Multivariate curve resolution-soft independent modelling of class analogy (MCR-SIMCA).

BACKGROUND: Various classification, class modeling, and clustering techniques operate within abstract spaces, utilizing Principal Components (e.g., Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA)) or latent variable spaces (e.g., Partial Least Squares Discriminant Analysis (PLS-DA)). It's important to note that PCA, despite being a mathematical tool, defines its Principal Components under certain mathematical constraints, it has a wide range of applications in the analysis of real-world systems. In this research, we assess the viability of employing the Multivariate Curve Resolution (MCR) subspace within class modeling techniques, as an alternative to the PC subspace. (92). RESULTS: This study evaluates the use of the MCR subspace in class modeling methods, specifically in tandem with soft independent modeling of class analogy (SIMCA), to investigate the advantages of employing the meaningful physico-chemical subspace of MCR over the mathematical subspace of PCA. In the MCR-SIMCA strategy, the model is constructed by applying MCR to training samples from a target class. The MCR model effectively partitions the data into two smaller sub-matrices: the contribution matrix and the corresponding response matrix. In the next step, the contribution matrix resulting from the decomposition of the training set develops a distance plot (DP). First, the theory of the MCR-SIMCA model is discussed in detail. Next, two real experimental datasets were analyzed, and their performance was compared with the DD-SIMCA model. In most cases, the results were as good as or even more satisfactory than those obtained with the DD-SIMCA model. (146). SIGNIFICANCE: The suggested class modeling method presents a promising avenue for the analysis of real-world natural systems. The study's results emphasize the practical utility of the MCR approach, underscoring the significance of the MCR subspace advantages over the PCA subspace. (39).

Release kinetic modeling of Satureja Khuzestanica Jamzad essential oil from fish gelatin/succinic anhydride starch nanocomposite films: The effects of temperature and nanocellulose concentration.

Fish gelatin (FG) and octenyl succinic anhydride starch (OSAS) composite films loaded with 1, 2, 3 and 4 wt% bacterial nanocellulose (BNC) and Satureja Khuzestanica Jamzad essential oil (SKEO) were achieved successfully and their physicochemical and release properties were investigated. The results revealed that incorporation of BNC improved the tensile strength which was associated with FE-SEM, FTIR and XRD. Moreover, this study focused on the release modeling of SKEO in 4, 25 and 37 °C from nanocomposite films using different release kinetic and Arrhenius models. Also, analysis of variance-simultaneous component analysis (ASCA) and exploratory data visualization by principal component analysis (PCA) were carried out to investigate the effects of two controlled factors. Consequently, the Peleg model showed the best fitting of experimental data. The activation energies decreased by increasing the BNC concentration. This research demonstrated the nanocomposite film containing SKEO would be a suitable candidate for active food packaging.

A simple self modelling curve resolution (SMCR) method for two-component systems.

Multivariate self-modeling curve resolution (SMCR) methods are the best choice for analyzing chemical data when there is not any prior knowledge about the chemical or physical model of the process under investigation [[1Q3: The reference '1' is only cited in the abstract and not in the text. Please introduce a citation in the text.]]. However, the rotational ambiguity is the main problem of SMCR methods, yielding a range of feasible solutions. It is, therefore, important to determine the range of all feasible solutions of SMCR methods. Different methods have been presented in the literature to find feasible solutions of two, three, and four component systems. Here, a novel simple SMCR method is presented for calculating the boundaries of feasible solutions of two-component systems. At first, the simple strategy is presented for calculating the feasible solutions of two-component systems. Next, four different experimental two-component systems are analyzed in detail for calculating the boundaries of feasible solutions in both spaces, including complex formation equilibrium, keto-enol tautomerization kinetic, lipidomics data, and a case for quantification of an analyte in gray systems. In all cases, the boundaries of range of feasible solutions are properly determined by the proposed simple strategy.