CAJ | 학술논문

为实现对牛肉嫩度的预测和分级,构建了试验用高光谱检测系统,在400～1000nm波长范围内获取牛肉表面的高光谱散射图像.从高光谱图像中提取牛肉的反射光谱曲线,用step-wise逐步回归法选择 430、496、510、725、760和828nm 6个波长建立了多元线性回归模型,用全交叉验证法验证模型的预测效果,模型的预测相关系数为0.96,预测标准差为0.64kg.以嫩度6.0kg为界将样本分为嫩牛肉和粗糙牛肉2类,特征波长处反射值为变量,建立了正则判别函数对牛肉嫩度分级,用全交叉验证法验证训练的效果.嫩牛肉分级准确率为83.3%,较粗糙牛肉分级准确率为90.9%,总的分级准确率为87.0%.研究表明该预测和分级技术具有一定的可行性.
위실현대우육눈도적예측화분급,구건료시험용고광보검측계통,재400～1000nm파장범위내획취우육표면적고광보산사도상.종고광보도상중제취우육적반사광보곡선,용step-wise축보회귀법선택 430、496、510、725、760화828nm 6개파장건립료다원선성회귀모형,용전교차험증법험증모형적예측효과,모형적예측상관계수위0.96,예측표준차위0.64kg.이눈도6.0kg위계장양본분위눈우육화조조우육2류,특정파장처반사치위변량,건립료정칙판별함수대우육눈도분급,용전교차험증법험증훈련적효과.눈우육분급준학솔위83.3%,교조조우육분급준학솔위90.9%,총적분급준학솔위87.0%.연구표명해예측화분급기술구유일정적가행성.
To predict and classify beef tenderness, a laboratory hyperspectral imaging system was developed to capture hyperspectral scattering images from the surface of beef samples in the spectral region of 400～1000nm. Reflectance spectral characters were obtained from hyperspectral image. By using the method of step-wise regression, six optimal bands, 430, 496, 510, 725, 760 and 828nm were selected for establishing the multi-linear regression (MLR) model. The model gives good prediction values of beef WBSF with the correlation coefficient of cross validation of 0.96 and the standard error of cross validation of 0.64kg. Based on the measured tenderness values, samples were divided into two classes, i.e., group 0 (<6.0kg) and group 1 (>6.0kg). From selected bands, canonical discriminant functions were built to divide samples into two classes. The full cross validation was employed with the classification accuracy of 83.3% and 90.9%. Resultingly, the overall accuracy of classification is 87.0%. This research demonstrates that the hyperspectral imaging technique is useful for nondestructive determination of beef tenderness.