and address inconsistencies in post-harvest pest detection, this study presents a multi-source fusion approach combining hyperspectral imaging, X-ray imaging, and visible/near-infrared (Vis/NIR) spectroscopy. Traditional single-sensor methods are limited in detecting pest damage, particularly in lychees with complex skins, as they often fail to capture both external and internal fruit characteristics. By integrating multiple sensors, our approach overcomes these limitations, offering a more accurate and robust detection system. Significant differences were observed between pest-free and infested lychees. Pest-free lychees exhibited higher hardness, soluble sugars (11% higher in flesh, 7% higher in peel), vitamin C (50% higher in flesh, 2% higher in peel), polyphenols, anthocyanins, and ORAC values (26%, 9%, and 14% higher, respectively). The Vis/NIR data processed with SG+SNV+CARS yielded a partial least squares regression (PLSR) model with an R2 of 0.82, an RMSE of 0.18, and accuracy of 89.22%. The hyperspectral model, using SG+MSC+SPA, achieved an R2 of 0.69, an RMSE of 0.23, and 81.74% accuracy, while the X-ray method with support vector regression (SVR) reached an R2 of 0.69, an RMSE of 0.22, and 76.25% accuracy. Through feature-level fusion, Recursive Feature Elimination with Cross-Validation (RFECV), and dimensionality reduction using PCA, we optimized hyperparameters and developed a Random Forest model. This model achieved 92.39% accuracy in pest detection, outperforming the individual methods by 3.17%, 10.25%, and 16.14%, respectively. The multi-source fusion approach also improved the overall accuracy by 4.79%, highlighting the critical role of sensor fusion in enhancing pest detection and supporting the development of automated non-destructive systems for lychee stem borer detection.

STARBUCKS INDONESIA'S BREWING CONTROVERSY : EXPLORING THE CONSUMER BEHAVIOUR AND PRODUCT PERCEPTION IN THE SHADOW OF THE ISRAELI-PALESTINIAN CONFLICT

The main objective of this study is to examine how the role and influence of religiosity, consumer animosity and consumer ethnocentrism on the assessment of Starbucks Indonesia products that affect consumer purchasing behavior after the Israeli-Palestinian conflict broke out again in October 2023. Considering Indonesia is the biggest country with Muslim population in the world, how will it react to such a global brand, the biggest coffee corporation in the world and what drives them. The research using quantitative method with validated questionnaires derived from literature is used for data collection. Data were collected from 250 respondents Starbucks Indonesia consumers who are Muslims. This research using the Structural Equation Modeling (SEM) method with a focus on Partial Least Squares (PLS) for framework analysis. The study attempts to add value and give comprehensive details to the existing literature on consumer behavior, especially the role of religiosity, animosity, and ethnocentrism among consumers in Indonesia. The research emphasizes the importance of understanding consumer behavior in diverse cultural contexts and aims to guide brand managers in developing targeted strategies to navigate potential challenges arising from religiously motivated consumer sentiments in the aftermath of the Israeli-Palestinian conflict. The practical implications suggest that brand managers should adopt nuanced marketing strategies in conflict-affected regions, recognizing the diverse consumer sentiments and tailoring campaigns accordingly. Effective communication and CSR initiatives are crucial for maintaining brand loyalty and trust, with a focus on community engagement and interfaith projects. Additionally, ongoing research and feedback mechanisms are essential to monitor and adapt to the changing consumer sentiments in these regions.

Determination of Sugar Concentrations in Aqueous Solution Using Multivariate Predictions Based on 1H‐NMR Spectroscopy

AbstractRenewable chemicals from carbohydrate‐rich wastes, like furfural and 5‐hydroxymethylfurfural (HMF), are gaining prominence as alternatives to petroleum‐based resources. Assessing the suitability of biomass as feedstock for furfural and HMF production requires knowledge of its composition. This study focuses on developing and validating predictive models for individual sugar concentrations in hydrolysates using quantitative 1H NMR data. Utilizing partial least square (PLS) regression, the dataset includes 137 NMR spectra of multi‐component sugar standards (arabinose, fructose, galactose, glucose, mannose, maltose, sucrose, and xylose). The best‐performing model achieved an R2 of 0.987–0.999 and RMSECV of 0.37–1.56 mM and is based on the non‐overlapping area of the NMR spectrum. Real‐world samples were used for validation, resulting in predicted sugar concentrations with a mean standard deviation of 0.5 mM. This high accuracy and streamlined analysis process make these models practical for quantifying large sample sets, showcasing the reliability and accessibility of extracting statistical information from H‐NMR data.

