Multiway Analysis Research Articles

Laban Effort in Empty-Handed Interactions of Hindustani Dhrupad Vocal Improvisation

Effort, commonly understood as the power of an action toward an intended goal, is acknowledged as an important aspect of music expressivity. Previous studies in Hindustani Dhrupad vocal improvisation, particularly those focusing on manual interactions with imaginary objects, have revealed the intricate connection between effort and various movement and melodic variables. The study employed manual annotations by participants who visually inspected and assessed the amount of effort that such interactions were perceived to require. However, since effort is inherently perceptual and subjective and the way that an observer makes assessments on effort levels remains a non-transparent process, the paper seeks to examine the applicability of the Laban Movement Analysis (LMA) system in this task. For this, it relies on a multi-way analysis of variance (ANOVA) to infer manually annotated (numerical) effort levels from Laban’s (categorical) Effort Factors, namely Weight, Flow, Time, and Space, for two Dhrupad performances. The results suggest that apart from the Space factor, which was excluded for reasons delineated, a good part of effort’s variance can be explained through the remaining three statistically significant Effort Factors, leading to the rejection of the null hypothesis that they are unrelated. By ascertaining this relationship, effort-related melodic aspects in Dhrupad improvisation can be predicted using the three Laban Effort Factors.

Combining different metal artifact reduction levels with sharpening filters and slice thickness for the visualization of mandibular canals perforated by implants.

To investigate the influence of combining levels of metal artifact reduction (MAR) tool with different filters and slice-thickness in the detection of mandibular canals perforated by implants on CBCT scans and to objectively assess the impact of the aforementioned combinations. Implants were placed above (8 implants) and inside (10 implants) mandibular canals of dried-mandibles. CBCT scans were obtained with the Eagle 3D unit (85 kVp, 8mA, 5 × 5cm FOV, 130μm voxel size, and off/medium/high MAR levels). Examiners evaluated the scans under each MAR level and across different conditions: no filter, Sharpen 1×, Sharpen 2×; 0mm, 1mm, and 2mm slice-thickness. The gray values on axial reconstructions were assessed. Area under the receiver operating characteristic curve (AUC), sensitivity, specificity, mean and standard deviation (SD) for gray values were calculated and compared by multi-way Analysis of Variance. Overall, the AUC and sensitivity decreased with high-level MAR (p < 0.0001), regardless other variables tested. Enabling MAR tool decreased mean gray values (p < 0.01) and increasing MAR levels reduced the SD values on scans with Sharpen 2× (p < 0.012). High-level MAR impairs the visualization of mandibular canals perforated by implants. Moreover, enabling MAR tool decreases the mean gray values. MAR tool, filters, and slice thickness influence the image quality of CBCT scans. Therefore, it is important to evaluate the impact of these parameters on the diagnosis of mandibular canals perforated by implants.

Computation of Outer Inverse of Tensors Based on t‐Product

ABSTRACTTensor computations play an essential role in various fields of science and engineering, including multiway data analysis. In this study, we established a few basic properties of the range and null space of a tensor by using block circulant matrices and a discrete Fourier matrix. We then discuss the outer inverse of the tensors based on ‐product with a prescribed range and kernel of third‐order tensors. We address the relation of this outer inverse with other generalized inverses, such as the Moore–Penrose inverse, group inverse, and Drazin inverse. In addition, we present a few algorithms for computing the outer inverses of the tensors. In particular, a ‐QR decomposition based algorithm was developed to compute outer inverses. It is well known that the confidentiality of information transmitted through the virtual world grows exponentially, and color image and video security have become a significant concern when communicating over the internet. As an application, a ‐QR decomposition based algorithm was demonstrated for concealing secret color images and videos.

Will Southeast Asia be the next global manufacturing hub? A multiway cointegration, causality, and dynamic connectedness analyses

We propose a novel framework to examine relationships among drivers of economic growth in Southeast Asia, a region poised to become a significant manufacturing destination. However, unbalanced economic growth among countries poses risks to multinational companies considering offshoring decisions. Our two-stage framework uses multi-way cointegration analysis and a vector error correction model (VECM) to investigate critical drivers of economic growth. We apply a QVAR model to evaluate dynamic connectedness and spillover effects of offshoring decisions. Using World Bank data, our results show that Southeast Asian countries are interconnected through complex relationships featuring multi-way cointegration and dynamic connectedness, informing evidence-based policy.

Half the price, twice the gain: How to simultaneously decrease animal numbers and increase precision with good experimental design.

