Superior gallstone dissolubility and safety of tert-amyl ethyl ether over methyl-tertiary butyl ether

Extraction of Lipids and Functional Properties of Defatted Egg Yolk Powder Obtained Using a One-Step Organic Solvent Lipid Extraction Process

A one-step organic solvent lipid extraction method was used to separate lipids from spray-dried egg yolk. Organic solvents tested were chloroform:methanol (CM, 2:1, v:v), methyl-tert-butyl ether (MTBE), or hexane:isopropanol (HI, 3:2, v:v). The resulting defatted egg yolk powder had between 21 and 30% more protein and between 22 and 25% less lipid than the initial spray-dried egg yolk powder (p < 0.05). The solubility of the powder decreased from 20% to 4% (p < 0.05) when CM was used as the organic solvent, likely due to protein denaturation by the chloroform. Gels made from MBTE and HI-extracted protein concentrates had similar hardness (p > 0.05) and were both harder than gels made using the initial egg yolk powder (p < 0.05). MTBE gels were springier, more cohesive, and gummier (p < 0.05) with similar resistance to the initial egg yolk powder (p > 0.05). The results of this study showed that the functionality of the protein in the defatted egg yolk powder was best retained when MTBE was used as the lipid extraction solvent.

New methods based on back propagation (BP) and radial basis function (RBF) artificial neural networks (ANNs) for predicting the occurrence of haloketones in tap water

Haloketones (HKs) is one class of disinfection by-products (DBPs) which is genetically toxic and mutagenic. Monitoring HKs in drinking water is important for drinking water safety, yet it is a time-consuming and laborious job. Developing predictive models of HKs to estimate their occurrence in drinking water is a good alternative, but to date no study was available for HKs modeling. This study was to explore the feasibility of linear, log linear regression models, back propagation (BP) as well as radial basis function (RBF) artificial neural networks (ANNs) for predicting HKs occurrence (including dichloropropanone, trichloropropanone and total HKs) in real water supply systems. Results showed that the overall prediction ability of RBF and BP ANNs was better than linear/log linear models. Though the BP ANN showed excellent prediction performance in internal validation (N₂₅ = 98-100%, R² = 0.99-1.00), it could not well predict HKs occurrence in external validation (N₂₅ = 62-69%, R² = 0.202-0.848). Prediction ability of RBF ANN in external validation (N₂₅ = 85%, R² = 0.692-0.909) was quite good, which was comparable to that in internal validation (N₂₅ = 74-88%, R² = 0.799-0.870). These results demonstrated RBF ANN could well recognized the complex nonlinear relationship between HKs occurrence and the related water quality, and paved a new way for HKs prediction and monitoring in practice.