牛肉质构特性的近红外光谱无损检测

为了建立基于近红外光谱技术的牛肉质构特性快速检测方法,该试验采集了202个新鲜牛肉样品在800~2 500 nm波长范围内的漫反射光谱,测定了牛肉的硬度、弹性、咀嚼性和黏附性,经小波消噪后,分别采用平滑、一阶微分、二阶微分等6种方法预处理,建立了牛肉质构特性的偏最小二乘回归模型,并用最优模型进行预测。结果表明：经小波消噪后采用二阶微分预处理方法建立的牛肉硬度、弹性、咀嚼性的检测模型效果最好,其校正集相关系数r均在0.9以上,校正集均方根误差（root means square error of calibration,RMSEC）分别为6.247 N、0.760 mm、14.954 m J,预...

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Bibliographic Details
Published in农业工程学报 Vol. 32; no. 16; pp. 286 - 292
Main Author 李志刚 贾策 王晓闻 刘强 董常生
Format Journal Article
LanguageChinese
Published 山西农业大学食品科学与工程学院,太谷,030801%山西农业大学动物科技学院,太谷,030801 2016
Subjects
无损检测
牛肉
质构
近红外光谱
牛肉
近红外光谱
texture
near infrared spectroscopy
beef
无损检测
nondestructive determination
质构
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ISSN1002-6819
DOI10.11975/j.issn.1002-6819.2016.16.038

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Summary:为了建立基于近红外光谱技术的牛肉质构特性快速检测方法,该试验采集了202个新鲜牛肉样品在800~2 500 nm波长范围内的漫反射光谱,测定了牛肉的硬度、弹性、咀嚼性和黏附性,经小波消噪后,分别采用平滑、一阶微分、二阶微分等6种方法预处理,建立了牛肉质构特性的偏最小二乘回归模型,并用最优模型进行预测。结果表明：经小波消噪后采用二阶微分预处理方法建立的牛肉硬度、弹性、咀嚼性的检测模型效果最好,其校正集相关系数r均在0.9以上,校正集均方根误差（root means square error of calibration,RMSEC）分别为6.247 N、0.760 mm、14.954 m J,预测集相关系数均在0.664以上,预测集均方根误差（root means square error of prediction,RMSEP）分别为8.887 N、0.951 mm、22.117 m J,相对预测误差（ratio of prediction to deviation,RPD）值分别为2.43、1.88、2.32,预测精度较高,能够有效地预测牛肉的硬度、咀嚼性,可以检测精度要求不高的牛肉弹性;试验所建立的牛肉黏附性检测模型的预测性能不是很理想,虽然其校正集和预测集相关系数较高（分别为0.720、0.694）,RMSEC和RESEP均较小（分别为0.302、0.243 N·mm）,但其RPD值小于1.5,模型预测精度较差,不可以用于预测未知样品的黏附性,此方法还需进一步研究。研究结果为牛肉质构特性的快速无损评价提供了理论依据。
Bibliography:11-2047/S
Li Zhigang,Jia Ce,Wang Xiaowen,Liu Qiang,Dong Changsheng （1. College of Food Science and Engineering, Shanxi Agricultural University, Taigu 030801, China; 2. College of Animal Science and Veterinary Medicine, Shanxi Agricultural University, Taigu 030801, China）
This study aims to establish a rapid detective method for the characteristics of beef texture through near infrare spectroscopy. The diffuse reflectance spectra at 800-2500 nm of 202 fresh beef samples were collected. The hardness springiness, chewiness and the adhesiveness of beef samples were tested by texture analyzer. After wavelet denoising, the dat were collected through processing the diffuse reflectance spectra by smoothness, first order differential, second orde differential, standard normal variate（SNV）, SNV combined with first order differential or SNV combined with second orde differential respectively, and then the partial least square regression（PLSR） statistical analysis models for the near-infrare spectra of beef samples were es
ISSN:1002-6819
DOI:10.11975/j.issn.1002-6819.2016.16.038