Animal research often involves experiments in which the effect of several factors on a particular outcome is of scientific interest. Many researchers approach such experiments by varying just one factor at a time. As a consequence, they design and analyze the experiments based on a pairwise comparison between two groups. However, this approach uses unreasonably large numbers of animals and leads to severe limitations in terms of the research questions that can be answered. Factorial designs and analyses offer a more efficient way to perform and assess experiments with multiple factors of interest. We will illustrate the basic principles behind these designs, discussing a simple example with only two factors before suggesting how to design and analyze more complex experiments involving larger numbers of factors based on multiway analysis of variance.

Investigating ‘Land-Use Trajectories’ in Mediterranean Rural Areas with Official Statistics and a Multiway Factor Analysis

While displaying diversified economic values, agro-ecosystems remain a relevant component of rural landscapes in Southern Europe. Coupled with the expansion of ecologically fragile areas because of climate warming, intensification and simplification of rural landscapes—e.g., in lowland—represent frequent trajectories of rural land-use change (RLUC) in the Mediterranean region and demonstrated to harm ecosystem functionality and ecological quality, especially in a context of socioeconomic transformations of landscapes. Additionally, an incipient ‘extensivation’ of geographically remote and economically marginal agricultural systems was also observed, likely following depopulation and land abandonment. The present study elaborated in this direction, providing an integrated RLUC evaluation scheme based on a multivariate analysis of land-use indicators derived from official statistics. This approach was applied to a continuous, long-term RLUC monitoring of a rural landscape in an ecologically complex Mediterranean region (Latium, Central Italy) experiencing multiple trends (e.g., lowland urbanization, crop intensification in gently sloping areas, and land abandonment/depopulation in steep zones) with diversified environmental implications. Based on administrative inventories, such evaluation was carried out over fifty years (1970–2020) in the study area, considering trends over time in selected crop surfaces at the municipal scale as inputs of a multiway factor analysis (MFA). This analysis quantifies stability in the main rural land-use types and possible changes in the dominant farming systems. Simplified indicators of land configuration (namely a crop intensity index and a rural woodland index reflecting ‘intensification’ or ‘extensivation’ of local farming systems) made available at the same spatial and temporal scales completed the informative picture corroborating MFA results. Taken together, our findings delineate multiple changes in rural landscapes, discriminating land-use trajectories in coastal and inland districts. These development trajectories were basically dependent on processes of (i) land abandonment in relict and remote areas, (ii) crop intensification in dynamic rural districts, and (iii) fringe urbanization along the coastal strip.

Cost-effectiveness analysis of acalabrutinib in treatment of patients with relapsed/refractory form of mantle cell lymphoma who previously received at least one line of therapy

Objective: to conduct a clinical and economic study of acalabrutinib for the treatment of adult patients with relapsed/refractory form of mantle cell lymphoma who previously received at least one line of therapy in the Russian Federation.Material and methods. Acalabrutinib and ibrutinib were considered as compared treatment options. Based on the adjusted indirect comparison, they were found to be comparable in terms of overall survival and progression-free survival. Therefore, a cost minimization analysis method was selected for the study. Based on results of ACE-LY-004 clinical trial a distributed survival model was developed. The model was used to calculate drug costs over a 3-year horizon. To explore uncertainty in the results of economic evaluations we conducted a sensitivity analysis. A number of patients in the hypothetical cohort who could be further treated with a less expensive drug was determined during a missed opportunity analysis. Both mean life years gained and mean progression-free life years were calculated too.Results. Based on simulations over a 3-year horizon, the mean life years gained were 2.45, and the mean progression-free life years were 1.75. The average costs of acalabrutinib therapy both over 1 year and in total over 3 years were 36.6% lower compared to ibrutinib. So, in the 1st year, the costs for acalabrutinib were 3,546,237,60 rubles, for ibrutinib – 5,591,391,00 rubles (a difference of 2,045,153,40 rubles). Using acalabrutinib for 3 years was associated with costs of 7,252,980,60 rubles compared to 11,435,852,60 rubles for ibrutinib (a decrease of 4,182,872,00 rubles). Univariate multi-way sensitivity analysis showed the robustness of modeling results to price fluctuations in the range ±10%. For a hypothetical cohort of 100 patients, using acalabrutinib instead of the more expensive ibrutinib within the same budget will provide therapy to additional 57 patients per year.Conclusion. The results of the study demonstrated that acalabrutinib compared to ibrutinib for the treatment of adult patients with relapsed/ refractory form of mantle cell lymphoma can reduce costs for the 1st year of therapy by 2,045,153,40 rubles (36.6%), and by 4,182,872,00 rubles (36.6%) for 3 years. Thus, acalabrutinib is the cost-saving option for previously treated adult patients with relapsed/refractory mantle cell lymphoma in the Russian Federation.

