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.

Wide Angle of Incidence-Insensitive Polarization-Independent THz Metamaterial Absorber for Both TE and TM Mode Based on Plasmon Hybridizations.

An ultra-wide-angle THz metamaterial absorber (MA) utilizing sixteen-circular-sector (SCR) resonator for both transverse electric (TE) and transverse magnetic (TM) mode is designed and investigated numerically. At normal incidence, the absorptivity of the proposed MA is higher than 93.7% at 9.