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A large data set of over 30 thousand organic compounds containing carbon, nitrogen, oxygen, fluorine, and hydrogen was collected, and the density of each compound was predicted by 1D descriptors derived from its molecular formula and 2D descriptors derived from its constitutional structural features. The 2D structural features are composed of Benson's groups, corrected groups, and 2D structural features of the whole molecular structures. All the descriptors were extracted by an in-house program in Java with a function to ensure that each atom (or bond) of molecules is represented by Benson's groups once for atom-based (or bond-based) descriptors. Partial least square (PLS) and random forest (RF) methods were used separately to build models to predict the density. Further, the variable selection of descriptors was performed by variable importance of RF. For partial least square, the combination of the models constructed by descriptors based on the atoms and the bonds achieved the best results in this paper: for the cross-validation of the training set, the Pearson correlation coefficient (R) = 0.9270, mean absolute error (MAE) = 0.0270 g·cm−3, and root mean squared error (RMSE) = 0.0426 g·cm−3; for the prediction of the test set, R = 0.9454, MAE = 0.0263 g·cm−3, and RMSE = 0.0375 g·cm−3. [ABSTRACT FROM AUTHOR]

Seeking sustainable development, companies voluntarily implement Corporate Social Responsibility (CSR) through the Triple Bottom Line (TBL) approach, considering economic, social and environmental aspects of interest to global society. Searching for sustainable development, the Circular Economy (CE) emerged as a new philosophy of life to meet the new challenges in society. This research links CSR and CE but considering jointly the Institutional and Stakeholder Theories to delimit the sustainable development framework from the EC approach driven by CSR. According to this framework, it was tested whether the CSR practices of companies positively and significantly impacts on a business orientation towards CE, through two models (Model A and Model B) each of them analyzed with two independent samples of companies in Spain. Both samples are composed of Small and Medium Enterprises (SMEs) at two different stages of CSR adoption. The hypotheses were analyzed through a structural equation modeling‐fuzzy‐set qualitative comparative analysis (SEM‐fsQCA). The result of the SEM supports the hypothesis in both models. So, CSR practices are driving companies towards CE, incorporating propositions on stakeholders' value creation. Moreover, an fsQCA revealed combinations of relationships that ensure the consistency of obtained results, generating five configurations based on two models defined. Main contribution of this work is CSR practices of companies and their orientation towards CE can be linked. This encourages to deep in analysis about the relations between CE and CSR, and of the specific CSR practices that should be enhanced for their beneficial effect on the implementation of CE. [ABSTRACT FROM AUTHOR]

Nowadays, team learning and blended learning are becoming increasingly important to today's university students. Knowledge sharing (KS), as an important factor that is the bridge between learning and cooperation and communication, can affect the effectiveness of students' learning. However, current research has paid less attention to knowledge-sharing behavior (KSB) under blended team-based learning (BTBL). This paper explored the factors that influence KSB in BTBL, 322 samples were analyzed using the partial least squares structural equation model (PLS-SEM). The results showed that team atmosphere, reciprocity, sharing attitude, and knowledge self-efficacy (KSE) had significant positive effects on KSB. Meanwhile, team learning ability (TLA) significantly negatively moderated sharing attitude and significantly positively moderated KSE. [ABSTRACT FROM AUTHOR]

In the university context, the self-regulated learning processes play an instrumental role as a strategy that allows students to achieve a greater academic self-concept due to their awareness of their academic development and their active participation in this process. This study involved 276 students of the Primary Education Degree of the University of Jaén, (Spain), participated, of which 213 (77.2%) were female and 63 (22.8%) were male, with the average age of 19.97 years (± 2.834). The Inventory of Self-Regulation of Learning Processes (IPAA) and the Academic Self-Concept Scale (EAA) were applied. The objective was to provide enough evidence on the complementarity of self-regulation learning processes with academic self-concept. This study introduces the combined used of structural equation modeling (PLS-SEM) and necessary conditions analysis (NCA) to investigate the proposed hypotheses. The results of the model found high coefficients of determination Execution [(Q2 = 0.261); (R² =0.547)]; Assessment [(Q2 = 0.249); (R² =0.393)]; and Academic self-concept [(Q2 = 0.074); (R²=0.329)]. The results have corroborated the composite use of PLS-SEM and NCA allow to identify some of the essential factors of academic self-concept according to the rationale of need. The combination of both perspectives supports the theoretical underpinning of this topic. [ABSTRACT FROM AUTHOR]

Fluorescence spectroscopy is an attractive candidate for analyzing samples of nylon. Impurities within the polymers formed during the synthesis and processing of nylons give rise to the observed fluorescence, allowing for nylons to be analyzed based on their impurities. Nylons from the same source are expected to display similar fluorescence profiles, and nylons with different fluorescence are expected to be from different sources. This paper investigates an important case where different nylons displayed similar fluorescence, preventing easy discrimination. Samples of Nylon 6 and Nylon 6/12 had visually indistinguishable excitation–emission matrices (EEM), excitation spectra, fluorescence spectra, and synchronous fluorescence spectra at larger Δλ. By collecting synchronous fluorescence spectra at smaller Δλ, additional features in the fluorescence profiles were identified that allowed for some discrimination between the two nylons. Combining the EEM and synchronous fluorescence data with chemometric algorithms provided a clearer differentiation between the two nylons. parallel factor analysis, principal component analysis, and common dimension partial least squares (ComDim-PLS) showed two distinct clusters in the data, with ComDim-PLS providing the greatest distinction between the clusters. The loadings revealed the variables of interest to the ComDim-PLS were the 400 nm and 335 nm bands for all synchronous fluorescence spectra, the 460 nm and 310 nm bands for the Δλ = 20 nm and Δλ = 30 nm synchronous fluorescence spectra, and the 440 nm band for the Δλ = 20 nm synchronous fluorescence spectra. The linear discriminant analysis performed with the PLS data yielded a classification accuracy of 95% with the EEM data and 100% with the synchronous fluorescence data, displaying the power of this technique to differentiate two different nylons with visually indistinguishable fluorescence spectra. [ABSTRACT FROM AUTHOR]