Influence of tube current and metal artifact reduction on the diagnosis of external cervical resorption in teeth adjacent to a dental implant in CBCT: an ex-vivo study.

This ex-vivo study aimed to assess the influence of tube current (mA) and metal artifact reduction (MAR) on the diagnosis of early external cervical resorption (EECR) in cone-beam computed tomography (CBCT) in the presence of an adjacent dental implant. Twenty-three single-rooted teeth were sectioned longitudinally and EECR was induced using a spherical drill and 5% nitric acid in 10 teeth. Each tooth was positioned in the socket of the lower right canine of a dry human mandible and CBCT scans were acquired using 90 kVp, voxel of 0.085 mm, field of view of 5 x 5 cm, and varying tube current (4, 8 or 12 mA), MAR (enabled or disabled) and implant conditions (with a zirconia implant in the socket of the lower right first premolar or without). Five oral radiologists evaluated the presence of EECR in a 5-point scale and the diagnostic values (area under the receiver operating characteristic curve - AUC, sensitivity, and specificity) were compared using multi-way Analysis of Variance (α = 0.05). Kappa test assessed intra-/inter-evaluator agreement. The tube current only influenced the AUC values in the presence of the implant and when MAR disabled; in this case, 8 mA showed lower values (p<0.007). MAR did not influence the diagnostic values (p>0.05). In general, the presence of an implant reduced the AUC values (p<0.0001); sensitivity values with 8 mA and MAR disabled, and specificity values with 4 mA and MAR enabled and 8 mA regardless MAR were also decreased (p<0.0001). Variations in tube current and MAR were unable to improve EECR detection, which was impaired by the presence of an adjacent implant. Increasing tube current or activating MAR tool does not improve EECR diagnosis, which is hampered by the artifacts generated by dental implants.

Understanding Factors Associated with Interest in Sustainability-Certified Wine among American and Italian Consumers.

The wine industry has been witnessing a growth in businesses crafting sustainability-certified wines and in the attention of consumers to sustainability, especially in the United States and Italy. To identify the characteristics of consumers who prefer sustainability-certified wine, this study analysed the relationship between consumers' demographics, wine buying behaviour, and interest in sustainability-certified wine, focusing on these two countries for comparison. Data were collected through an online survey of US and Italian consumers. Through correspondence analysis, k-modes clustering analysis, and multi-way correspondence analysis, this study revealed a stronger relationship between demographics and interest in sustainability-certified wine among US consumers than Italian consumers. In particular, middle-aged US consumers exhibited a greater interest than seniors. The patterns of connections between consumers' wine buying behaviour and interest in sustainable wine were similar for the two countries. In particular, consumers who purchase wine weekly had a keen interest, and those who purchase wine sporadically had no or little interest. Furthermore, this study uncovered the intricate relationship among various variables, providing a comprehensive understanding of the association between wine consumer characteristics and their interest in sustainability-certified wine.

Hypergraph regularized nonnegative triple decomposition for multiway data analysis

Tucker decomposition is widely used for image representation, data reconstruction, and machine learning tasks, but the calculation cost for updating the Tucker core is high. Bilevel form of triple decomposition (TriD) overcomes this issue by decomposing the Tucker core into three low-dimensional third-order factor tensors and plays an important role in the dimension reduction of data representation. TriD, on the other hand, is incapable of precisely encoding similarity relationships for tensor data with a complex manifold structure. To address this shortcoming, we take advantage of hypergraph learning and propose a novel hypergraph regularized nonnegative triple decomposition for multiway data analysis that employs the hypergraph to model the complex relationships among the raw data. Furthermore, we develop a multiplicative update algorithm to solve our optimization problem and theoretically prove its convergence. Finally, we perform extensive numerical tests on six real-world datasets, and the results show that our proposed algorithm outperforms some state-of-the-art methods.

