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Hyperspectral Imaging (HSI) combines microscopy and spectroscopy to assess the spatial distribution of spectroscopically active compounds in objects, and has diverse applications in food quality control, pharmaceutical processes, and waste sorting. However, due to the large size of HSI datasets, it can be challenging to analyze and store them within a reasonable digital infrastructure, especially in waste sorting where speed and data storage resources are limited. Additionally, as with most spectroscopic data, there is significant redundancy, making pixel and variable selection crucial for retaining chemical information. Recent high-tech developments in chemometrics enable automated and evidence-based data reduction, which can substantially enhance the speed and performance of Non-Negative Matrix Factorization (NMF), a widely used algorithm for chemical resolution of HSI data. By recovering the pure contribution maps and spectral profiles of distributed compounds, NMF can provide evidence-based sorting decisions for efficient waste management. To improve the quality and efficiency of data analysis on hyperspectral imaging (HSI) data, we apply a convex-hull method to select essential pixels and wavelengths and remove uninformative and redundant information. This process minimizes computational strain and effectively eliminates highly mixed pixels. By reducing data redundancy, data investigation and analysis become more straightforward, as demonstrated in both simulated and real HSI data for plastic sorting. [ABSTRACT FROM AUTHOR]

Supramolecular carriers as a targeted drug delivery system have a significant role in managing the pharmacokinetics and pharmacodynamics of drugs. Therefore, in this project, we used cyclodextrin (CD) as a host molecule for tamoxifen (TMX) and methotrexate (MTX) to evaluate apoptosis mechanism and statistical patterns of release. The flow cytometry analysis showed that combination therapy by anticancer drugs was successful in apoptosis induction compared to free TMX and MTX. Also, real-time PCR and western blot analyses were performed to measure gene expression of Bak1, Bclx, and caspase-3 in estrogen and folate receptor-positive MCF-7 cell lines. All results confirmed that MNPs containing TMX and MTX can significantly increase Bak1/Bclx ratios plus the activity of caspase-3, while free MTX and free TMX showed no effects on triggering apoptosis after 24 h of exposure. According to results of pharmacokinetic studies onto TMX and MTX release, it is revealed that both models that consist of Sahlin-Peppas and Ritger-Peppas create the good fitting curves more than other statistical patterns at both low and high temperatures (37°C and 42°C). For this reason, activation parameters (ΔH‡, ΔS‡, and ΔG‡) were calculated to estimate the favorable model. The lower energy barrier indicates that the Sahlin-Peppas is the favorable model with both diffusion and relaxation process. Therefore, combination therapy by two anticancer drugs, TMX and MTX, utilizing cyclodextrin-based host–guest supramolecular magnetic nanoparticles (CD-MNPs) provided new results for induction of apoptosis and pharmacokinetic model. [ABSTRACT FROM AUTHOR]

Self‐modeling curve resolution (SMCR) techniques are widely applied for resolving chemical data to the pure‐component spectra and composition profiles. In most circumstances, there is a range of mathematical solutions to the curve resolution problem. The mathematical solutions generated by SMCR obey the applied constraints coming from a priori physicochemical information about the system under investigation. However, several studies demonstrate that a unique solution can be obtained by implementing some constraints such as trilinearity, equality, zero concentration region, correspondence, local‐rank, and non‐negativity under data‐based uniqueness (DBU) condition. In this research, a general rule for uniqueness (GRU) is proposed to unify all the different information that lead to a unique solution in one framework. Moreover, GRU can be a guide for developing new constraints in SMCR to get more accurate solutions. The authors are delighted to dedicate this manuscript to Professor Paul J. Gemperline in recognition of his significant contributions to the field of chemometrics. We believe that the chemometrics society's success in addressing its mission owes a great deal to his vision, passion for learning and teaching, and extensive scientific efforts over the years. We honor his friendship and generous supports. [ABSTRACT FROM AUTHOR]

Modern analytical instruments provide measurement data arrays with full of hidden and redundant information. Multivariate curve resolution (MCR) techniques decompose data set to physic‐chemically meaningful abstract profiles. On the other hand, for such data matrices, Borgen‐Rajkó self‐modeling curve resolution (SMCR) techniques reveal all possible solutions analytically under the minimal assumption. Although Lawton‐Sylvestre (LS) and Borgen methods have been proposed for the non‐negative curve resolution of two‐component and three‐component systems, there is still a great deal of interest to include further restrictions on the Borgen‐Rajkó SMCR. As modern hyphenated analytical instruments produce multiway (eg, three‐way) arrays, multiway analysis (eg, trilinear decomposition) was received much more popularity by chemists. This contribution is decicated to the extension of the Borgen algorithm to the trilinear data sets. The Borgen method incorporating trilinearity constraint is approached in an analogy to the trilinear Lawton‐Sylvester method. The proposed analytical triadic decomposition is applied to the simulated three‐way arrays with full rank and rank overlap (a type of rank deficiency) loadings. Finally, the proposed algorithm is further exemplified with a three‐component spectrofluorimetric study of acid‐base equilibrium of the pyridoxine. Investigating feasible regions of the simulated and experimental three‐component arrays reveal interesting additional information. Borgen method incorporating trilinearity constraint is approached in an analogy to the trilinear Lawton‐Sylvester method. The proposed analytical triadic decomposition is applied to the simulated three‐way arrays with full rank and rank overlap (a type of rank‐deficiency) loadings. Finally, the proposed algorithm is further exemplified with a three‐component spectrofluorimetric study of acid‐base equilibrium of the pyridoxine. Investigating feasible regions of the simulated and experimental three‐component arrays reveal interesting additional information. [ABSTRACT FROM AUTHOR]

