Partial Least Squares Method Research Articles

UNDERSTANDING THE IMPACT OF SOCIAL INFLUENCE ON ENGAGEMENT IN IOT SPORTS TECHNOLOGY: A MEDIATION ANALYSIS

The Internet of Things (IoT) is a major revolution that is transforming the world of sports and enabling athletes to improve their physical performance. The use of IoT devices for sports is based primarily on the sharing of athletes' achievements online and the interaction between them, which allows them to motivate and encourage each other to improve their physical performance. Our research analyzes the influence of family and friends (INF_SOC) on engagement (ENGAG) in the ongoing and long-term use of these IoT devices and studies the mediating effect of perceived control (CP), perceived provider credibility (CRD), and trust (CONF) in this relationship. The population is the athletes who use IoT to measure their performance. Convenience sampling was deployed to collect the data using a questionnaire. 313 respondents participated in this study. To analyze our theoretical model, we used the method of partial least squares under SmartPls. The results confirmed all the theoretical hypotheses introduced and validated the mediating effects. They allowed us to generate interesting recommendations for IoT providers and users to reinforce engagement in the use of the Internet of Things for sports.

Спектрофотометрическое определение некоторых β-лактамных антибиотиков в их бинарных смесях с использованием метода проекций на латентные структуры

The method has been proposed for the spectrophotometric determination of antibiotics of the cephalosporin (cefuroxime, ceftriaxone, cefotaxime, cefazolin) and penicillin (amoxicillin) groups using chemometric data processing (method of partial least squares, PLS). The absorption spectra of 24 binary mixtures of “cephalosporin – cephalosporin” and 16 binary mixtures of “cephalosporin – amoxicillin” with various concentrations in the range of 3–27 and 5–140 μg/ml have been recorded. The optimal number of latent variables for each chemometric method (PLS-1 and PLS-2) has been selected, which ranged from 2 to 7 depending on the type of model and the nature of the antibiotics. The smallest root mean square calibration errors (RMSEC) have been found for the ceftriaxone–cefazolin system, which have been 0.09 and 0.08 for PLS-1 and PLS-2, respectively. The smallest root mean square errors of prediction (RMSEP) for the mixture “cefuroxime – amoxicillin” have been 0.07 for both methods (PLS-1 and PLS-2). It has been established that equations of linear relationships “measured-predicted” are characterized by a regression coefficient and the square of the approximation coefficient close to 1; which indicates the high quality of the models. The relative error of determination in test mixtures has been 0.4–6.2% by the PLS-1 method for “cephalosporin – cephalosporin” mixtures, and 0.07–6.0% by the PLS-2 method. At the same time, there is no significant difference in the error in determining the components of the mixture, in contrast to the “cefuroxime – amoxicillin” system, for which the error is 0.1–1.1% for the first component and 2.6–14.7% for the second component using the PLS-1 method and 0.2–1.2% for the first component and 4.3–13.3% for the second component using the PLS-2 method. Thus, it has been shown that the use of chemometric methods ensures high accuracy of the spectrophotometric determination of the studied antibiotics with overlapping absorption bands in their mixtures.

Heterogeneous hybrid governance mechanisms of information technology and contract in supply chain quality management

AbstractThis study scrutinizes the heterogeneous hybrid governance mechanisms of information technology and contact in supply chain quality management. Drawing on governance theory and features of information technology, a conceptual model is proposed. Based on the survey data obtained from 154 manufacturing firms' managers, we use the structural equation modeling method to test our conceptual model. The method of partial least squares is used to analyze the structural equation model. We find that Information transparency and contract completeness are substitutes, and information traceability and contract completeness are complements.

Classification of wheat powdery mildew based on hyperspectral: From leaves to canopy

