Huang H, Liu L, Ngadi M O, Gariépy C
Department of Bioresource Engineering, McGill University, Macdonald Campus. 21,111 Lakeshore Road, Ste-Anne-de-Bellevue, Quebec, Canada H9X 3V9.
Department of Bioresource Engineering, McGill University, Macdonald Campus. 21,111 Lakeshore Road, Ste-Anne-de-Bellevue, Quebec, Canada H9X 3V9.
Talanta. 2014 Feb;119:385-95. doi: 10.1016/j.talanta.2013.11.015. Epub 2013 Nov 12.
Having acquired near infrared (NIR) hyperspectral images of intact pork loin samples through an NIR hyperspectral imaging system, the efficiency of a variety of image processing techniques including texture pattern analysis techniques were applied to process hyperspectral images so as to determine the intramuscular fat (IMF) content non-destructively. After the segmentation of region of interest (ROI), the raw spectral, texture-based spectral and textural characteristics of pork images were extracted by spectral averaging and pattern recognition techniques namely Gabor filter and improved gray level co-occurrence matrix (GLCM), respectively. First derivatives of the non-filtered and the Gabor filtered spectra were also investigated. Full waveband partial least squares regression (PLSR) was employed to determine the optimal parameters of Gabor filter and GLCM, and to select optimal wavelengths for IMF prediction. A stepwise procedure was applied to the optimal wavelengths to further optimize them to key wavelengths. Multiple linear regression (MLR) models were built based on the key wavelengths. Mean spectra and the Gabor filtered spectra outperformed GLCM. The best result, represented by correlation coefficients of calibration (Rc), cross validation (Rcv) and prediction (Rp) of 0.89, 0.89, and 0.86, respectively, was achieved using the first derivative of Gabor filtered spectra at 1193 and 1217 nm. To visualize the IMF content in pork, the distribution maps of IMF content in pork were drawn using a mean spectra-based MLR model. These promising results highlight the great potential of NIR hyperspectral imaging for non-destructive prediction of IMF content of intact pork.
通过近红外（NIR）高光谱成像系统获取完整猪里脊肉样本的近红外高光谱图像后，应用包括纹理模式分析技术在内的多种图像处理技术来处理高光谱图像，以便无损测定肌内脂肪（IMF）含量。在分割感兴趣区域（ROI）后，分别通过光谱平均以及模式识别技术（即Gabor滤波器和改进的灰度共生矩阵（GLCM））提取猪肉图像的原始光谱、基于纹理的光谱和纹理特征。还研究了未滤波光谱和Gabor滤波光谱的一阶导数。采用全波段偏最小二乘回归（PLSR）来确定Gabor滤波器和GLCM的最佳参数，并选择用于IMF预测的最佳波长。对最佳波长应用逐步程序以进一步将其优化为关键波长。基于关键波长建立多元线性回归（MLR）模型。平均光谱和Gabor滤波光谱的表现优于GLCM。使用1193和1217nm处Gabor滤波光谱的一阶导数获得了最佳结果，校准（Rc）、交叉验证（Rcv）和预测（Rp）的相关系数分别为0.89、0.89和0.86。为了可视化猪肉中的IMF含量，使用基于平均光谱的MLR模型绘制了猪肉中IMF含量的分布图。这些有前景的结果突出了近红外高光谱成像在无损预测完整猪肉IMF含量方面的巨大潜力。
