The experimental determination of toxicological properties of commercial chemicals being costly and time consuming process, there is the need to develop mathematical predictive tools to theoretically quantify such properties. In this background, we have modeled the nonspecific toxicity of 51 substituted benzenes to the yeast Saccharomyces cerevisiae using extended topochemical atom (ETA) indices. Principal component factor analysis (FA) was used as the data-preprocessing step to reduce the dimensionality of the data matrix and identify the important variables that are devoid of collinearities. Multiple linear regression (MLR) analyses show that the best ETA model has the following statistical quality:We have also modeled the toxicity data using other topological descriptors including Wiener, Hosoya Z, molecular connectivity, kappa shape, Balaban J and E-state parameters apart from physicochemical parameters like AlogP98, MolRef, H_bond_acceptor and H_bond _donor. The best model shows the following quality: n ¼ 51,6. An attempt to use a combined set including both ETA and non-ETA parameters comes out with the following results:Besides FA-MLR, stepwise regression analysis and partial least squares (PLS) analysis were used as additional statistical tools. The use of the ETA indices suggested negative contributions of functionalities of amino and carboxylic acid substituents on the benzene ring and the presence of the electronegative atoms and positive contributions of branching and functionality of chloro substituent. Using factor scores as independent variables, principal component regression analysis (PCRA) was performed and the derived relations were of the following statistical qualities: Q 2 values being 0.926, 0.878 and 0.869 while R 2 values being 0.942, 0.903 and 0.899 for factor scores derived from ETA, non-ETA and combined matrices respectively. Thus, it appears that the ETA descriptors have significant potential in QSAR/QSPR/QSTR, which warrants extensive evaluation.

Bioconcentration refers to the absorption or uptake of a chemical from the media to an organism's tissues leading to greater concentration in tissues than that in the surrounding environment. Considering the importance of bioconcentration from the viewpoint of ecological safety assessment, a QSPR study was conducted based upon log BCF of 122 non-ionic organic compounds in fish using the recently introduced extended topochemical atom (ETA) indices. In deriving the models, principal component factor analysis (FA) followed by multiple linear regression (MLR), stepwise regression, partial least squares (PLS) and principal component regression analysis (PCRA) were applied as statistical tools. This was repeated with non-ETA (topological and physicochemical) descriptors and a combination set including both the ETA and non-ETA descriptors. The ETA indices suggested negative contributions of functionalities of nitro, amino and hydroxy substructures and positive contributions of branching, volume and functionality of chloro substituents. Again, the predictive ability of the developed models was compared with the previously reported models. Finally the validation of all the QSAR models was discussed based on random division, sorted log BCF data and K-means clusters for the factor scores of the original variable (ETA) matrix without the response property values. The results suggest that ETA parameters are sufficiently rich in chemical information to encode the structural features contributing to the bioconcentration of the non-ionic organic compounds in fish and thus these merit further assessment to explore their potential in QSAR/QSPR/QSTR modelling.

Considering n-octanol/water partition coefficient as an important chemical property in modeling the fate and persistence of chemicals, recently introduced Extended Topochemical Atom (ETA) indices have been used to model n-octanol/water partition coefficient data of 122 nonionic organic compounds which are highly persistent in the environment and reported to bioconcentrate considerably in lipid tissues. In deriving the models, principal component Factor Analysis (FA) followed by Multiple Linear Regression (MLR), stepwise regression, Partial Least Squares (PLS), and Principal Component Regression Analyses (PCRA) were applied as the statistical tools. The model development process was repeated with non-ETA (topological and physicochemical) descriptors and a combination set comprising both the ETA and non-ETA descriptors. The models with ETA indices suggested negative contributions of groups capable of hydrogen bonding and/or polar interactions and positive contributions of volume and degree of halogen substitution to the partition coefficient. Finally, we discuss validation of QSPR models by dividing the dataset into training and test sets based on different strategies, e.g., random division, sorted log K ow data and K-means clusters for the factor scores of the original variable (ETA) matrix without the response property values. The ETA (FA-MLR and stepwise regression) models were also applied on a purely external dataset (n ¼ 35) and acceptable predictive r 2 values (0.582 and 0.651 respectively) were obtained. The results suggest that ETA parameters are sufficiently rich in chemical information to encode the structural features contributing to the n-octanol/water partition coefficient of nonionic organic compounds and thus these merit further assessment to explore their potential in QSAR/QSPR/QSTR modeling.

Food packaging is used worldwide and is a common technique for protecting food safety and quality while increasing shelf life. Environmental issues caused by using polymers in packaging derived from petroleum are becoming more significant and more well‐known. Interest in ecofriendly packaging materials made of renewable resources (biopolymers) has steadily increased, particularly for temporary and throw‐away packaging applications. However, biopolymers frequently have poor processability, poor mechanical, and poor barrier characteristics, restricting their industrial application and scalable manufacturing. Researchers have created bionanocomposites with improved packaging qualities like antibacterial function, mechanical toughness, optical clarity, and gas and water barrier properties to overcome these restrictions. This review seeks to inform readers about recent advances in active food packaging that use biopolymers and bionanocomposite materials. The difficulties and possibilities presented by such resources for the food packaging sector have been examined. This review is timely given the recent spike in interest in research projects both in academia and industry seeking to create a new group of materials for packaging based on biopolymer for food with potential uses elsewhere.

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