Multivariate Data Processing Research Articles

Pressure Sensor Array Based on Four DNA-Nanoenzymes with Catalase-like Activity for Portable Multiple Detection of Foodborne Pathogens.

This study has developed a pressure sensor array based on four functionalized DNA-nanoenzymes with catalase-like activity for multiple detections of foodborne pathogens through a portable pressure manometer. Benefiting from functionalization of 4-mercaptophenylboronic acid and β-mercaptoethylamine, the diversity of nonspecific interactions between four DNA-nanoenzymes and each of the nine bacteria leads to differences in pressure response patterns by catalyzing H2O2 to generate exclusive "fingerprints". As effective statistical tools for processing multivariate data, principal component analysis and hierarchical clustering analysis are employed to identify nine foodborne pathogens by analyzing pressure response patterns. Furthermore, the as-prepared sensor array can discriminate different mixtures of bacteria and achieve quantitative detection, with an average detection limit of 102 and 104 CFU mL-1 for Gram-positive and Gram-negative bacteria, respectively, demonstrating its desirable practicality and satisfactory accuracy for real sample detection. This study expands insights into multiple analyses of foodborne pathogens for food safety monitoring.

New approach to multivariate statistical process control and its application to wastewater treatment process monitoring

This paper presents a new process monitoring and fault diagnosis approach based on a modified Multivariate StatisticalProcess Control (MSPC) and evaluates its applicability to municipal wastewater treatment process monitoring. Firstly,a conventional MSPC, based on Principal Component Analysis (PCA), is adjusted to provide an easy-to-understand userinterface and then a new yet simplified reconfigurable diagnostic model is introduced. The user interface that has beendeveloped is designed to integrate MSPC seamlessly with existing process monitoring systems that use the so-called trendgraphs. The proposed diagnostic model is constructed by aggregating small models with either one or two inputs, whichenhances the tractability of the diagnostic model. The effectiveness of the modified MSPC is demonstrated through a series of offline and online experiments, using a set of real multivariate process data from a municipal wastewater treatment.plant.

Rapid determination of residual pefloxacin in mutton based on hyperspectral imaging and data fusion

Non-destructive detection of veterinary drug residues in meat products is critical for ensuring food safety and human health. The feasibility of using visible near-infrared (Vis-NIR) and near-infrared (NIR) hyperspectral imaging (HSI) system combined with data fusion for the prediction of pefloxacin residues in mutton was investigated in this study. A partial least squares regression optimization model was developed by multivariate data processing methods. The results showed that the low-level fusion (LLF) produced better results with R2P = 0.907 and RMSEP = 0.462 as compared to individual data blocks. Furthermore, intermediate-level fusion (ILF) based on iterative retention of informative variables algorithm showed the best results with R2P = 0.940 and RMSEP = 0.375. Finally, the visualized distributions of Vis-NIR and NIR based on the optimal model combination were mapped. The results have demonstrated that a combination of HSI with data fusion may be applied to rapidly and non-destructively detect pefloxacin residues in mutton.

Rapid quantification models for assessing melamine adulteration in sport nutrition supplements via benchtop and portable NIRS instruments

Sport nutrition supplements (SNS) are vulnerable to adulteration with melamine, artificially augmenting their protein content as determined by conventional assay methodologies. Vibrational spectroscopy techniques are suitable for the detection of adulteration because they allow rapid analysis, require minimal sample preparation, and can perform numerous analyses in a short time. The aim of this study was to develop rapid quantification models for the determination of melamine adulteration in a variety of SNS matrices using NIRS (near-infrared spectroscopy) in combination with multivariate data processing. Moreover, a comparison of benchtop and portable NIR instruments was carried out. Employing a stepwise approach involving OPLS-DA and PLS analysis, matrix discrimination and prediction ability were investigated. The benchtop instrument effectively discriminated among matrices (R2Y = 0.964, Q2 = 0.933), while the portable device, although showing a slightly altered pattern, maintained favorable discrimination capability (R2Y = 0.966, Q2 = 0.931). The quantitative PLS models for each SNS matrix exhibited comparable statistical indicators for both instruments with reasonable errors for melamine content estimation and prediction (RMSEE: 0.3–2.4 %, RMSEP: 0.98–2.99 %). Higher estimation and prediction errors were observed for protein-containing samples in both acquisition modes, probably due to the tendency of protein agglomeration and adhesion to different surfaces, which affects the homogeneity of the powder. Despite data loss due to the narrower spectral range and lower resolution of the portable instrument, all models were found to be suitable for predicting melamine content in sport nutrition supplements.

