Partial Least Squares Method Research Articles

New approach for rapid estimation of leaf nitrogen, phosphorus, and potassium contents in apple-trees using Vis/NIR spectroscopy based on wavelength selection coupled with machine learning

Timely and rapid monitoring of apple trees nutrition status is vital for accurate management of nutrient fertilizers in order to improve the yield and quality, as well as to reduce the risk of environmental degradation. As a most frequently used method, tissue analysis used to assess the nutritional status of apple tree leaves is laborious, costly, time-consuming, environmentally unfriendly, and destructive. Ground-based sensors are able to efficiently provide information on nutritional status using leaf spectra reflectance. This research aims to establish a novel cost-effective and non-destructive approach for rapidly estimating the status of nitrogen (N), phosphorus (P), and potassium (K) in apple tree leaves based on Visible/Near-infrared (Vis/NIR) spectroscopy (500–1000 nm) coupled with machine learning. The Vis/NIR spectra of apple trees’ leaves were acquired. Then, leaf chemical contents of NPK elements were considered as reference points. Different pre-processing techniques were used to pre-treat the spectra. Four different chemometrics analysis consist of support vector machine (SVM), Artificial neural network (ANN), Random Forest (RF) and partial least square (PLS) were applied to predict NPK contents in comparison to actual values. In order to simplify the models, the sensitive wavelengths were extracted using three wavelength selection approaches, variable importance in projection (VIP), partial least squares (PLS), and random frog (Rfrog). The extracted feature wavelengths from PLS, VIP, and Rfrog methods were widely distributed in the visible and near-infrared regions of the spectrum. The performance of the developed models was tested using Residual Prediction Deviation (RPD) and the Ratio of Performance to Interquartile Distance (RPIQ). The results demonstrated that among all models, the non-linear modeling methods were superior to the linear model. The best results for estimation of Nitrogen (N), Phosphorus (P), and Potassium (K) elements were achieved by the models of MSC + D2-Rfrog-RF (rp = 0.985, RMSEP = 0.029%, RPD = 8.77, RPIQ = 7.72), SNV + D2-Rfrog-RF (rp = 0.977, RMSEP = 0.0053%, RPD = 6.42, RPIQ = 5.09) and SNV + D2-Rfrog-RF (rp = 0.978, RMSEP = 0.018%, RPD = 8.16, RPIQ = 7.01), respectively. The findings of the current approach may provide an efficient approach to predict in-situ NPK contents of apple trees based on leaf spectral reflectance.

Rapid quantitative analysis of calcium in infant formula powder assisted by long short-term memory with variable importance using laser-induced breakdown spectroscopy.

Calcium is the main mineral responsible for healthy bone growth in infants. Laser-induced breakdown spectroscopy (LIBS) was combined with a variable importance-based long short-term memory (VI-LSTM) for the quantitative analysis of calcium in infant formula powder. First, the full spectra were used to establish PLS (partial least squares) and LSTM models. The R2 and root-mean-square error (RMSE) of the test set (R P2 and R M S E P) were 0.1460 and 0.0093 in the PLS method, respectively, and 0.1454 and 0.0091 in the LSTM model, respectively. To improve the quantitative performance, variable selection based on variable importance was introduced to evaluate the contribution of input variables. The variable importance-based PLS (VI-PLS) model had R P2 and R M S E P of 0.1454 and 0.0091, respectively, whereas the VI-LSTM model had R P2 and R M S E P of 0.9845 and 0.0037, respectively. Compared with the LSTM model, the number of input variables in the VI-LSTM model was reduced to 276, R P2 was improved by 114.63%, and R M S E P was reduced by 46.38%. The mean relative error of the VI-LSTM model was 3.33%. We confirm the predictive ability of the VI-LSTM model for the calcium element in infant formula powder. Thus, combining VI-LSTM modeling and LIBS has great potential for the quantitative elemental analysis of dairy products.

