Hybrid Linear Analysis Research Articles

Aquaphotomics based screening of tomato powder extracts reveals susceptibility to bulking and coloring agents

Gaining prominence as indispensable culinary ingredients, tomato powders aren't exempt from fraudulent practices. While the complexity of conventional authentication methods hinders their adoption as routine assessment tools, our study examines the efficiency of aquaphotomics as a potential alternative, that entails limited preparation steps and involves no solvent extractions. Tested across three tomato varieties, aqueous blends comprising concentrations in the range [0%–20%] of two adulterant types (bulking agent, coloring agent), added separately or concurrently, were considered. Upon scanning with an XDS Near infrared spectrometer operating in transmission mode, the data was subjected to chemometrics based analysis over the 1st and 2nd water overtone ranges. While attempting to perform classification based on the level of adulteration, running hybrid principal component analysis and linear discriminant analysis (PCA-LDA) qualitative modeling on single adulterant mixtures allowed for recognition and prediction rates varying from [97.76%–100%] and [88.59%–100%], respectively. Slightly lower accuracies were obtained when coupling the bulking and coloring agent. Partial least squares regression (PLSR) models gave good estimates of the extent of adulteration, with respective R2 and RMSECV values reaching at best, 0.98 and 0.45%w/w. The aquagrams revealed distinctive spectral patterns highlighting the gradual level of adulteration. Assuredly, these findings ascertain the contributions aquaphotomics promises in ensuring the authenticity of tomato powders, when analyzed in their liquid state.

A hybrid linear discriminant analysis and genetic algorithm to create a linear model of aging when performing motor tasks through inertial sensors positioned on the hand and forearm

Background During the aging process, cognitive functions and performance of the muscular and neural system show signs of decline, thus making the elderly more susceptible to disease and death. These alterations, which occur with advanced age, affect functional performance in both the lower and upper members, and consequently human motor functions. Objective measurements are important tools to help understand and characterize the dysfunctions and limitations that occur due to neuromuscular changes related to advancing age. Therefore, the objective of this study is to attest to the difference between groups of young and old individuals through manual movements and whether the combination of features can produce a linear correlation concerning the different age groups.Methods This study counted on 99 participants, these were divided into 8 groups, which were grouped by age. The data collection was performed using inertial sensors (positioned on the back of the hand and on the back of the forearm). Firstly, the participants were divided into groups of young and elderly to verify if the groups could be distinguished through the features alone. Following this, the features were combined using the linear discriminant analysis (LDA), which gave rise to a singular feature called the LDA-value that aided in verifying the correlation between the different age ranges and the LDA-value.Results The results demonstrated that 125 features are able to distinguish the difference between the groups of young and elderly individuals. The use of the LDA-value allows for the obtaining of a linear model of the changes that occur with aging in the performance of tasks in line with advancing age, the correlation obtained, using Pearson’s coefficient, was 0.86.Conclusion When we compare only the young and elderly groups, the results indicate that there is a difference in the way tasks are performed between young and elderly individuals. When the 8 groups were analyzed, the linear correlation obtained was strong, with the LDA-value being effective in obtaining a linear correlation of the eight groups, demonstrating that although the features alone do not demonstrate gradual changes as a function of age, their combination established these changes.

LDA enhanced one-bit compressive sensing method for high-throughput mass spectrometry data feature selection

Mass spectrometry (MS) is commonly used for the proteins detection of biological samples. However, it generates high-throughput data, posing huge computational challenges for signal processing. In previous studies, we have proposed a one-bit perturbed block CS (OB-PBCS) method to realize feature selection (FS) and obtain a feature set with reduced dimension for classification. However, its feature selection is realized by solving the reconstruction problem with simple greedy strategies, which fails to consider the final classification objective during reconstruction. Considering such problem, in this paper, we propose a hybrid linear discriminant analysis (LDA) enhanced one-bit CS method, called L-OBPBCS, which selects features by considering the subsequent classification and consists of three parts. The first part roughly filters out the features that do not favor the classification task with the proposed single feature decision coefficient. Then, OB-PBCS obtains significant features from the filtered feature set. Afterwards, LDA algorithm is further used for refining, where the amplitude of the optimal projection vector is gathered as the linear discrimination mapping coefficients to identify important features. To show the efficiency of our proposed L-OBPBCS method, classification based on the selected features for both synthesized dataset and real ovarian cancer MS dataset is evaluated. Results show that the feature set derived by L-OBPBCS ensures excellent classification performance, which surpasses the existing one-bit type FS method, and the computational complexity of L-OBPBCS is obviously reduced compared with the existing OB-PBCS method.

