Parallel Factor Analysis Research Articles

Evaluation of Groundwater Infiltration in Sewer Networks Using Fluorescence Spectroscopy

Diagnosing water infiltration is imperative to assess the integrity and operation performance of sewer networks, which is challenging and costly due to the complex nature of these networks. This study proposes a simple approach to evaluate the extent of groundwater infiltration via a fluorescence spectroscopy method, i.e., the identification and quantification of the fluorescent signature components of the dissolved organic matter sewage. A newly built sewer network in Shantou, Southern China, was selected for the case study, and a mass balance method based on water quality characteristic factors (total phosphorus and NH4+-N) was applied in parallel for comparison. The results showed that the mass balance method was substantially influenced by fluctuations in sewage and external water concentrations, rendering it unreliable due to the extensive data and calculations required. Conversely, three-dimensional excitation–emission matrix–parallel factor analysis enabled the identification of terrestrial humus compounds as the signatures of underground water sources. The estimation indicates that the groundwater proportion across the four surveyed inspection wells along the pipeline network ranged from 10.8 ± 2.5% to 9.6 ± 3.5%, conforming to the allowable groundwater infiltration limits set for municipal sewage pipelines (10–15%). This study presents a simple method for the in-depth analysis of groundwater infiltration in urban sewage networks, providing valuable insights into maintaining water quality and network integrity.

Identification and Quantification of the Main Psychoactive Ingredients of Cannabis in Urine Using Excitation-Eemission Matrix Fluorescence Coupled with Parallel Factor Analysis.

Cannabis is the most prevalent abused substance after alcohol, and its consumption severely harms human health and thus adversely impacts society. The identification and quantification of cannabis in urine play important roles in practical forensics. Excitation-emission matrix (EEM) fluorescence spectroscopy coupled with parallel factor (PARAFAC) analysis was developed to identify and quantify the four main ingredients of cannabis in urine samples. The main ingredients of cannabis including Δ-9-tetrahydrocannabinol (THC), cannabidiol, cannabinol, and tetrahydrocannabinolic acid (THC-COOH) exhibited diverse fluorescence characteristics, and the concentrations of these compounds depicted a positive linear relationship with the fluorescence intensity at the ng/mL level. The EEM/PARAFAC method adequately characterized and discriminated the four ingredients in calibration and prediction samples with a low root-mean-square error of prediction (RMSEP; 0.03-0.07 μg/mL) and limit of quantitation (LOQ; 0.26-0.71 μg/mL). The prediction results of the EEM/PARAFAC method well correlated with that of GC-MS with a low RMSEP range (0.01-0.05 μg/mL) and LOQ range (0.07-0.44 μg/mL) in urine samples. The EEM spectroscopic investigation coupled with the PARAFAC algorithm results in an organic, solvent-less, fast, reliable tool to perform accurate and rapid screening of cannabis abusers.

Fractionation and transport of compost‐derived dissolved organic matter fractions in saturated red soil and black soil columns

AbstractThe fractionation and transport of compost‐derived dissolved organic matter (DOMC) could affect the transport and fate of nutrients and DOM‐associated pollutants in the soil environment. In this study, the humic acid fraction of DOMC (HAC) and fulvic acid fraction of DOMC (FAC) were selected to investigate the fractionation and transport of DOMC in repacked soil columns of a red soil and a black soil under different KCl concentrations. The effluent DOMC fractions were monitored by ultraviolet (UV)‐visible (Vis) light and fluorescence spectroscopy. The results showed that the molecular weight (MW) of the effluent DOM approached that of the influent DOMC fractions with the injection of DOMC fractions. Three‐dimensional fluorescence excitation emission matrices (3D‐EEMs) coupled with parallel factor analysis resolved three fluorescent components, that is, low MW UV humic‐like substances (C1), high MW UV humic‐like substances (C2), and protein‐like substances (C3). The mobility of HAC and FAC decreased with increasing KCl concentrations (1 mM–50 mM), implying that electrostatic interaction was an important mechanism for the retention of DOMC in soil columns. The fact that the mobility of DOMC fractions in the black soil was greater than that in the red soil could be attributed to the high free Fe oxide content in the red soil. The retained DOMC fractions did not entirely desorb by the background electrolyte solution, suggesting that a part of the DOMC fractions retained in soil columns was strongly bound. These results are helpful in understanding the fractionation and transport of DOMC in soil environments.

