首页|Studies Conducted at Shenyang Agricultural University on Support Vector Machines Recently Published (A fresh-cut papaya freshness prediction model based on part ial least squares regression and support vector machine regression)
Studies Conducted at Shenyang Agricultural University on Support Vector Machines Recently Published (A fresh-cut papaya freshness prediction model based on part ial least squares regression and support vector machine regression)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News - New study results on have been publish ed. According to news reporting out of Liaoning, People’s Republic of China, by NewsRx editors, research stated, “This study investigated the physicochemical an d flavor quality changes in fresh-cut papaya that was stored at 4 °C.” The news journalists obtained a quote from the research from Shenyang Agricultur al University: “Multivariate statistical analysis was used to evaluate the fresh ness of fresh-cut papaya. Aerobic plate counts were selected as a predictor of f reshness of fresh-cut papaya, and a prediction model for freshness was establish ed using partial least squares regression (PLSR), and support vector machine reg ression (SVMR) algorithms. Freshness of fresh-cut papaya could be well distingui shed based on physicochemical and flavor quality analyses. The aerobic plate cou nts, as a predictor of freshness of fresh-cut papaya, significantly correlated w ith storage time. The SVMR model had a higher prediction accuracy than the PLSR model.”
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