Integrated UPLC-MS and multivariate data processing approach to explore the chemical components of Uncaria rhynchophylla and its absorbed components in rats following oral adminstration

Uncaria rhynchophylla (Miq.) Jacks (Gouteng in Chinese, UR), a common herbal medicine, is chosen for treating cerebral diseases such as convulsions, hypertension, epilepsy, and eclampsia. However, comprehensive research on the chemical components of UR and its absorbed components has not been revealed. To develop an integrative UP LC-Q-TOF-MS and multivariate data processing approach for determining the chemical constituents of UR and its absorbed constituents in rat serum after oral adminstration. A UP LC-Q-TOF-MS equipped with a Waters UP LCT M HSS T3 was applied for sample seperation. P CA and OP LS-DA were applied to extract differential variables. Based on the self-built databases combined with mass spectrometry cleavage patterns and reference standards, the components structural identification were carried out. In total, 40 compounds, including alkaloids, flavonoids, terpenoids were identified in UR. Amongst them, 26 prototype constituents were found in rat serum, which might be the potential active constituents.The identified constituents absorbed in blood could be the effective constituents. This work provided a solid experimental data for further research on the pharmacological substance basis and demonstrated the practicability of the UP LC-Q-TOF-MS for chemical constituents structural elucidation from herbal medicines.

Modeling risk characterization networks for chemical processes based on multi-variate data

Risk characterization plays a crucial role in ensuring safe and stable chemical processes. However, existing studies cannot mine the deep-level relationships between multi-variate process data, resulting in un-detailed characterization results. Hence a multi-variate data-driven modeling method of risk characterization networks is proposed for chemical processes in this paper. The method particularly combines the Spearman's rank correlation coefficient and Apriori algorithm. Firstly, the Spearman's rank correlation coefficient is used to analyze the correlations between multi-variate process data. Secondly, the Apriori algorithm is further applied to mine the internal association rules of process data. Thirdly, a risk characterization network model is established using the complex network theory. To illustrate its validity, the Tennessee Eastman Process (TEP) and a catalytic cracking process are selected as test cases. Results show that the risk states of chemical processes can be effectively characterized, and the risk roots can also be reasonably identified and traced.

Analysis of Clinical Samples of Pancreatic Cyst's Lesions with A Multi-Analyte Bioelectronic Simot Array Benchmarked Against Ultrasensitive Chemiluminescent Immunoassay.

Pancreatic cancer, ranking as the third factor in cancer-related deaths, necessitates enhanced diagnostic measures through early detection. In response, SiMoT-Single-molecule with a large Transistor multiplexing array, achieving a Technology Readiness Level of 5, is proposed for a timely identification of pancreatic cancer precursor cysts and is benchmarked against the commercially available chemiluminescent immunoassay SIMOA (Single molecule array) SP-X System. A cohort of 39 samples, comprising 33 cyst fluids and 6 blood plasma specimens, undergoes detailed examination with both technologies. The SiMoT array targets oncoproteins MUC1 and CD55, and oncogene KRAS, while the SIMOA SP-X planar technology exclusively focuses on MUC1 and CD55. Employing Principal Component Analysis (PCA) for multivariate data processing, the SiMoT array demonstrates effective discrimination of malignant/pre-invasive high-grade or potentially malignant low-grade pancreatic cysts from benign non-mucinous cysts. Conversely, PCA analysis applied to SIMOA assay reveals less effective differentiation ability among the three cyst classes. Notably, SiMoT unique capability of concurrently analyzing protein and genetic markers with the threshold of one single molecule in 0.1mL positions it as a comprehensive and reliable diagnostic tool. The electronic response generated by the SiMoT array facilitates direct digital data communication, suggesting potential applications in the development of field-deployable liquid biopsy.