Air nanobubble water improves plant uptake and tolerance toward cadmium in phytoremediation

Heavy metal contamination continues to be a persistent environmental problem. To address this issue, this study evaluated the impact of air nanobubbles (NBs) in water on the uptake of heavy metals by Alternanthera philoxeroides (A. philoxeroides), a common aquatic plant in China known for its rapid growth, strong vitality, and high capacity for heavy metal remediation. This study found that diluted air NBs (25% concentration) boosted cadmium uptake of A. philoxeroides by 17.39%. They also enhanced plant growth (25–50%) and photosynthetic pigments (10–20%) even at low cadmium levels (0.1 mM). Furthermore, the incorporation of 25% air NBs has been demonstrated to significantly amplify the performance of key antioxidant enzymes, such as superoxide dismutase and catalase, alongside heightened levels of crucial antioxidants such as malondialdehyde. This heightened activity of antioxidant defenses offers a compelling explanation for the potential amelioration of cadmium toxicity and concurrent enhancements in overall plant growth rates. Notably, a comprehensive analysis utilizing the excitation emission matrix–parallel factor analysis (EEM-PARAFAC) technique has revealed alterations in the composition of rhizosphere dissolved organic matter due to the presence of NBs. This ncomposition change of the rhizosphere dissolved organic mattermposition has subsequently exerted an influence on plant complexation processes and the subsequent uptake of cadmium. This study demonstrates that the strategic implementation of air NBs in water systems holds the potential to significantly enhance the plant's ability to detoxify cadmium and improve the uptake of heavy metals during phytoremediation processes.

Spectral Characteristics and Source Analysis of Chromophoric Dissolved Organic Matter in Surface Water of Taihu Lake Before Cyanobacterial Blooming

Chromophoric dissolved organic matter (CDOM) is an important part of the nutrient biogeochemical cycle in aquatic ecosystems. To explore the characteristics and sources of CDOM components in the surface water of Taihu Lake, UV-visible spectroscopy and excitation emission matrix fluorescence spectroscopy-parallel factor analysis were used to analyze CDOM components in surface water. Combined with CDOM optical parameters (a355, SUVA254, a250/a365, FI, BIX, and HIX), the spatial differences and pollution sources were identified, and a preliminary comparison was made between this study and the historical data of CDOM components in Taihu Lake. According to the results, a355, SUVA254, and a250/a365 showed the characteristics of high concentration, high aromatic ability, and low relative molecular weight of CDOM in the surface water of the eastern part of Taihu Lake; however, the northern part showed the opposite characteristics. Four components were isolated from CDOM using parallel factor analysis:one tyrosine-like (C1), two types of tryptophan (C2 and C4), and one fulionic acid (C3). The main component C1 had a strong linear relationship with the C2 and C3 components, suggesting that different components originated from similar pollution sources. The fluorescence index showed that CDOM in different areas of Taihu Lake were differently affected by endogenous and terrestrial inputs; however, the overall humification degree was low. This indicated that the CDOM components in Taihu Lake were primarily protein-like (C1, C2, and C4) (>85%) and autogenous, with good biochemical availability.

Characteristics of optical properties of DOM and nutrients in rainwater of different ecological areas of a large reservoir in China

