首页|Qingdao Agricultural University Researchers Update Current Data on Food Safety ( Food safety testing by negentropy-sorted kernel independent component analysis b ased on infrared spectroscopy)
Qingdao Agricultural University Researchers Update Current Data on Food Safety ( Food safety testing by negentropy-sorted kernel independent component analysis b ased on infrared spectroscopy)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News ; Research findings on food safety are d iscussed in a new report. According to newsreporting originating from Qingdao, People’s Republic of China, by NewsRx correspondents, research stated,“In the f ield of food safety testing, variety, brand, origin, and adulteration are four i mportant factors.”Our news reporters obtained a quote from the research from Qingdao Agricultural University: “Inthis study, a novel food safety testing method based on infrared spectroscopy is proposed to investigatethese factors. Fourier transform infrar ed spectroscopy data are analyzed using negentropy-sorted kernelindependent com ponent analysis (NS-kICA) as the feature optimization method. To rank the components, negentropy is performed to measure the non-Gaussian independent components . In our experiment,the proposed method was run on four datasets to comprehensi vely investigate the variety, brand, origin,and adulteration of agricultural pr oducts. The experimental results show that NS-kICA outperformsconventional feat ure selection methods. The support vector machine model outperforms the backpropagation artificial neural network and partial least squares models.”
Qingdao Agricultural UniversityQingdaoPeople’s Republic of ChinaAsiaEmerging TechnologiesFood PoisoningFood SafetyFoodborne Diseases and ConditionsGastroenterologyMachine LearningS upport Vector MachinesVector Machines
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