Powdery mildew is considered to be one of the important diseases that threaten wheat production. It can cause serious economic losses and damage food security. Therefore, early detection of powdery mildew infection is essential to limit its amplification in reproduction. The purpose of this study is to use the hyperspectral image (HSI) to detect wheat powdery mildew (WPM) in an objective and accurate way. A method is proposed to classify the infection degree of WPM by combining cascade interval selection sensitive band with support vector machine (SVM). Firstly, HSI of leaf and canopy scale are obtained, and the image was segmented based on HSV (hue, saturation, value) color space and OTSU algorithm (HSV + OTSU). The spectral reflectance data were extracted after removing the background region of interest (ROI), and then the multiple scattering correction (MSC) was performed. The spectral data after MSC was called RAW, and then the first derivative (D1) and second derivative (D2) operations were performed respectively. Subsequently, the method of backward interval partial least squares (BiPLS) was used to screen out the characteristic bands of the sensitive interval. Finally, the classification models of WPM disease degree at the leaf scale and the canopy scale (RAW-BiPLS-SVM, D1-BiPLS-SVM and D2-BiPLS-SVM) were established respectively. The results showed that the spectral data after derivative operation was enhanced, and the classification effect of D1-BiPLS-SVM model had advantages at both leaf and canopy scales, in which the overall accuracy (OA) at the leaf scale was 95.2 %, and the OA at canopy scale was 74.4 %. In order to increase the difference between diseases, the canopy-scale disease severity levels were integrated. The correct rates of health and disease were 96.9 % and 99.2 %, respectively, and the OA of disease grade recombination was 87.9 %. Therefore, the method proposed in this paper can not only accurately identified healthy and infected wheat, but also realized the classification of different degrees of WPM, and provide technical support for the prevention and control of WPM.

Investigating the common pharmacophoric points of PDK1 inhibitors as anti-cancer agents using an alignment independent 3D-QSAR, molecular docking and molecular dynamic simulation

The glycolytic enzyme 3-Phosphoinositide-dependent protein kinase-1 (PDK1), a member of the serine/threonine kinase family, is a promising target for developing novel anticancer drugs due to its vital role in growth and survival of cancer cells and tumor angiogenesis. A MIF base alignment independent, Three-Dimensional Quantitative Structure-Activity Relationship (3D-QSAR) study was conducted to highlight the correlation between PDK1 inhibitors and their structural characteristics, thus indicating the most effective molecular properties of the inhibitors as anticancer agents. Using the Kennard-Stone algorithm to separate calibration and validation subsets, Partial Least-Square (PLS) method application shows excellent agreement between experimental and predicted values (R2=0.85, Q2LOO=0.77 and R2Perd=0.93), making it a reliable model for future drug design approaches. In addition, the active site of the PDK1 receptor protein was examined via Molecular Docking with both passive and active compounds. In order to study the effect of inhibitors on PDK1 structure, 10 ns Molecular Dynamics (MD) simulation of free protein and protein-ligand complex was performed and compared. Comparing the 3D-QSAR, docking and molecular simulation outcomes, the prevailing pharmacophoric use of PDK1 inhibitors as anti-cancer agents have been highlighted. The comprehensive information of this approach reveals important structural variables and their relationship with PDK1 inhibitory activity and PDK1 cavity residues which could be valuable for designing anticancer candidates.

A novel approach for determining coconut drink adulteration by means of laser light backscattering imaging

AbstractIn this work, the simulated adulteration of coconut drink by dilution with water was investigated using laser-light backscattering (LLB) imaging. The laser vision system consisted of six low power laser modules, emitting 1 mm diameter beams at wavelengths of 532, 635, 780, 808, 850 and 1,064 nm. The backscattering images were acquired by a grey scale camera with 12 bit resolution. Eight parameters were extracted to describe the backscattering profile. The methods of linear discriminant analysis (LDA) and partial least squares (PLS) regression were performed on LLB parameters for classifying and predicting dilution level of adulterated coconut drink samples. Based on the results, LLB signals responded sensitively to adulteration. LDA results showed that adulterated samples were correctly recognized with accuracies between 60 and 100%. PLS models were able to estimate the adulteration level of samples with coefficients of determination of 0.57–0.97 in validation. This result demonstrated the potential of laser-light backscattering imaging as a rapid and non-destructive optical technique for evaluation of coconut drink adulteration.