Numerous studies have focused on the spectral characteristics and seasonal variations of dissolved organic matter (DOM) in rainwater. However, the relationship between the optical indices of DOM and nutrients in rainwater from different ecological areas of large reservoirs is poorly understood. A one-year monitoring study was conducted between March 2019 and February 2020 in the Danjiangkou Reservoir in Henan Province, China, to compare the composition, spectral characteristic parameters, and relationship between the optical indices of DOM and nutrients in rainwater under different ecological environments. The study showed that the average value of a300 in all samples was 5.29 ± 2.16 m-1 and showed a seasonal trend of higher in spring and winter and lower in summer and autumn as well as a regional difference of agricultural area > urban area > reservoir area. A three-dimensional fluorescence with parallel factor analysis (EEM-PARAFAC) revealed four components of the rainwater: C1 and C2 as UV humic-like substances, and C3 and C4 as protein-like substances. The protein-like components of rainwater from agricultural areas had a high fluorescence intensity, whereas the UV humic-like components of rainfall from urban and reservoir areas had a high fluorescence intensity. Analysis of the fluorescence indices showed that rainwater DOM humification was low and had a strong endogenous character in the Danjiangkou Reservoir. The redundancy analysis revealed that NO3−-N, DTN, and SO42− mainly influenced the DOM optical indices of rainwater in urban areas, EC, DTN, and DOC had the highest interpretation of the DOM optical indices of rainwater in agricultural areas, and SO42−, DOC, and DTN had the highest interpretation of the DOM optical indices of rainwater in the reservoir. Overall, understanding the characteristics of rainfall DOM fluorescence and the relationships with nutrients in different ecological regions provides important information for comprehending biogeochemical processes in reservoirs.

Assessing spatiotemporal variability in the concentration and composition of dissolved organic matter and its impact on iron solubility in tropical freshwater systems through a machine learning approach

Dissolved organic matter (DOM) plays important roles not only in maintaining the productivity and functioning of aquatic ecosystems but also in the global carbon cycle, although the sources and biogeochemical functions of terrestrially derived DOM have not been fully elucidated, particularly in the tropics and subtropics. This study aimed to evaluate the factors influencing spatiotemporal variability in (i) the concentration and composition of DOM, including dissolved organic carbon (DOC), ultraviolet absorption coefficient at 254-nm wavelength (a254), and components identified by fluorescence excitation–emission matrix coupled with parallel factor analysis (EEM-PARAFAC), and (ii) the concentration of dissolved iron (DFe) across freshwater systems (rivers, forested streams, and dam reservoirs) on a tropical island (Ishigaki Island, Japan) based on the results of water quality monitoring at 2-month intervals over a 2-year period. Random forests (RF) machine learning algorithm was employed, with the catchment characteristics (land use, soil type) and water temperature as the predictor variables for DOM and the composition of DOM (EEM-PARAFAC components) and hydrochemistry (water temperature, pH, and concentrations of divalent cations) as the predictor variables for DFe. The RF models for DOC, a254, and three humic-like components exhibited excellent predictive performance, indicating that these DOM properties are not only seasonally variable but also strongly influenced by the compositions of land uses and soil types in the upstream watershed. Poorly drained riparian lowland soil (Gleyic Fluvisols) was identified as the most important catchment parameter that positively influences these DOM variables. The RF model also explained a large portion of the variation in DFe, while terrestrial humic-like components were the most important parameters, emphasizing their significance as organic ligands for iron. These results improve our understanding of the impacts of terrestrial DOM and iron loadings on tropical and subtropical coastal ecosystems as well as on regional and global carbon budgets.

Unraveling the impact of dissolved organic matter on arsenic mobilization in alluvial aquifer of the lower Yellow River basin, Northern China

Increased exposure to arsenic (As) has been reported in many arid and semi-arid areas, where drinking water and agricultural irrigation strongly rely on groundwater. High-As groundwater occurred widely in the Northern Henan Plain, which belongs to the lower Yellow River alluvial plain area, posing a new threat to the health of local residents. Based on 77 groundwater samples collected from different hydrogeological units, the sources and optical characteristics of dissolved organic matter (DOM) components in groundwater were analyzed using a three-dimensional fluorescence spectroscopy excitation-emission matrix (3D-EEM) and parallel factor analysis (PARAFAC), to reveal the influence of organic matter on As mobilization and enrichment in Quaternary alluvial aquifers. The results showed that high-As groundwater was concentrated in the areas of the piedmont alluvial depressions and the Yellow River crevasse splay, which was closely related to the richness of organic matter in the buried sediments of the Yellow River paleochannels. Vertically, the variability of groundwater As was governed by the redox conditions and anthropogenic activities, leading to elevated As levels in deep aquifers. High-As groundwater was characterized by high content of humic-like components (C2+C3), strong humification, and weak authigenic sources, which can be attributed to the effects of frequent diversion of the Yellow River and the introduction of more terrestrial humic-like components by intensive agricultural activities (e.g., irrigation). The significant correlations between Fe2+ and As and NH4+ (r = 0.65, 0.48, p < 0.05) as well as the maximum fluorescence intensity of C2 and C3 with As (r = 0.59, 0.74, p < 0.05) reflected the dominant impact of DOM on As migration. The terrestrial-derived high molecular weight organic matter C2 facilitated As mobilization through complexation reactions, while the labile humic-like component C3 triggered the reductive dissolution of iron oxides/hydroxides through microbial metabolic processes, which together contributed to the enrichment of As in groundwater from the Northern Henan Plain.