Rapid Quantitative Simultaneous Determination of Vitamin A and Vitamin D in a Skin Ointment Pharmaceutical Formulation Based on Net Analyte Signal and Partial Least Squares using the UV Spectrophotometric Method

Objective: The aim of this study was to propose an easy, fast, and precise method, which can be satisfactorily applied to the simultaneous determination of the drugs in pharmaceutical dosage forms instead of the high-performance liquid chromatography (HPLC) method. Methods: Complicated and costly instruments, as well as spending a lot of time for analysis are the limitations of the chromatography method. Hence, UV spectrophotometric technique based on net analyte signal (NAS) and partial least squares (PLS) was proposed and validated for the simultaneous determination of vitamin A and vitamin D in binary mixtures and commercial skin ointment. The chromatographic technique was implemented to compare with the proposed approach for the simultaneous analysis of drugs. Results: The NAS method was able to determine vitamin A and vitamin D in the concentration range of 1–7 and 2-13 μg mL-1 at λmax=323 and λmax=261, respectively. The limit of detection (LOD) and limit of quantification (LOQ) related to the NAS method were found to be 0.092, 0.053 μg/mL and 0.214, 0.170 μg/mL for vitamin A and vitamin D, respectively. The root mean square error (RMSE) of the test series was 0.0607 and 0.0547 for vitamins A and D, respectively. Recoveries by the PLS method were found in the range of 99.98%-100.49% and 99.57%-100.88% for vitamins A and D, respectively. The results obtained from the NAS and PLS procedures were statistically compared with the reference method (HPLC) via analysis of variance (ANOVA) test, where no significant difference was observed. Conclusion: The spectrophotometry along with NAS and PLS methods are proper, rapid, and economic for the determination of pharmaceutical components in quality control laboratories.

Multi-parameter optimization for Vis–NIR spectroscopic analysis of multiple indicators of soil heavy metal in the tideland reclamation area of the Pearl River Delta

ABSTRACT Using visible and near-infrared (Vis–NIR) spectroscopy combined with novel chemometric methods, the rapid reagent-free simultaneous analysis model for Cu, Zn, Ni, and Cr contents in tideland reclamation soil in the Pearl River Delta was established. Based on Savitzky–Golay (SG) smoothing and partial least squares (PLS) regression, a multi-parameter optimization platform (SG-PLS) covering 264 modes was constructed to select appropriate spectral preprocessing mode for each indicator. The equidistant combination PLS (EC-PLS) method with three cycle parameters was adopted for the first large-scale screening of wavelength models. In addition, wavelength phase-out PLS (WSP-PLS) and repetition rate priority combination methods were used as the secondary optimization method. The well-executed competitive adaptive reweighted sampling PLS was also used for comparison. The validation samples that were not involved in modeling were used to validate the selected three group models. For the four indicators, the method of EC-PLS combined with WSP-PLS achieved the best validation effect. In validation, the root mean square error (SEPV), relative root mean square error (RSEPV), and correlation coefficients (RP,V) of prediction were 2.46 mg kg−1, 4.5%, and 0.959 for Cu; 51.37 mg kg−1, 24.6%, and 0.900 for Zn; 3.59 mg kg−1, 9.1%, and 0.739 for Ni; and 9.15 mg kg−1, 8.4%, and 0.843 for Cr, respectively. Results indicated that low relative error and high prediction correlation confirmed the feasibility of using Vis–NIR spectroscopy to analyze soil heavy metal contents. The proposed multi-parameter, multi-stage integrated optimization algorithm could be applied to a wider field of spectral analysis.

THE PORTUGUESE ACCOUNTANTS: A IMPORTANT ROLE ON MANAGEMENT ACCOUNTING USE? AND IN FIRM PERFORMANCE?

