Application of Time Series Hyperspectral Imaging (TS-HSI) for Determining Water Distribution Within Beef and Spectral Kinetic Analysis During Dehydration
Application of Time Series Hyperspectral Imaging (TS-HSI) for Determining Water Distribution Within Beef and Spectral Kinetic Analysis During Dehydration
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DOI：
10.1007/s11947-012-0928-0
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发表时间：
2013-11-01
影响因子：
5.6
通讯作者：
Yao, Jiansong
中科院分区：
文献类型：
--
作者：
Wu, Di;Wang, Songjing;Yao, Jiansong
本研究的目的是使用时间序列高光谱成像 (TS-HSI) 快速、无创地测定脱水过程中牛肉内部的水分分布。在脱水过程的不同时期获取牛肉片的高光谱图像（380-1,700 nm）。使用图像分割过程从 TS-HSI 图像中提取牛肉的光谱。进行主成分分析以获得脱水过程中系统光谱变化的概述。首次采用有效波长的选择来代替传统的数据挖掘策略来应对TS-HSI图像中的大型多元数据结构，以减少TS-HSI数据的计算负担并简化校准建模。在利用连续投影算法（SPA）识别有效波长的基础上，比较了偏最小二乘回归、最小二乘支持向量机和多元线性回归（MLR）三种光谱校准算法。带有 Spectral Set I 的 SPA-MLR 模型被认为是测定牛肉片水分含量的最佳模型。该模型的确定系数 () 为 0.953，交叉验证估计的均方根误差为 1.280%。通过将定量模型转移到图像中的每个像素，最终生成脱水过程中牛肉切片内水分分布的可视化，以确定牛肉样品所有点的水分含量。还首次对 TS-HSI 图像进行了动力学分析，以分析牛肉在脱水过程中的光谱变化。结果表明，TS-HSI 具有以合理的精度快速、无创地定量可视化脱水过程中牛肉水分含量的潜力。
This study was carried out for rapid and noninvasive determination of water distribution within beef during dehydration using time series hyperspectral imaging (TS-HSI). Hyperspectral images (380-1,700 nm) of beef slices were acquired at different periods of dehydration process. The spectra of beef were extracted from the TS-HSI images using image segmentation process. Principal component analysis was conducted to obtain an overview of the systematic spectral variations during dehydration. Instead of the traditional data mining strategies to cope with the large multivariate data structures in the TS-HSI images, the selection of effective wavelengths was conducted for the first time to reduce the computational burden of the TS-HSI data and predigest calibration modeling. On the basis of the effective wavelengths identified by using successive projections algorithm (SPA), three spectral calibration algorithms of partial least squares regression, least squares support vector machines, and multiple linear regression (MLR) were compared. The SPA-MLR model with Spectral Set I was considered to be the best for determining water content of beef slice. The model led to a coefficient of determination () of 0.953 and root mean square error estimated by cross-validation of 1.280 %. The visualization of water distribution within beef slice during dehydration was finally generated by transferring the quantitative model to each pixel in the image to determine water content in all spots of the beef sample. Kinetic analysis of the TS-HSI images was also conducted for the first time to analyze spectral changes of beef during dehydration. The results demonstrate that TS-HSI has the potential of quantitatively visualizing water content of beef rapidly and noninvasively during dehydration in a reasonable accuracy.