Soft independent modeling of class analogy (SIMCA) was successful in classifying a large library of 758 commercially available, non-blended samples of acetate, cotton, polyester, rayon, silk and wool 89% - 98% of the ...Soft independent modeling of class analogy (SIMCA) was successful in classifying a large library of 758 commercially available, non-blended samples of acetate, cotton, polyester, rayon, silk and wool 89% - 98% of the time at the 95% confidence level (p = 0.05 significance level). In the present study, cotton and silk had a 62% and 24% chance, respectively, of being classified with their own group and also with rayon. SIMCA correctly identified a counterfeit “silk” sample as polyester. When coupled with diffuse NIR reflectance spectroscopy and a large sample library, SIMCA shows considerable promise as a quick, non-destructive, multivariate method for fiber identification. A major advantage is simplicity. No sample pretreatment of any kind was required, and no adjust-ments were made for fiber origin, manufacturing process residues, topical finishes, weave pattern, or dye content. Increasing the sample library should make the models more robust and improve identification rates over those reported in this paper.展开更多
The demand for spectacle independence at all ages continues to grow,as our population ages and life expectancy continues to rise.Younger and older individuals alike expect greater freedom to pursue their active lifest...The demand for spectacle independence at all ages continues to grow,as our population ages and life expectancy continues to rise.Younger and older individuals alike expect greater freedom to pursue their active lifestyles.However,as demands for higher levels of visual function increase,ophthalmologists have more technological options for refractive corrections than ever before,making it more important for surgeons to employ the most up to date methods and technology to fulfill patients’ever rising expectations of precise visual outcomes.展开更多
文摘Soft independent modeling of class analogy (SIMCA) was successful in classifying a large library of 758 commercially available, non-blended samples of acetate, cotton, polyester, rayon, silk and wool 89% - 98% of the time at the 95% confidence level (p = 0.05 significance level). In the present study, cotton and silk had a 62% and 24% chance, respectively, of being classified with their own group and also with rayon. SIMCA correctly identified a counterfeit “silk” sample as polyester. When coupled with diffuse NIR reflectance spectroscopy and a large sample library, SIMCA shows considerable promise as a quick, non-destructive, multivariate method for fiber identification. A major advantage is simplicity. No sample pretreatment of any kind was required, and no adjust-ments were made for fiber origin, manufacturing process residues, topical finishes, weave pattern, or dye content. Increasing the sample library should make the models more robust and improve identification rates over those reported in this paper.
文摘The demand for spectacle independence at all ages continues to grow,as our population ages and life expectancy continues to rise.Younger and older individuals alike expect greater freedom to pursue their active lifestyles.However,as demands for higher levels of visual function increase,ophthalmologists have more technological options for refractive corrections than ever before,making it more important for surgeons to employ the most up to date methods and technology to fulfill patients’ever rising expectations of precise visual outcomes.