Deep Learning-Based Quantitative Assessment of Melamine and Cyanuric Acid in Pet Food Using Fourier Transform Infrared Spectroscopy.

Melamine and its derivative, cyanuric acid, are occasionally added to pet meals because of their nitrogen-rich qualities, leading to the development of several health-related issues. A nondestructive sensing technique that offers effective detection must be developed to address this problem. In conjunction with machine learning and deep learning technique, Fourier transform infrared (FT-IR) spectroscopy was employed in this investigation for the nondestructive quantitative measurement of eight different concentrations of melamine and cyanuric acid added to pet food. The effectiveness of the one-dimensional convolutional neural network (1D CNN) technique was compared with that of partial least squares regression (PLSR), principal component regression (PCR), and a net analyte signal (NAS)-based methodology, called hybrid linear analysis (HLA/GO). The 1D CNN model developed for the FT-IR spectra attained correlation coefficients of 0.995 and 0.994 and root mean square error of prediction values of 0.090% and 0.110% for the prediction datasets on the melamine- and cyanuric acid-contaminated pet food samples, respectively, which were superior to those of the PLSR and PCR models. Therefore, when FT-IR spectroscopy is employed in conjunction with a 1D CNN model, it serves as a potentially rapid and nondestructive method for identifying toxic chemicals added to pet food.

Two-Stage Classification Model for the Prediction of Heart Disease Using IoMT and Artificial Intelligence.

Internet of Things (IoT) technology has recently been applied in healthcare systems as an Internet of Medical Things (IoMT) to collect sensor information for the diagnosis and prognosis of heart disease. The main objective of the proposed research is to classify data and predict heart disease using medical data and medical images. The proposed model is a medical data classification and prediction model that operates in two stages. If the result from the first stage is efficient in predicting heart disease, there is no need for stage two. In the first stage, data gathered from medical sensors affixed to the patient’s body were classified; then, in stage two, echocardiogram image classification was performed for heart disease prediction. A hybrid linear discriminant analysis with the modified ant lion optimization (HLDA-MALO) technique was used for sensor data classification, while a hybrid Faster R-CNN with SE-ResNet-101 modelwass used for echocardiogram image classification. Both classification methods were carried out, and the classification findings were consolidated and validated to predict heart disease. The HLDA-MALO method obtained 96.85% accuracy in detecting normal sensor data, and 98.31% accuracy in detecting abnormal sensor data. The proposed hybrid Faster R-CNN with SE-ResNeXt-101 transfer learning model performed better in classifying echocardiogram images, with 98.06% precision, 98.95% recall, 96.32% specificity, a 99.02% F-score, and maximum accuracy of 99.15%.

Application of Fourier Transform Infrared Spectroscopy and Multivariate Analysis Methods for the Non-Destructive Evaluation of Phenolics Compounds in Moringa Powder

This study performed non-destructive measurements of phenolic compounds in moringa powder using Fourier Transform Infrared (FT-IR) spectroscopy within a spectral range of 3500–700 cm−1. Three major phenolic compounds, namely, kaempferol, benzoic acid, and rutin, were measured in five different varieties of moringa powder, which was approved with respect to the high-performance liquid chromatography (HPLC) method. The prediction performance of three different regression methods, i.e., partial least squares regression (PLSR), principal component regression (PCR), and net analyte signal (NAS)-based methodology, called hybrid linear analysis (HLA/GO), were compared to achieve the best prediction model. The obtained results for the PLS regression method resulted in better performance for the prediction analysis of phenolic compounds in moringa powder. The PLSR model attained a correlation coefficient (Rp2) value of 0.997 and root mean square error of prediction (RMSEP) of 0.035 mg/g, respectively, which is comparatively higher than the other two regression models. Based on the results, it can be concluded that FT-IR spectroscopy in conjugation with a suitable regression analysis method could be an effective analytical tool for the non-destructive prediction of phenolic compounds in moringa powder.

