高光谱技术结合特征波长筛选的牛肉品种多波段识别
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摘要
利用可见/近红外(400～1 000 nm)及近红外(900～1 700 nm)高光谱成像技术结合特征波长筛选方法对安格斯牛、力木赞牛、西门塔尔牛3个品种的牛肉进行鉴别研究,且测定肉样的色泽、嫩度、pH值以及水分、脂肪、蛋白质含量。根据不同波段光谱的特点,分别对原始光谱进行预处理,并利用SPA、IRF和IRF-SPA方法筛选特征波长,建立基于全波段及特征波长下的PLS-DA牛肉品种鉴别模型。结果显示:400～1 000 nm波段采用SNV-IRF-SPA-PLS-DA方法建立的模型最优,校正集与预测集准确率分别为98.56%和97.12%,900～1 700 nm波段采用SG-SPA-PLS-DA方法建立的模型准确率为94.09%和96.04%,说明不同波段高光谱对牛肉品种识别均有较好的效果;400～1 000 nm波段的识别准确率优于900～1 700 nm,说明3种牛肉在色泽纹理上的差异比成分含量显著。研究表明,利用高光谱成像技术结合特征波长筛选方法能够获得较好的牛肉品种鉴别效果。
This paper focused on the research on identifying and classifying for beef varieties of Angus, Limousin and Simmental, by using visible/near-infrared(400～1 000 nm) and near infrared(900～1 700 nm) hyperspectral technologies combined with different characteristic wavelengths selection methods. Meanwhile, the contents of color, tenderness, pH value, moisture, fat and protein were measared. Pretreatment methods were used to process original spectrum respectively according to the characteristics of different spectrum bands; the characteristic wavelengths were extracted by using SPA, IRF and IRF-SPA; then PLS-DA model was applied to identify the different beef varieties under characteristic wavelengths and fullwave bands. Results showed that SNV-IRF-SPA-PLS-DA models achieved the optimal performance in 400～1 000 nm, and the accuracy of the correction set and prediction set was 98.56% and 97.12%, respectively. SG-SPA-PLS-DA models achieved the optimal performance in 900～1 700 nm, and the accuracy of the correction set and prediction set was 94.09% and 96.04%, respectively. There were good effects for beef varieties identification in different hyperspectral bands. The identification accuracy in 400～1 000 nm bands was better than in 900～1 700 nm bands, which explained that the differences of color and texture were more significant than the component contents among the 3 varieties beef. The research indicated that combined hyperspectral technologies with characteristic wavelengths selection methods can obtain a better recognition effect of beef varieties.
This paper focused on the research on identifying and classifying for beef varieties of Angus, Limousin and Simmental, by using visible/near-infrared(400～1 000 nm) and near infrared(900～1 700 nm) hyperspectral technologies combined with different characteristic wavelengths selection methods. Meanwhile, the contents of color, tenderness, pH value, moisture, fat and protein were measared. Pretreatment methods were used to process original spectrum respectively according to the characteristics of different spectrum bands; the characteristic wavelengths were extracted by using SPA, IRF and IRF-SPA; then PLS-DA model was applied to identify the different beef varieties under characteristic wavelengths and fullwave bands. Results showed that SNV-IRF-SPA-PLS-DA models achieved the optimal performance in 400～1 000 nm, and the accuracy of the correction set and prediction set was 98.56% and 97.12%, respectively. SG-SPA-PLS-DA models achieved the optimal performance in 900～1 700 nm, and the accuracy of the correction set and prediction set was 94.09% and 96.04%, respectively. There were good effects for beef varieties identification in different hyperspectral bands. The identification accuracy in 400～1 000 nm bands was better than in 900～1 700 nm bands, which explained that the differences of color and texture were more significant than the component contents among the 3 varieties beef. The research indicated that combined hyperspectral technologies with characteristic wavelengths selection methods can obtain a better recognition effect of beef varieties.
引文
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[18] BALLABIO D,CONSONNI V.Classification tools in chemistry.Part 1:Linear models.PLS-DA [J].Anal.Methods,2013,5(16):3790-3798.
[19] 丁佳兴,杨晓玉,房盟盟,等.可见/近红外高光谱成像技术对鸡蛋种类无损判别 [J].发光学报,2018,39(3):394-402.DING J X,YANG X Y,FANG M M,et al..Non-destructive discrimination of different kinds egg by Vis/NIR hyperspectral imaging technique [J].Chin.J.Lumin.,2018,39(3):394-402.(in Chinese)
[20] 王松磊,吴龙国,马天兰,等.高光谱技术的羊肉品种多波段识别研究 [J].光谱学与光谱分析,2016,36(9):2937-2945.WANG S L,WU L G,MA T L,et al..Study on multi-bands recognition for varieties of mutton by using hyperspectral technologies [J].Spectrosc.Spect.Anal.,2016,36(9):2937-2945.(in Chinese)
[2] VLACHOS A,ARVANITOYANNIS I S,TSERKEZOU P.An updated review of meat authenticity methods and applications [J].Crit.Rev.Food Sci.Nutr.,2016,56(7):1061-1096.