Nonlinear dimensionality reduction methods for potentiometric multisensor systems data analysis

AbstractElectrochemical multisensor systems were proven to be a very perspective research direction in modern analytical chemistry. The multisensor approach assumes an employment of cross‐sensitive chemical sensors in combination with multivariate data processing methods. Dimensionality reduction of the data obtained from multisensor systems is a very important step and it is mostly based on the traditional tools of chemometrics, such as Principal Component Analysis (PCA). In case of chemically complex samples, the response of multisensor systems may have a complex nonlinear nature and the use of linear modelling methods does not seem optimal. However, the potential of nonlinear dimensionality reduction methods in the processing of multisensor data has not yet been systematically studied. In this report we aim to fill this gap and assess the performance of various nonlinear dimensionality reduction tools: Isomap, Self‐Organizing Kohonen Maps, and Autoencoder. These methods were explored using three datasets from potentiometric multisensor systems obtained in various real applications. It was shown that nonlinear dimensionality reduction methods give the possibility to obtain additional and more detailed information about the analyzed objects/processes compared to PCA. However, calculation time for nonlinear dimensionality reduction methods essentially exceeds that for PCA, and it can be a limiting factor for application of such algorithms.

New approach to multivariate statistical process control and its application to wastewater treatment process monitoring

This paper presents a new process monitoring and fault diagnosis approach based on a modified Multivariate Statistical Process Control (MSPC) and evaluates its applicability to municipal wastewater treatment process monitoring. A conventional MSPC based on Principal Component Analysis (PCA) is firstly adjusted to have an easy-to-understand user interface and then a new yet simplified decomposable diagnostic model is introduced. The developed user interface is designed to seamlessly connect MSPC to existing process monitoring system adopting the so-called trend graphs. The proposed diagnostic model is derived in a constructive way by aggregating small size models with one input or two inputs to improve tractability of the diagnostic model. The effectiveness of the modified MSPC is illustrated through some offline and on-line experiments by using a set of real multivariate process data at a municipal wastewater treatment plant.

Correlation between Administering Misoprostol Vaginal and Intravenous Oxytocin with Successful Delivery

Labor induction is the stimulation of labor before there are signs of spontaneous labor. Induction of labor is performed if the risks of waiting for spontaneous delivery are judged to outweigh the risks of shortening the duration of pregnancy. Several labor induction methods are recommended, namely pharmacological and mechanical or non-pharmacological. Pharmacological induction is induction by administering prostaglandin E¹ analogs, namely misoprostol, and oxytocin, which will affect uterine contraction. This study aims to determine the relationship between vaginal administration of misoprostol and intravenous oxytocin with successful delivery. This research uses observational analytics with a cross-sectional approach. Sampling used the accidental sampling method with a sample of 32 respondents, which was carried out in August 2022. The instrument used for data collection was an observation sheet. The results showed that of the 32 respondents for successful delivery, almost all experienced successful delivery, with 24 respondents (75%). Multivariate data processing using the Spearman Rank test calculated using SPSS obtained a p-value < α = 0.001 (value α = 0.05). It can be concluded that H1 is accepted, which means there is a relationship between the vaginal administration of misoprostol and intravenous oxytocin with successful delivery in pregnant women.

A New EWMA Control Chart for Monitoring Multinomial Proportions

Control charts have been widely used for monitoring process quality in manufacturing and have played an important role in triggering a signal in time when detecting a change in process quality. Many control charts in literature assume that the in-control distribution of the univariate or multivariate process data is continuous. This research develops two exponentially weighted moving average (EWMA) proportion control charts to monitor a process with multinomial proportions under large and small sample sizes, respectively. For a large sample size, the charting statistic depends on the well-known Pearson’s chi-square statistic, and the control limit of the EWMA proportion chart is determined by an asymptotical chi-square distribution. For a small sample size, we derive the exact mean and variance of the Pearson’s chi-square statistic. Hence, the exact EWMA proportion chart is determined. The proportion chart can also be applied to monitor the distribution-free continuous multivariate process as long as each categorical proportion associated with specification limits of each quality variable is known or estimated. Lastly, we examine simulation studies and real data analysis to conduct the detection performance of the proposed EWMA proportion chart.

