食品科学 ›› 2016, Vol. 37 ›› Issue (22): 180-186.doi: 10.7506/spkx1002-6630-201622027

• 安全检测 • 上一篇    下一篇

近红外光谱的||预处理对羊肉TVB-N模型的影响

邹 昊1,田寒友1,刘 飞1,李文采1,王 辉1,李家鹏1,陈文华1,狄艳全2,乔晓玲1,*   

  1. 1.中国肉类食品综合研究中心,肉类加工技术北京市重点实验室,北京 100068;
    2.聚光科技(杭州)股份有限公司,浙江 杭州 310052
  • 收稿日期:2016-05-05 出版日期:2016-11-16 发布日期:2017-02-22
  • 通讯作者: 乔晓玲(1964—),女,教授级高级工程师,学士,研究方向为肉制品加工技术。E-mail:cmrcsen@126.com
  • 作者简介:邹昊(1988—),男,学士,研究方向为肉品品质无损检测技术。E-mail:1016039906@qq.com

Effects of Spectral Pretreatments on Prediction of Total Volatile Basic Nitrogen (TVB-N) Content in Mutton Using Near Infrared Spectroscopy

ZOU Hao1, TIAN Hanyou1, LIU Fei1, LI Wencai1, WANG Hui1, LI Jiapeng1, CHEN Wenhua1, DI Yanquan2, QIAO Xiaoling1,*   

  1. 1. Beijing Key Laboratory of Meat Processing Technology, China Meat Research Center, Beijing 100068, China;
    2. Focused Photonics Inc., Hangzhou 310052, China
  • Received:2016-05-05 Online:2016-11-16 Published:2017-02-22

关键词: 近红外光谱分析技术, 预处理方法, 生鲜羊肉, TVB-N, 便携式近红外仪

Abstract: This study aimed at in situ, rapid and nondestructive detection of total volatile basic nitrogen (TVB-N) content in fresh raw mutton using near infrared spectroscopy. We checked whether the impact of portable near infrared spectrometer and microstructure of samples on the spectral information of the samples could be reduced or even eliminated by adjusting algorithm parameters and combing different algorithms for the purpose of improving the accuracy and robustness of the prediction model developed. Various individual algorithms with different parameter combinations and various algorithm combinations were used to pretreat the spectral information of the samples for modeling. The effects of algorithm parameters and algorithm combinations on the performance of the model in terms of predictive accuracy and stability were evaluated and discussed to find the optimal pretreatment method. The results showed that different algorithm parameter combinations and different algorithm combinations distinctly affected the model performance. When the spectral information of the sample was pretreated with difference derivatives (window parameter is 6, and order of differentiation is 1), the best model performance was achieved. The standard error of calibration (SEC) and standard error of prediction (SEP) of the model were 1.21 and 1.31, respectively, with SEP/SEC = 1.08 < 1.2. The number of principal components was 10. The correlation coefficients of calibration and prediction were 0.94 and 0.92, respectively. Our study verified that spectral information pretreatment with proper algorithm parameter combination and algorithm combination can significantly improve the model performance and allow fast, non-destructive and on-the-spot detection of TVB-N in mutton.

Key words: near infrared spectroscopy, pretreatment, fresh raw mutton, TVB-N, portable near infrared spectrometer

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