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Chromium-laced medical capsules detection based on hyperspectral imaging technology
KTH, School of Technology and Health (STH).
2016 (English)In: International Agricultural Engineering Journal, ISSN 0858-2114, Vol. 25, no 4, p. 293-302Article in journal (Refereed) Published
Abstract [en]

Medicinal gelatin hollow hard capsule has been widely used in people's daily life, but the toxic capsule event has drew public concern on its safety issue in 2012, since the casings were made with an industrial gelatin with high amounts of metal chromium. In this paper, the traditional method, atomic absorption spectrophotometer, was used for detecting medicinal gelation hollow hard capsule as a control group; then hyperspectral image was used for detecting 347 normal capsule samples and 701 toxic capsule samples, which were analyzed and discussed from different image bands and regions of interest (ROIs). At the beginning, hyperspectral processing was used for dimension reduction by Principal Component Analysis (PCA) method, and then two kinds of capsules spectral data were obtained to do qualitative analysis according to ROIs. Partial Least Squares Discriminant Analysis(PLS-DA) and Principal Component Analysis- Artificial Neural Network(PCA-ANN) were used to identify Chromium-laced capsule based on spectral data processing, of which 60% sample are used for modeling, the remaining 40% are used for predicting. In the end, we can draw a conclusion that if chosen 4 Latent Variables (LVs) as input features in the PLSDA model, high classification accuracy would be achieved, with the accuracy of 100%. The correlation coefficient of cross validation and sample prediction are 0.923 and 0.972 respectively; sensitivity and specificity are 100% as well. The result prove that it is feasible to use hyperspectral data for chromium-laced capsule qualitative analysis, the method greatly reduces the complexity of the traditional detection and try to provide a new thought in chromium detection of capsule. © 2016, Asian Association for Agricultural Engineering. All rights reserved.

Place, publisher, year, edition, pages
Asian Association for Agricultural Engineering , 2016. Vol. 25, no 4, p. 293-302
Keywords [en]
Atomic absorption spectrophotometer, Chromium-laced capsule, Detection, Hyperspectral imaging, Spectral data processing, Accident prevention, Chromium, Complex networks, Data handling, Discriminant analysis, Error detection, Gelation, Imaging techniques, Least squares approximations, Medical imaging, Meteorological instruments, Neural networks, Spectrophotometers, Spectroscopy, Classification accuracy, Correlation coefficient, Hyperspectral imaging technologies, Partial least squares discriminant analyses (PLSDA), Qualitative analysis, Sensitivity and specificity, Principal component analysis
National Category
Medical Biotechnology
Identifiers
URN: urn:nbn:se:kth:diva-207570Scopus ID: 2-s2.0-85012040147OAI: oai:DiVA.org:kth-207570DiVA, id: diva2:1102698
Note

Export Date: 22 May 2017; Article; CODEN: IAEJE; Correspondence Address: Chen, F.; Hangzhou Dianzi University, College of Life Information Science and Instrument EngineeringChina; email: fnchen@hdu.edu.cn; Funding details: CSC, China Scholarship Council; Funding text: We thank the National Nature and Science Foundation of China 2013 (Project No. 61305037) and Swedish Institute (05612/2015) for supporting this research. Besides, China Scholarship Council also supports the research. QC 20170530

Available from: 2017-05-30 Created: 2017-05-30 Last updated: 2017-05-30Bibliographically approved

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