Employing PCA and Rietveld refinement on X-ray diffraction data to explore and quantify the crystalline phases in the cathode of depleted Zn-MnO2 alkaline batteries

A combined approach of the principal component analysis (PCA), a method of processing multivariate data, and Rietveld refinement on X-ray diffraction (XRD) data were employed to analyze the crystalline phases present in samples extracted from the cathode's internal and external surfaces of a depleted Zn-MnO2 alkaline primary battery. PCA allowed discrimination of the cathode surface region where the samples came from (six regions), inferring the number of crystalline phases of the samples and the relative concentrations as a function of the sample region. The PCA results were afterward confirmed by crystallographic analysis of the X-ray diffraction data. Rietveld refinements carried out on the X-ray diffractograms furnished the relative weight of the identified phases in the samples of the two cathode surfaces.

Fault detection and isolation using probabilistic wavelet neural operator auto-encoder with application to dynamic processes

Fault detection and isolation are crucial aspects that need to be considered for the safe and reliable operation of process systems. The modern industrial process frequently employs various sensors to measure multiple process variables. However, complex temporal dependencies and intra-channel, as well as inter-channel correlations in the observed multivariate data pause major challenges during fault detection. In this paper, we present the probabilistic wavelet neural operator auto-encoder (PWNOAE), an operator learning model that aims to learn the distribution of these multivariate process data and apply them for fault detection and isolation. Neural operators are networks that can efficiently learn operator dynamics in addition to function dynamics and therefore have better generalizability compared to other models. The proposed PWNOAE utilizes healthy data for learning the distribution, which is then used as a reference for detecting and isolating faults online. For learning the distribution of multivariate time series, the proposed PWNOAE combines the integral kernel with wavelet transformation in a probabilistic fashion. As wavelets help in the time-frequency localization of time series, PWNOAE exploits them to learn complex time-frequency characteristics underlying the multivariate datasets. The fidelity of the proposed PWNOAE is demonstrated using the Tennessee Eastman benchmark data and industrial data recorded from a pressurized heavy-water nuclear reactor operated by the Nuclear Power Corporation of India. The obtained results demonstrate the proposed method’s notable efficacy and success in detecting and isolating faults in the time series data compared to various well-established baselines in the literature.

Geochemical identification and classification of cherts using handheld laser induced breakdown spectroscopy (LIBS) supported by supervised machine learning algorithms

The elemental analysis of cherts is very important for understanding their geological history, formation and origin. In this work, chert samples collected from a mining complex located in the Gargano promontory (Apulia, Italy) have been investigated by laser-induced breakdown spectroscopy (LIBS) using a commercial handheld (h) LIBS instrument. In order to discriminate chert samples according to their mining site, the broadband LIBS spectra acquired on the specimens were subjected to multivariate data analysis. In particular, machine learning (ML) algorithms, including discriminant analysis (DA), support vector machine (SVM), and k-nearest neighbors (KNN), were applied to the data set from the averaged LIBS spectra of each sample dimensionally reduced by principal components analysis (PCA). Among the classifiers tested, the highest accuracy (72.34%) in identifying the chemical features of five different chert suites and discriminating their sources was achieved by Leave-One-Out Cross-Validation (LOO-CV) using the cosine KNN ML algorithm. The exclusion of PC1, which appeared to be mainly influenced by elements featuring large intra-class variation and engraved the clusterization, allowed to improve the accuracy of the classification reaching the value of 80.85% using the linear DA algorithm. The classification and discrimination between 2 classes (1 × 1) reached a high rate of correct predictions, which implied that this discrimination could be used as a subsequent mean to confirm if the class predicted in the previously performed multiclass classification was correct. In conclusion, the results of this study demonstrated that even chemically similar geological materials could be discriminated using LIBS analysis combined with appropriate multivariate chemometric data processing, in particular with the aim of recognizing and differentiating specific geological chert sources and identifying the origin of chert archaeological artifacts.