Natural indigo, the most widely produced and utilized natural dye, encounters quality challenges due to the lack of standardization in the natural dye industry. Rapid determination of natural indigo dye contents before the dyeing process appears extremely important. In this study, two prediction models for different concentrations were established using partial least squares in conjunction with near-infrared analysis quantitatively to analyze the natural indigo dye content. A total of 228 indigo samples were collected from 14 different dyestuffs across various regions, with concentrations ranging from 100 to 1000 mg/L and 10 to 100 mg/L, respectively. The spectral pre-processing methods of multiplicative scatter correction plus first-order derivative and Savitzky–Golay smoothing plus band normalization plus first-order derivative were selected to enhance the model prediction accuracy. The optimized model exhibited excellent prediction accuracy. Within the concentration range of 100–1000 mg/L, the model has an R 2 value of 0.9994, and a root mean square error of prediction value of 6.36 mg/L. In the concentration range of 10–100 mg/L, the model returned an R 2 value of 0.9907, and a root mean square error of prediction value of 2.80 mg/L. The model's detection limit stands at 49.2 mg/L. The results demonstrated that the near-infrared models developed in this study can be used rapidly and accurately for the quantitative determination of natural indigo dyes. [ABSTRACT FROM AUTHOR]

Due to the issues present in the urban waste management system in the metropolis of Tehran and the inherent dynamics of the waste management system, it is necessary to use a system dynamic model to examine it. In this study, a system dynamic model was first designed and validated for optimal solid waste management in the Tehran metropolis using the landfill reduction approach based on expert judgment. The statistical population included 45 municipal waste management experts, and the opinions were gathered using a questionnaire, the validity of which was confirmed by experts and the reliability confirmed by Cronbach's alpha. Data analysis was then conducted using heuristic factor analysis and structural equation modeling through the least squares method. Based on the findings, the system dynamic model consists of six distinctive components: technological and managerial (intra-municipality), population, education level, cultivation, citizenship, and economy (external factors). The variance values for each of the above-mentioned components were 0.819, 0.574, 0.990, 1.000, 0.983, and 0.978, respectively. According to the results, these variables could explain a total of 0.683% of the variability associated with waste landfills in Tehran, indicating an appropriate model fit. Concerning the derived effect size, the technological component (F2=90.567) had the highest effect on waste landfill reduction in Tehran. All model paths had t values greater than 1.96, confirming all research hypotheses. The obtained results indicated that the developed dynamic model has a good fit and may be utilized in planning for landfill reduction in Tehran. [ABSTRACT FROM AUTHOR]

A novel, simple, rapid, and non-expensive analytical method based on square wave voltammogram at Pt-microelectrode coupled with partial least square multivariate calibration was used for the simultaneous quantitation of thymol (THY) and carvacrol (CAR) in thyme and oregano essential oils. Results demonstrated that the multivariate calibration method successfully exploited the first-order advantage, rendering highly satisfactory quantitative figures (average recoveries not statistically different than 100%). Moreover, the results agree well with those obtained from the official analytical method. Last, the method's environmental sustainability was asserted using the AGREE metric, highlighting its eco-friendly nature. More importantly, the proposed analytical procedure does not require previous sample preparation or electrode surface modification. The results underscore the suitability of the method for determining THY and CAR in essential oils at low concentrations (LOD ~ 7.6 µM) with REP% below 5.6%, meeting the requirements of the green analytical chemistry. [ABSTRACT FROM AUTHOR]

Near-infrared spectroscopy has become an important methodology for rapid and non-destructive detection in food and agricultural fields. However, the accuracy of quantitative analysis was seriously restricted by the severe overlap of spectra and the high cost of standard samples. In order to reduce the impact of these problems especially that of small sample size problem, a novel method named weighted clustering ensemble partial least squares (WCE-PLS) is proposed for the protein content analysis of corn. Firstly, the clustering and sampling strategy is introduced in the calibration sets of corn to create different subsets for generating sub-models. Then, root mean square errors of cross-validation (RMSECV) in those sub-models as the crucial criterion are computed for model optimization. Finally, in integrating step, two Gaussian weighted functions used to determine the weights of sub-models are defined. The validation performance of the proposed method is tested with the near infrared spectral data sets of corn and compared with single PLS, bagging PLS, boosting PLS, and data augmentation (DA) PLS. To further demonstrate the effectiveness of the method, another data set of soil was used for supplementary verification. Results of the prediction sets indicated that the RMSEP values of the WCE-PLS are obviously smaller than that of boosting PLS. And the RMSEP of WCE-PLS and bagging PLS is relatively small in most cases. Furthermore, the correlation coefficients between predicted value and chemical value are respectively 0.96587 and 0.90849 for two data sets, which computed by the WCE-PLS is obviously higher than that computed by the other four methods. And the t test also showed the WCE-PLS has smaller t values and larger p values. [ABSTRACT FROM AUTHOR]

Pyrus spinosa (PS), also known as wild pear, is an indigenous species to the Mediterranean basin. It has attracted interest for its potential use in the food and beverage industries due to its antioxidant properties. This research aims to develop an antioxidant-rich PS fruit extract by optimizing the extraction parameters. More specifically, through a comprehensive study of the extraction parameters (including extraction duration, temperature, and ethanol concentration), the optimal conditions were determined that can achieve the highest antioxidant properties. High-Performance Liquid Chromatography (HPLC) was employed for the identification and quantitation of the polyphenolic compounds present in PS fruits. The optimized extraction conditions significantly enhanced the antioxidant properties of the extract, with the total polyphenol content increasing by up to 345% (reaching a value of 50.97 mg gallic acid equivalents per g of dry weight in the optimum sample), total flavonoid content by up to 273%, and ascorbic acid content by up to 653%. Furthermore, the antioxidant capacity of the extracts increased by 2356% (by FRAP method) and 1622% (by the DPPH method), with varying extraction parameters. These findings highlight the importance and the effectiveness of optimizing the extraction parameters in order to increase the antioxidant properties of PS fruit extract. Based on these findings, PS extracts can be further utilized in the food and beverage industries to develop new products that will benefit from the antioxidant properties. [ABSTRACT FROM AUTHOR]

Partial least squares regression (PLSR) and support vector regression (SVR) models were optimized for the quantification of U(VI) (10-320 g L-1) and HNO3 (0.6-6 M) by Raman spectroscopy with optimized calibration sets chosen by optimal design of experiments. The designed approach effectively minimized the number of samples in the calibration set for PLSR and SVR by selecting sample concentrations with a quadratic process model, despite complex confounding and covarying spectral features in the spectra. The top PLS2 model resulted in percent root mean square errors of prediction for U(VI), HNO3, and NO3 - of 3.7%, 3.6%, and 2.9%, respectively. PLS1models performed similarly despitemodeling an analyte with a majority linear response (i.e., uranyl symmetric stretch) and another with more covarying vibrational modes (i.e., HNO3). Partial least squares (PLS) model loadings and regression coefficients were evaluated to better understand the relationship between weaker Raman bands and covarying spectral features. Support vector machine models outperformed PLS1 models, resulting in percent root mean square error of prediction values for U(VI) and HNO3 of 1.5% and 3.1%, respectively. The optimal nonlinear SVR model was trained using a similar number of samples (11) compared with the PLSR model, even though PLS is a linear modeling approach. The generic D-optimal design presented in this work provides a robust statistical framework for selecting training set samples in disparate two-factor systems. This approach reinforces Raman spectroscopy for the quantification of species relevant to the nuclear fuel cycle and provides a robust chemometric modeling approach to bolster online monitoring in challenging process environments. [ABSTRACT FROM AUTHOR]