Currently the role of accountants in firms is changing. Increasingly, accountants should have adequate skills not only in terms of financial accounting, but also skills in terms of management accounting that help in the decision-making process and in the performance of firms.The use of management accounting practices, and the respective instruments, has been gaining more and more importance in the business sector, presenting very satisfactory results, contributing greatly to the achievement of a companys competitive advantage over others operating in the same field of activity.There are some studies that suggest a weak influence on the role of management accounting in the decision-making process and in firm or organizational performance, however, they are already somewhat old, namely from the end of the 20th century. However, more recent studies show that many small and medium-sized firms cannot survive in the first years of activity, possibly due to a lack of specialists in decision-making support and they didnt implement Management Accounting (MA).The research objective of this article isto analyse the role of the Portuguese accountant, his skills and how the use of MA can contribute to the firm performance.To meet the research objective, it was decided to apply a quantitative analysis. It was developed a reflective structural equation model (SEM), using a Partial Least Squares method (PLS-SEM), which reveals the influence and impact of the Portuguese accountant and the MA on the firm performance.The results obtained allowed us to conclude that accountants in Portugal recognize the relevance of financial accounting and management accounting for the firm performance, however, they do not give relevance to the complementarity, use and applicability of these two accountings. For accountants in Portugal, management accounting is a complement to the firm performance, not being relevant to financial performance and is less important than financial accounting.

THE ROLE OF ADVOCACY SERVICES, TALENT DEVELOPMENT AND MEMBER SATISFACTION WITH THE PERFORMANCE OF THE SERANG REGENCY SPN MANAGEMENT

Low Human Resource Performance is a common problem in developing countries such as Indonesia, from a survey conducted in February 2022 by BPS Banten Province. Human development indicators are indisputable, especially in attack districts, which are related to very small performance, which is only 66.38%. It is in the third lowest order after pandeglang district and lebak district. And the performance of the SPN organizational management is one of the problems that needs to be examined related to the performance of human resources in Serang regency. Efforts to improve HR performance are the responsibility of all levels of society and especially for workers and the management of worker organizations in the SPN Organization. for this reason, this research was carried out in order to provide solutions to improve the performance of SPN management to make it even better. With research variables, the role of advocacy services, talent development, member satisfaction, and the performance of SPN management. In data analysis, the author applied the calculation method of structural equation modelling partial least square (SEM-PLS) analysis. with a total sample of 398 employees who are members of SPN throughout Serang Regency and use Stratified Random Sampling data analysis tools using Smart PLS and SPSS using Likert calculation guidelines. The exogenous variables in this study are advocacy services using 10 indicators, talent development 13 indicators, the role of satisfaction 10 indicators, and endogenous performance variables administrators with 11 indicators. The number of hypotheses tested in this study was 7 hypotheses. The results of the outer model test show that of the 44 indicators used, there are 4 invalid struments becausethey have a loading factor value of < 0.70, while the rest are as many as 40 indicators are valid. The conclusion of the hypothesis test results obtained 5 hypotheses accepted and 2 hypotheses in has no effect, with details: There is no significant influence between Advocacy services and satisfaction. There is a significant influence between talent development and satisfaction. There is a significant influence between Advocacy Services and Performance. There is a significant influence between Talent development and performance. There is a significant influence between Satisfaction and Performance. There is no significant influence between Advocacy for Performance through Satisfaction. There is a significant influence between talent development on performance through satisfaction.

Peran Strategi Bisnis Terhadap Nilai Perusahaan dengan Good Corporate Governance sebagai Variabel Mediasi

This study aims to determine the role of good corporate governance in mediating the effect of business strategy on firm value in non-cyclical consumer companies listed on the Indonesia Stock Exchange. This research includes quantitative research. With a population of all non-cyclical consumer companies listed on the Indonesia Stock Exchange. The sampling technique used purposive sampling technique, in order to obtain 26 companies as samples. Data collection techniques used documentation techniques with secondary data obtained from non-cylicals consumer companies listed on the Indonesia Stock Exchange. The data analysis technique in this study is the mediation path analysis using Smart PLS (Partial Least Square) version 3.3.9 with the PLS method consisting of Outer Model Analysis, Inner Model Analysis, and Hypothesis Testing. The results of this study prove that business strategy has no effect on firm value, business strategy has no effect on firm value with the audit committee as a mediating variable, business strategy does not affect firm value with institutional ownership as a mediating variable, business strategy does not affect firm value with the proportion independent board of commissioners as a mediating variable.