Wine Lees as Source of Antioxidant Molecules: Green Extraction Procedure and Biological Activity

An ultrasound-assisted extraction method, employing ethanol and water as solvents at low temperature (30 °C) and reduced time (15 min), was proposed to extract bioactive molecules from different cultivars (Magliocco Canino, Magliocco Rosato, Gaglioppo, and Nocera Rosso) of wine lees. All the extract yields were evaluated and their contents of phenolic acids, flavonoids, and total polyphenols were determined by means of colorimetric assays and high-performance liquid chromatography coupled with diode-array detection (HPLC-DAD) and Fourier transform infrared (FTIR) techniques. Radical scavenging assays were performed and the Magliocco Canino extracted with a hydroalcoholic mixture returned the best results both against ABTS (0.451 mg mL-1) and DPPH (0.395 mg mL-1) radicals. The chemometric algorithms principal component analysis (PCA) and partial least square regression (PLS) were used to process the data obtained from all qualitative-quantitative sample determinations with the aim of highlighting data patterns and finding possible correlations between composition and antioxidant features of the different wine lees cultivars and the extraction procedures. Wine lees from Magliocco Canino and Magliocco Rosato were found to be the best vegetable matrices in terms of metabolite content and antioxidant properties. The components extracted with alcoholic or hydroalcoholic solvents, specifically (-)-epigallocatechin gallate, chlorogenic acid, and trans-caftaric acid, were found to be correlated with the antioxidant capacity of the extracts. Multivariate data processing was able to identify the compounds related to the antioxidant features. Two PLS models were optimized by using their concentration levels to predict the IC50 values of the extracts in terms of DPPH and ABTS with high values of correlation coefficient R2, 0.932 and 0.824, respectively, and a prediction error lower than 0.07. Finally, cellular (SH-SY5Y cells) antioxidant assays were performed on the best extract (the hydroalcoholic extract of Magliocco Canino cv) to confirm its biological performance against radical species. All these recorded data strongly outline the aptness of valorizing wine lees as a valuable source of antioxidants.

Green Chemometric Determination of Cefotaxime Sodium in the Presence of Its Degradation Impurities Using Different Multivariate Data Processing Tools; GAPI and AGREE Greenness Evaluation

Four eco-friendly, cost-effective, and fast stability-indicating UV-VIS spectrophotometric methods were validated for cefotaxime sodium (CFX) determination either in the presence of its acidic or alkaline degradation products. The applied methods used multivariate chemometry, namely, classical least square (CLS), principal component regression (PCR), partial least square (PLS), and genetic algorithm-partial least square (GA-PLS), to resolve the analytes' spectral overlap. The spectral zone for the studied mixtures was within the range from 220 to 320 nm at a 1 nm interval. The selected region showed severe overlap in the UV spectra of cefotaxime sodium and its acidic or alkaline degradation products. Seventeen mixtures were used for the models' construction, and eight were used as an external validation set. For the PLS and GA-PLS models, a number of latent factors were determined as a pre-step before the models' construction and found to be three for the (CFX/acidic degradants) mixture and two for the (CFX/alkaline degradants) mixture. For GA-PLS, spectral points were minimized to around 45% of the PLS models. The root mean square errors of prediction were found to be (0.19, 0.29, 0.47, and 0.20) for the (CFX/acidic degradants) mixture and (0.21, 0.21, 0.21, and 0.22) for the (CFX/alkaline degradants) mixture for CLS, PCR, PLS, and GA-PLS, respectively, indicating the excellent accuracy and precision of the developed models. The linear concentration range was studied within 12-20 μg mL-1 for CFX in both mixtures. The validity of the developed models was also judged using other different calculated tools such as root mean square error of cross validation, percentage recoveries, standard deviations, and correlation coefficients, which indicated excellent results. The developed methods were also applied to the determination of cefotaxime sodium in marketed vials, with satisfactory results. The results were statistically compared to the reported method, revealing no significant differences. Furthermore, the greenness profiles of the proposed methods were assessed using the GAPI and AGREE metrics.

