There has been a considerable and continuous interest to develop equations for rapid and accurate prediction of the ME of meat and bone meal. In this study, an artificial neural network (ANN), a partial least squares (PLS), and a multiple linear regression (MLR) statistical method were used to predict the TME(n) of meat and bone meal based on its CP, ether extract, and ash content. The accuracy of the models was calculated by R(2) value, MS error, mean absolute percentage error, mean absolute deviation, bias, and Theil's U. The predictive ability of an ANN was compared with a PLS and a MLR model using the same training data sets. The squared regression coefficients of prediction for the MLR, PLS, and ANN models were 0.38, 0.36, and 0.94, respectively. The results revealed that ANN produced more accurate predictions of TME(n) as compared with PLS and MLR methods. Based on the results of this study, ANN could be used as a promising approach for rapid prediction of nutritive value of meat and bone meal.
A simple and rapid vortex assisted ionic liquid based liquid–liquid microextraction technique (VALLME) was proposed for preconcentration of trace levels of cadmium. According to this method, the extraction solvent was dispersed into the aqueous samples by the assistance of vortex agitator. Cadmium preconcentration was mediated by chelation with the 8-hydroxyquinoline (oxine) reagent and an IL, 1-octyl-3-methylimidazolium hexafluorophosphate ([Omim][PF6]) was chosen as the extraction solvent to extract the hydrophobic complex. Several variables such as sample pH, concentration of oxine, volume of [Omim][PF6] and extraction time were investigated in details and optimum conditions were selected. Under the optimum conditions, the limit of detection (LOD) was 2.9 μg L−1 for Cd (ІІ) and relative standard deviation (RSD%) for five replicate determinations of 125 μg L−1 was 4.1%. The method was successfully applied to the determination of cadmium in tap water, apple and rice samples.
A Quantitative structure -property relationships analysis has been applied to a series of chemical compounds which were of interest because of their roles in environmental samples. The incorporation neutral solute (log K s ) in anionic micelle (SDS) has been predicted by using molecular structural descriptors for a heterogeneous set of 62 neutral solutes with a range of more than 4log units. Two linear correlating models, Multiple Linear Regression (MLR) and Partial Least Squares (PLS) regression methods were used. The stepwise MLR of SPSS software was used for the selection of the variables. After variables selection, MLR and PLS methods used leave-one-out cross-validation for building the regression models. Appropriate models with low standard errors and high correlation coefficients were selected. The results showed that both MLR and PLS methods could model the relationship between solubility and their electronic and thermodynamic descriptors perfectly. The predictive quality of the QSPR models were tested for an external prediction set of 11 compounds randomly chosen from 62 compounds. The squared regression coefficients of prediction for the MLR and PLS regression methods were 0.9679 and 0.9728 respectively.
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