To on-line monitor the process variables of different components in solid state fermentation of wheat bran by spectral technology, the contents of protein, water, total phenol and crude fiber in 61 wheat bran solid fermented feed were measured based on Chinese national standard metheds. The near infrared spectrum and Fourier transform infrared spectrometer of the samples was collected and analyzed. The spectra was performed with 9 different preprocessing methods, such as standard normal variate transformation (SNV), multiplicative scatter correction (MSC) and smoothing (SG). NIR and FT-IR quantitative models of feed nutrients were established and compared by partial least squares (PLS). The results showed that the Rc² and Rp² were both great than 0.8, root mean square error of cross validation (RMSECV) was less than 2.0, root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were less than 1.0, respectively. NIR and FT-IR quantitative analysis models have good stability. The process parameters of solid-state fermentation of wheat bran were rapidly monitored by NIR and FT-IR is feasible. [ABSTRACT FROM AUTHOR]

Background: In China, community rehabilitation workers are facing a growing challenge related to heavy occupational stress, which is having an impact on employment turnover. Previous studies have explored the effect of the public service motivation of workers in "helping" jobs on occupational stress or turnover intention, but there is a lack of clarification of the impact of altruism on turnover intention in the case of complex pathways involving various factors. Methods: A stratified sampling method was used, and a total of 82 community rehabilitation workers who assist disabled people from 34 community health centres in Jiangmen city were included in the study from August to October 2022. The turnover intention, occupational stress, burnout, quality of life, altruism, and certain sociodemographic information of community rehabilitation workers were measured using a structured questionnaire. The partial least squares method was employed to construct and test the structural equation model. Results: Although altruism had no direct impact on occupational stress or turnover intention, altruism moderated the effect of occupational stress on burnout (βMod = −0.208) and quality of life (βMod = 0.230) and weakened the mediation of burnout and quality of life between occupational stress and turnover intention. Conclusions: This study proposes to address the dilemma of "strong function" and "weak specialty" in community rehabilitation services and to conduct positive psychological interventions for community rehabilitation workers through the guidance of altruistic values. [ABSTRACT FROM AUTHOR]

Dynamic models generating time‐dependent model predictions are typically associated with high‐dimensional input spaces and high‐dimensional output spaces, in particular if time is discretized. It is computationally prohibitive to apply traditional global sensitivity analysis (SA) separately on each time output, as is common in the literature on multivariate SA. As an alternative, we propose a novel method for efficient global SA of dynamic models with high‐dimensional inputs by combining a new polynomial chaos expansion (PCE)‐driven partial least squares (PLS) algorithm with the analysis of variance. PLS is used to simultaneously reduce the dimensionality of the input and output variables spaces, by identifying the input and output latent variables that account for most of their joint variability. PCE is incorporated into the PLS algorithm to capture the non‐linear behavior of the physical system. We derive the sensitivity indices associated with each output latent variable, based on which we propose generalized sensitivity indices that synthesize the influence of each input on the variance of entire output time series. All sensitivities can be computed analytically by post‐processing the coefficients of the PLS‐PCE representation. Hence, the computational cost of global SA for dynamic models essentially reduces to the cost for estimating these coefficients. We numerically compare the proposed method with existing methods by several dynamic models with high‐dimensional inputs. The results show that the PLS‐PCE method can obtain accurate sensitivity indices at low computational cost, even for models with strong interaction among the inputs. [ABSTRACT FROM AUTHOR]

To solve the problem that the multi‐stage process with dynamicity and nonlinear is hard to monitor effectively, the time‐space neighborhood standardization (TSNS) method is proposed, which is further applied to partial least squares (PLS) to propose TSNS and PLS (TSNS‐PLS) method for process fault detection. TSNS can transform multi‐stage data into single‐stage data that approximately obeys a standard normal distribution, remove temporal correlation between samples at previous and subsequent moments in the process data, and separate online fault samples. TSNS makes the transformed process data satisfy the requirements of the PLS method for process data and can significantly improve the fault detection rate of the PLS method. Finally, the performance of TSNS‐PLS was examined by a numerical simulation process and the penicillin fermentation process design fault detection experiment. To solve the problem that the multi‐stage process with dynamicity and nonlinear is hard to monitor effectively, the time‐space neighborhood standardization (TSNS) method is proposed, which is further applied to partial least squares (PLS) to propose TSNS and PLS (TSNS‐PLS) method for process fault detection. [ABSTRACT FROM AUTHOR]

Background: The main challenge in heritage education is to identify the verbs—and their hierarchical relations—that explain heritage learning as based on empirical evidence. The Heritage Learning Sequence (HLS) selects seven verbs (Knowing-Understanding-Respecting-Valuing-Caring-Enjoying-Transmitting) on the basis of (a) theoretical studies, (b) analyses of international standards, and (c) evaluation of heritage education programs. The study has the following objectives: (a) to clarify the heritage learning process; (b) to test a theoretical model that groups the verbs that make up the Heritage Learning Sequence (HLS), as well as the relationships between them; (c) to identify possible sub-models that explain the different heritage learning itineraries. Methods: The Q-Herilearn scale (previously calibrated using SEM and IRT models) was administered to N = 1454 individuals, focusing on seven factors (corresponding to each HLS verb) that measure heritage learning. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used as a general analytical strategy. Findings: The results obtained provided sufficient guarantees to validate the HLS and showed the adequate explanatory and predictive power and general fit of the proposed model (Heritage Learning Model); all twelve hypothesized direct influence relations between the main verbs that define heritage learning were confirmed. The statistical significance values suggested the existence of other internal subsequences that could be explored in further studies. Contribution: Learning modeling provides a key structural framework for (a) the design of effective, efficient, and comprehensive tools to measure heritage learning and (b) their operationalization in heritage education designs. [ABSTRACT FROM AUTHOR]

The objective of this study was to discriminate thyroid and parathyroid tissues using Raman spectroscopy combined with an improved support vector machine (SVM) algorithm. In thyroid surgery, there is a risk of inadvertently removing the parathyroid glands. At present, there is a lack of research on using Raman spectroscopy to discriminate parathyroid and thyroid tissues. In this article, samples were obtained from 43 individuals with thyroid and parathyroid tissues for Raman spectroscopy analysis. This study employed partial least squares (PLS) to reduce dimensions of data, and three optimization algorithms are used to improve the classification accuracy of SVM algorithm model in spectral analysis. The results show that PLS‐GA‐SVM algorithm has higher diagnostic accuracy and better reliability. The sensitivity of this algorithm is 94.67% and the accuracy is 94.44%. It can be concluded that Raman spectroscopy combined with the PLS‐GA‐SVM diagnostic algorithm has significant potential for discriminating thyroid and parathyroid tissues. [ABSTRACT FROM AUTHOR]