Prediction of Caffeine and Protein of Arabica Coffee Beans Using Near Infrared Spectroscopy (NIRS)

Testing the chemical components of coffee beans has so far been carried out conventionally with laboratory analysis which requires a long time and is expensive. Technological advances allow testing of chemical components to be carried out quickly and accurately using the NIRS (Near Infra Red Spectroscopy) method. This research aims to develop a prediction model for caffeine and protein in Solok Radjo coffee beans using the NIRS method. Solok Radjo coffee is a type of Arabica with specialty grade because of its very strong character, aroma and taste. A total of 30 samples with a weight of 6 g per sample were used in this study. This research uses NIRS Type FT-IR IPTEK T-1516 with a wavelength of 1000 - 2500 nm. The partial least squares (PLS) method was used to process the data with several SNV, MN, and MSC pretreatments to improve the model. The research results show that caffeine is found at wavelengths of 1456 - 1475 nm, 1937 - 1974 nm. Proteins 1455 - 1475 nm, and 1935 - 1974 nm. MSC pretreatment is able to improve PLS performance results. Caffeine calibration values are R2 = 0.996 and SEC = 0.002%, validation values R2 = 0.989, SEP = 0.002%, and RPD 11.869 while protein calibration R2 = 0.999 and SEC = 0.004%, Validation values R2 = 0.999, SEP = 0.010%, and RPD 19,943. NIRS can be used to predict the chemical components of Solok Radjo coffee non-destructively using the PLS method. Key work: Caffeine, NIRS, PLS, predict, protein

Analysis of The Influence of Service Quality on Customer Satisfaction and Its Impact on Reuse Intention of Mobile Banking Payment in E-Commerce Transactions

This study intends to determine how consumer satisfaction with mobile banking services affects repurchase interest. Customers of Jabodetabek who utilize mobile banking make up the study's sample. Purposive sampling was used in this investigation, and a non-probability sample of 100 participants was used. Questionnaires will be used by researchers to collect data. The structural equation method of partial least squares, or SEM-PLS, is employed in this study. Higher-order formative and lower-order reflecting constructs were utilised in this study. According to the study's findings, the level of mobile banking services directly affects repurchase interest. The effectiveness of mobile banking services directly affects how satisfied customers are. Repurchase interest is directly influenced by customer satisfaction. Customer satisfaction has an indirect impact on repurchase interest through the quality of mobile banking services. Customer interest in repurchasing mobile applications for e-commerce payment services is influenced by service quality. Customers will be satisfied with good service quality in mobile applications, particularly in e-commerce payment services. It has been demonstrated that the positive indirect effect of mobile banking services is moderated by customers' satisfaction with e-commerce payment services provided by mobile banking.

Pengaruh Bauran Pemasaran (Marketing Mix) Terhadap Keputusan Pembelian Pisang Sunpride (Studi Kasus: Supermarket Superindo Tropodo)

This research was conducted with the aim of describing the characteristics of cavendish sunpride banana consumers at Superindo Tropodo, to analyze the marketing mix which consists of product, price, place, promotion, people, process, physical evidence influences consumer decisions in purchasing cavendish sunpride bananas at Superindo Tropodo, to analyze which marketing mix variables have the most influence on consumer decisions in purchasing cavendish sunpride bananas at Superindo Tropodo. The analysis technique and hypothesis testing in this study used Partial Least Square (PLS) with the help of Warp PLS software. The use of the PLS method as an analytical technique and hypothesis testing has advantages such as the sample size does not have to be large and can be used to develop a theory. The results of this study are the characteristics of respondents based on the gender of the majority of respondents with female. Characteristics of respondents based on the age of the majority of respondents aged 20-30 years. The characteristics of respondents based on their last education were the majority of Bachelor / Equivalent education level. Characteristics of respondents based on the work of the majority of respondents whose jobs are the most are self-employed. Characteristics of respondents based on the income of the majority of respondents whose income is the largest, namely income above Rp. 4,000,000. Product variables, price, place, promotion, people, process, physical evidence have a positive and significant effect on purchasing decisions. The physical evidence variable that has the most high influence on purchasing decisions.

Quantitative Analysis of Biodiesel Adulterants Using Raman Spectroscopy Combined with Synergy Interval Partial Least Squares (siPLS) Algorithms