Towards the non-destructive analysis of multilayered samples: A novel XRF-VNIR-SWIR hyperspectral imaging system combined with multiblock data processing

The new challenge in the investigation of cultural heritage is the possibility to obtain stratigraphical information about the distribution of the different organic and inorganic components without sampling. In this paper recently commercialized analytical set-up, which is able to co-register VNIR, SWIR, and XRF spectral data simultaneously, is exploited in combination with an innovative multivariate and multiblock high-throughput data processing for the analysis of multilayered paintings. The instrument allows to obtain elemental and molecular information from superficial to subsurface layers across the investigated area. The chemometric strategy proved to be highly efficient in data reduction and for the extraction and integration of the most useful information coming from the three different spectroscopies, also filling the gap between data acquisition and data understanding through the combination of principal component analysis (PCA), brushing, correlation diagrams and maps (within and between spectral blocks) on the low-level fused. In particular, correlation diagrams and maps provide useful information for the reconstruction of a stratigraphic structure without the need to take any sample, thanks to the effective account for inter-correlation among data (variables), which is able to effectively characterize the possible combinations of components located in the same depth level. The highly innovative technology and the data processing strategy are applied for the multi-level characterization of a complex painting reproduction as an illustrative pilot study.

Latent Variable Method Demonstrator — software for understanding multivariate data analytics algorithms

The ever-increasing quantity of multivariate process data is driving a need for skilled engineers to analyze, interpret, and build models from such data. Multivariate data analytics relies heavily on linear algebra, optimization, and statistics and can be challenging for students to understand given that most curricula do not have strong coverage in the latter three topics. This article describes interactive software – the Latent Variable Demonstrator (LAVADE) – for teaching, learning, and understanding latent variable methods. In this software, users can interactively compare latent variable methods such as Partial Least Squares (PLS), and Principal Component Regression (PCR) with other regression methods such as Least Absolute Shrinkage and Selection Operator (lasso), Ridge Regression (RR), and Elastic Net (EN). LAVADE helps to build intuition on choosing appropriate methods, hyperparameter tuning, and model coefficient interpretation, fostering a conceptual understanding of the algorithms’ differences. The software contains a data generation method and three chemical process datasets, allowing for comparing results of datasets with different levels of complexity. LAVADE is released as open-source software so that others can apply and advance the tool for use in teaching or research.

Multiplexed COVID-19 antibody quantification from human sera using label-free nanoplasmonic biosensors.

Serological assays that can reveal immune status against COVID-19 play a critical role in informing individual and public healthcare decisions. Currently, antibody tests are performed in central clinical laboratories, limiting broad access to diverse populations. Here we report a multiplexed and label-free nanoplasmonic biosensor that can be deployed for point-of-care antibody profiling. Our optical imaging-based approach can simultaneously quantify antigen-specific antibody response against SARS-CoV-2 spike and nucleocapsid proteins from 50 µL of human sera. To enhance the dynamic range, we employed multivariate data processing and multi-color imaging and achieved a quantification range of 0.1-100 µg/mL. We measured sera from a COVID-19 acute and convalescent (N = 24) patient cohort and negative controls (N = 5) and showed highly sensitive and specific past-infection diagnosis. Our results were benchmarked against an electrochemiluminescence assay and showed good concordance (R∼0.87). Our integrated nanoplasmonic biosensor has the potential to be used in epidemiological sero-profiling and vaccine studies.

Comparative Multidimensional Analysis of the Current State of European Economies Based on the Complex of Macroeconomic Indicators

The stability of the economy of any country is primarily determined by the totality of macroeconomic indicators that describe the current economic state. This article provides a multi-dimensional analysis of the macroeconomic situation in Europe according to the data of 2020. The purpose of the article is to give a clear idea of the relative position of the economies of European countries, their proximity or the significance of their differences to determine each country’s place in the overall European economic system. Research objectives: (1) to identify the necessary macroeconomic indicators for the research; (2) to determine the direction of the impact of these indicators on the economic situation of European countries; (3) to carry out a cluster division of the studied countries with the identification of the main characteristics of each cluster; (4) to identify the main macroeconomic indicators that determine the level of welfare of European countries, (5) to reduce the dimension of the multi-dimensional economic space using integrated latent factors, (6) to build a fuzzy mathematical model to predict the level of welfare of the country when the specified values of latent factors are achieved. The methodological basis of the analysis is the methods of processing multi-dimensional information, such as multi-dimensional scaling, cluster analysis, factor analysis, multivariate regression analysis, analysis of variance, discriminant analysis, and fuzzy modelling methods. The multivariate data processing was performed using the SPSS and FuzzyTech computer programs. The results obtained in the article can be useful in carrying out macroeconomic reforms to improve the economic condition of the countries.