Partial Least Squares - Diffusion Tensor Imaging (PLS-DTI): A novel approach for biomarker imaging in breast cancer

Currently, magnetic resonance imaging is the most sensitive imaging technique for detecting cancer processes in early stages. Regarding breast cancer, due to the characteristics of the tissue as it is formed by ducts (tubular structure), anisotropic diffusion should be considered instead of general isotropic Diffusion Weighted Imaging (DWI). Anisotropic diffusion is studied by applying a technique called Diffusion Tensor Imaging (DTI), where the diffusion gradient is applied with several different directions, calculated by Ordinary Least Squares (OLS) in clinical practice. In this paper, we propose a new DTI calculation method based on Partial Least Squares (PLS), which has some advantages over the traditional OLS calculation: i) the PLS model provides valid biomarkers (non-negative eigenvalues) in a larger percentage of pixels, improving the traditional OLS calculation and reducing the effect of noisier images; ii) OLS tensors are calculated pixel-by-pixel, whereas the PLS method calculates only one model taking advantage of the correlation structure between pixels with similar characteristics, obtaining more reliable estimations; iii) PLS performance is quite reliable when lowering the number of directions of the magnetic field, while this is not the case of OLS. PLS keeps providing a good solution even with low functional resolution equipment, reducing costs and acquisition times, which is an important advantage for its widespread use in value-based medicine-oriented clinical practice.

Quantitative and qualitative prediction of sulfur content in diesel by near infrared spectroscopy

This study explored the application of near infrared spectroscopy for quantitative and qualitative prediction of sulfur content in diesel fuel in the range of 10.3–1038.0 mg kg−1. The original spectra were preprocessed through various methods such as decentralization, normalization, multivariate scattering correction, and a smoothing (15-point window with second order polynomial fit). The performances of models based on partial least squares (PLS) regression, the bootstrapping soft shrinkage (BOSS), competitive adaptive reweighted sampling and Monte Carlo uninformative variable elimination algorithms in quantitative analysis of diesel samples were compared. The model for quantitative prediction of sulfur content in diesel samples using the BOSS-PLS algorithm had the highest performance and accuracy with a RMSEP of 36.20 mg kg−1 and r2 of 0.98 using a Savitzky-Golay second derivative. Diesel fuel samples were classified into five groups according to the sulfur content for qualitative analysis. The interval PLS method was then used to determine the characteristic spectra of the diesel samples. The experimental results indicated that the discriminant partial least squares qualitative analysis model had the highest performance with the characteristic spectrum from 12,493 to 10,892 cm−1, with 92.04% accuracy.

Core, Coating, or Corona? The Importance of Considering Protein Coronas in nano-QSPR Modeling of Zeta Potential.

To control stability in a biological medium, several factors affecting the zeta potential (ζ) of nanoparticles (NPs) must be considered, including complex interactions between the nanostructure and the composition of the protein corona (PC). Effective in silico methods (based on machine learning and quantitative structure-property relationship (QSPR) models) could help predict and characterize the relationship between the physicochemical properties of NP and the formation of PC and biological outcomes in the medium at an early stage of the experiment. However, the models currently developed are limited to simple descriptors that do not represent the complex interactions between the core, the coating, and their PC fingerprints. To be useful, the models developed should be described as a function of both the structural properties determined by the core and coating of the NPs and the biological medium determined by the formation of the protein corona. We have developed a set of complex descriptors that describe the quantitative relationship between the value of the zeta potential (ζ), core, the coating of NPs, and their PC fingerprints (the so-called nano-QSPR model). The nano-QSPR model was developed based on a genetic algorithm using a partial least-squares regression method (GA-PLS), which is characterized by high external predictive power (Q2EXT = 0.89). The GA-PLS model was developed using descriptors that describe (i) the core structure (determined by 7 different types of polymer-based NMs in the range of 20 different sizes), (ii) the coating structure with 7 different functional groups, and (iii) 80 different types of protein compositions adsorbed on the surface of the NPs. The presented study answers the question of how complex interactions between the corona and NP determine the zeta potential (ζ) of NP in a given medium. Moreover, our current study is a proof-of-concept that the zeta potential of NPs modeled on the original structure depends not only on the NPs themselves but also on the structure and properties determined by the NP core and coating, as well as the biological medium determined by the formation of the protein corona. On the basis of these results, our studies will be useful in determining the stability and mechanism of cell uptake, toxicity, and ability to predict the zeta potential of compounds not yet tested.