Biodiesel has emerged as an alternative to traditional fuels with the aim of reducing the impact on the environment. It is produced by the esterification of oleaginous seeds, animal fats, etc., with short-chain alcohols in an alkaline solution, which is one of the most commonly used methods. This increases the oxygen content (from the fatty acids) and promotes the fuel to burn faster and more efficiently. The accurate quantification of biodiesel is of paramount importance to the fuel market due to the possibility of adulteration, which can result in economic losses, engine performance issues and environmental concerns related to corrosion. In response to achieving this goal, in this work, synergy interval partial least squares (siPLS) algorithms in combination with Raman spectroscopy are used for the quantification of the biodiesel content. Different pretreatment methods are discussed to eliminate a large amount of redundant information of the original spectrum. The siPLS technique for extracting feature variables is then used to optimize the input variables after pretreatment, in order to enhance the predictive performance of the calibration model. Finally, the D1-MSC-siPLS calibration model is constructed based on the preprocessed spectra, the selected input variables and the optimized model parameters. Compared with the feature variable selection methods of interval partial least squares (iPLS) and backward interval partial least squares (biPLS), results elucidate that the D1-MSC-siPLS calibration model is superior to the D1-MSC-biPLS and the D1-MSC-iPLS in the quantitative analysis of adulterated biodiesel. The D1-MSC-siPLS calibration model demonstrates better predictive performance compared to the full spectrum PLS model, with the optimal determination coefficient of prediction (R2P) being 0.9899; the mean relative error of prediction (MREP) decreased from 9.51% to 6.31% and the root--mean-squared error of prediction (RMSEP) decreased from 0.1912% (v/v) to 0.1367% (v/v), respectively. The above results indicate that Raman spectroscopy combined with the D1-MSC-siPLS calibration model is a feasible method for the quantitative analysis of biodiesel in adulterated hybrid fuels.

Faktor Faktor yang Memengaruhi Minat Beli Ulang di Digerati House Bekasi

There are many factors that can influence consumer attitudes when making a purchase of a product or service, and when consumers are satisfied with the product or services purchased, it can imply on the intensity of making re-purchase in the future. Based on observations and reviews, there are 3 (three) most dominant factors of customer satisfaction that can affect interest in re-purchase and supported by increased sales in the coffee menu category. The aim of this study is to know the great influence of servicescape, campaign messages, and quality of coffee products on re-buy interest in Digerati House Bekasi. The research method used is quantitative using the method of analysis PLS (Partial Least Square), the sample in this study amounted to 100 respondents with the criterion of having made purchases more than 2 (two) times in Digarati House bekasi. The results of this study were that servicescape had a positive and significant influence on re-purchase intention with Tstatistic > Ttable (4,186 > 1,984) and p-value < 0,05, campaign messages had a significant and positive influence on re-purchase intention with TStatistic> Ttable (2,291 > 1, 984) and the p-value was < 0,05 and the quality of coffee products had a positive and significant influecne on re-purchase intention with Tstatistic > Ttabel (2,175 > 1, 984) and p-value < 0, 05.

Rapid screening and multicomponent quantifications of active components of oral syrup over-the-counter medications by Raman and UV–visible spectroscopy and multivariate regression analysis

Over-the-counter medications (OTCMs) are frequently recommended as a first-line treatment for common ailments, diseases, and illnesses. Oral liquid dosage forms are advantageous for rapid absorption with no dissolution time and are easier for pediatric and geriatric consumers to swallow. The production of these medicines by pharmaceutical industry makes them readily available to the public. Although the US Food and Drug Administration (FDA) provides strict guidelines to drug manufacturers of these products; the risk of counterfeiting is a global issue. This can lead to several adverse effects and health issues. Here, we report a fast screening and quality assurance method using Raman and UV–visible spectroscopy combined with Principal Component Analysis (PCA) and Partial-Least-Square (PLS) regression of commonly used OTCM oral syrups. PLS regressions of UV–visible absorption spectra were used for multicomponent quantifications of the active component (acetaminophen, guaifenesin, dextromethorphan HBr, and phenylephrine HCl) concentrations of OTMCs in flavored (sugar or sugar-free) oral syrups. Raman and UV–visible spectral responses varied based on the type and concentration of the active component analyzed. PCA of the spectral data provided pattern recognition of the oral syrup OTCM. The developed PLS method demonstrated good linearity with an R2 > 0.9784 and high sensitivity with a low detection limit of 0.02 mg/mL for acetaminophen and guaifenesin. Moreover, the simultaneous quantification of concentrations of all active components by the described method yielded good accuracies ranging from 88 to 94%. This study provides an example of the benefits of the combined use of Raman and UV–vis spectral profiling, PCA, and PLS regression for the quality analysis of oral syrups OTCM providing multicomponent quantification of active components with no need for sample extraction. The reported method can be easily adapted and scaled for online detection analysis used in the drug manufacturing industry, both in-situ and field analysis, and for the quality control of syrups OTCM by regulatory agencies and quality control officers.