One of the most remarkable techniques recently introduced into the field of bioprocess engineering is machine learning. Bioprocess engineering has drawn much attention due to its vast application in different domains like biopharmaceuticals, fossil fuel alternatives, environmental remediation, and food and beverage industry, etc. However, due to their unpredictable mechanisms, they are very often challenging to optimize. Furthermore, biological systems are extremely complicated; hence, machine learning algorithms could potentially be utilized to improve and build new biotechnological processes. Gaining insight into the fundamental mathematical understanding of commonly used machine learning algorithms, including Support Vector Machine, Principal Component Analysis, Partial Least Squares and Reinforcement Learning, the present study aims to discuss various case studies related to the application of machine learning in bioprocess engineering. Recent advancements as well as challenges posed in this area along with their potential solutions are also presented. [ABSTRACT FROM AUTHOR]

Determining anthracnose-infested mango can involve laborious and time-consuming assays, resulting in delayed postharvest management and decreased fruit marketability. Near infrared spectroscopy (NIRS) is proposed to detect the fungus in fully matured 'Namdokmai Sithong' mango. Inoculation of Colletotrichum gloeosporioides (1 × 106 conidia/mL) was artificially made onto one side of the fruit's peel at the center of mango fruit while the other side was left intact. Interactance measurements were conducted at both inoculated and intact locations for 104 mango samples every 24 h until anthracnose symptoms visibly appeared. The classification approaches included a partial least squares discriminant analysis (PLS-DA) and a conventional artificial neural network (ANN). Results of our study revealed increased absorbance values corresponding with days after inoculation. Relatively high classification accuracies were obtained from all chemometrics approaches (˃ 89%). In the early hours after inoculation (24 h), the best classification result was obtained from the ANN model (98.1%), confirming that early detection was possible. Applications of PLS-DA and ANN are discussed. [ABSTRACT FROM AUTHOR]

Recently, there has been a significant transition in the dietary preferences of consumers toward foods containing health-promoting compounds. In addition, as people's environmental awareness increases, they are increasingly looking for sustainable solutions. Palm oil, an oil used extensively by the food industry, does not fit these criteria. This study investigated the development of a complex oil blend consisting of commonly used vegetable oils such as corn, rapeseed, sunflower, and palm oil. The aim was to find the optimal blended oil and compare this combination with palm oil in terms of its oxidative stability, antioxidant capacity, and the composition of bioactive compounds (i.e., fatty acids, tocopherols, and carotenoids). Palm oil was found to have greater oxidative stability as a result of its increased concentration of saturated fatty acids. The optimal blended oil, which consisted of corn and rapeseed oil at a ratio of 4:3 w/w, inhibited the superior antioxidant activity, showing a ~33% increase in DPPH• inhibition activity. ATR-FTIR spectra further verified the existence of a significant quantity of saturated fatty acids in palm oil and unsaturated fatty acids in the blended oil. Finally, several correlation analyses revealed interesting connections between oil samples and investigated parameters. This work has the potential to establish a basis for the mass production of oil blends that possess high concentrations of antioxidant compounds and reduce the use of palm oil. [ABSTRACT FROM AUTHOR]

This study constructs a Chinese lottery index (LI) based on six popular lottery preference variables by using the partial least squares method and examines the relationship between the LI and future stock market returns during the period from January 2000 to December 2021. We find that the LI can negatively predict stock market excess returns in‐sample and out‐of‐sample. In addition, the LI can generate a large economic gain for a mean–variance investor. Finally, the predictive sources of the LI stem from a cash flow channel and can be explained by the positive volume–volatility relationship and investor attention. [ABSTRACT FROM AUTHOR]

Abstract Background In China, community rehabilitation workers are facing a growing challenge related to heavy occupational stress, which is having an impact on employment turnover. Previous studies have explored the effect of the public service motivation of workers in “helping” jobs on occupational stress or turnover intention, but there is a lack of clarification of the impact of altruism on turnover intention in the case of complex pathways involving various factors. Methods A stratified sampling method was used, and a total of 82 community rehabilitation workers who assist disabled people from 34 community health centres in Jiangmen city were included in the study from August to October 2022. The turnover intention, occupational stress, burnout, quality of life, altruism, and certain sociodemographic information of community rehabilitation workers were measured using a structured questionnaire. The partial least squares method was employed to construct and test the structural equation model. Results Although altruism had no direct impact on occupational stress or turnover intention, altruism moderated the effect of occupational stress on burnout (β Mod = −0.208) and quality of life (β Mod = 0.230) and weakened the mediation of burnout and quality of life between occupational stress and turnover intention. Conclusions This study proposes to address the dilemma of “strong function” and “weak specialty” in community rehabilitation services and to conduct positive psychological interventions for community rehabilitation workers through the guidance of altruistic values.

Abstract Background The main challenge in heritage education is to identify the verbs—and their hierarchical relations—that explain heritage learning as based on empirical evidence. The Heritage Learning Sequence (HLS) selects seven verbs (Knowing-Understanding-Respecting-Valuing-Caring-Enjoying-Transmitting) on the basis of (a) theoretical studies, (b) analyses of international standards, and (c) evaluation of heritage education programs. The study has the following objectives: (a) to clarify the heritage learning process; (b) to test a theoretical model that groups the verbs that make up the Heritage Learning Sequence (HLS), as well as the relationships between them; (c) to identify possible sub-models that explain the different heritage learning itineraries. Methods The Q-Herilearn scale (previously calibrated using SEM and IRT models) was administered to $$N = 1454$$ N = 1454 individuals, focusing on seven factors (corresponding to each HLS verb) that measure heritage learning. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used as a general analytical strategy. Findings The results obtained provided sufficient guarantees to validate the HLS and showed the adequate explanatory and predictive power and general fit of the proposed model (Heritage Learning Model); all twelve hypothesized direct influence relations between the main verbs that define heritage learning were confirmed. The statistical significance values suggested the existence of other internal subsequences that could be explored in further studies. Contribution Learning modeling provides a key structural framework for (a) the design of effective, efficient, and comprehensive tools to measure heritage learning and (b) their operationalization in heritage education designs.