Applying fluorescence spectroscopy and DNA pyrosequencing with 2D-COS and co-occurrence network to deconstruct dynamical DOM degradation of air-land-water sources in an urban river

In an urban river, comprehending the interplay between dissolved organic matter (DOM) and atmospheric, terrestrial, and aquatic sources is crucial. This encompassed investigating temporal variations in DOM and its association with the bacterioplankton community to gain profound insights into the biogeochemical dynamics and biodegradability of DOM. DOM was extracted from PM2.5, soil, sediment, bait, and terrestrial/aquatic plant residuals collected along the Wenyuhe River in Beijing, China - a region predominantly supplied with reclaimed water. Subsequently, mixed microbial communities from the river were introduced into DOM samples originating from each source and incubated for 10 days. Principal component analysis (PCA) applied to reassembled excitation-emission matrix (EEM) data revealed two distinct clusters: cluster 1 comprising soil, sediment, and PM2.5 samples; and cluster 2 consisting of bait as well as terrestrial/aquatic plant residuals. According to parallel factor analysis, C1 (microbial humic-like) and C2-C3 (fulvic-like) dominated the DOM from soil, sediment, and PM2.5. These components were continuously degraded during incubation, except for PM2.5. DOM from bait and terrestrial/aquatic plants contained representative components of C6 (phenolic-like) and C7 (tryptophan-like), which underwent extensive decomposition. Interestingly, DOM in PM2.5 contained aliphatic compounds and polycyclic aromatic hydrocarbons (PAHs) but exhibited weak degradation with the complete disappearance of C6 and C7. Rhodococcus was a unique species capable of degrading PAHs, which might be particularly important considering the specificity of PM2.5 pollution. Based on two-dimensional correlation spectroscopy (2D-COS), variations in DOM components such as C6, and C7 were significantly larger compared to those of C1, C2, C3, and C5 (terrestrial humic-like) from bait samples, sediments, and residual terrestrial plants. MW-2D-COS analysis revealed that DOM from bait samples and terrestrial/aquatic plants experienced substantial degradation by the second day while DOM from soil or sediment decomposed mainly on the fourth day. Notably, the decomposition of DOM fractions in PM2.5 occurred throughout the entire four-day period. Co-occurrence network analysis classified sources of DOM into two clusters similar to PCA results: cluster 1 showed significant microbial degradation of fulvic-like compounds while cluster 2 demonstrated deep microbial decomposition of tyrosine-like and phenolic compounds. Therefore, the artificial loading of DOM into rivers not only expands the chemical diversity within DOM but also perturbs bacterioplankton diversities.

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.

Origin, distribution and spatial characteristics of dissolved organic matter in the Heilongjiang River

The Heilongjiang River Basin is a vast area with significant DOM sources and composition differences. The mechanism of DOM degradation under spatial variation remains unclear. This research investigated the degradation characteristics of DOM in different watersheds of the Heilongjiang River. DOM levels were higher in midstream waters, while DOM degradation rates were higher in midstream and downstream waters. The parallel factor analysis (PARAFAC) results showed that the upstream amino acid fraction was significantly depleted, the midstream was dominated by the degradation of DOM of terrestrial origin, and the downstream humic acid fraction was decreased considerably. Gene sequencing results indicated that the upstream, middle, and downstream water bodies' microbial community composition and structure differed significantly. The network analysis results revealed microorganisms in upstream water bodies mainly utilized amino acid-like substances and small molecule humic acids. Microorganisms in the middle and lower reaches of the water column were characterized by the utilization of humic acid-like fractions. In this study, we further screened the key driving microorganisms (e.g., Flavobacterium and Lacibacter) responsible for the difference in the DOM utilization function of upstream-to-midstream and midstream-to-downstream microorganisms in the Heilongjiang River. These findings will help identify the cycling process of DOM under spatial variation and predict the succession pattern of microbial communities.