Using multi-way correspondence analysis to examine the Nobel Prize data in STEM related disciplines

There are two purposes of this paper. The first, and primary, purpose is to examine the association between the Country of nationality, the Discipline, and Time period of Nobel Prize laureates in the Science, Technology, Engineering, and Mathematics (STEM) related disciplines. This is done by performing a multi-way correspondence analysis (MWCA). The second purpose is to provide a technical, bibliographic and practical overview of MWCA. Our investigation of the variables shows that there is a statistically significant three-way association between them, as well as between only one pair of the variables: Country and Time period. The row-tube biplot provides a visual description of this association.

Trend Projections of Potential Evapotranspiration in Yangtze River Delta and the Uncertainty

Global warming may increase potential evapotranspiration (ETp), reducing the water resources in Yangzi River Delta. Therefore, it is important to investigate the trend of ETp there under the background of climate change. To this purpose, the systematic biases in temperature outputs of 24 global climate models (GCMs) under 3 shared socioeconomic pathways—representative concentration pathways (SSPs) emission scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5)—are first corrected by using 8 bias correction methods. Then, the trend of ETp in Yangtze River Delta is projected by using 4 ETp calculation formulas (Blaney–Criddle, Hargreaves–Samani, Makkink, and Priestley–Taylor). The uncertainty of the projections is estimated and decomposed by using multi-way analysis of variance frameworks. The influence of uncertainty on the projected change signal is quantified by using the signal-to-noise ratio. The results show that all emission scenarios indicate robust increments of ETp. Specifically, relative to 1971~2000, ETp will increase by 0.14~0.17 mm d−1 (5.7~6.8%) during 2021~2050 and by 0.21~0.41 mm d−1 (8.5~16.7%) during 2061~2090, respectively. During 2021~2050, the uncertainty of ETp projections is dominantly contributed by the main effects of GCM (63%) and the ETp calculation formula (24%). During 2061~2090, it is mainly contributed by the main effect of GCM (36%), followed by the main effects of the emission scenario (34%) and the ETp calculation formula (18%). The ETp projections are generally reliable and robust during the two projection periods.

Analyzing postprandial metabolomics data using multiway models: a simulation study

BackgroundAnalysis of time-resolved postprandial metabolomics data can improve the understanding of metabolic mechanisms, potentially revealing biomarkers for early diagnosis of metabolic diseases and advancing precision nutrition and medicine. Postprandial metabolomics measurements at several time points from multiple subjects can be arranged as a subjects by metabolites by time points array. Traditional analysis methods are limited in terms of revealing subject groups, related metabolites, and temporal patterns simultaneously from such three-way data.ResultsWe introduce an unsupervised multiway analysis approach based on the CANDECOMP/PARAFAC (CP) model for improved analysis of postprandial metabolomics data guided by a simulation study. Because of the lack of ground truth in real data, we generate simulated data using a comprehensive human metabolic model. This allows us to assess the performance of CP models in terms of revealing subject groups and underlying metabolic processes. We study three analysis approaches: analysis of fasting-state data using principal component analysis, T0-corrected data (i.e., data corrected by subtracting fasting-state data) using a CP model and full-dynamic (i.e., full postprandial) data using CP. Through extensive simulations, we demonstrate that CP models capture meaningful and stable patterns from simulated meal challenge data, revealing underlying mechanisms and differences between diseased versus healthy groups.ConclusionsOur experiments show that it is crucial to analyze both fasting-state and T0-corrected data for understanding metabolic differences among subject groups. Depending on the nature of the subject group structure, the best group separation may be achieved by CP models of T0-corrected or full-dynamic data. This study introduces an improved analysis approach for postprandial metabolomics data while also shedding light on the debate about correcting baseline values in longitudinal data analysis.

Multi-way Analysis of the Gender Dimension of the Sustainable Development Goals

The gender dimension of the Sustainable Development Goals (SDG) is integral to the goals established to foster the development of nations, as it is intertwined with other goals and enhances their attainability. Thus, this study examines 28 indicators belonging to the goals of the sustainable development objectives, distinguishing between the gender SDGs (6 indicators) and other SDGs (22 indicators), all of which were reported by 110 countries in the year 2023. The assessment considers four regions: the African continent (28 countries), the Americas region (21 countries), Asia-Oceania (29 countries), and Europe (32 countries). The STATICO multivariate technique is used (consisting of four Co-inertia analyses and one Partial Triadic Analysis) to study the interactions among the indicators. The goal is to determine whether differences or similarities exist between these indicators within each region and to make a comparative assessment across countries. The study’s findings show the existing covariances between the various targets of the 2030 Agenda and indicate that in Europe, the gender dimension is more closely integrated with the other SDGs than in the other regions. In Africa, substantial variations between countries are observed, while Asia, Oceania, and the Americas face challenges in specific indicators and countries in terms of achieving sustainable development.

