CAJ | 학술논문

牛肉含水率的高低不仅直接影响牛肉品质，而且会对消费者造成经济损失。为此，通过实验探究了采用高光谱图像技术对牛肉含水率进行检测的可行性，为检测牛肉品质提供依据。采用82个牛肉后腿样本作为实验材料，按5×4×1cm的规格通过国际烘干法测量其真实含水量，并采集它们的光谱图像；获取样本的光谱信息后，通过ENVI 及MatLab 软件获取感兴趣区域。同时，利用不同的预处理方法，分别建立BP 神经网络和偏最小二乘校正模型，通过比对两种模型结果，偏最小二乘校正模型能够更有效预测牛肉含水率，校正集相关系数为0．91，校正标准差为0．121，预测集的相关系数为0．89，预测标准差为0．118。研究结果证实，利用高光谱图像技术可以快速无损检测牛肉含水率。
우육함수솔적고저불부직접영향우육품질，이차회대소비자조성경제손실。위차，통과실험탐구료채용고광보도상기술대우육함수솔진행검측적가행성，위검측우육품질제공의거。채용82개우육후퇴양본작위실험재료，안5×4×1cm적규격통과국제홍간법측량기진실함수량，병채집타문적광보도상；획취양본적광보신식후，통과ENVI 급MatLab 연건획취감흥취구역。동시，이용불동적예처리방법，분별건립BP 신경망락화편최소이승교정모형，통과비대량충모형결과，편최소이승교정모형능구경유효예측우육함수솔，교정집상관계수위0．91，교정표준차위0．121，예측집적상관계수위0．89，예측표준차위0．118。연구결과증실，이용고광보도상기술가이쾌속무손검측우육함수솔。
The moisture content of beef not only can directly affect the beef quality , but also brings great economic dam-age to the consumers , therefore this experiment provides the basis for beef quality detection by exploring the feasibility of detecting beef moisture content through hyperspectral image technology .82 samples of cows ’ back-legs are adopted as experiment materials to measure their real moisture and to collect their hyperspectral images by way of international drying method according to the specification of 5 ×4 ×1 cm.After getting the hyperspectral information of the samples , with the help of ENVI and MATLAB software , interesting areas are gained .By different pretreatment methods , artificial neural network and PLS calibration model are separately built .Though comparison of the results of these two models , PLSR cali-bration model can better predict the beef moisture content .The correlation coefficient of correction is 0 .91 the RMSEC is 0.121 the correlation coefficient of prediction set is 0.89and the RMSEP is 0.118.The results show that beef moisture content can be quickly and intact detected through hyperspectral image technology .