Distribution, characteristics and fate of fluorescent dissolved organic matter (FDOM) in the Bay of Bengal

The Bay of Bengal (BoB) is the largest sink to retain discharges from major rivers and the Sundarbans Mangrove Forest in Bangladesh and upholds significant ecological and resource diversity. This study aims to characterize, and identify sources, spatial dynamics, and the fate of the principal ecological web driver that is fluorescent dissolved organic matter (FDOM) in the BoB using advanced techniques of excitation-emission matrix (EEM) fluorescence spectroscopy and multivariate parallel factor (PARAFAC) analyses. The identified four protein-, two humic- and one detergent-like FDOM components mostly showed higher abundance in the shallow water than deep unlike a protein-like component. Such exceptional protein-like component was identified to form colloidal structure under elevated salinity in deep water. Autochthonous humic-like FDOM originated from primary production and water temperature counteracted microbial polymerization in shallow and deep water, respectively. The annual mass deposition indicated the influx of anthropogenic pollutants from both terrestrial and internal marine systems.

Synthesis and Characterization of Thiol-Protected Silver Nanoclusters for Photodynamic Therapy Applications

Aqueous metal nanoclusters have emerged as versatile tools in biomedical applications, particularly in the scope of imaging and cancer therapeutics. This research paper investigates a thiol-protected silver nanocluster (AgNC), focusing on establishing a reproducible synthesis method to obtain a pure cluster species for use in biological stability studies. Conventionally, nanocluster synthesis employs reducing agents, like NaBH4, which often poses challenges in reaction control and product purity. This study adopts an alternative approach, utilizing a Norrish Type I reaction to enhance synthesis. This light-activated reaction leverages the regulated concentration of alpha-hydroxy radicals from a photoinitiator (Omnirad 2959) achieved through UVA-induced homolytic bond cleavage. Monitoring AgNC formation through ultraviolet-visible (UV-Vis) and fluorescence emission-excitation matrix (EEM) spectroscopy and confirming product purity via parallel factor analysis (PARAFAC), this work offers insight into the influence of parameters such as light intensity, reactant concentration, irradiation wavelength, and oxygen presence on consistency and reproducibility of the AgNC syntheses. Methods like Polyacrylamide Gel Electrophoresis (PAGE) and centrifugation are also employed to facilitate separation and purification, preparing the AgNC for subsequent evaluation within biologically significant contexts (tumor microenvironments and phosphate buffer solutions). Although diverse reaction conditions yielded an array of nanocluster species, characterization of the AgNCs reveals challenges in reproducing a pure cluster species consistently. EEM and PARAFAC analyses underscore similarities across synthesized clusters, yet significant variations in reaction conditions persist. The primary application envisioned for these AgNCs is in Photodynamic Therapy (PDT), a form of radiative therapy that exploits the optical properties of metal nanoclusters to induce reactive oxygen species, thus eliciting cancer cell death. It is evident that further investigation is necessary to deepen the comprehension of AgNCs and validate their potential use in PDT. Future research should focus on refining reaction conditions, and effective separation, of AgNC, allowing for advancement in therapeutic interventions such as PDT.

Molecular insight into DOM fate using EEM-PARAFAC and FT-ICR MS and concomitant heavy metal behaviors in biologically treated landfill leachate during coagulation: Al speciation dependence