How do medication errors occur in the nursing communication process? Investigating the relationship between error types and error factors.

Human error types and error factors are two important elements of error analysis. Understanding the relationship between them can contribute to new case analyses, the tendency of error occurrence statistics, error factor identification, and prevention of error recurrence. To provide evidence and guidance for the prevention and improvement of medication communication errors by quantitatively exploring the relationship between error types and error factors. Data were collected on self-reported errors in the medication administration process by nurses in all departments of three cooperative medical institutions, and an error sheet of specified style was adopted. Error types were determined by the systematic human error reduction and prediction approach method and human cognition processes. Error factors were extracted using the root cause analysis combined with Berlo's communication model, and the relationship between error types and error factors was quantitatively studied using the partial least-squares regression method. After a one-by-one analysis of 303 error cases, the communication errors occurring in the nursing medication process could be explained by six error types and 12 error factors. In addition, 20 correlation patterns between the error types and error factors were quantitatively obtained, and their path coefficient distributions ranged from 0.088 to 0.467. The results of this study may provide reference to understand errors and establish countermeasures from the statistics of error occurrence trends, extract error factors related to error types and determine key error factors.

A comparison of principal component analysis, partial least-squares, and reduced-rank regressions in the identification of dietary patterns associated with hypertension: YaHS-TAMYZ and Shahedieh cohort studies.

Limited data exist on the advantage of data reduction hybrid methodologies for evaluating the relationship between dietary patterns (DPs) and chronic diseases and they have led to inconsistent results. This study aimed to investigate the association between DPs extracted using principal component analysis (PCA), partial least-squares (PLS), and reduced-rank regressions (RRRs) in identifying DPs associated with hypertension (HTN) risk. The current study was conducted in the context of two cohort studies accomplished in Iran. DPs were generated using PCA, PLS, and RRR methods. Log-binomial logistic regression test was used to assess the association between DPs and the risk of HTN. From a total of 12,403 included participants aged 20-70 years, 507 incident cases of confirmed HTN were identified. The PCA-DP2 was associated with HTN in the fully adjusted model (T3 vs. T1: RR: 0.737, 95% CI: 0.57-0.93, P trend = 0.013). The PLS-DP2 and HTN risk were inversely associated in the multivariate model (T3 vs. T1: RR: 0.704, 95% CI: 0.54-0.91, P trend = 0.013). The RRR-DP2 was associated with an increased risk of HTN (T3 vs. T1: RR: 1.412, 95% CI: 1.11-1.80, P trend = 0.007). Our findings suggest that the RRR method reveals stronger results in association with HTN risk. However, further investigations are required to confirm the association between DPs derived by PLS and RRR methods by incorporating biomarkers related to HTN as the response variables.

The association between dietary patterns derived by three statistical methods and type 2 diabetes risk: YaHS-TAMYZ and Shahedieh cohort studies

Findings were inconsistent regarding the superiority of using recently introduced hybrid methods to derive DPs compared to widely used statistical methods like principal component analysis (PCA) in assessing dietary patterns and their association with type 2 diabetes mellitus (T2DM). We aimed to investigate the association between DPs extracted using principal component analysis (PCA), partial least-squares (PLS), and reduced-rank regressions (RRR) in identifying DPs associated with T2DM risk. The study was conducted in the context of two cohort studies accomplished in central Iran. Dietary intake data were collected by food frequency questionnaires (FFQs). DPs were derived by using PCA, PLS, and RRR methods considering. The association between DPs with the risk of T2DM was assessed using log-binomial logistic regression test. A total of 8667 participants aged 20–70 years were included in this study. In the multivariate-adjusted models, RRR-DP3 characterized by high intake of fruits, tomatoes, vegetable oils, and refined grains and low intake of processed meats, organ meats, margarine, and hydrogenated fats was significantly associated with a reduced T2DM risk (Q5 vs Q1: RR 0.540, 95% CI 0.33–0.87, P-trend = 0.020). No significant highest-lowest or trend association was observed between DPs derived using PCA or PLS and T2DM. The findings indicate that RRR method was more promising in identifying DPs that are related to T2DM risk compared to PCA and PLS methods.