This work focuses on dimension-reduction techniques for modelling conditional extreme values. Specifically, we investigate the idea that extreme values of a response variable can be explained by nonlinear functions derived from linear projections of an input random vector. In this context, the estimation of projection directions is examined, as approached by the extreme partial least squares (EPLS) method—an adaptation of the original partial least squares (PLS) method tailored to the extreme-value framework. Further, a novel interpretation of EPLS directions as maximum likelihood estimators is introduced, utilizing the von Mises–Fisher distribution applied to hyperballs. The dimension reduction process is enhanced through the Bayesian paradigm, enabling the incorporation of prior information into the projection direction estimation. The maximum a posteriori estimator is derived in two specific cases, elucidating it as a regularization or shrinkage of the EPLS estimator. We also establish its asymptotic behavior as the sample size approaches infinity. A simulation data study is conducted in order to assess the practical utility of our proposed method. This clearly demonstrates its effectiveness even in moderate data problems within high-dimensional settings. Furthermore, we provide an illustrative example of the method’s applicability using French farm income data, highlighting its efficacy in real-world scenarios. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the structure-function relationship of angiotensin-converting enzyme (ACE) inhibitory peptides and to elucidate the action mechanism of food-derived ACE inhibitory peptides. Based on the amino acid sequences of recently reported ACE inhibitory pentapeptides and their half-maximal inhibitory concentration (IC50) values, a library of ACE inhibitory pentapeptides was generated and the structures of the ACE inhibitory pentapeptides were characterized using three amino acid descriptors, Z-scales, VHSE and SVHEHS. A partial least square (PLS) model for describing the quantitative structure-activity relationship (QSAR) of the ACE inhibitory peptides with the hydrophobic properties, steric properties, and electrical properties of amino acids as the independent variables and the lg IC50 of the ACE inhibitory pentapeptides as the dependent variable was established using Matlab software. The results showed that the R2 and Q2 of the QSAR model based on Z-scales descriptor were 0.641 1 and 0.536 9, respectively, and Gln-Arg-Pro-Asn-Met showed higher ACE inhibitory activity as predicted by this model. The predicted and measured IC50 were 0.051 7 and (0.040 0 ± 0.008 3) μmol/L, respectively, and the error between them was 0.011 7 μmol/L. The R2 and Q2 of the QSAR model based on VHSE descriptor were 0.763 6 and 0.508 1, respectively, and Leu-Arg-Ala-Phe-Gln exhibited better ACE inhibitory activity as predicted by this model. The predicted and measured IC50 were 0.043 8 and (0.027 3 ± 0.005 3) μmol/L, respectively, and the error between them was 0.016 5 μmol/L. The R2 and Q2 of the QSAR model based on SVHEHS descriptor were 0.840 5 and 0.400 5, respectively, and Leu-Arg-Ala-Phe-Gln displayed better ACE inhibitory activity as predicted by this model. The predicted and measured IC50 were 0.005 5 and (0.031 2 ± 0.004 2) μmol/L, and the error between them was 0.025 7 μmol/L. Among the three QSAR models, the one based on SVHEHS descriptor had the strongest fitting capability but weak predictive capacity, while the models based on Z-scales and VHSE descriptors could allow good QSAR analysis of the pentapeptides. Our modeling analysis showed that the activity of the ACE inhibitory peptides was negatively correlated with the hydrophobic characteristics of amino acids and positively correlated with the steric characteristics of amino acids. Molecular docking of three ACE inhibitory peptides to ACE protein (2X8J) showed that all the ACE inhibitory peptides could bind to ACE protein. This study provides a new tool for developing ACE inhibitory peptides and a theoretical basis for the development and application of food-derived ACE inhibitory peptides.

Abstract This paper employs a statistical mechanical model as a framework to investigate how socioeconomic factors of individuals such as gender and place of residence influence their decision when deciding between comprehensive and third-party motor insurance policies in Ghana. Data from a general insurance firm was used for this investigation taking five years’ worth of transactions into account. The methods of partial least squares and the ordinary least squares are, respectively, used to estimate the parameters of the interacting and the non-interacting models in the Multipopulation Currie-Weiss model in a discrete choice framework. The findings showed that both location and gender have discernible influences on how people choose their motor insurance. We encourage insurance companies to intensify their campaign on the importance of motor insurance to all vehicle/car owners, especially those in rural areas in order to reduce the risk and associated losses in vehicular accidents on Ghanaian roads.

Abstract Background The progress of the cell cycle of yeast involves the regulatory relationships between genes and the interactions proteins. However, it is still obscure which type of protein plays a decisive role in regulation and how to identify the vital nodes in the regulatory network. To elucidate the sensitive node or gene in the progression of yeast, here, we select 8 crucial regulatory factors from the yeast cell cycle to decipher a specific network and propose a simple mixed K2 algorithm to identify effectively the sensitive nodes and genes in the evolution of yeast. Results Considering the multivariate of cell cycle data, we first utilize the K2 algorithm limited to the stationary interval for the time series segmentation to measure the scores for refining the specific network. After that, we employ the network entropy to effectively screen the obtained specific network, and simulate the gene expression data by a normal distribution approximation and the screened specific network by the partial least squares method. We can conclude that the robustness of the specific network screened by network entropy is better than that of the specific network with the determined relationship by comparing the obtained specific network with the determined relationship. Finally, we can determine that the node CDH1 has the highest score in the specific network through a sensitivity score calculated by network entropy implying the gene CDH1 is the most sensitive regulatory factor. Conclusions It is clearly of great potential value to reconstruct and visualize gene regulatory networks according to gene databases for life activities. Here, we present an available algorithm to achieve the network reconstruction by measuring the network entropy and identifying the vital nodes in the specific nodes. The results indicate that inhibiting or enhancing the expression of CDH1 can maximize the inhibition or enhancement of the yeast cell cycle. Although our algorithm is simple, it is also the first step in deciphering the profound mystery of gene regulation.