[3] MONTOWSKA M,FORNAL E.Label-free quantification of meat proteins for evaluation of species composition of processed meat products [J].Food Chem.,2017,237:1092-1100.
[4] NAVEENA B M,JAGADEESH D S,JAGADEESH BABU A,et al..OFFGEL electrophoresis and tandem mass spectrometry approach compared with DNA-based PCR method for authentication of meat species from raw and cooked ground meat mixtures containing cattle meat,water buffalo meat and sheep meat [J].Food Chem.,2017,233:311-320.
[5] PRANDI B,LAMBERTINI F,FACCINI A,et al..Mass spectrometry quantification of beef and pork meat in highly processed food:application on Bolognese sauce [J].Food Control,2017,74:61-69.
[6] ALI M E,RAZZAK M A,HAMID S B A,et al..Multiplex PCR assay for the detection of five meat species forbidden in Islamic foods [J].Food Chem.,2015,177:214-224.
[7] ZHONG J F,QIN X L.Rapid quantitative analysis of corn starch adulteration in konjac glucomannan by chemometrics-assisted FT-NIR spectroscopy [J].Food Anal.Methods,2016,9(1):61-67.
[8] 王松磊,吴龙国,康宁波,等.基于高光谱图谱融合技术的宁夏滩羊肉嫩度检测方法研究 [J].光电子·激光,2016,27(9):987-995.WANG S L,WU L G,KANG N B,et al..Study on Tan-Lamb mutton tenderness by using the fusion of hyperspectral spectrum and image information [J].J.Photoelectron.Laser,2016,27(9):987-995.(in Chinese)
[9] JIANG H Z,YOON S C,ZHUANG H,et al..Tenderness classification of fresh broiler breast fillets using visible and near-infrared hyperspectral imaging [J].Meat Sci.,2018,139:82-90.
[10] XIONG Z J,SUN D W,PU H B,et al..Combination of spectra and texture data of hyperspectral imaging for differentiating between free-range and broiler chicken meats [J].LWT - Food Sci.Technol.,2015,60(2):649-655.
[11] BALAGE J M,DA LUZ E SILVA S,GOMIDE C A,et al..Predicting pork quality using Vis/NIR spectroscopy [J].Meat Sci.,2015,108:37-43.
[12] 王靖,丁佳兴,郭中华,等.基于近红外高光谱成像技术的宁夏羊肉产地鉴别 [J].食品工业科技,2018,39(2):250-254.WANG J,DING J X,GUO Z H,et al..Identification of geographical origins of mutton in Ningxia based on the near infrared hyperspectral imaging technique [J].Sci.Technol.Food Ind.,2018,39(2):250-254.(in Chinese)
[13] GALV?O R K H,ARAUJO M C U,JOSE G E,et al..A method for calibration and validation subset partitioning [J].Talanta,2005,67(4):736-740.
[14] 袁莹,王伟,褚璇,等.光谱特征波长的SPA选取和基于SVM的玉米颗粒霉变程度定性判别 [J].光谱学与光谱分析,2016,36(1):226-230.YUAN Y,WANG W,CHU X,et al..Selection of characteristic wavelengths using SPA and qualitative discrimination of mildew degree of corn kernels based on SVM [J].Spectrosc.Spect.Anal.,2016,36(1):226-230.(in Chinese)
[15] LIU K,CHEN X J,LI L M,et al..A consensus successive projections algorithm-multiple linear regression method for analyzing near infrared spectra [J].Anal.Chim.Acta,2015,858:16-23.
[16] YUN Y H,LI H D,WOOD L R E,et al..An efficient method of wavelength interval selection based on random frog for multivariate spectral calibration [J].Spectrochim.Acta A:Mol.Biomol.Spectrosc.,2013,111:31-36.
[17] 郎玉苗,沙坤,李海鹏,等.中国培育品种牛肉品质特性比较分析 [J].肉类研究,2013,27(5):31-35.LANG Y M,SHA K,LI H P,et al..Comparative analysis of quality characteristics of beef from Chinese cultivated breed [J].Meat Res.,2013,27(5):31-35.(in Chinese)
[18] BALLABIO D,CONSONNI V.Classification tools in chemistry.Part 1:Linear models.PLS-DA [J].Anal.Methods,2013,5(16):3790-3798.
[19] 丁佳兴,杨晓玉,房盟盟,等.可见/近红外高光谱成像技术对鸡蛋种类无损判别 [J].发光学报,2018,39(3):394-402.DING J X,YANG X Y,FANG M M,et al..Non-destructive discrimination of different kinds egg by Vis/NIR hyperspectral imaging technique [J].Chin.J.Lumin.,2018,39(3):394-402.(in Chinese)
[20] 王松磊,吴龙国,马天兰,等.高光谱技术的羊肉品种多波段识别研究 [J].光谱学与光谱分析,2016,36(9):2937-2945.WANG S L,WU L G,MA T L,et al..Study on multi-bands recognition for varieties of mutton by using hyperspectral technologies [J].Spectrosc.Spect.Anal.,2016,36(9):2937-2945.(in Chinese)