Effects of Geographical Origin and Tree Age on the Stable Isotopes and Multi-Elements of Pu-erh Tea

Pu-erh tea is a famous tea worldwide, and identification of the geographical origin of Pu-erh tea can not only protect manufacture’s interests, but also boost consumers’ confidence. However, tree age may also influence the fingerprints of Pu-erh tea. In order to study the effects of the geographical origin and tree age on the interactions of stable isotopes and multi-elements of Pu-erh tea, 53 Pu-erh tea leaves with three different age stages from three different areas in Yunnan were collected in 2023. The δ13C, δ15N values and 25 elements were determined and analyzed. The results showed that δ13C, δ15N, Mg, Mn, Fe, Cu, Zn, Rb, Sr, Y, La, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, and Lu had significant differences among different geographical origins (p &lt; 0.05). Mn content was significantly influenced by region and tree age interaction. Based on multi-way analysis of variance, principal component analysis and step-wised discriminant analysis, 24 parameters were found to be closely related to the geographical origin rather than tree age, and the geographical origin of Pu-erh tea can be 100.0% discriminated in cross-validation with six parameters (δ13C, δ15N, Mn, Mg, La, and Tb). The study could provide references for the establishment of a database for the traceability of Pu-erh tea, and even the identification of tea sample regions with different tree ages.

The effects of segmentation on cognitive load, vocabulary learning and retention, and reading comprehension in a multimedia learning environment

BackgroundSegmentation is a common pedagogical approach in multimedia learning, but its effects on cognitive processes and learning outcomes have yet to be comprehensively explored. Understanding the role of segmentation is crucial, as it has the potential to influence the way instructional materials are designed and delivered in digital learning environments.ObjectivesThis research aims to fill this gap by examining the impact of segmentation on cognitive load, vocabulary acquisition, retention, and reading comprehension in a multimedia learning context.MethodologyParticipants were selected from two language schools in Zhengzhou through a multi-stage random sampling method. Ninety teenage students were randomly assigned to six experimental groups. The study utilized a 2 × 3 factorial design to examine segmentation and textual augmentation effects. Four assessment instruments were employed: a Reading Comprehension Test, a Vocabulary Assessment Test, a Cognitive Load Assessment Scale, and a Prior Knowledge Test. The experiment comprised four stages: pre-test, Instruction, post-test, and follow-up. Data analysis was performed using SPSS 22 software, involving descriptive statistics, one-way, and multi-way analysis of variance.ResultsResults indicated that high segmentation significantly impacts cognitive load, vocabulary learning, retention, and reading comprehension across various aspects of multimedia learning. In essence, segmentation reduces cognitive load, supports learning efficiency, and facilitates more profound understanding, vocabulary learning, and retention.Conclusions and implicationsHigh segmentation in multimedia learning significantly impacts cognitive load, vocabulary learning, comprehension, and retention. Educators should prioritize segmentation for more effective and engaging e-learning experiences.

Potential impact of metal crowns at varying distances from a carious lesion on its detection on cone-beam computed tomography scans with several protocols.

This study evaluated the impact of artifacts generated by metal crowns on the detection of proximal caries lesions in teeth at various distances using cone-beam computed tomography (CBCT). Additionally, the diagnostic impacts of tube current and metal artifact reduction (MAR) were investigated. Thirty teeth were arranged within 10 phantoms, each containing 1 first premolar, 1 second premolar, and 1 second molar. A sound first molar (for the control group) or a tooth with a metal crown was placed. Of the 60 proximal surfaces evaluated, 15 were sound and 45 exhibited enamel caries. CBCT scans were acquired using an OP300 Maxio unit (Instrumentarium, Tuusula, Finland), while varying the tube current (4, 8, or 12.5 mA) and enabling or disabling MAR. Five observers assessed mesial and distal surfaces using a 5-point scale. Multi-way analysis of variance was employed for data comparison, with P<0.05 indicating statistical significance. The area under the curve (AUC) varied from 0.40 to 0.60 (sensitivity: 0.28-0.45, specificity: 0.44-0.80). The diagnostic accuracy was not significantly affected by the presence of a metal crown, milliamperage, or MAR (P>0.05). However, the overall AUC and specificity were significantly lower for surfaces near a crown (P<0.05). CBCT-based caries detection was not influenced by the presence of a metal crown, variations in milliamperage, or MAR activation. However, the diagnostic accuracy was low and was further diminished for surfaces near a crown. Consequently, CBCT is not recommended for the detection of incipient caries lesions.