Can inner and outer containment counteract pulls and pushes toward delinquency? A test of Walter Reckless’s containment theory

The aim of the current study is to provide an empirical test of containment theory of Walter Reckless (1899–1988). The theory proposes that outer and inner containment hold adolescents back from delinquency even when external factors pull and push them toward it. This early control theory was ahead of its time, but never received the empirical attention it deserves. This article outlines the core theoretical concepts and the basic propositions in order to empirically examine their validity. We employed hybrid linear regression analysis using longitudinal survey data of 612 adolescents (12–18 years old) in the city of The Hague, the Netherlands. The results indicate that outer and inner containment can be meaningfully distinguished, and that several but not all propositions of the theory are supported. Inner and outer containment function as a buffer against external pulls and are able to counteract the effect of increases in environmental pulls during adolescence. We conclude that containment theory is still a promising interaction theory that can help us understand why adolescents who experience external pulls toward delinquency are able to resist these influences.

Hybrid Linear Discriminant Analysis (HLDA) based Dimensionality Reduction and Unary Potential based on Fuzzy Possibilistic C-Means (FPCM) Clustering for Remote Sensing Images

Hybrid Linear Discriminant Analysis (HLDA) based Dimensionality Reduction and Unary Potential based on Fuzzy Possibilistic C-Means (FPCM) Clustering for Remote Sensing Images

Review of Power Sharing, Voltage Restoration and Stabilization Techniques in Hierarchical Controlled DC Microgrids

In order to overcome the problem of power generation in distributed energy, microgrid(MG) emerges as an alternative scheme. Compared with the ac microgrids, the dc microgrids have the advantages of high system efficiency, good power quality, low cost, and simple control. However, due to the complexity of the distributed generation system, the conventional droop control shows the drawbacks of low current sharing accuracy. Therefore, the improved primary control methods to enhance current sharing accuracy are systematically reviewed, such as particle swarm optimization programming, probabilistic algorithm and voltage correction factor scheme. However, it is difficult to achieve stable and coordinated operation of the dc microgrids by relying on the primary control. Hence, the various secondary control approaches, such as dynamic current sharing scheme, muti-agent system (MAS) control and virtual voltage control methods have been summarized for voltage regulation. Furthermore, the energy management system (EMS), modular-based energy router (MBER) and other coordinated control methods are reviewed to achieve power management. Besides, various control methods to compensate the effect of communication delay are summarized. Moreover, linear matrix inequality (LMI), Lyapunov-Krasovskii functional stability and Takagi-Sugeno model prediction scheme can be adopted to eliminate the influence of communication delay. In addition, due to the constant power loads (CPL) exhibit negative impedance characteristics, which may result in the output oscillation of filter. Thus, various control approaches have been reviewed to match the impedance, such as the nonlinear disturbance observer (NDO) feedforward compensation method, linear programming algorithm, hybrid potential theory and linear system analysis of polyhedral uncertainty. The merits and drawbacks of those control strategies are compared in this paper. Finally, the future research trends of hierarchical control and stability in dc microgrids and dc microgrid clusters are also presented.

Five modified classical least squares based models for stability indicating analysis of cyclobenzaprine HCl with its major degradation products: A comparative study

Five modified multivariate calibration models based on classical least squares (CLS) in addition to traditional CLS model are developed and validated for assaying cyclobenzaprine HCl (CZ) with its major degradants; dibenzocycloheptatrienone (DZ) and anthraquinone (AQ), whether in its pure form or in pharmaceutical dosage form. The five models are net analyte processing CLS (NAP-CLS), orthogonal signal correction CLS (OSC-CLS), direct orthogonal signal correction CLS (DOSC-CLS) and hybrid linear analysis following the strategy of Xu and Schechter (HLA-XS) or Goicoechea et al. (HLA-GO). The five modified CLS models in addition to traditional CLS were subjected to a comparative study through manipulation of ultra-violet absorption data in the region of 220–350 nm. Three factor four level experimental design was adopted which results in 16 mixtures calibration set covering various concentrations of CZ, DZ and AQ. An extra validation set, composed of nine mixtures, was prepared for validation of the prediction power of the presented models. Experimental results showed high capability of the proposed modified CLS models for assaying CZ successfully without any interference from the co-existing degradation products (DZ and AQ). A statistical comparison between the results of CZ analysis in its dosage form by the six CLS based models and the reported HPLC method was carried out presenting no significant difference in regards to precision and accuracy. Significance of CLS based models is a consequent of their high quantitative and qualitative power for assaying multi-components mixtures.

