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Haixia R.,Zhengzhou University | Weiqi L.,Xiamen Products Quality Supervision and Inspection Institute | Weimin S.,Zhengzhou University | Qi S.,Zhengzhou University
Analytical Letters | Year: 2013

The K-means algorithm has some limitations including dead-unit properties, heavy dependence on the initial choice of cluster centers, convergence to local optima, and sensitivity to the number of clusters. This paper presents an efficient algorithm that optimizes K-means clustering by a hybrid particle swarm algorithm. The modified discrete algorithm is used to select variables and is continuously applied to update cluster centers simultaneously. The nearest center classification is then employed to classify the test samples. The proposed algorithm was applied to discriminate various edible oil varieties by employing Fourier transform infrared spectroscopy. As a comparison, the common K-means clustering, principal component analysis, and partial least squares techniques were also applied to classify these edible oil samples. Results demonstrated that the proposed method is an accurate and rapid strategy for identifying edible oils. © 2013 Taylor and Francis Group, LLC. Source

Yu W.,Shandong University | Jiang X.,Xiamen Products Quality Supervision and Inspection Institute | Meng F.,Tianjin Lishen Battery Joint Stock Co. | Zhang Z.,Shandong University | And 2 more authors.
RSC Advances | Year: 2016

Herein, the rational design and synthesis of manganese oxides (MnO2 and MnO) have been achieved and both of them show petal-like microsphere structures. As anodes for LIBs, MnO exhibits a higher capacity of 751.4 mA h g-1 after 400 cycles (492.7 mA h g-1 for MnO2 after 300 cycles) at 2000 mA g-1. © The Royal Society of Chemistry 2016. Source

Sun X.,Zhengzhou University | Lin W.,Xiamen Products Quality Supervision and Inspection Institute | Li X.,Zhengzhou University | Shen Q.,Zhengzhou University | Luo H.,Xiamen Medical College
Analytical Methods | Year: 2015

Detection of adulteration in extra virgin olive oil (EVOO) is one of the main aspects in the quality control. In this study, we sought to identify the adulterated oil from EVOO to discriminate the type of adulterants and to quantify the levels of adulteration using FT-IR spectroscopy coupled with chemometrics. Supervised locally linear embedding (SLLE) was employed to reduce the dimensionality of variables and then compared with principal component analysis and locally linear embedding. The results show that SLLE gave satisfactory results. Nearest centroid classification and PLS regression methods were applied to establish the classification and quantification models for EVOO adulteration using the compressed low dimensional FT-IR data. The results have shown that we can clearly identify which edible oils are adulterated and accurately quantify the percentage of adulteration in EVOO. This journal is © The Royal Society of Chemistry 2015. Source

Zhuang Q.,Xiamen University | Zhuang Q.,Xiamen Products Quality Supervision and Inspection Institute | Yang Z.,Xiamen University | Kang J.,Xiamen University
Applied Physics Letters | Year: 2013

Zn/Zn2SiO4 core-shell nanocables are convenient to grow and have ability to carry ultraviolet (UV) information, which makes them a promising structure in the future application, the nano-waveguide amplifier. In this study, the propagation and enhancement characteristics of surface plasmon polaritons in metal-dielectric core-shell nanocables are experimentally and theoretically studied. The strong coupling effect is also determined. The results demonstrate that UV signals can be effectively transmitted and significantly enhanced in the nanocables. © 2013 AIP Publishing LLC. Source

Ren H.,Zhengzhou University | Lin W.,Xiamen Products Quality Supervision and Inspection Institute | Shi W.,Zhengzhou University | Shen Q.,Zhengzhou University | Wang S.,Zhengzhou University
Analytical Letters | Year: 2014

The Gaussian mixture model (GMM) and regression (GMR) are widely used statistical tools in pattern classification and nonlinear regression. In this paper, the suitability score was used for variable selection to improve GMM and GMR. The improved GMM was used to characterize peanut oil adulterated with palm oil using Fourier transform infrared spectroscopy. The improved GMR was applied to determine the concentration of palm oil contaminant present. As comparison, GMM and GMR with principal component analysis for feature extraction, support vector machine, back-propagation artificial neural network, nearest centroid classification, and partial least-squares analysis were also used to classify and quantify peanut oil. It was demonstrated that the method is a new classification and regression strategy for the detection of adulterated edible oil. © 2014 Copyright Taylor & Francis Group, LLC. Source

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