基于计算机图像处理技术与红外特征光谱融合的兔肉熟食识别方法Identification Method of Cooked Rabbit Meat Based on the Fusion of Computer Image Processing Technology and Infrared Characteristic Spectrum
孙乃旭
摘要(Abstract):
利用计算机图像处理技术与近红外光谱融合测量和预测、识别了兔肉熟食中猪肉的掺假比例。采用PLS和BP-ANN对红外光谱数据进行处理,并建立了预测模型。结果表明,红外光谱PLS模型和BP-ANN模型均具有较好的预测能力和精确度,尤其是BP-ANN模型。PLS模型和BP-ANN模型中的预测相对误差分别低于8%和6%。综上,基于计算机图像处理技术与红外特征光谱融合的兔肉熟食识别方法具有可行性。
关键词(KeyWords): 特征光谱;PLS模型;BP-ANN模型;识别
基金项目(Foundation): 河南省卫生厅课题(wjcg2015061)
作者(Author): 孙乃旭
参考文献(References):
- [1]苏爱国.兔肉酱制品工艺条件的研究[J].中国调味品,2017,42(12):121-128.
- [2]王东.发酵兔肉酱制品工艺条件的研究[J].中国调味品,2017,42(10):103-110.
- [3]董福凯,周秀丽,查恩辉.电子鼻在掺假牛肉卷识别中的应用[J].食品工业科技,2018,39(4):219-221,227.
- [4]周巍,周正,刘东,等.实时荧光PCR在鉴别鸡精中鸡源性成分的应用研究[J].中国调味品,2011,36(10):22-25.
- [5]白京,李家鹏,邹昊,等.近红外特征光谱定量检测羊肉卷中猪肉掺假比例[J].食品科学,2019,40(2):287-292.
- [6]张玉华,孟一,姜沛宏,等.近红外技术对不同动物来源肉掺假的检测[J].食品工业科技,2015,36(3):316-319,334.
- [7]Balage J M,Saulo D L E S,Gomide C A,et al.Predicting pork quality using Vis/NIR spectroscopy[J].Meat Science,2015,108:37-43.
- [8]Schmutzler M,Beganovic A,Bhler G,et al.Methods for detection of pork adulteration in veal product based on FT-NIR spectroscopy for laboratory,industrial and on-site analysis[J].Food Control,2015,57:258-267.
- [9]万新民.基于近红外光谱分析技术和计算机视觉技术的猪肉品质检测的研究[D].镇江:江苏大学,2010.
- [10]屠振华,朱大洲,籍保平,等.基于近红外光谱技术的蜂蜜掺假识别[J].农业工程学报,2011,27(11):382-387.
- [11]王楠.基于近红外技术检测鱼油品质的研究[D].北京:中国农业科学院,2017.
- [12]Hussain M N,Abdul Khir M F,Hisham M H,et al.Feasibility study of detecting canola oil adulteration with palm oil using NIR spectroscopy and multivariate analysis[C].International Conference on Information.IEEE,2015.
- [13]张虽栓,李延垒.近红外光谱对食醋pH值测定的研究[J].中国调味品,2015,40(1):65-68.
- [14]Cocchi M,Durante C,Foca G,et al.Durum wheat adulteration detection by NIR spectroscopy multivariate calibration[J].Talanta,2006,68(5):1505-1511.
- [15]Mabood F,Jabeen F,Ahmed M,et al.Development of new NIR-spectroscopy method combined with multivariate analysis for detection of adulteration in camel milk with goat milk[J].Food Chemistry,2017,221:746.