Prediction of soil organic carbon and total nitrogen affected by mine using Vis-NIR spectroscopy coupled with machine learning algorithms in calcareous soils.

The utilization of visible-near infrared (Vis-NIR) spectroscopy presents a nondestructive, fast, reliable and cost-effective approach to predicting total nitrogen (TN) and organic carbon (OC) levels. This study employed a combination of Vis-NIR spectroscopy, partial least-squares regression (PLSR), and support vector machine (SVM) models to investigate the effects of mining on TN and OC stocks in both the topsoil (0-10 cm) and subsoil (10-40 cm). 105 soil samples were collected from agricultural areas near an iron mine, polluted, moderately-polluted, and non-polluted sites. Results indicated that soils at the non-polluted site had the highest of soil OC stocks (7.5 kg m-2) and total nitrogen (2.5 kg m-2), followed by the moderately-polluted site. Furthermore, it was observed that soils from the polluted site displayed the highest spectral reflectance. The spectral bands in the range of 500-700 nm showed the strongest correlation with soil organic carbon content. Notably, the SVM method utilizing Vis-NIR spectroscopy provided superior predictions for both subsoil and topsoil organic carbon and total nitrogen compared to the PLSR methods. Additionally, SVM demonstrated better performance in predicting topsoil soil organic carbon (R2 = 0.87, RMSE = 0.13%, and RPD = 2.8) and total nitrogen (R2 = 0.91, RMSE = 0.13%, and RPD = 2.4) compared to the subsoil, owing to the larger OM content in the topsoils.

The Impact of Store Atmosphere, Service Quality, and Social Media Marketing on Purchase Decisions: Insight from the Coffee Shop Market (Case Study at Kedai Kopi Distrik Metropolitan Surabaya)

This research aims to identify the influence of store atmosphere, service quality, and social media marketing on purchasing decisions at the Surabaya Metropolitan District Coffee Shop. Using quantitative methods with non-probability sampling techniques through purposive sampling, this research involved 115 respondents aged over 17 years who had bought and visited shops and had social media. Data was collected via Google Form and analyzed using Partial Least Square (PLS). The research results show that store atmosphere, service quality, and social media marketing have a positive influence on purchasing decisions. Consumers consider the design at Kedai Distrik Metropolitan to be quite unique by combining Vintage Chinatown-Sumatra themes. With a unique place and cheap product prices, consumers are happy to visit Kedai Kopi Distrik Metropolitan Surabaya, especially among teenagers.

Comparison between consumer-oriented and laboratory benchtop near-infrared spectrometers for total soluble solids measurement in grape

Portable near-infrared (NIR) spectrometers are gaining interest as a non-destructive tool for fruit quality determination in the decade. In this study, the performance of the portable NIR spectrometer for TSS prediction of grapes was evaluated and compared to the benchtop spectrometer. A total of 105 table grapes were individually measured NIR spectra at short-wavelength (740 – 1070 nm) and long-wavelength NIR region (1000 – 2500 nm) using SCiO and NIRFlex N-500 spectrometers, respectively. Partial least square (PLS) regression analysis was performed on berry spectra from both devices afterward significant wavelengths were identified and used to develop multiple linear regression (MLR) models. The best PLS prediction model was obtained from SNV pretreating spectra for both devices and spectral data acquired from SCiO showed better prediction performance (= 0.854, SEP = 0.452°Brix) than NIRFlex N-500 spectra (= 0.667, SEP = 0.675°Brix). Low predicting ability was gained for the MLR calibration model for both device with an RPD of about 1.70. From the results, a pocket-size spectrometer has the potential to be a non-destructive sorting and screening tool in fruit industries.

The Cultural Roots of Green Stays: Understanding Touristic Accommodation Choices Through the Lens of the Theory of Planned Behavior

Based on the Theory of Planned Behavior (TPB), this study presents and tests an integrated model to investigate how individual cultural values influence tourists’ decisions when selecting sustainable accommodation. This study aims to examine the cultural impact on sustainable accommodation choices from the perspectives of tourists in three culturally distinct countries. Data were gathered from 1855 participants in Spain, Norway, and Lithuania using a questionnaire survey method. The data was analyzed using the Partial Least Squares (PLS) method, with statistical analysis based on Structural Equation Modeling (SEM). This study found that uncertainty avoidance and long-term orientation significantly influence attitude. Additionally, collectivism and power distance notably impact subjective norms, while masculinity affects perceived behavioral control. Tourists’ intentions regarding sustainable accommodation choices were determined to be influenced by attitude, subjective norms, and perceived behavioral control. These findings contribute to the theoretical discussion of sustainable purchasing by emphasizing the intricate role of individual cultural values and provide practical insights for developing marketing strategies that resonate with these values.