Prediction of body condition in Jersey dairy cattle from 3D-images using machine learning techniques.

The body condition of dairy cows is a crucial health and welfare indicator that is widely acknowledged. Dairy herds with a well-management body condition tend to have more fertile and functional cows. Therefore, routine recording of high-quality body condition phenotypes is required. Automated prediction of body condition from 3D images can be a cost-effective approach to current manual recording by technicians. Using 3D-images, we aimed to build a reliable prediction model of body condition for Jersey cows. The dataset consisted of 808 individual Jersey cows with 2,253 phenotypes from three herds in Denmark. Body condition was scored on a 1 to 9 scale and transformed into a 1 to 5 scale with 0.5-unit differences. The cows' back images were recorded using a 3D camera (Microsoft Xbox One Kinect v2). We used contour and back height features from 3D-images as predictors, together with class predictors (evaluator, herd, evaluation round, parity, lactation week). The performance of machine learning algorithms was assessed using H2O AutoML algorithm (h2o.ai). Based on outputs from AutoML, DeepLearning (DL; multi-layer feedforward artificial neural network) and Gradient Boosting Machine (GBM) algorithms were implemented for classification and regression tasks and compared on prediction accuracy. In addition, we compared the Partial Least Square (PLS) method for regression. The training and validation data were divided either through a random 7:3 split for 10 replicates or by allocating two herds for training and one herd for validation. The accuracy of classification models showed the DL algorithm performed better than the GBM algorithm. The DL model achieved a mean accuracy of 48.1% on the exact phenotype and 93.5% accuracy with a 0.5-unit deviation. The performances of PLS and DL regression methods were comparable, with mean coefficient of determination of 0.67 and 0.66, respectively. When we used data from two herds for training and the third herd as validation, we observed a slightly decreased prediction accuracy compared to the 7:3 split of the dataset. The accuracies for DL and PLS in the herd validation scenario were > 38% on the exact phenotype and > 87% accuracy with 0.5-unit deviation. This study demonstrates the feasibility of a reliable body condition prediction model in Jersey cows using 3D-images. The approach developed can be used for reliable and frequent prediction of cows' body condition to improve dairy farm management and genetic evaluations.

The Limiting Indicator of Variables in the Performance Index Modeling for Simple Waterworks

This research intends to evaluate a variable scheme based on the condition of a simple waterworks system in the Brantas river area to obtain the variables that influence each other. This research was conducted in 30 water sources that are distributed in the Brantas river area such as Batu, Malang Regency, Blitar Regency, Jombang Regency, Kediri Regency, Nganjuk Regency, and Tulungagung Regency. The methodology consists of identifying the affected variables to the bron-capturing and to evaluate the indicators that are influenced the variables, then to conduct the statistic test of the indicators to the variables. The research indicators about the performance index model of simple waterworks are obtained through the partial least square (PLS) method. The PLS method is used for evaluating the influenced relationship among the variables that are used in this research, such as the variables of technical, social, and management aspects. The technical aspect consists of physical infrastructure, service, operation, environment damage, and maintenance; the social aspect consists of economy and socio-culture conditions; the management aspect consists of water management institutions, human resources, and monetary aspect. Based on the t calculated analysis of the inner model, where t variable statistic of the technical aspect is 11.632, the social aspect is 3.017, and management aspect is 4.665, each variable has the t calculated higher than t critic (1.682) due to the level of significance (α) = 0.05 or the p value of 0.000, and it is less than the critic value (0.05). Based on the regression analysis, it can be concluded that the technical aspect has the most effect (72.1%) on the performance index model of simple waterworks in the Brantas river area. The second biggest effect is the management aspect (26.7%) in influencing the performance index model of simple waterworks in the Brantas river area. Then, the lowest effect is the social aspect (15.1%) in affecting the performance index model of simple waterworks in the Brantas river area. This result is necessary for supporting the performance index modeling of simple waterworks. Keywords : indicator, variable, bron-capturing, waterworks. https://doi.org/10.55463/issn.1674-2974.50.2.9