The 8–17E DNAzyme is a temperature-dependent DNA metalloenzyme catalyzing RNA trans esterification in the presence of Pb2+ metal ions. Labeling the stems of the substrate and DNAzyme with the Cy3 and Cy5 respectively, the considered DNAzyme was studied by the fluorescence spectroscopy. The temperature-dependent variability of the Pb2+-specific 8–17E DNAzyme catalytic sensor was investigated trough a number of successive temperature fluctuations from 4 to 25 °C to obtain information. Investigating underlined biochemical system reveals that in this sensor, free single strands Enzyme (Cy5-E) and Substrate (Cy3-S) have higher fluorescence intensities than hybridized forms, suggesting that the fluorophores are in a contact quenched. Increasing the temperature has three effects: 1) Fluorescence intensities for the free fluorophores were reduced, 2) stability of the hybridized form was reduced and cleavage of substrate in presence of Pb2+was occurred, and 3) conformation of ES hybridized form was changed (before cleavage). As a result of conformation changes in ES, S was more affected than E in the ES. Pb2+ ion shows quenching effect on both fluorophores and in the absence of N2(g) purge the effect was more considerable. A main goal that we had in mind was to find if significantly lower concentrations of Pb2+ and ES, compared to previous reports, can generate any observable cleavage in substrate. Analysis of the cleavage reaction for 50 nM ES indicates that S is cleaved at 25 °C in presence of N2(g) and 0.5 μM Pb2+, while in same condition no apparent change occurs in the 4 or 10 °C. The rapid, sensitive and low cost strategy presented here can be applicable to study temperature-dependent behavior of other nucleic acid-based biosensors. [ABSTRACT FROM AUTHOR]

Several curve resolution techniques have been developed to analyze higher-order data structures, among which one can name parallel profiles with linear dependencies (PARALIND). The PARALIND is a variant of the Parallel Factor Analysis (PARAFAC) to cope with linear dependencies. Applying a PARAFAC model with different random initializations to a noisy three-way data with linearly dependent loadings always converges to a best-fitted noisy solution. Since PARAFAC maximally fits the noise part of the data, rank-deficiency is broken, and it causes a noise-induced unique decomposition. The uniqueness due to the presence of noise in rank-deficient cases is an artificial phenomenon called 'surface uniqueness.' The PARALIND model causes non-uniqueness to be revealed, ie, it leads to a different set of profiles in each run. However, it might not give us all the possible solutions. Therefore, there is still a demand for determining all and every possible solution. The aim of this paper is 3-fold: (1) A method is developed for determining feasible regions using the PARALIND model. (2) The surface uniqueness phenomenon is clarified. (3) The effect of interaction matrices in PARALIND as an active constraint is emphasized and discussed in detail using simulated noisy and noise-free two- and three-component data arrays. [ABSTRACT FROM AUTHOR]

Parallel factor analysis (PARAFAC) is believed to be one of the most widely used multiway curve resolution techniques. The superiority of PARAFAC is due to its uniqueness property that is provided under mild assumptions. Unambiguously recovered chemically and physically meaningful profiles by PARAFAC are reliable for quantitative and qualitative application. The essential uniqueness condition of PARAFAC breaks totally or partially in data sets with linearly dependent loadings. PARAFAC uniqueness levels include full uniqueness, partial uniqueness, uni-mode uniqueness, and full non-uniqueness. Although PARAFAC uniqueness conditions are more or less known, they cannot cover the whole of existing uniqueness problems, especially complex rank-deficient systems. The questions investigated here are as follows: Under which conditions is PARAFAC still unique and what is the extent of deterioration of PARAFAC uniqueness by linearly dependent loadings? These are among the most important points in dealing with three-way data arrays with PARAFAC models. Direct visualization of feasible bands using an explicit, well-defined, and trustworthy method is a key feature of our approach to the investigation of uniqueness and recasting partial uniqueness rules in this study. Different simulated examples with different uniqueness levels were designed, and rotational ambiguity was calculated for each case. In this way, the full implication of partial uniqueness was investigated. Direct visualization of feasible bands is beneficial not only for confirmation of previous rules of uniqueness and partial uniqueness but also for development of new theorems. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Multivariate curve resolution techniques try to estimate physically and/or chemically meaningful profiles underlying a set of chemical or related measurements. However, the estimation of profiles is not generally unique and it is often complicated by intensity and rotational ambiguities. Constraints as further information of chemical entities can be imposed to reduce the extent of ambiguities. Not only a long list of constraints has been introduced but also some of them can be applied in different ways. Either investigating constraint effects on the extent of rotational ambiguity or how they can be applied during curve resolution can shed light on curve resolution studies. The motivation behind this contribution is to pave the way to a clarification about the closure constraint. Considering simulated equilibrium and kinetic spectrophotometric data sets, different approaches to closure implementation were applied to demonstrate the geometrical explanation of closure constraint and its effect on multivariate curve resolution‐alternating least squares results. Besides, the closure constraint is compared with normalization and it is proved that the closure constraint is a Borgen norm and has the same effect as other Borgen norms in multivariate curve resolution. Finally, to further examine the closure constraint, a real data set was investigated. Investigating constraint effects on the extent of rotational ambiguity or how they can be applied during curve resolution can shed light on the curve resolution studies. The motivation behind this contribution is to pave the way to a clarification about closure constraint. Different approaches to closure implementation were applied to demonstrate the geometrical explanation of closure constraint. Additionally, the closure constraint is compared with normalization, and it is proved that the closure constraint is a Borgen norm. [ABSTRACT FROM AUTHOR]