Impact of the spatial orientation of the patient's head, metal artifact reduction, and tube current on cone-beam computed tomography artifact expression adjacent to a dental implant: A laboratory study using a simulated surgical guide.

The aim of this study was to evaluate image artifacts in the vicinity of dental implants in cone-beam computed tomography (CBCT) scans obtained with different spatial orientations, tube current levels, and metal artifact reduction algorithm (MAR) conditions. One dental implant and 2 tubes filled with a radiopaque solution were placed in the posterior region of a mandible using a surgical guide to ensure parallel alignment. CBCT scans were acquired with the mandible in 2 spatial orientations in relation to the X-ray projection plane (standard and modified) at 3 tube current levels: 5, 8, and 11 mA. CBCT scans were repeated without the implant and were reconstructed with and without MAR. The mean voxel and noise values of each tube were obtained and compared using multi-way analysis of variance and the Tukey test (α=0.05). Mean voxel values were significantly higher and noise values were significantly lower in the modified orientation than in the standard orientation (P<0.05). MAR activation and tube current levels did not show significant differences in most cases of the modified spatial orientation and in the absence of the dental implant (P>0.05). Modifying the spatial orientation of the head increased brightness and reduced spatial orientation noise in adjacent regions of a dental implant, with no influence from the tube current level and MAR.

Classification of water by bacterial presence using chemometrics associated with excitation-emission matrix fluorescence spectroscopy

Bacterial presence in water is an important indicator of water quality and, when found in high concentrations, may risk human health. The detection of total coliforms, thermotolerant coliforms, and Escherichia coli (E. coli) in water through standard methods involves time-consuming and expensive laboratory tests, which may not always provide timely and accurate results. An alternative approach is excitation-emission matrix fluorescence spectroscopy (EEMFS), which offers fast detection of bacteria in water by analyzing fluorescent compounds. Chemometrics methods can be used to process EEMF spectrum, extract the relevant information, and differentiate water samples based on the presence of bacteria using classification models. In this study, various classification algorithms were applied to EEMFS datasets, including k-nearest neighbors (k-NN), partial least squares discriminant analysis (PLS-DA), multiway-PLS (NPLS-DA), principal component analysis with discriminant analysis (PCA-DA), support vector machines (SVM), and random forest (RF). Models were developed after the unfold multiway and parallel factor analysis (PARAFAC) to classify groundwater, freshwater, saltwater, and treated water samples according to the presence of E. coli, thermotolerant coliforms, and total coliforms. Among these models, PLS-DA, SVM, and RF demonstrated superior performance in discriminating the samples in most cases. In the test sets, the accuracy of the best models for total coliforms varied from 85.2% to 100% for groundwater, 71.4% to 98.2% for freshwater, 64.6% to 81.3% for treated water, and 65.8% to 71.1% for saltwater. Accuracy for E. coli and thermotolerant coliforms ranged from 89.3% to 100% in groundwater and from 64.7% to 87.5% for treated water. Results for thermotolerant coliforms in freshwater were 64.8% to 95.2%, and 63.3% to 76.7% for saltwater. For E. coli, accuracy ranged from 73.8% to 78.6% for freshwater and 65.0% to 80.0% for saltwater.

A multiway spectral analysis model to monitor the kinetic chlorination reaction of caffeine and determine its content in commercial liquid and solid drinks.

A multiway data analysis model, namely parallel factor analysis (PARAFAC) was proposed to decompose a three-way array of second-order kinetic UV measurements, for the chlorination reaction of caffeine with NaOCl, into a set of the spectra, time, and concentration matrices. The multiway resolution provided the simultaneous estimation of spectral, kinetic, and quantitative analysis of caffeine. The ability of the PARAFAC tool was checked by analyzing the validation samples in the presence of interferences. The added recovery and relative standard deviations for caffeine in the spiked samples were calculated as 99.1%-99.5% and 0.52%-1.34% for Iced Coffee Black liquid coffee (ICB), 99.5%-103.0% and 0.42%-1.03% for Jacobs Monarch Gold 100% Instant Coffee (JMG) and 99.5%-101.4% and 0.11%-0.13% for Çaykur Black Filter (Süzen) Bag Tea (BTB). Caffeine in commercial drinks was analyzed using the concentration matrices of the PARAFAC application. The PARAFAC results were statistically compared to those obtained by the developed UPLC method.