Henan University of Technology

About: Henan University of Technology is a education organization based out in Zhengzhou, China. It is known for research contribution in the topics: Catalysis & Chemistry. The organization has 7648 authors who have published 6503 publications receiving 73067 citations. The organization is also known as: Hénán Gōngyè Dàxué.

Oxidative stability of sunflower oil flavored by essential oil from Coriandrum sativum L. during accelerated storage

Abstract: The aim of the work was to assess the oxidative stability of sunflower oil flavored by the essential oils extracted of Coriandrum sativum (coriander, CEO) during the accelerated storage of 24 days. In the study, CEO was extracted by Soxhlet apparatus, and its chemical composition was analyzed by gas chromatography with flame ionization detector (GC-FID) and gas chromatography and mass spectrometry (GC-MS), and then, the evaluation for antioxidant effect in vitro displayed that CEO possessed markedly antioxidant potential. Furthermore, after the addition of CEO, acid value (AV), peroxide value (PV), iodine value (IV), p-anisidine value (AnV), thiobarbituric acid reactive substances (TBARS), free fatty acid (FFA), total polar compounds (TPC), tocopherols (TOC) in sunflower oils during the accelerated storage were measured every 4 days, and the results herein exhibited the addition of CEO at 1200 ppm could not only increase the oxidative stability of the sunflower oils, but also exert synergistic effect with TBHQ. Meanwhile, the values of K232 and K268 and fatty acid composition were investigated. The sensory evaluation of the oil samples revealed that the addition of CEO at 1200 ppm could increase aroma flavor and consumers' acceptability, so that it could be developed as convenient condiment.

Ensemble extreme learning machines for compound-fault diagnosis of rotating machinery

Abstract: The data-driven fault indicator for rotating machinery is designed to reveal the possible fault scenarios from the observed statistical vibration signals. This study develops a novel ensemble extreme learning machine (EELM) network to replace the conventional layout by combining binary classifiers (e.g., binary relevance) for compound-fault diagnosis of rotating machinery. The proposed EELMs consist of two sub-networks, namely, the first extreme learning machine (ELM) for clustering, and the second for multi-label classification. The first network generates the Euclidean distance representations from each point to every centroid with unsupervised clustering, and the second identifies potential output tags through multiple-output-node multi-label learning. Compared to the existing multi-label classifiers (e.g., multi-label radial basis function, rank support vector machine, back-propagation multi-label learning, and binary classifiers with binary relevance), the theoretical verification reveals EELMs perform the best in hamming loss, one-error, training time, and achieves the best overall evaluation for the two real-world databases (e.g., Yeast and Image). Regarding the real test for the compound-fault diagnosis of rotating machinery, this paper applies the particle swarm optimization-based variational mode decomposition to decompose the raw vibration signals into a series of intrinsic modes, and selects ten time-domain indicators and five frequency-domain statistical characteristics for feature extraction. The experimental results illustrate that the EELM-based fault diagnosis method achieves the best overall performance.