Organic management increases beneficial microorganisms and promotes the stability of microecological networks in tea plantation soil.

Organic agriculture is highly regarded by people for its commitment to health, ecology, care, and fairness. The soil microbial community responds quickly to environmental changes and is a good indicator for evaluating soil microecology. Therefore, from the perspective of soil microbial communities, elucidating the impact of organic management on soil microecology in tea plantations has great significance for improving local tea plantation systems. The study collected bulk soil from organic management (OM) and conventional management (CM) tea plantations in Pu'er City, a major tea-producing area in China, and analyzed their species diversity, structural composition, and co-occurrence networks using metagenomics technology. Compared with CM, the diversity index (Shannon) and evenness index (Heip) of soil fungi increased by 7.38% and 84.2% in OM tea plantations, respectively. The relative abundance of microorganisms related to the nitrogen cycle increased. Specifically, there was a significant increase in Rhodobiales, a 2-fold increase in Nitrospirae, and approximately 1.95 and 2.03 times increases in unclassified genera within Betaproteobacteria and Deltaproteobacteria, respectively. The relative abundance of plant residue degradation species, Gemmatimonadetes, Ascomycota, and Basidiomycota, increased by 2.8, 1, and 1.4 times, respectively. The OM was conducive to the establishment of collaborative relationships among bacterial species and increased the diversity and complexity of species relationships in fungal communities. The network stability of soil ecosystems was promoted. The organic tea plantations' keystone taxa contained mycorrhizal fungi (Pezoloma_ericae, Rhizophagus_irregularis, Rhizophagus_clarus), as well as species involved in soil nitrogen metabolism (Acidobacteria_bacterium, Acidobacteriia_bacterium_AA117, Sphingomonas_sp._URHD0007, Enhydrobacter_aerosaccus), pathogen (Erysiphe_pulchra), and parasites (Paramycosporidium saccamoeba). The partial least squares method (PLS-SEM) indicated that OM affected N-NH negatively, increasing the abundance of fungi, and thereby positively affecting the Shannon index. In brief, reasonable organic management can improve the diversity of soil microorganisms, increase the relative abundance of beneficial bacteria in tea plantation soil, and promote the stability of the soil microbial ecological network.

A Partial Least Squares Structural Equation Modelling Analysis of the Primary Barriers to Sustainable Construction in Iran

This paper outlines the obstacles to sustainable construction growth in Iran and thereafter examines the effect and relation between these barriers and the direction of sustainable construction growth as one of the essential objectives for achieving sustainable cities and infrastructure. The study is applied for research purposes that are based on descriptive survey data gathering and correlational data analysis techniques. The statistical population for this study consists of 120 construction-related engineers and university professors who were assessed on a five-point Likert scale. Using SmartPLS software version 4, the responses to the questionnaire were examined. The Kolmogorov–Smirnov assessment was utilized to evaluate the normalcy of the variables, as this assessment is typically employed for this purpose. For data analysis, the PLS (partial least squares) method was used, while SEM (structural equation modeling) methods have been used to assess the study hypotheses. Cronbach’s alpha and the composite reliability coefficient (CR) were applied to determine the instrument’s viability, and the results show that the coefficient connected to all variables is above 7.0, which is an acceptable value. The AVE (average variance extracted) was also used to evaluate the questionnaire’s validity, which was greater than 0.4 and deemed acceptable for coefficients of significance (T-values), coefficient of predictive power (Q2), and coefficient of determination (R2). The obtained results support and confirm all research hypotheses, including that the identified obstacles directly affect the performance of sustainable construction. According to the results of the Friedman test, the legal restrictions variable (CL) is the most significant obstacle to sustainable construction in Iran, with a rank of 4.24. The indicators of political limits (CP) and social and cultural constraints (CSC) came in at second and third, respectively. The results could help government officials make better decisions about where to focus their attention and how to distribute scarce resources.

