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基于高光谱成像技术的冷鲜羊肉pH值无损检测

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[目的]快速无损检测冷鲜羊肉pH值.[方法]利用可见/近红外光谱(400~1 000 nm)成像技术对冷鲜羊肉外表的散射图像进行采集,并提取样本感兴趣区域的反射光谱曲线以获取原始光谱数据.分别采用平滑滤波(SG)、多元散射校正(MSC)、标准正态变量转换(SNV)和一阶导数(FD)4种单一方法以及MSC-SG、SNV-SG、FD-SG 3种组合方法对原始光谱数据进行预处理.用偏最小二乘回归(PLSR)以及随机森林(RF)、支持向量回归(SVR)、极端梯度增强回归(XGB)分别构建了基于全波长下的冷鲜羊肉pH值的预测模型.[结果]FD-SG为最优预处理方法,XGB模型为最优模型,其校正集相关系数和测试集相关系数分别为0.930 1,0.830 0,校正均方根误差和预测均方根误差分别为0.052 0,0.079 2.利用XGB模型计算了冷鲜羊肉样本中每个像素点下的pH值,并建立伪彩色图像来更直观地展示冷鲜羊肉样本pH值的空间分布情况.[结论]可见/近红外高光谱成像技术可以实现对冷鲜羊肉pH值的无损检测.
Nondestructive detection of chilled mutton pH value using hyperspectral imaging technique
[Objective]To detect the pH value of chilled mutton quickly and non-destructively.[Methods]Visible/near-infrared hyperspectral(400~1 000 nm)imaging technology was used to collect scattering images on the surface of chilled mutton,and the reflectance spectral curve of the region of interest of the sample was extracted to obtain the original spectral data.Four single methods of savitzky-golay(SG),multiplicative scatter correction(MSC),standard normal variant transformation(SNV)and first-order derivative(FD)and three combination methods of MSC-SG,SNV-SG and FD-SG were used to preprocess the original spectral data.Linear regression model:partial least squares regression(PLSR)and nonlinear regression model:Random Forest(RF),support vector regression(SVR),extreme gradient boosting(XGB)were used to construct the prediction model of pH value of chilled mutton based on full wavelength.[Results]FD-SG was the optimal pretreatment method,and the XGB model was the optimal model.The R2C and R2P were 0.930 1 and 0.830 0,and the RMSEC and RMSEP were 0.052 0 and 0.079 2.The XGB model was used to calculate the pH value of each pixel in the chilled mutton sample,and a pseudo-color image was established to show the spatial distribution of the pH value of the chilled mutton sample more intuitively.[Conclusion]Visible/near-infrared hyperspectral imaging technology can realize non-destructive detection of pH value of chilled mutton.

hyperspectral imaging technologychilled muttonpH valuenondestructive detectionextreme gradient boosting

李孟辕、张文广、姜新华、徐子洋、石彩霞

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内蒙古农业大学动物科学学院,内蒙古 呼和浩特 010018

内蒙古自治区农业基因组大数据工程研究中心,内蒙古 呼和浩特 010018

内蒙古农业大学计算机学院,内蒙古 呼和浩特 010018

内蒙古自治区高校动物营养与饲料科学重点实验室,内蒙古 呼和浩特 010018

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高光谱成像技术 冷鲜羊肉 pH值 无损检测 极端梯度增强

2024

食品与机械
长沙理工大学

食品与机械

CSTPCD北大核心
影响因子:0.89
ISSN:1003-5788
年,卷(期):2024.40(11)