摘要
为了探讨高光谱图像技术对不同储存时间和取样部位的牛肉颜色检测的可行性,采集具有代表性的牛肉后腿、里脊和背脊共82个牛肉样品的高光谱图像,并测量其亮度、红度、黄度和饱和度等颜色参数。选取感兴趣区域获取样品代表性光谱,通过选择适宜的谱区范围和预处理方法,建立并评价了预测各颜色参数的偏最小二乘校正模型。对于亮度、红度、黄度和饱和度,校正集的相关系数分别为0.80、0.91、0.91和0.93,校正标准差分别为2.23、1.18、0.82和1.12,预测集的相关系数分别为0.92、0.88、0.87和0.89,预测标准差分别为1.66、1.45、0.80和1.27。研究结果表明,高光谱图像技术可用于快速无损检测不同储存时间下、不同部位的牛肉颜色。
In order to explore the feasibility of hyperspectral imaging technique to estimate beef color parameters under different storage time and sampling positions, hyperspectral images of 82 representative beef samples were acquired. Color parameters, including brightness （ L＊ ）, redness （ a ＊ ）, yellowness （ b ＊ ） and saturation （ C＊ ） were also determined. Their representative spectra were obtained by selecting regions of interest （ROIs）. By comparing and choosing appropriate spectral regions and pretreatment methods, optimum partial least squares （PLS） calibration models of each beef color parameters were established and evaluated, respectively. As for L＊ , a ＊ , b ＊ and C＊ , the correlation coefficients of calibration were 0. 80, 0.91, 0.91 and 0.93, and root mean square errors of calibration were 2.23, 1.18, 0. 82 and 1.12, respectively. The correlation coefficients of prediction were 0.92, 0. 88, 0.87 and 0.89, and root mean square errors of prediction were 1.66, 1.45, 0.80 and 1.27, respectively. The results showed that hyperspectral imaging technique could be used to rapidly and non-destructively analyze beef color parameters under different storage time and sampling positions.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2013年第7期165-169,共5页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金资助项目(31160329)
石河子大学高层次人才科研启动项目(RCZX200943)
关键词
牛肉
颜色参数
高光谱图像
偏最小二乘
Beef Color parameters Hyperspeetral imaging Partial least squares
作者简介
朱荣光，副教授，主要从事农畜产品无损检测研究，E—mail：rgzh_jd@shzu．edu．cn
通讯作者：姚雪东，副教授，主要从事农产品加工研究，E-mail：yaoxuedong@126．com