Various combined processes with pre-coagulation have been developed for biologically treated landfill leachate, but the microscopic-level processes occurring during coagulation remain largely unknown. Herein, dissolved organic matter (DOM) fate using fluorescence excitation emission matrix spectroscopy combined with parallel factor analysis and electrospray ionization coupled Fourier transform-ion cyclotron resonance mass spectrometry and concomitant heavy metal (HM) behaviors were explored at the molecular level. In addition, AlCl3 and two polyaluminum chloride (PACl) species (dominated by [AlO4Al12(OH)24(H2O)12]7+ and [(AlO4)2Al28(OH)56(H2O)26]18+, respectively) were used. The results show that all coagulants are efficient at removing DOM. PACl was found to be advantageous over AlCl3 in overcoming pH fluctuation, which is ascribed to the different dominant mechanisms, namely, entrapment and sweep flocculation for AlCl3 and charge neutralization for PACl. Consequently, PACl was more effective at removing humic substances, usually high-molecular-weight, oxygen-rich and unsaturated, than protein substances. For HM removal, PACl was likewise better and more stable, where As, Cu, Ni, Co and Hg were removed predominantly via adsorption, and Cr, Zn, Pb, Cd and Mn were removed via coprecipitation. Correlation analysis showed that humic substances tended to complex with HMs and be removed synergistically due to the ubiquitous occurrences of aromatic structures and oxygen-containing functional groups.

Excitation-emission fluorescence spectroscopy coupled with PARAFAC and MCR-ALS with area correlation for investigation of jet fuel contamination

The contamination of jet fuel has gained attention in the past years as a notable factor in aircraft accidents. Identifying the contamination sources is still a challenge, especially when they have a similar composition to the fuel, such as kerosene solvent (KS). A novel analytical methodology was developed by combining a set of excitation-emission matrix (EEM) fluorescence to area constrained multivariate curve resolution with alternating least-squares (MCR-ALS) and PARAllel FACtor (PARAFAC) analysis, in order to identify KS in blends with JET-A1. For this purpose, a dataset with 50 samples (KS and JET-A1 blends, 2.0–100% v/v) was used to build the multivariate models. Both PARAFAC and MCR-ALS allowed fuel quantification with 4.64% and 3.46% RMSEP, respectively; both models (PARAFAC and MCR-ALS) could quantify KS with high accuracy (RMSEP <5.36%). In addition, MCR-ALS model was able to recover the pure spectral profiles of KS, JET-A1 and interferers. GC-MS data of the samples proved the composition similarities between both petroleum distillates, thus being inefficient for identifying the contamination. These results indicate that the development of multivariate models using EEM was the key for contributing with a new low-cost and accurate method for on-line screening of jet fuel contamination.

Spatial variation of dissolved organic matter in the Tuojiang River Basin in Chengdu, China: insights based on EEMs-PARAFAC analysis.

Three-dimensional excitation-emission matrix fluorescence spectroscopy coupled with parallel factor analysis was adopted to investigate the characteristics of dissolved organic matter (DOM) components in water samples collected from the Tuojiang River Basin in Chengdu, including its main stream and tributaries. Four DOM components that matched with three fluorescence peaks were identified in the whole river basin and tributaries; while three components corresponding to four fluorescence peaks were identified in the main stream. In all cases, humic-like components accounted for high proportions of the DOM. Correlation analysis revealed the same sources for four components in the whole river basin and its tributaries, whereas two components had different sources in the main stream. Ultraviolet absorbance parameters (SUVA254, SR) and fluorescence parameters (BIX, HIX, FI, β:α) indicated the dominant autochthonous sources of DOM in the whole river basin. Higher terrestrial inputs of DOM were observed in the tributaries than in the main stream. In the areas influenced by human activities (6#, 17#, 18#), the sources of DOM showed strong terrestrial characteristics and high degrees of humification and aromatization, as well as serious pollution. The results of this study have potentially far-reaching implications for environmental water management in the area.

Application of two- and multiway chemometric strategies for describing elementomic changes in pepper plants exposed to cadmium stress by multielement determination

