期刊
APPLIED SPECTROSCOPY
卷 54, 期 6, 页码 894-899出版社
SOC APPLIED SPECTROSCOPY
DOI: 10.1366/0003702001950292
关键词
infrared spectroscopy; visible spectroscopy; authenticity; meat; chemometrics; discriminant analysis; SIMCA; K-nearest neighbors analysis; discriminant PLS
Mid-infrared (2000-800 cm(-1)), near-infrared (750-2498 nm), and visible (400-750 nm) reflectance spectra of 230 homogenized meat samples (chicken, turkey, pork, beef, and lamb) were collected. Species identification was attempted by using factorial discriminant analysis (FDA), soft independent modeling of class analogy (SIMCA), K-nearest neighbor (KNN), analysis and discriminant partial least-squares (PLS) regression. A variety of wavelength ranges was investigated for optimum accuracy. Particular difficulty was encountered in distinguishing between chicken and turkey; models were therefore developed with the use of five separate meat classes and again with the use of four, with chicken and turkey samples being treated as one group. Discriminant PLS, FDA, and KNN models provided similar levels of accuracy in this application. Correct classification rates in excess of 90% were achieved in all cases.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据