Quantitative analysis of Sudan dye adulteration in paprika powder using FTIR spectroscopy

ABSTRACTAs adulteration of foodstuffs with Sudan dye, especially paprika- and chilli-containing products, has been reported with some frequency, this issue has become one focal point for addressing food safety. FTIR spectroscopy has been used extensively as an analytical method for quality control and safety determination for food products. Thus, the use of FTIR spectroscopy for rapid determination of Sudan dye in paprika powder was investigated in this study. A net analyte signal (NAS)-based methodology, named HLA/GO (hybrid linear analysis in the literature), was applied to FTIR spectral data to predict Sudan dye concentration. The calibration and validation sets were designed to evaluate the performance of the multivariate method. The obtained results had a high determination coefficient (R2) of 0.98 and low root mean square error (RMSE) of 0.026% for the calibration set, and an R2 of 0.97 and RMSE of 0.05% for the validation set. The model was further validated using a second validation set and through the figures of merit, such as sensitivity, selectivity, and limits of detection and quantification. The proposed technique of FTIR combined with HLA/GO is rapid, simple and low cost, making this approach advantageous when compared with the main alternative methods based on liquid chromatography (LC) techniques.

UV-Vis Spectrophotometry and Multivariate Calibration Method for Simultaneous Determination of Theophylline, Montelukast and Loratadine in Tablet Preparations and Spiked Human Plasma.

Resolution of binary mixtures of theophylline (THEO), montelukast (MKST) and loratadine (LORA) with minimum sample pre-treatment and without analyte separation has been successfully achieved by multivariate spectrophotometric calibration, together with partial least-squares (PLS-1), principal component regression (PCR) and hybrid linear analysis (HLA). Data of analysis were obtained from UV-Vis spectra of three compounds. The method of central composite design was used in the ranges of 2-14 and 3-11 mg L-1 for calibration and validation sets, respectively. The models refinement procedure and their validation were performed by cross-validation. The minimum root mean square error of prediction (RMSEP) was 0.173 mg L-1 for THEO with PCR, 0.187 mg L-1 for MKST with PLS1 and 0.251 mg L-1 for LORA with HLA techniques. The limit of detection was obtained 0.03, 0.05 and 0.05 mg L-1 by PCR model for THEO, MKST and LORA, respectively. The procedure was successfully applied for simultaneous determination of the above compounds in pharmaceutical tablets and human plasma. Notwithstanding the spectral overlapping among three drugs, as well as the intrinsic variability of the latter in unknown samples, the recoveries are excellent.

Resolution of overlapped quaternary spectral bands by net analyte signal based methods; an application to different combinations in tablets and capsules

Net analyte signal (NAS) based methods (NBMs); namely NAS preprocessing (NAP) and hybrid linear analysis (HLA) were applied as multivariate calibration methods for resolving quaternary spectrally overlapped bands of Chlorzoxazone (CXZ), Paracetamol (PAR), Ibuprofen (IBU) and Ketoprofen (KTO) which are commonly co-formulated. The calibration set was composed of 17 mixtures within the concentrations ranges of 8.80–13.20, 10.56–15.84, 7.04–10.56 and 1.76–2.64 μg/mL for CXZ, PAR, IBU and KTO, respectively. A wavelength selection procedure was presented for the introduced NBMs. Figures of merit as limit of detection, selectivity and analytical sensitivity were also calculated based on the NAS concept. The developed NBMs models used smaller number of wavelengths as well as smaller number of latent variables which make NAP and HLA models more simple and interpretable than the conventional partial least squares (PLS). The models were successfully applied to tablets and capsules, and ANOVA was applied in comparison with an HPLC method.