The objective of this work was to evaluate elemental changes in pepper exposed to Cd stress through different chemometric tools. For this purpose, pepper plants were grown under five different treatments with different Cd concentrations in the nutrient solution. Considering the hypothesis that pepper plants exposed to Cd stress during growth undergo changes in the macro- and microelemental distribution in leaves, stems, and roots, principal component analysis (PCA) and parallel factor (PARAFAC) analysis were applied to compare bidirectional and multivariate chemometric strategies to assess elemental changes in pepper plants. Since the number of variables and the data generated were large and complex, the application of chemometric tools was justified to facilitate the visualization and interpretation of results. The mineral composition, namely the Ca, Cd, Cu, Fe, K, Mg, Mn, N, and P contents, was assessed in 180 samples of leaves, stems, and roots of the cultivated peppers. Then, PCA and PARAFAC analysis were applied to compare bidirectional and multivariate chemometric strategies to assess elemental changes throughout pepper plants. The visualization of the trend on each sample and their intrinsic relationship with the variables were possible with the application of PCA. The use of PARAFAC analysis permitted the simultaneous study of all samples in a straightforward representation of the information that facilitated a quick and comprehensive understanding of the spatial distribution of elements in plants. Thus, macroelements (Ca, K, Mg, N, and P) that were found in higher concentrations in leaves did not present significant differences in the distribution along the plants under different treatment conditions. In contrast, a significant impact on the microelement (Cu, Fe, and Mn) distribution was produced between uncontaminated and contaminated samples. This analysis revealed a significant accumulation of Cd in roots and adverse effects on normal plant growth, demonstrating their level of phytotoxicity to pepper.

Fluorescence Excitation-Emission-Matrix Imaging.

We present a 4-dimensional (4D) fluorescence imaging system in which each of the 65,536 pixels in the image array contains an excitation-emission-matrix spectrum with 31 excitation wavelengths and 8 emission wavelengths (x, y, λexc, λem). Hadamard-transform multiplexing of the excitation light from a 31-channel programmable light source allows for an increase in the data acquisition rate so that each 65,536-pixel image can be obtained within 8 s. The system is demonstrated and characterized using, first, a 4D image of 10 capillaries filled with four dye solutions and their binary and ternary mixtures, and, second, using a sequence of about 100 images of layered fluorescent dye solutions and their changing fluorescence as a function of temperature. Multivariate analysis using parallel factor analysis produces images of the spatial distribution of the fluorophores together with their relative intensity as a function of time.

Integrated data detection and video restoration for optical camera communications

Optical Camera Communication (OCC) technology enables the use of Image Sensors (IS) with Visible Light Communications (VLC), one of the key technologies for the Sixth Generation (6G) of mobile communications. For Screen-to-Camera (S2C) applications in which Light-Emitting Diode (LED) screens modulate information symbols onto video pixels, increasing either the (transmitted) image resolution or frame rate may have a negative effect on the bit error performance and visual quality of OCC-encoded videos. This work proposes a novel OCC/S2C coding approach that lets both symbol transmission and video quality can be improved simultaneously. For this dual task, a Parallel Factor Analysis (PARAFAC) decomposition is applied for the first time in the literature to model an OCC system. In addition to validating the correct formulation of the proposed models, hypotheses, and algorithms, computational simulations demonstrate that increasing the number of video frames can result in time diversity gains. In accordance with the parameters of the system, this work also addresses the practical aspects of data rate, data volume, and computational complexity of the receiver.

Rapid Fluorescence EEM Spectroscopy Using Super-Cycle Hadamard-Transform Multiplexing.

Hadamard-transform (HT) multiplexing has recently been applied to increasingly complex spectroscopic techniques. It had been shown that the data acquisition time for fluorescence excitation emission matrix (EEM) spectroscopy can be reduced by 1 or 2 orders of magnitude using HT multiplexing of the excitation light using a programmable light source. In these previous studies, the data acquisition rate had been limited by the time it took to record an EEM, that is, to complete one cycle of multiplexed excitation spectra. The extraction of chemical information, such as concentration and chemical identity, is then obtained from parallel factor (PARAFAC) analysis of the sequence of EEMs. In this contribution, we increase the data acquisition rate by another order of magnitude, limited ultimately by the time it takes to record a single excitation spectrum. Our algorithm is entirely based on improved data processing, that is, it can be applied to previously recorded HT multiplexed data sets. The algorithm is based on three previously unexplored approaches: (1) we perform a PARAFAC multivariate analysis on the raw (multiplexed) data set, (2) the time-independent PARAFAC loading vectors are obtained prior to obtaining the time-dependent score vectors, and (3) when loading vectors are difficult to obtain from the EEMs, we instead use a rolling-average approach to considerably increase the stability of the fit. Analysis of experimental data shows that the scores of fluorescence EEMs with seven excitation wavelengths and over 1000 emission wavelengths can be obtained in less than 20 ms.