首页|Jiangsu University Reports Findings in Support Vector Machines(Production monit oring and quality characterization of black garlicusing Vis-NIR hyperspectral i maging integrated with chemometricsstrategies)
Jiangsu University Reports Findings in Support Vector Machines(Production monit oring and quality characterization of black garlicusing Vis-NIR hyperspectral i maging integrated with chemometricsstrategies)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Support Vector Machine s is the subject of a report. According tonews reporting from Zhenjiang, People ’s Republic of China, by NewsRx journalists, research stated, “As anew deep-pro cessing garlic product with notable health benefits, the accurate discrimination of processingstages and prediction of key physicochemical constituents in blac k garlic are vital for maintaining productquality. This study proposed a novel method utilizing hyperspectral imaging technology to both rapidlymonitor the pr ocessing stages and quantitatively predict changes in the key physicochemical co nstituentsduring black garlic processing.”The news correspondents obtained a quote from the research from Jiangsu Universi ty, “Multiplemethods of noise reduction and feature screening were used to proc ess the acquired hyperspectral information.To differentiate processing stages, pattern recognition methods including linear discriminantanalysis (LDA), K-near est neighbor (KNN), support vector machine classification (SVC) analysis were utilized, achieving a discriminant accuracy of up to 98.46 %. Further more, partial least squares regression(PLSR) and support vector machine regress ion (SVR) analysis were performed to achieve quantitativeprediction of the key physicochemical constituents including moisture and 5-HMF. PLSR models outperformed SVR models, with correlation coefficient of prediction of 0.9762 and 0.9744 for moisture and 5-HMFcontent, respectively.”
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