A Hybrid Least Squares and Principal Component Analysis Algorithm for Raman Spectroscopy

Raman spectroscopy is a powerful technique for detecting and quantifying analytes in chemical mixtures. A critical part of Raman spectroscopy is the use of a computer algorithm to analyze the measured Raman spectra. The most commonly used algorithm is the classical least squares method, which is popular due to its speed and ease of implementation. However, it is sensitive to inaccuracies or variations in the reference spectra of the analytes (compounds of interest) and the background. Many algorithms, primarily multivariate calibration methods, have been proposed that increase robustness to such variations. In this study, we propose a novel method that improves robustness even further by explicitly modeling variations in both the background and analyte signals. More specifically, it extends the classical least squares model by allowing the declared reference spectra to vary in accordance with the principal components obtained from training sets of spectra measured in prior characterization experiments. The amount of variation allowed is constrained by the eigenvalues of this principal component analysis. We compare the novel algorithm to the least squares method with a low-order polynomial residual model, as well as a state-of-the-art hybrid linear analysis method. The latter is a multivariate calibration method designed specifically to improve robustness to background variability in cases where training spectra of the background, as well as the mean spectrum of the analyte, are available. We demonstrate the novel algorithm’s superior performance by comparing quantitative error metrics generated by each method. The experiments consider both simulated data and experimental data acquired from in vitro solutions of Raman-enhanced gold-silica nanoparticles.

The Prevalence Rates of Reported SBS Symptom and Gender: A Case Study in a Middle School

PP-29-148 Background/Aims: To describe the gender difference on the reported SBS symptom in a middle school case study. Methods: All students in Grade 1 and Grade 2 were selected in middle school A, Beijing. The SBS symptoms in past 1 year and in past 1 week were reported by the self-filling questionnaire. Finally, 385 students finished. Of them, 50.9% were boys and 49.1% were girls. A total of 165 students in Grade 1 finished reporting on 6 main SBS symptoms at exactly same point in 3 successive days in spring, summer, and winter. Database was established with Epidata 3.0. Logistic regression and fitted hybrid linear model provided by SPSS/PC13.0 were used. Results: Reported prevalence rates of SBS symptom both in past 1 year and past 1 week were closely related to the gender. After controlling other factors, reported prevalence rates of fatigue, eye itch or dry, overall SBS symptoms for girls in the last year were 1.851 times (OR = 1.851, P = 0.007), 1.672 times (OR = 1.672, P = 0.055), 1.600 times (OR = 1.600, P = 0.070) of those for boys, respectively; and the reported prevalence rates of fatigue, headache for girl in the last week were 1.871 times (OR = 1.871, P = 0.007), 1.685 times (OR = 1.685, P = 0.089)of those for boys, respectively. These results were similar to investigations on foreign adults' SBS symptoms. Fitted hybrid linear model analysis on repeated measure in different seasons indicated that gender was one of the impact factors related to the reported strength of SBS symptoms. After controlling other factors, the reported strength of eye itch or dry, concentration difficult, throat sore or dry, stuffy nose, overall SBS symptoms for boys were higher than those for girls, which were not seen any report before. Conclusion: Gender was related to SBS symptoms. Girls more likely suffered higher reported rates on SBS symptoms, while boys more likely suffered higher reported strength on SBS symptoms. All these indicated that girls had higher reported rates with lower strength threshold, while boys had lower reported rates with higher strength threshold.

Simultaneous analysis of three catecholamines by a kinetic procedure: comparison of prediction performance of several different multivariate calibrations

AbstractA rapid kinetic method for the simultaneous determination of levodopa, dopamine, and dobutamine was examined and developed. It was based on a consecutive reaction of a reduction of Cu(II) to Cu(I) by catecholamines, followed by the complexation of Cu(I) with neocuproine to form a yellow product in an acetic acid-acetate buffer. Spectrophotometric data were recorded at 453 nm (wavelength at the yellow complex absorption maximum) for 300 s. Linear calibrations were obtained in the concentration ranges of (0.08–1.44) × 10−5 mol L−1, (0.08–1.44) × 10−5 mol L−1, and (0.16–1.44) × 10−5 mol L−1 for levodopa, dopamine, and dobutamine, respectively. A variety of multivariate calibration models was developed for simultaneous analysis of the three analytes; while most models produced satisfactory prediction results for synthetic samples, the hybrid linear analysis method was arguably the best-performing (relative prediction error, RPET = 6.6 %). The proposed method was applied to an analysis of spiked rabbit serum samples and the results showed good agreement with the high performance liquid chromatography measurements.