The Influence of Budget Planning, Bureaucratic Environment and Budget Evaluation on Budget Absorption with Management Commitment as a Moderator at the Directorate of Yogyakarta State University

This research is research with a quantitative approach carried out at the Directorate of Yogyakarta State University. This research aims to determine whether or not there is an influence of budget planning, bureaucratic environment, budget evaluation on budget absorption with management commitment as a moderator. The data collection method in this research is a questionnaire. The sampling method used was the purposive sampling method. The population that was the object of research was the financial planning and management team at the Directorate of Yogyakarta State University. The sample in this study was 100 members of the Directorate of Yogyakarta State University. Hypothesis testing was carried out with Partial Least Square (PLS) through the SmartPLS 4.0 program. Based on the research results with a significance level of 5%, the conclusions obtained are: (1) There is a positive and significant influence of Budget Planning on Budget Absorption with at statistic of 3.255 and a significant value of 0.001; (2) There is a positive and significant influence of the Bureaucratic Environment on Budget Absorption with at statistic of 2.410 and a significant value of 0.008; (3) There is a positive and significant influence of Budget Evaluation on Budget Absorption with at statistic of 1.688 and a significant value of 0.046; (4) Management Commitment strengthens the positive influence of Budget Planning on Budget Absorption with at statistic of 2.071 and a significant value of 0.019; (5) Management Commitment does not strengthen the positive influence of the Bureaucratic Environment on Budget Absorption with at statistic of 0.534 and a significant value of 0.297; (6) Management Commitment strengthens the positive influence of Budget Evaluation on Budget Absorption with at statistic of 1.685 and a significant value of 0.046.

Mediating effect of self-control on financial literacy and savings behaviour of rural women entrepreneurs

Background: Savings behaviour is crucial for any economic and investment activity. The ability of rural enterprises to save is difficult because of their ignorance of the benefits of savings, low-income levels and inability to commit personal savings, which falls to certain factors that include financial literacy (financial knowledge, financial attitude) and self-control.Aim: The article aims to examine the mediating effect of self-control on financial literacy (financial knowledge, financial attitude) and savings behaviour of rural women entrepreneurs in Mokwa Local Government, Niger State, Nigeria.Setting: The population comprised 340 rural women trained by the Department of Commerce on financial literacy applications in the 11 wards of Mokwa Local Government. A total of 175 respondents were identified using the Krejcie and Morgan sample size table.Methods: Primary quantitative data were collected using structured questionnaires and analysed using partial least square (PLS) path modelling.Results: The empirical results revealed that financial knowledge and financial attitude exerted a positive effect on the savings behaviour of rural women entrepreneurs.Conclusion: The conclusion drawn from the study is that the financial literacy of rural women entrepreneurs is empirically related to how they save.Contribution: Based on the research findings, the study recommends that government agencies supervising and coordinating the activities of entrepreneurs like SMEDAN and also financial institutions like Microfinance Banks should facilitate flexible, understandable and acceptable programmes that will enlighten rural entrepreneurs on the importance of savings for investment and the nation at large.

Detection of the Pigment Distribution of Stacked Matcha During Processing Based on Hyperspectral Imaging Technology

Color is a key indicator for evaluating the quality of tea during processing; various processing procedures can significantly affect the content of fat-soluble pigments of tea, which in turn affects the color and quality of finished tea. Therefore, there is an urgent demand for the fast, non-destructive detection of pigments of stacked tea during processing. This paper presents the use of hyperspectral imaging technology (HSI), combined with machine learning algorithms, to detect chlorophyll a, chlorophyll b, and carotenoids in stacked matcha tea during processing. Firstly, a quantitative relationship between HSI data of tea and their pigment contents was developed based on regression analysis, and the results showed that exceptional prediction performance was achieved by the partial least squares regression (PLSR) algorithm combined with the feature band algorithm of competitive adaptive reweighting (CARS), and the Rp2 values of detection models of chlorophyll a, chlorophyll b and carotenoids were 0.90465, 0.92068 and 0.62666, respectively. Then, these quantitative detection models were extended to each pixel in hyperspectral images, achieving point-by-point prediction of pigment components, so the distribution of pigments of stacked tea leaves during processing procedures was successfully visualized on the processing line in situ. By integrating a hyperspectral imaging system into the real-world environment, operators can monitor pigment levels in real time and thus dynamically adjust processing parameters based on real-time data. This study enhances pigment detection efficiency in tea processing, supports process optimization, and aids in quality control.