Impacts of online-community-based collaborative learning on project scope appropriateness and stakeholder management

ABSTRACT Most corporate projects suffer from the problem of scope creep, despite of the methodologies available to cope with this issue. Organisations have utilised computer-supported collaborative learning (CSCL) approaches to enhance employees’ project management (PM) training. Most prior studies on CSCL and PM focus on the application of CSCL for PM training in the contexts of educational institutions. It is thus uncertain that their findings are applicable to the real-world PM situations. Additionally, those studies did not specifically investigate the effects of key PM-related CSCL features on PM learning. Therefore, this study identifies and empirically investigate the effects of the key CSCL features, namely goal orientation, individual accountability, and structured interaction, on the level of scope completeness and the effectiveness of stakeholder management (SHM) in a context in which an online case-based collaborative learning system was implemented for PM learning purposes. Data collected from 264 PM professionals were analyzed using the method of partial least squares to examine the proposed research model. The results show that all the three key CSCL features have a significant direct/indirect influence on the level of scope completeness and the SHM effectiveness. Implications for theory and practice, research limitations, and future research directions are discussed correspondingly.

Faktor Penentu Minat Investasi di Pasar Modal Syariah: Studi Pada Generasi Z Kota Malang

The Islamic capital market has enormous potential to encourage sustainable economic and financial development. Investors in the Islamic capital market are still very small when compared to the total population of Indonesia. Public interest in investing is quite low, but they do not have sufficient knowledge to assess the risks and returns of the Islamic capital market. Thus, the purpose of this study is to examine the factors that influence Gen Z's interest in investing in the Islamic capital market, risk perception, social media influencers, and disposable income. This research utilizes a quantitative methodology focused on the generation Z population in Malang City. A total of 200 research samples were collected using the incidental sampling method. The collected information was analyzed using the PLS (Partial Least Squares) method. The results of data analysis show that interest in investing in Islamic capital markets is influenced by Islamic capital market literacy, risk perception, and social media influencers. However, the results of data analysis show that disposable income does not affect investment interest. This study has practical implications for increasing Gen Z participation in Malang City in investing in the Islamic capital market. Educational efforts aimed at improving Islamic capital market literacy, effective risk management, and utilizing the positive influence of social media influencers can motivate Gen Z's interest in investing in the Islamic capital market.

Assessing Fermentation Broth Quality of Pineapple Vinegar Production with a Near-Infrared Fiber-Optic Probe Coupled with Stability Competitive Adaptive Reweighted Sampling.

In this study, the performance of a near-infrared (NIR) fiber-optic probe coupled with stability competitive adaptive reweighted sampling (SCARS) was investigated for the analysis of acetic acid, ethanol, total soluble solids, caffeic acid, gallic acid, and tannic acid in the broth of pineapple vinegar during fermentation. The NIR spectra of the broth samples in the region of 11,536-3956 cm-1 were collected during vinegar fermentation promoted by Acetobacter aceti. This continuous biological process led to changes in the concentrations of all analytes studied. SCARS provided optimized and stabilized NIR spectral variables for the construction of a partial least squares (PLS) model for each analyte using a small number of optimal variables (under 88 variables). The SCARS-PLS model outperformed the conventional PLS model, and achieved excellent accuracy in accordance with ISO 12099:2017 for the four prediction models of acetic acid, ethanol, caffeic acid, and gallic acid, with root-mean-square error of prediction values of 0.137%, 0.178%, 0.637 μg/mL and 0.640 μg/mL, respectively. In contrast, only an acetic acid content prediction model constructed via the conventional PLS method and using the whole spectral region (949 variables) could pass with acceptable accuracy. These results indicate that the NIR optical probe coupled with SCARS is an appropriate method for the continuous monitoring of multianalytes during vinegar fermentation, particularly acetic acid and ethanol contents, which are indicators of the finished fermentation of pineapple vinegar.

A Simple UV Spectrophotometric Method Based on Continuous Wavelet Transform and Multivariate Calibration Model for the Concurrent Analysis of Three Water-Soluble Vitamins in Fertility Supplements.

