Bioconcentration assessment is important in the scientific evaluation of risks that chemicals may pose to humans and environment and is a current focus of regulatory effort. In this work, a new QSAR model by adopting electronic topological properties and flexibility of chemicals to predict the bioconcentration factor (BCF) in fish was established based on a large number of diverse compounds. Multiple linear regression (MLR) and partial least squares (PLS) were used to build reliable QSARs, which were evaluated with internal five cross-validations (Qcv2) and an external validation (Qex2). The proposed MLR model showed reasonable predictivity of BCF (Qcv2 = 0.79,Qex2 = 0.79) and included seven molecular descriptors, namely SsCl, SaasC, SaaaC, SsNH2, Hmin, SssO, and Phia. The PLS model (Qcv2 = 0.83, Qex2 = 0.80) was shown to be slightly better than the MLR one in prediction accuracy, using six PLS latent components. In addition, the relationship between the log BCF and the theoretical calculated log Kow was extensively investigated. These studies may help to understand the factors influencing the bioconcentration process of chemicals and to develop alternative methods for prescreening of environmental toxic compounds.

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The objective of this study was to develop a multiple linear regression (MLR) model using near infrared (NIR) spectroscopy combined with chemometric techniques for soluble solids content (SSC) in pomegranate samples at different storage periods. A total of 135 NIR diffuse reflectance spectra with the wavelength range of 950-1650 nm were acquired from pomegranate arils. Based upon sampling error profile analysis (SEPA), outlier diagnosis was conducted to improve the stability of the model, and four outliers were removed. Several pretreatment and variable selection methods were compared using partial least squares (PLS) regression models. The overall results demonstrated that the pretreatment method of the first derivative (1D) was very effective and the variable selection method of stability competitive adaptive re-weighted sampling (SCARS) was powerful for extracting feature variables. The equilibrium performance of 1D-SCARS-PLS regression model for ten times was similar to 1D-PLS regression model, so that the advantage of wavelength selection was inconspicuous in PLS regression model. However, the number of variables selected by 1D-SCARS was less to 9, which was enough to establish a simple MLR model. The performance of MLR model for SSC of pomegranate arils based on 1D-SCARS was receivable with the root-mean-square error of calibration set (RMSEC) of 0.29% and prediction set (RMSEP) of 0.31%. This strategy combining variable selection method with MLR may have a broad prospect in the application of NIR spectroscopy due to its simplicity and robustness.