中国农业科学 ›› 2016, Vol. 49 ›› Issue (1): 155-162.doi: 10.3864/j.issn.0578-1752.2016.01.014

• 贮藏·保鲜·加工 • 上一篇    下一篇

近红外光谱法检测鸡、鱼肉加热终点温度

刘功明1,孙京新 1,李鹏1,徐幸莲2,张万刚2,黄明2,周光宏2

 
  

  1. 1青岛农业大学食品科学与工程学院/山东省肉类食品质量控制工程技术研究中心,山东青岛 266109
    2南京农业大学食品科技学院/农业部农畜产品加工与质量控制重点开放实验室,南京 210095
  • 收稿日期:2015-05-14 出版日期:2016-01-01 发布日期:2016-01-01
  • 通讯作者: 孙京新,E-mail:jxsun20000@163.com
  • 作者简介:刘功明,E-mail:15265279136@163.com
  • 基金资助:
    国家公益性行业(农业)科研专项(201303083)、山东省现代农业产业技术体系家禽创新团队项目(SDAIT-13-011-11)、北方特色酱卤熏菜肴加工关键技术及产业化(2014BAD04B11)、国家肉鸡现代产业技术体系(CARS-42)

Detection of Endpoint Temperature of Chicken and Fish by Near-Infrared Spectroscopy

LIU Gong-ming1, SUN Jing-xin1, LI Peng1, XU Xing-lian2, ZHANG Wan-gang2HUANG Ming2, ZHOU Guang-hong2   

  1. 1College of Food Science and Engineering, Qingdao Agricultural University/Center of Meat Quality and Safty Control of Shandong Province, Qingdao 266109, Shandong
    2College of Food Science and Technology, Nanjing Agricultural University/Key Laboratory of Meat Processing and Quality Control, Ministry of Education, Nanjing 210095
  • Received:2015-05-14 Online:2016-01-01 Published:2016-01-01

摘要: 【目的】在肉制品生产中,加热终点温度(endpoint temperature,EPT)是控制食源性疾病的关键因素。现有的EPT检测方法诸多,如酶活性测定法,凝血试验和聚丙烯酰胺凝胶电泳(SDS-PAGE电泳)法等,但普遍存在耗时、样品处理繁杂等不足。采用近红外光谱(near-infrared spectroscopy,NIR)结合偏最小二乘法(partial least squares,PLS)检测鸡、鱼肉加热终点温度,为研究近红外光谱法检测肉类EPT的可行性提供参考。【方法】分别将肉样以1·min-1的升温速率进行9个不同温度的加热处理(50、55、60、65、70、75、80、85和90℃),当达到终点温度时,迅速取出,冰水冷却到4℃。冷却后的肉样和释放的肉汁同时放到均质机中,均质1 min,绞碎成肉糜状。均质后的肉样存放于4℃的冰箱中,共制得144个样品(鸡、鱼肉样品数分别为77和67)。在近红外光谱仪上,采用硫化铅(PbS)检测器和旋转样品池,每个样品连续采集光谱3次,在11 000—4 000 cm-1波数范围内,以8 cm-1的分辨率扫描64次。将所采集的鸡、鱼肉的光谱数据分别随机分为校正集(样品总数108,其中鸡肉样58,鱼肉样50)和检验集(样品总数36,其中鸡肉样19,鱼肉样17),校正集用于校正模型的建立,检验集用于检验模型的预测能力。在建立模型时,采用标准正则变换、一阶微分和Norris Derivative滤波平滑(N-D)3种方法结合对原始光谱进行处理,采用内部交叉验证均方差(cross-validation mean square error,RMSECV)确定主成分数,利用模型对检验集样品的预测均方差(prediction mean square error,RMSEP)、预测值与实测值间的相关系数r及预测标准差σ考察模型的预测性能。【结果】采用校正集的内部交叉验证均方差(RMSECV)确定鸡肉、鱼肉的主成分数分别为9和11,此时校正集的RMSECV值最小,分别为1.59%和0.96%;所得校正模型的预测温度与实际加热温度之间的相关系数分别为0.9844和0.9936;由所建模型对检验集样品的检验结果可看出,实际加热温度与近红外模型预测的加热温度具有很高的相关性,预测值的相关系数r分别为0.9966和0.9832;预测均方差RMSEP分别为3.02%和2.94%;预测标准差σ为0.97和1.63。【结论】本研究所建模型具有很好的预测性能,近红外光谱用于肉制品EPT检测具有很大潜力。

关键词: 鸡肉, 鱼肉, 近红外光谱, 偏最小二乘法, 加热终点温度

Abstract: 【Objective】Endpoint temperature (EPT) of heat treated meat products is a determinant factor to control food-borne diseases. EPT of cooked meat and meat products has been determined using different methods including determination of enzyme activity, coagulation test and Sodium Dodecyl Sulfate-Polyacrylamide Gel Electrophoresis (SDS-PAGE) etc. However, these methods are time consuming, destructive and labor intensive. The objective of this study was to investigate if near-infrared spectroscopy (NIR) in combination with partial least squares (PLS) data analysis can be used to determine the EPT in cooked meat.【Method】The meat samples (chicken and fish) were heat treated until they reached a final internal temperature of 50 to 90 (5 intervals) at the rate of 1·min-1. The cooked samples were removed from the waterbath and cooled immediately in ice water to 4. The 144 samples in total (the number of chicken and fish was 77 and 67 respectively) were homogenized for 1 minute and deposited at 4 before measurement. Then NIR reflectance spectra were collected using a scanning monochromator equipped with a rotating sample cup with a quartz window between 11 000 and 4 000 cm-1 at 8 cm-1 intervals. Each sample was scanned for three times. Each spectrum, an average of 64 scans, was recorded over the selected wavenumber range. Randomly selected 108 spectra from chicken and fish samples were assigned to a calibration set while remaining 36 spectra were used for a validation set. The calibration sets were used for establishing the calibration model and the validation sets were used to test model prediction ability.The spectra was processed using the method of Standard Normal Variate, first-order differential and Norris Derivative. The principal component number was determined by the cross-validation mean square error (RMSECV). The prediction mean square error (RMSEP), correlation index (r) and the standard deviation (σ) were used to evaluate prediction ability of the calibration model.【Result】By RMSECV, the principal components of the chicken and fish were 9 and 11 ascertained through RMSECV value (1.59% and 0.96%) and r value (0.9844 and 0.9936). The lower the RMSECV and lower the r values, the more accurate is the calibration model. As can be observed in the study, prediction of EPT using the NIR spectra showed very high correlation (r=0.9966 for chicken and r=0.9832 for fish) with a prediction error (RMSECV) of 3.02% and 2.94%, respectively.【Conclusion】The results showed that NIR spectroscopy has the potential for use in the food processing industry and food inspection to ensure that all meat products have reached the recommended EPT, keeping it safe. And the model established in this paper has a good prediction ability of EPT for meat products, which is a non-destructive, simple and reliable technique to monitor the ?nal quality of a product.

Key words: chicken, fish, near-infrared spectroscopy, partial least squares, endpoint temperature