IntroductionApple is one of the most frequently consumed fruits in the world. It is a source of minerals, fiber, various biological compounds such as vitamin C, and phenolic compounds (natural antioxidants). The amount of nutrients plays a significant role in the growth, reproduction, and performance of agricultural products and plants. Chemical inputs can be accurately managed by predicting these elements. Thus, timely and accurate monitoring and managing the status of crop nutrition is crucial for adjusting fertilization, increasing the yield, and improving the quality. This approach minimizes the application of chemical fertilizers and reduces the risk of environmental degradation. In crop plants, leaf samples are typically analyzed to diagnose nutrient deficiencies and imbalances, as well as to evaluate the effectiveness of the current nutrient management system. Therefore, the main aim of this study is to estimate the level of Nitrogen (N), Phosphorus (P), and Potassium (K) elements in the leaves of the apple tree using the non-destructive method of Visible/Near-infrared (Vis/NIR) spectroscopy at the wavelength range of 500 to 1000 nm coupled with chemometrics analysis.Materials and MethodsThis research investigated the potential of the Vis/NIR spectroscopy coupled with chemometrics analysis for predicting NPK nutrient levels of apple trees. In this study, 80 leaf samples of apple trees were randomly picked and transferred to the laboratory for spectral measurement. The Green-Wave spectrometer (StellarNet Inc, Florida, USA) was utilized to collect the spectral data. In the next step, the spectral data were transferred to the laptop using the Spectra Wiz software (StellarNet Inc, Florida, USA). For this purpose, spectroscopy of the leaf samples was done in interactance mode. Ten random points were selected on each leaf to capture reflectance spectra and the averaged spectrum was used to determine the reflectance (R). The data was then transformed into absorbance (log 1/R) for chemometrics analysis. Following the spectroscopy measurements, the NPK contents were measured using reference methods. Afterward, Partial Least Square (PLS) multivariate calibration models were developed based on the reference measurements and spectral information using different pre-processing techniques. To remove the unwanted effects, various pre-processing methods were utilized to obtain an accurate calibration model. To evaluate the proposed models, the Root Mean Square Error of calibration and prediction sets (RMSEC and RMSEP), as well as the correlation coefficient of calibration and prediction sets (rc and rp), and Residual Predictive Deviation (RPD) were calculated.Results and DiscussionThe statistical metrics were calculated for the evaluation of PLS models and the results indicated that the PLS models could efficiently predict the NPK contents with satisfactory accuracy. The model with the best performance for nitrogen prediction was based on the standard normal variate pre-processing method in combination with the second derivative (SNV+D2) and resulted in rc= 0.988, RMSEC=0.028%, rp=0.978, RMSEP=0.034%, and RPD of 7.47. The best model for P content prediction resulted in rc= 0.967, RMSEC=0.0051%, rp=0.958, RMSEP=0.0057%, and RPD of 5.96. Additionally, the PLS model based on MSC+D2 pre-processing method resulted in rc= 0.984, RMSEC=0.017%, rp=0.976, RMSEP=0.021%, and RPD of 7.10, indicating the high potential of PLSR model in predicting K content. Moreover, the weakest performing model was related to the estimation of P content without pre-processing with rc = 0.774, RMSEC = 0.013%, rp = 0.685, RMSEP = 0.018%, and RPD value of 1.87. Based on the obtained results, the proposed PLS models coupled with suitable pre-processing methods were able to predict the nutrient content with high precision.ConclusionField spectroscopy has recently gained popularity due to its portability, ease of use, and low cost. Consequently, the use of a portable system for estimating nutrient levels in the field can significantly save time and lower laboratory expenses. Therefore, due to the accuracy of the Vis/NIR spectroscopy technique and according to the obtained results, this method can be used to actualize a portable system based on Vis/NIR spectroscopy to estimate the nutrient elements needed by the apple trees in the orchards and to increase the productivity of the orchards.

This paper employs a statistical mechanical model as a framework to investigate how socioeconomic factors of individuals such as gender and place of residence influence their decision when deciding between comprehensive and third-party motor insurance policies in Ghana. Data from a general insurance firm was used for this investigation taking five years' worth of transactions into account. The methods of partial least squares and the ordinary least squares are, respectively, used to estimate the parameters of the interacting and the non-interacting models in the Multipopulation Currie-Weiss model in a discrete choice framework. The findings showed that both location and gender have discernible influences on how people choose their motor insurance. We encourage insurance companies to intensify their campaign on the importance of motor insurance to all vehicle/car owners, especially those in rural areas in order to reduce the risk and associated losses in vehicular accidents on Ghanaian roads. [ABSTRACT FROM AUTHOR]

Background: The progress of the cell cycle of yeast involves the regulatory relationships between genes and the interactions proteins. However, it is still obscure which type of protein plays a decisive role in regulation and how to identify the vital nodes in the regulatory network. To elucidate the sensitive node or gene in the progression of yeast, here, we select 8 crucial regulatory factors from the yeast cell cycle to decipher a specific network and propose a simple mixed K2 algorithm to identify effectively the sensitive nodes and genes in the evolution of yeast. Results: Considering the multivariate of cell cycle data, we first utilize the K2 algorithm limited to the stationary interval for the time series segmentation to measure the scores for refining the specific network. After that, we employ the network entropy to effectively screen the obtained specific network, and simulate the gene expression data by a normal distribution approximation and the screened specific network by the partial least squares method. We can conclude that the robustness of the specific network screened by network entropy is better than that of the specific network with the determined relationship by comparing the obtained specific network with the determined relationship. Finally, we can determine that the node CDH1 has the highest score in the specific network through a sensitivity score calculated by network entropy implying the gene CDH1 is the most sensitive regulatory factor. Conclusions: It is clearly of great potential value to reconstruct and visualize gene regulatory networks according to gene databases for life activities. Here, we present an available algorithm to achieve the network reconstruction by measuring the network entropy and identifying the vital nodes in the specific nodes. The results indicate that inhibiting or enhancing the expression of CDH1 can maximize the inhibition or enhancement of the yeast cell cycle. Although our algorithm is simple, it is also the first step in deciphering the profound mystery of gene regulation. [ABSTRACT FROM AUTHOR]

Accurate prediction of nitrogen oxide (NOx) emission is crucial for effectively controlling pollution in municipal solid waste incineration processes. However, it is challenging to construct a NOx emission prediction model with high prediction accuracy and easy engineering application. To address this, this paper proposes a robust and easily applicable NOx emission trend prediction model oriented to engineering applications, utilizing the partial least squares (PLS) method with the time series reconstruction and exponential weighting (TS‐EW‐PLS). The model is verified using operational data from an actual waste incineration process, and comparative analysis with the PLS model showed that the TS‐EW‐PLS model achieved a remarkable improvement of 27–38 % in prediction performance. [ABSTRACT FROM AUTHOR]