Owing to the presence of overlapping spectra in pharmaceutical components, classical spectrophotometry is hard for concurrent determination. The advance of chemometrics along with UV-Vis spectrophotometry has contributed to solving this problem. In this study, a fast, easy, precise, accurate, low-cost, and eco-friendly spectrophotometric technique was introduced and validated for the simultaneous analysis of vitamin B6, vitamin B12, and vitamin C in fertility supplements for men and women using continuous wavelet transform (CWT) and partial least squares (PLS) techniques without using time-consuming extraction process and organic solvents. In the CWT method, the zero-crossing technique was applied to obtain the optimum points for plotting calibration curves for each component. The validation of both methods was evaluated by analyzing several mixtures with different concentrations. The efficiency of the proposed methods was also surveyed on commercial capsules. Wavelet families, including Symlet (sym2) at 230, Biorthogonal (bior1.3) at 378 nm, and Daubechies (db2) at 261, were considered for vitamins B6, B12, and C, respectively. The linear range was found to be 8-20, 8-20, and 10-25 μg/mL with the coefficient of determination (R2) equal to 0.9982, 0.9978, and 0.9701 for B6, B12, and C, respectively. Low limit of detection (LOD) (<0.09 μg/mL) and limit of quantification (LOQ) <0.9 μg/mL were achieved. The mean recovery values in synthetic mixtures were from 98.38 to 98.89% and from 99.83 to 99.99%, where root-mean-square error (RMSE) of not more than 0.4 and 0.05 using the CWT and PLS methods, respectively. The obtained results from the commercial capsules, applying the suggested techniques, were compared to those yielded by the high-performance liquid chromatography (HPLC) method using the analysis of variance (ANOVA) test. According to the results, there are no significant differences, and they were in good agreement. According to all the mentioned cases, the proposed approaches can replace the time-consuming and costly HPLC method in quality control laboratories. Green spectrophotometry coupling chemometrics methods were proposed. Simultaneous determination of three water-soluble vitamins in fertility supplements was done using these approaches. Rapidity, simplicity, low cost, and accuracy are the benefits of the proposed methods. A HPLC technique was used as a reference method to compare with the chemometrics methods.

Consumer Behavior and Perceive to Buy from Ready-to-Eat Meals Meat-Based Products: a Case Study of Rendang Tastes Processed Meats in Indonesia

Consumer behavior plays a pivotal role in the success of marketing strategies as it determines how consumers perceive and interact with products in the market. This study examines consumer behavior and perceptions in buying rendang-flavored processed meat as ready-to-eat (RTE) products in Indonesia. The research focuses on identifying the factors influencing consumers to purchase rendang taste processed meats, understanding their perceptions of the product, and assessing the impact of marketing strategies on their purchasing decisions. Data were collected using a survey with 211 final respondents, a quantitative method of Partial Least Squares - Structural Equation Modelling (PLS-SEM) was used to analyze the data. The findings of this study offer valuable insights into consumer behavior and perception towards rendang-flavored processed meats in Indonesia. The research is anticipated to have intellectual merit by exploring consumer behavior towards RTE products specific to Indonesian consumers. The practical implications of this study can aid companies in developing effective marketing strategies that capitalize on consumer preferences and ultimately increase sales and profitability.

Convolutional neural network allows amylose content prediction in yam (Dioscorea alata L.) flour using near infrared spectroscopy.

Yam (Dioscorea alata L.) is the staple food of many populations in the intertropical zone, where it is grown. The lack of phenotyping methods for tuber quality has hindered the adoption of new genotypes from breeding programs. Recently, near-infrared spectroscopy (NIRS) has been used as a reliable tool to characterize the chemical composition of the yam tuber. However, it failed to predict the amylose content, although this trait is strongly involved in the quality of the product. This study used NIRS to predict the amylose content from 186 yam flour samples. Two calibration methods were developed and validated on an independent dataset: partial least squares (PLS) and convolutional neural networks (CNN). To evaluate final model performances, the coefficient of determination (R2), the root mean square error (RMSE), and the ratio of performance to deviation (RPD) were calculated using predictions on an independent validation dataset. The tested models showed contrasting performances (i.e., R2 of 0.72 and 0.89, RMSE of 1.33 and 0.81, RPD of 2.13 and 3.49 respectively, for the PLS and the CNN model). According to the quality standard for NIRS model prediction used in food science, the PLS method proved unsuccessful (RPD < 3 and R2 < 0.8) for predicting amylose content from yam flour but the CNN model proved to be reliable and efficient method. With the application of deep learning methods, this study established the proof of concept that amylose content, a key driver of yam textural quality and acceptance, can be predicted accurately using NIRS as a high throughput phenotyping method. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.