Zastosowanie modelowania PLS-SEM do badania innowacyjności gospodarek krajów Unii Europejskiej

Purpose – The article aims to present the possibility of using PLS-SEM method in research on the innovativeness of EU economies. Research method – Structural equation modeling is a method that allows the study of relationships between variables that are unobservable. The SEM model consists of two sub-models: structural and measurement. Covariance-based (CB) or partial least squares (PLS) methods are used to estimate the model. The choice depends on the empirical context, research objectives, sample size and statistical properties of the data. Due to the small size of the population, the PLS method was used in the study. The models were estimated on the basis of cross-sectional data for 2018 and 2021. Results – The article presents the stages of construction of the PLS-SEM model, from model specification, through estimation, statistical verification, to interpretation of the results. Particular attention is paid to the possibility of formulating conclusions based on various elements of the estimated model. Originality / value / implications / recommendations – PLS-SEM is a relatively new econometric method belonging to the group of second generation multivariate analysis methods. Its dynamic development can be observed especially in management sciences. In the discipline of economics, the method is not widely known and used, but it has great potential and can be useful in the study of relationships between unobservable categories. The empirical research presented in the article fills the cognitive gap in the use of advanced econometric methods in economic research.

Spectrophotometric quantification of paracetamol and tramadol hydrochloride by chemometric calibration methods.

The results of UV spectrophotometric analysis were analysed using partial least squares (PLS) and principal component regression (PCR) techniques to allow simultaneous evaluation of tramadol hydrochloride (TRA) and p-acetaminophen (PAR) in tablets. A calibration set of 16 mixtures, each containing PAR and TRA in various amounts, was created using a 24-full fractional design. The absorbance data set for the calibration set were obtained between 215-280 nm (Δλ = 0.1 nm). Subsequently, the concentration and absorbance sets were used to generate PCR and PLS calibrations. The ratio spectra- first derivative method was devised as a solution to the same problem to compare the outcomes of the chemometric methods used for the same experiment. After the proposed methods were shown to be accurate in the testing of validation samples, they were used in analysing commercial tablet samples. The analytical results showed that PCR and PLS methods can be used as alternative methods to high-performance liquid chromatography (HPLC). Correlation coefficients were determined for the working concentration range of 6-36 g.mL-1 for PAR and 4-22 g.mL-1 for TRA. The limits of detection and quantification were calculated as 0.9104 μg.mL-1 and 3.0347 μg.mL-1, respectively. The test results of the chemometric analyses and ratio spectra- first derivative method of the commercial tablet form are in agreement with the results of the one-way ANOVA with a confidence interval of 95%. This study shows that the ratio spectra- first derivative method, PCR and PLS models based on spectrophotometric measurements are very useful and straightforward techniques for the quantitative resolution of a two-component pharmaceutical preparation, requiring little sample preparation and little time for analysis.

Just-In-Time Learning Based Functional Spectral Data Modeling for In-Situ Measurement of Slurry Component Concentrations via Infrared Spectroscopy

For using the infrared spectroscopy to conduct in-situ measurement of slurry component concentrations during batch crystallization or fermentation process in engineering practice, a novel just-in-time learning (JITL) based functional modelling method is proposed for spectral data analysis in this paper to improve on-line measurement accuracy, by addressing engineering issues of very limited assay samples, spectral absorption nonlinearity and high-dimensional spectral variables. The traditional univariate or multivariate partial least-squares (PLS) model is subtly extended to the corresponding functional form for describing the relationship between the measured spectra and slurry component concentrations, such that the salient problem of only a small number of slurry component concentration samples available in practice for spectral data modeling could be effectively solved. The wavelet functions are adopted to properly approximate the measured spectral curves owing to their multi-scale and orthogonal properties. Accordingly, a functional spectral data model is established by wavelet functional PLS for on-line measurement of single and multiple component concentrations, respectively. Meanwhile, a JITL strategy is introduced to select the most representative samples for such modeling, which could efficiently handle the disparity issue involved with real-time samples and guarantee the model validity. Experimental results show that root mean square error of model prediction on the solution concentration during L-glutamic acid cooling crystallization process and multiple componts of ethanol fermentation process, could be evidently reduced over 40% and 50%, respectively, compared with the traditional PLS and support vector regression methods.