Real‐time selective protein quantification is an integral component of operating continuous chromatography processes. Partial least squares models fit with spectroscopic UV‐Vis absorbance data have demonstrated the ability to selectively quantify proteins. With standard continuous chromatography equipment that is only capable of measuring absorbance at a few user‐defined wavelengths, the problem of selecting appropriate wavelengths that maximize the measurement capability of the instrument remains unaddressed. Therefore, we propose a method for selecting wavelengths for continuous chromatography equipment. We illustrate our method using sets of protein mixtures composed of bovine serum albumin and lysozyme. The first step is to refine the raw wavelength set with a statistical t‐test and an absorbance magnitude test. Then, the wavelengths within the refined spectroscopic range are ranked. Three existing techniques are evaluated – sequential forward search, variable importance to projection scores, and the least absolute shrinkage and selection operator. The best technique (in this case, sequential forward search) determines a subset of three wavelengths for further evaluation on the BioSMB PD. We use an exhaustive approach to determine the final wavelength set. We show that soft sensor models trained from the method's wavelength selections can quantify the two proteins more accurately than from the wavelength set of 230, 260 and 280 nm, by a factor of four. The method is shown to determine appropriate wavelengths for different path lengths and protein concentration ranges. Overall, we provide a tool that alleviates the analytical bottleneck for practitioners seeking to develop advanced monitoring and control methods on standard equipment. Application of a wavelength selection method that consists of a spectroscopic wavelength refinement step, followed by a wavelength ranking step and then a final wavelength verification step using in‐line absorbance data. The soft sensor partial least square models trained from the method's wavelength selections can selectively quantify proteins more accurately than from a set of 230, 260, and 280 nm wavelengths. The proposed method is shown to generate appropriate wavelengths for data sets of different path length and concentration ranges. [ABSTRACT FROM AUTHOR]

Regression component analysis (RCA) replaces the factors in a factor analysis model with weighted composites of the model's observed variables. The weight matrix may be calculated from the factor model's parameter estimates. Thus, RCA parameter estimates can be obtained using factor model software, but RCA composites have determinate scores, rather than the indeterminate scores of factors. Analytically, RCA equates to modeling with "regression method" factor scores, except that, while those scores will be inconsistent with the original factor model, they are strictly consistent with the RCA model. When the original factor model is strictly correct in the population and the composites in RCA are standardized, RCA parameter estimates replicate those from regression-weighted forms of partial least squares (PLS) path modeling and generalized structured component analysis (GSCA)—affirming that those methods also equate to modeling with regression method factor scores under the same conditions. Parallel measurement allows RCA to replicate both correlation weight and regression weight versions of PLS and GSCA. These results suggest that RCA and regression-weighted forms of PLS and GSCA are all consistent approaches for modeling data that conforms to a factor model. All analytical methods are described using one consistent symbol palette. Complete R syntax is provided. [ABSTRACT FROM AUTHOR]

In this study, simultaneous quantification of 2-nitrophenol and 4-nitrophenol in their binary mixtures was investigated spectrophotometrically. Since the signals of analytes were highly overlapped, a multivariate partial least squares (PLS) technique was proposed to analyze the data. The PLS method makes the analysis possible without the need for the separation of analytes by tedious separation procedures or using expensive instrumentation techniques such as chromatographic methods. Both 2-nitrophenol and 4-nitrophenol possess acid-base properties and it was required to investigate the effect of pH on the FOM of the calibration. Therefore, at three pH conditions, the calibration processes were evaluated and the results showed the best FOM and the least root mean squares error of prediction (RMSEP) for both analytes were achieved for the augmented data at 3.45 and 8.95 of pHs where only neutral or anionic forms of analytes were present in the solution. The analytical sensitivity, limit of detection, R², and RMSEP were 108.3 ppm-1, 0.08ppm, 1.00, 0.04 ppm and; 163.2 ppm-1, 0.06 ppm, 0.9999, 0.04 ppm for 2-nitrophenol and 4-nitrophenol, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study aims to apply the European Foundation for Quality Management (EFQM) Model 2020 in the Greek Public Sector context and investigate the causal relationships between the model's criteria. The research uses a structured questionnaire based on the self-assessment tool and the guidelines on the concept and structure of the EFQM Model 2020, translated from English into Greek using forward-backward translation. Two focus groups and a pilot study were conducted to ensure the validity and reliability of the questionnaire. Subsequently, a large-scale quantitative research was conducted using Partial Least Square Structural Equation Modelling (PLS-SEM) to test the research hypotheses on a national sample of 177 managers from public administrative services. The study results indicate that the EFQM Model 2020 is indeed a reliable and valid framework for the study of the public sector and reveal significant relationships between the model's criteria. The study is one of the first comprehensive investigations of the relationships between the EFQM Model 2020 criteria in Europe and, therefore, provides insights into the understanding of the model. As this research was geographically limited, the findings should be treated and generalised with caution, and further research should be conducted in different contexts. [ABSTRACT FROM AUTHOR]

Copyright of Laser Technology is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of RIED: Revista Iberoamericana de Educación a Distancia is the property of Revista Iberoamericana de Educacion a Distancia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study focuses on six variables that impact teachers' use of technology in their instructional leadership: usage expectancy (UE), social influence (SI), enabling circumstances (FC), behavioural intention (BI), computer self-efficacy (CSE) and blended teaching competency. This study aimed to examine the reliability and validity of modified scales incorporating UE scales including the PE scale, EE scale, SI scale, FC scale, CSE scale, BTC scale, BI scale and TIIL scale. A total of 60 in-service university teachers participated in this research. The PLS-SEM approach was employed to examine the reliability and validity of all scales. Composite reliability (CR) and Cronbach's alpha determine internal consistency and reliability. Convergent validity was assessed by the outer loading and the average variance extracted (AVE). Assessment of discriminant validity was conducted by the Fornell-Larcker criterion, cross-loadings and Heterotrait-Monotrait Ratio (HTMT). After deleting nine items that were lower than .40, Cronbach's alpha and CR values were all higher than .70. All scales' item values fulfilled the criteria of AVE (>.50), Fornell-Larcker criterion, crossloading and HTMT(<.90). Assessment results indicate that all modified scales have established validity and reliability for in-depth research. This research contributed to the PLS-SEM research technique, examined TIIL's influencing elements in the Chinese environment, enhanced the theoretical model of TIIL and provided useful assistance for the field's advancement. [ABSTRACT FROM AUTHOR]

The appearance of dried fruit clearly influences the consumer's perception of the quality of the product but is a subtle and nuanced characteristic that is difficult to quantitatively measure, especially online. This paper describes a method that combines several simple strategies to assess a suitable surrogate for the elusive quality using imaging, combined with multivariate statistics and machine learning. With such a convenient tool, this study also shows how one can vary the pretreatments and drying conditions to optimize the resultant product quality. Specifically, an image batch processing method was developed to extract color (hue, saturation, and value) and morphological (area, perimeter, and compactness) features. The accuracy of this method was verified using data from a case study experiment on the pretreatment of hot-air-dried kiwifruit slices. Based on the extracted image features, partial least squares and random forest models were developed to satisfactorily predict the moisture ratio (MR) during drying process. The MR of kiwifruit slices during drying could be accurately predicted from changes in appearance without using any weighing device. This study also explored determining the optimal drying strategy based on appearance quality using principal component analysis. Optimal drying was achieved at 60 °C with 4 mm thick slices under ultrasonic pretreatment. For the 70 °C, 6 mm sample groups, citric acid showed decent performance. [ABSTRACT FROM AUTHOR]

