A classification and identification model of extra virgin olive oil adulterated with other edible oils based on pigment compositions and support vector machine.

Adulteration identification of extra virgin olive oil (EVOO) is a vital issue in the olive oil industry. In this study, chromatographic fingerprint data of pigments combined with machine learning methodologies were successfully identified and classified EVOO, refined-pomace olive oil (R-POO), rapeseed oil (RO), soybean oil (SO), peanut oil (PO), sunflower oil (SFO), flaxseed oil (FO), corn oil (CO), extra virgin olive oil adulterated with rapeseed oil (EVOO-RO) and extra virgin olive oil adulterated with corn oil (EVOO-CO). Support vector machine (SVM) classification of EVOO, other edible oils, and EVOO adulteration identification achieved 100% accuracy for the training set sample and 94.44% accuracy for the test set sample. As a result, this SVM model could identify effectively the adulteration EVOO with the limit of 1% RO and 1% CO. Therefore, the excellent classification and predictive power of this model indicated pigments could be used as potential markers for identifying EVOO adulteration.

Dissipation of Sulfonamides in Soil Emphasizing Taxonomy and Function of Microbiomes by Metagenomic Analysis.

Sulfonamides (SAs) are widespread in soils, and their dissipation behavior is important for their fate, risk assessment, and pollution control. In this work, we investigated the dissipation behavior of different SAs in a soil under aerobic condition, focusing on revealing the relationship between overall dissipation (without sterilization and in dark) and individual abiotic (sorption, hydrolysis)/biotic (with sterilization and in dark) factors and taxonomy/function of microbiomes. The results showed that dissipation of all SAs in the soil followed the pseudo-first-order kinetic model with dissipation time at 50% removal (DT50) of 2.16-15.27 days. Based on, experimentally, abiotic/biotic processes and, theoretically, partial least-squares modeling, a relationship between overall dissipation and individual abiotic/biotic factors was developed with microbial degradation as the dominant contributor. Metagenomic analysis showed that taxonomic genera like Bradyrhizobium/Sphingomonas/Methyloferula and functions like CAZy family GT51/GH23/GT2, eggNOG category S, KEGG pathway ko02024/ko02010, and KEGG ortholog K01999/K03088 are putatively involved in SA microbial degradation in soil. Spearman correlation suggests abundant genera being multifunctional. This study provides some new insights into SA dissipation and can be applied to other antibiotics/soils in the future.

Enhancing the Stability of Medium Range and Misalignment Wireless Power Transfer System by Negative Magnetic Metamaterials.

The misalignment of the resonant coils in wireless power transfer (WPT) systems causes a sharp decrease in transfer efficiency. This paper presents a method which improves the misalignment tolerance of WPT systems. Based on electromagnetic simulations, the structural unit parameters of the electromagnetic material were extracted, and an experimental prototype of a four-coil WPT system was built. The influence of electromagnetic metamaterials on the WPT system under the conditions of lateral misalignment and angular offset was investigated. Experiments showed that the transfer efficiency of the system could be maintained above 45% when the transfer distance of the WPT system with electromagnetic metamaterials was 1 m and the resonant coils were shifted laterally within one coil diameter. Furthermore, the system transfer efficiency could be stabilized by more than 40% within an angle variation range of 70 degrees. Under the same conditions, the transfer efficiency of a system without electromagnetic metamaterials was as low as 30% when lateral migration occurred, and less than 25% when the angle changed. This comparison shows that the stability of the WPT system loaded with electromagnetic metamaterials was significantly enhanced.