A rapid identification method of rice protein in rice starch based on synchronous fluorescence spectroscopy

Rice protein is a protein with high nutritional value and its essential amino acids are complete. Rice protein content is related to the Nutritional value of rice powder mixture. Three-dimensional synchronous fluorescence spectroscopy combined with Parallel factor analysis (PARAFAC) and Partial least squares (PLS) was applied for Protein content in rice starch. Synchronous Fluorescence Spectroscopy were obtained at 250-400 nm (excitation) and 260-750 nm (wave-length deviation). Firstly, data of Rice starch were collected using synchronous fluorescence spectroscopy equipment and the protein content of samples were got by Dumas Nitrogen analyzer ; secondly, three improved PLS methods, including interval partial least squares (iPLS), backward interval partial least squares (biPLS) and synergy interval partial least squares (siPLS),were used to find the most informative range. Lastly, models with better predictive turbidity of fragrance vinegar using PLS was established in the whole wavelength range .The results showed that the models by siPLS method, which separated the whole spectra into 10 intervals, and combine four intervals of [1 3 4 5]had the best predictive ability, the RMSECV and RMSEP were 0.1482 and 0.1696, and the calibration and prediction correlation coefficient were 0.8513 and 0.8236. Using three-dimensional synchronous fluorescence spectroscopy for rapid detection of Protein content in rice starch was feasible.

Smart chemometrics spectrophotometry for rapid simultaneous quantitative determination of paracetamol, diphenhydramine, and phenylephrine in commercial tablet compared to high-performance liquid chromatography as a reference method

The simultaneous separation of components in a multi-component drug is not possible by spectrophotometry alone due to their overlap. In this work, UV–Vis spectrophotometry along with continuous wavelet transform (CWT) and multivariate calibration method named partial least squares (PLS) as a fast, easy, inexpensive, and precise method was introduced for the simultaneous determination of paracetamol (PAR), diphenhydramine (DPE), and phenylephrine (PHE) in tablet dosage form without using the time-consuming extraction step. The validation of these techniques was surveyed by analyzing diverse synthetic mixtures with specific concentrations. The absorption of standard solutions of three pure components was processed through different mother wavelet families in the CWT approach. The best wavelet families were selected based on the appropriate zero crossing point and calibration curve. The best results were related to the Daubechies (db1) wavelet with a scaling factor of 21 at 214 nm, Coiflet (Coif2) with scaling factor of 22 at 262 nm, and Gaussian (Gaus2) with a scaling factor of 28 at 249 nm for PAR, DPE, and PHE, respectively. The limit of detection (LOD) and limit of quantification (LOQ) were lower than 0.4 μg mL−1 and 0.6 μg mL−1, respectively. The mean recovery values of synthetic mixtures were found to be 98.65%, 99.33%, and 99.43% for PAR, DPE, and PHE, respectively, where the root mean square error (RMSE) was not more than 1.5. Based on the k-Fold cross-validation of the PLS method, the optimum number of components of PAR, DPE, and PHE were 7, 7, and 6 that their related MSEP values were obtained at 6.04 × 10−4, 0.5258, and 0.0397, respectively. In this method, the mixtures are divided into two parts, training and testing. Mean recovery values of 100.04% for PAR and DPE, as well as 100.02% for PHE were achieved, where RMSE values were <0.07. The proposed methods were applied to the results of the commercial tablet, which were compared to the outcomes of high-performance liquid chromatography (HPLC) using a one-way analysis of variance (ANOVA) test. Regarding to the results, they were in good agreement and revealed no significant differences.