Background and objective: Standard normal variate (SNV) preprocessing is widely applied to spectroscopic data prior to multivariate modeling, under the assumption that it mitigates undesired background variations while preserving analyte signal information. However, the scaling step in SNV, where each spectrum is divided by its standard deviation, could potentially distort the covariance between spectral intensities and component concentrations. This study systematically evaluates the effect of SNV preprocessing on the ability to develop accurate quantitative models for determining analyte concentrations in mixtures, with a focus on the determination of moisture in corn using NIR spectroscopy. Materials and methods: Simulations were performed to generate single and multi-component spectroscopic datasets with varying levels of multiplicative scatter and baseline offset effects. Additionally, an experimental near-infrared (NIR) dataset for corn samples with reference moisture values was utilized. Partial least squares (PLS) regression was employed to model the simulated and experimental data, with and without SNV preprocessing. Model calibration and prediction performance metrics were assessed. Results and conclusion: For simulated datasets without background interferences, SNV preprocessing eliminated useful concentration-related variations by forcing all sample spectra to equal lengths, severely degrading PLS calibration and prediction abilities. In scenarios with multiplicative/additive perturbations, while SNV mean-centering helped mitigate these undesired effects, the subsequent scaling step obscured analyte concentration information in the spectral intensities. PLS models built from raw corn NIR spectra provided excellent determination of moisture in corn using NIR spectroscopy, whereas SNV preprocessing led to significantly higher prediction errors. The findings demonstrate that indiscriminate application of SNV can be detrimental for precise quantitative spectroscopic analysis by disrupting the covariance between signals and analyte levels. Therefore, preprocessing strategies should be judiciously evaluated based on the specific data characteristics and modeling objectives. [ABSTRACT FROM AUTHOR]

Building construction is always undergoing rework. This can happen due to design errors, the application of work methods that are not in accordance with procedures, or lack of supervision during the construction process. In previous studies, the factors that cause rework include design, management, resources, and the environment. This study analyzes how these factors have an impact on the quality of performance in the implementation of construction projects. Construction projects in Denpasar City are the subject of quantitative and qualitative research. Data was collected through interviews, brainstorming, and questionnaires with experts. With the purposive sampling method, respondents are selected based on predetermined criteria. SmartPLS 3.0 software is used to process the data through the analysis of the average square. The test results show that design, management, resources, and environment affect the quality of construction project implementation performance by 94.40 percent; Outside of the study, this influence was 5.60 percent. With an initial sample value (O) of 0.401, management was the most influential rework factor on the quality of project execution performance, compared to 0.226 for design, 0.261 for resources, and 0.222 for the environment. Therefore, contractors must have the ability to improve management functions during the construction project execution process so that they can reduce the amount of work that has to be done again at a later date. [ABSTRACT FROM AUTHOR]

Introduction Apple is one of the most frequently consumed fruits in the world. It is a source of minerals, fiber, various biological compounds such as vitamin C, and phenolic compounds (natural antioxidants). The amount of nutrients plays a significant role in the growth, reproduction, and performance of agricultural products and plants. Chemical inputs can be accurately managed by predicting these elements. Thus, timely and accurate monitoring and managing the status of crop nutrition is crucial for adjusting fertilization, increasing the yield, and improving the quality. This approach minimizes the application of chemical fertilizers and reduces the risk of environmental degradation. In crop plants, leaf samples are typically analyzed to diagnose nutrient deficiencies and imbalances, as well as to evaluate the effectiveness of the current nutrient management system. Therefore, the main aim of this study is to estimate the level of Nitrogen (N), Phosphorus (P), and Potassium (K) elements in the leaves of the apple tree using the non-destructive method of Visible/Near-infrared (Vis/NIR) spectroscopy at the wavelength range of 500 to 1000 nm coupled with chemometrics analysis. Materials and Methods This research investigated the potential of the Vis/NIR spectroscopy coupled with chemometrics analysis for predicting NPK nutrient levels of apple trees. In this study, 80 leaf samples of apple trees were randomly picked and transferred to the laboratory for spectral measurement. The Green-Wave spectrometer (StellarNet Inc, Florida, USA) was utilized to collect the spectral data. In the next step, the spectral data were transferred to the laptop using the Spectra Wiz software (StellarNet Inc, Florida, USA). For this purpose, spectroscopy of the leaf samples was done in interactance mode. Ten random points were selected on each leaf to capture reflectance spectra and the averaged spectrum was used to determine the reflectance (R). The data was then transformed into absorbance (log 1/R) for chemometrics analysis. Following the spectroscopy measurements, the NPK contents were measured using reference methods. Afterward, Partial Least Square (PLS) multivariate calibration models were developed based on the reference measurements and spectral information using different pre-processing techniques. To remove the unwanted effects, various pre-processing methods were utilized to obtain an accurate calibration model. To evaluate the proposed models, the Root Mean Square Error of calibration and prediction sets (RMSEC and RMSEP), as well as the correlation coefficient of calibration and prediction sets (rc and rp), and Residual Predictive Deviation (RPD) were calculated. Results and Discussion The statistical metrics were calculated for the evaluation of PLS models and the results indicated that the PLS models could efficiently predict the NPK contents with satisfactory accuracy. The model with the best performance for nitrogen prediction was based on the standard normal variate pre-processing method in combination with the second derivative (SNV+D2) and resulted in rc= 0.988, RMSEC=0.028%, rp=0.978, RMSEP=0.034%, and RPD of 7.47. The best model for P content prediction resulted in rc= 0.967, RMSEC=0.0051%, rp=0.958, RMSEP=0.0057%, and RPD of 5.96. Additionally, the PLS model based on MSC+D2 pre-processing method resulted in rc= 0.984, RMSEC=0.017%, rp=0.976, RMSEP=0.021%, and RPD of 7.10, indicating the high potential of PLSR model in predicting K content. Moreover, the weakest performing model was related to the estimation of P content without pre-processing with rc = 0.774, RMSEC = 0.013%, rp = 0.685, RMSEP = 0.018%, and RPD value of 1.87. Based on the obtained results, the proposed PLS models coupled with suitable pre-processing methods were able to predict the nutrient content with high precision. Conclusion Field spectroscopy has recently gained popularity due to its portability, ease of use, and low cost. Consequently, the use of a portable system for estimating nutrient levels in the field can significantly save time and lower laboratory expenses. Therefore, due to the accuracy of the Vis/NIR spectroscopy technique and according to the obtained results, this method can be used to actualize a portable system based on Vis/NIR spectroscopy to estimate the nutrient elements needed by the apple trees in the orchards and to increase the productivity of the orchards. [ABSTRACT FROM AUTHOR]