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生鲜猪肉水分含量的快速无损检测
引用本文:张海云,彭彦昆,王伟.生鲜猪肉水分含量的快速无损检测[J].食品安全质量检测技术,2012,3(1):23-26.
作者姓名:张海云  彭彦昆  王伟
作者单位:中国农业大学工学院; 山东理工大学机械工程学院;中国农业大学工学院;中国农业大学工学院
基金项目:公益性行业(农业)科研专项经费资助(项目编号: 201003008)
摘    要:目的 研究生鲜猪肉水分含量与1000~1680 nm范围内近红外吸收光谱之间的关系, 对生鲜肉的水分含量进行快速无损检测。方法 将原始光谱经中值平滑、多元散射校正和一阶导数复合预处理, 结合多元线性回归和偏最小二乘回归两种建模方法建立生鲜肉水分含量的预测模型。结果 应用所建立的模型对111个实际生鲜猪肉样品的水分含量进行预测, 得到较为满意的预测结果, 两种模型的预测相关系数分别为0.839和0.810。结论 所建模型适合于生鲜猪肉水分的无损快速检测。

关 键 词:生鲜猪肉    近红外光谱技术    水分含量    多元线性回归    偏最小二乘回归    无损检测

Rapid non-destructive detection of water content in fresh pork
ZHANG Hai-Yun,PENG Yan-Kun and WANG Wei.Rapid non-destructive detection of water content in fresh pork[J].Food Safety and Quality Detection Technology,2012,3(1):23-26.
Authors:ZHANG Hai-Yun  PENG Yan-Kun and WANG Wei
Affiliation:College of Engineering, China Agricultural University; College of Mechanical Engineering, Shandong University of Technology;College of Engineering, China Agricultural University;College of Engineering, China Agricultural University
Abstract:Objective To investigate the relationship between the water content in the fresh pork and the near infrared absorption spectrum in the range of 1000 nm~1680 nm. And to detect the water content in the fresh pork by using the rapid non-destructive technology. Methods Through spectral analysis, it indicated that the op-timal pretreatment method was the combination of the median smooth, multiple scattering correction and first derivative. Two models were established with the multiple linear regression (MLR) and partial least square regression (PLSR) method. Results All 111 samples were predicted by the two models, both of which could give satisfactory results with the correlation coefficient of 0.839 and 0.810, respectively in the validation sets. Conclusion This research demonstrated that the model was suitable for the non-destructive rapid detection for the fresh pork water content.
Keywords:fresh pork  near infrared spectroscopy  water content  multiple linear regression  partial least square regression  non-destructive detection
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