Objective: This study aimed to establisha rapid and accurate prediction method of lamb adulteration by using visible/near-infrared (400~1 000 nm) and short-wave near-infrared (900~1 700 nm) hyperspectral imaging techniques.Methods: The data acquisition of lamb adulterated with different proportions of duck meat using visible/near-infrared (400~1 000 nm) and short-wave near-infrared (900~1 700 nm) hyperspectral imagers was performed to compare the effect of partial least squares (PLS) modeling with different spectral preprocessing methods in the two band ranges. Then the normalized preprocessing method was selected in the visible-NIR band, and the standard normal variate transformation (SNV) preprocessing method was selected in the short-wave infrared band. After the optimal preprocessing of the spectral data on to the two bands separately, the feature wavelengths were selected using the successive projections algorithm (SPA), the competitive adaptive reweighted sampling (CARS), the Interval random frog (iRF) and the Synergy intervals PLS (SiPLS).Results: The best lamb adulteration prediction using SNV-SPA-PLS model in the short-wave near-infrared (900~1 700 nm) bands, was achieved, and with the prediction set model evaluation coefficients of R2p=0.968 4, RMSEP=0.058 2, RPD=5.625 4, relaiable image inversion results were obtained.Conclusion: The rapid and nondestructive quantitative detection of lamb adulteration can be achieved by using hyperspectral imaging techniques in different wavebands.

Publication Date


First Page


Last Page





[1] DE SMET S,VOSSEN E.Meat:The balance between nutrition and health:A review[J].Meat Science,2016,120:145-156.
[2] 姜洪喆,蒋雪松,杨一,等.肉类掺杂掺假的高光谱成像检测研究进展[J].食品与发酵工业,2021,47(6):300-305.JIANG H Z,JIANG X S,YANG Y,et al.The progress of the detection of meats adulteration using hyperspectral imaging[J].Food and Fermentation Industries,2021,47(6):300-305.
[3] BOYACI I H,TEMIZ H T,UYSAL R S,et al.A novel method for discrimination of beef and horsemeat using Raman spectroscopy[J].Food Chemistry,2014,148(1):37-41.
[4] 范梦晨,韩爱云.肉类掺假检测技术的研究进展[J].食品安全质量检测学报,2021,12(1):236-241.FAN M C,HAN A Y.Research progress on meat adulteration detection technology[J].Journal of Food Safety and Quality,2021,12(1):236-241.
[5] FEI B.Hyperspectral imaging in medical applications[J].Data Handling in Science and Technology,2020,32:523-565.
[6] LU B,DAO P D,LIU J,et al.Recent advances of hyperspectral imaging technology and applications in agriculture[J].Remote Sensing,2020,12(16):2 659.
[7] RYAN J,DAVIS C,TUFILLARO N,et al.Application of the hyperspectral imager for the coastal ocean to phytoplankton ecology studies in monterey bay,CA,USA[J].Remote Sensing,2014,6(2):1 007-1 025.
[8] 朱亚东,何鸿举,王魏,等.高光谱成像技术结合线性回归算法快速预测鸡肉掺假牛肉[J].食品工业科技,2020,41(4):184-189.ZHU Y D,HE H J,WANG W,et al.Quick detection of beef adulteration using hyperspectral imaging technology combined with linear regression algorithm[J].Science and Technology of Food Industry,2020,41(4):184-189.
[9] 刘友华,白亚斌,邱祝福,等.基于高光谱图像技术和波长选择方法的羊肉掺假检测方法研究[J].海南师范大学学报(自然科学版),2015,28(3):265-269.LIU Y H,BAI Y B,QIU Z F,et al.Hyperspectral imaging technology and wavelength selection method for nondestructive detection of mutton adulteration[J].Journal of Hainan Normal University(Natural Science),2015,28(3):265-269.
[10] ZHAO H T,FENG Y Z,CHEN W,et al.Application of invasive weed optimization and least square support vector machine for prediction of beef adulteration with spoiled beef based on visible near-infrared(Vis-NIR)hyperspectral imaging[J].Meat Science,2019,151:75-81.
[11] 陈斌,邹贤勇,朱文静.PCA结合马氏距离法剔除近红外异常样品[J].江苏大学学报(自然科学版),2008,29(4):277-280.CHEN B,ZOU X Y,ZHU W J.Eliminating outlier samples in near-infrared model by method of PCA-mahalanobis distance[J].Journal of Jiangsu University(Natural Science Edition),2008,29:277-280.
[12] 陆婉珍.近红外光谱仪器[M].北京:化学工业出版社,2010:57.LU W Z.Near infrared spectrometers[M].Beijing:Chemical Industry Press,2010:57.
[13] GALVÃO R K H,ARAUJO M C U,JOSÉ G E,et al.A method for calibration and validation subset partitioning[J].Talanta,2005,67(4):736-740.
[14] 孙宗保,王天真,刘小裕,等.高光谱结合波长选择算法串联策略检测调理牛排新鲜度[J].光谱学与光谱分析,2020,40(10):3 224-3 229.SUNZ B,WANG T Z,LIU X Y,et al.Detection of prepared steaks freshness using hyperspectral technology combined with wavelengths selection methods combination strategy[J].Spectroscopy and Spectral Analysis,2020,40(10):3 224-3 229.
[15] 周宏平,胡逸磊,姜洪喆,等.基于高光谱成像的油茶籽含油率检测方法[J].农业机械学报,2021,52(5):308-315.ZHOU H P,HU Y L,JIANG H Z,et al.Detection method of oil content of camellia oleifera seed based on hyperspectral imaging[J].Transactions of the Chinese Society for Agricultural Machinery,2021,52(5):308-315.
[16] MUNCKA L,NIELSENA J P,MØLLERA B,et al.Exploring the phenotypic expression of a regulatory proteome-altering gene by spectroscopy and chemometrics[J].Analytica Chimica Acta,2001,446(1):169-184.
[17] 梅从立,陈瑶,尹梁,等.siPLS-LASSO的近红外特征波长选择及其应用[J].光谱学与光谱分析,2018,38(2):436-440.MEI C L,CHEN Y,YIN L,et al.Wavelength selection by siPLS-LASSO for NIR spectroscopy and its application[J].Spectroscopy and Spectral Analysis,2018,38(2):436-440.
[18] WOLD S,SJÖSTRÖM M,ERIKSSON L.PLS-regression:A basic tool of chemometrics[J].Chemometrics & Intelligent Laboratory Systems,2001,58(2):109-130.
[19] 褚小立.化学计量学方法与分子光谱分析技术[M].北京:化学工业出版社,2011.CHU X L.Molecular spectroscopy analytical technology combined with chemometrics and its applications[M].Beijing:Chemical Industry Press,2011.
[20] BAILLÈRES H,DAVRIEUS F,PICHAVANT F H.Near infrared analysis as a tool for rapid screening of some major wood characteristics in a eucalyptus breeding program[J].Annals of Forest Science,2002,59(5/6):479-490.
[21] 严衍禄.近红外光谱分析基础与应用[M].北京:中国轻工业出版社,2005:39-42.YAN Y L.Foundation and application of near infrared spectroscopy[M].Beijing:China Light Industry Press Ltd,2005:39-42.
[22] MAMANI-LINARES L W,GALLO C,ALOMAR D.Identification of cattle,llama and horse meat by near infrared reflectance or transflectance spectroscopy[J].Meat Science,2012,90(2):378-385.
[23] WORKMAN J,WEYER L J.Practical guide to interpretive near-infrared spectroscopy[M].Boca Raton:CRC Press,Inc.,2007:22-85.

Included in

Food Science Commons



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.