In situ determination of dissolved organic carbon in freshwaters using a reagentless UV sensor

Reliable, high temporal and spatial resolution data are essential for enhancing our understanding of aquatic dissolved organic carbon (DOC) biogeochemical cycling. This paper describes a novel UV spectrophotometric sensor for the real time, in situ, high resolution (every 30 s) mapping of DOC in freshwaters. The sensor incorporates high resolution, multi-wavelength spectral acquisition (256 channel photodiode array) and a hybrid linear analysis (HLA) curve fitting algorithm. The portable and reagentless in situ UV sensor has a good linear range (0.5-15 mg C L(-1)) and precision (mean RSD 8.5%, n = 7 standards each measured 5 times) and quantitative recoveries were obtained for spiked river water (93.8 +/- 6.2%, n = 35). The DOC field data were in good agreement with results from a laboratory high temperature combustion method (t test (p = 0.05) gave P = 0.20 (n = 14), 0.89 (n = 21) and 0.92 (n = 15) for three separate freshwater deployments). These data suggested that solar radiation, coupled with microbial uptake and release, together with the physico-chemical parameters of hydrological flow, temperature and pH were significant drivers of DOC cycling in this ecosystem. Real time data processing provided an immediate data stream for mapping diurnal and/or seasonal DOC cycling. This capability will enhance our understanding of DOC sources, delivery mechanisms and internal cycling and support sustainable catchment management.

Simultaneous determination of paracetamol, phenylephrine hydrochloride and chlorpheniramine maleate in pharmaceutical preparations using multivariate calibration 1

Resolution of binary mixtures of paracetamol, phenylephrine hydrochloride and chlorpheniramine maleate with minimum sample pre-treatment and without analyte separation has been successfully achieved by methods of partial least squares algorithm with one dependent variable, principal component regression and hybrid linear analysis. Data of analysis were obtained from UV–vis spectra of the above compounds. The method of central composite design was used in the ranges of 1–15 mg L −1 for both calibration and validation sets. The models refinement procedure and their validation were performed by cross-validation. Figures of merit such as selectivity, sensitivity, analytical sensitivity and limit of detection were determined for all three compounds. The procedure was successfully applied to simultaneous determination of the above compounds in pharmaceutical tablets.

Simultaneous determination of vitamin B12 and its derivatives using some of multivariate calibration 1 (MVC1) techniques

Resolution of binary mixtures of vitamin B12, methylcobalamin and B12 coenzyme with minimum sample pre-treatment and without analyte separation has been successfully achieved by methods of partial least squares algorithm with one dependent variable (PLS1), orthogonal signal correction/partial least squares (OSC/PLS), principal component regression (PCR) and hybrid linear analysis (HLA). Data of analysis were obtained from UV–vis spectra. The UV–vis spectra of the vitamin B12, methylcobalamin and B12 coenzyme were recorded in the same spectral conditions. The method of central composite design was used in the ranges of 10–80 mg L −1 for vitamin B12 and methylcobalamin and 20–130 mg L −1 for B12 coenzyme. The models refinement procedure and validation were performed by cross-validation. The minimum root mean square error of prediction (RMSEP) was 2.26 mg L −1 for vitamin B12 with PLS1, 1.33 mg L −1 for methylcobalamin with OSC/PLS and 3.24 mg L −1 for B12 coenzyme with HLA techniques. Figures of merit such as selectivity, sensitivity, analytical sensitivity and LOD were determined for three compounds. The procedure was successfully applied to simultaneous determination of three compounds in synthetic mixtures and in a pharmaceutical formulation.

Constrained Regularization: Hybrid Method for Multivariate Calibration

We present a hybrid multivariate calibration method, constrained regularization (CR), and demonstrate its utility via numerical simulations and experimental Raman spectra. In this new method, multivariate calibration is treated as an inverse problem in which an optimal balance between model complexity and noise rejection is achieved with the inclusion of prior information in the form of a spectral constraint. A key feature is that the constraint is incorporated in a flexible manner, allowing the minimization algorithm to arrive at the optimal solution. We demonstrate that CR, when used with an appropriate constraint, is superior to methods without prior information, such as partial least-squares, and is less susceptible to spurious correlations. In addition, we show that CR is more robust than methods in which the constraint is rigidly incorporated, such as hybrid linear analysis, when the exact spectrum of the analyte of interest as it appears in the sample is not available. This situation can occur as a result of experimental or sample variations and often arises in complex or turbid samples such as biological tissues.