Jiang Hongzhe, Ru Yu, Chen Qing, Wang Jinpeng, Xu Linyun
College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2021 Mar 15;249:119307. doi: 10.1016/j.saa.2020.119307. Epub 2020 Dec 15.
Hyperspectral imaging (HSI) technique was investigated to explore a feasible protocol for detecting the potential offal (lung) adulteration in ground pork. Tested samples (176 adulterated and 2 controls) were first prepared with adulterant of ground lung in range of 0%-100% (w/w) at 10% intervals. After hyperspectral images were acquired and calibrated in reflectance mode (400-1000 nm), full spectra were extracted from identified regions of interests (ROIs) and then transformed into absorbance and Kubelka-Munck spectral units, respectively. Partial least squares regression (PLSR) models based on full spectra showed that raw reflectance spectra with no preprocessings performed best with coefficient of determination (R) of 0.98, root mean square error (RMSEP) of 4.25%, and ratio performance deviation (RPD) of 7.53 in prediction set. To reduce the high dimensionality of spectra, data was further explored using principal component loadings, two-dimensional correlation spectroscopy (2D-COS), and regression coefficients (RC), respectively. The optimal performance of established simplified PLSR model were acquired using eleven featured wavelengths selected by PC loadings with R of 0.98, RMSEP of 4.47% and RPD of 7.16. Finally, the limit of detection (LOD) was calculated to be a satisfactory 7.58%, and readily discernible visualization procedure using preferred simplified PLSR model yielded satisfactory spatial distribution of adulteration situation. Control samples with known distribution were also visualized to successfully prove the validity. Consequently, this research offers an alternative assay for visually and rapidly detecting offal of lung adulteration in ground pork.
研究了高光谱成像（HSI）技术，以探索一种可行的方案来检测碎猪肉中潜在的内脏（肺）掺假情况。首先制备测试样品（176个掺假样品和2个对照样品），其中掺假物为磨碎的肺，掺假范围为0%-100%（w/w），间隔为10%。在以反射模式（400-1000nm）采集并校准高光谱图像后，从识别出的感兴趣区域（ROI）提取全光谱，然后分别转换为吸光度和库贝尔卡-蒙克光谱单位。基于全光谱的偏最小二乘回归（PLSR）模型表明，未经预处理的原始反射光谱表现最佳，预测集中的决定系数（R）为0.98，均方根误差（RMSEP）为4.25%，比率性能偏差（RPD）为7.53。为了降低光谱的高维性，分别使用主成分载荷、二维相关光谱（2D-COS）和回归系数（RC）对数据进行了进一步探索。使用通过主成分载荷选择的11个特征波长获得了所建立的简化PLSR模型的最佳性能，R为0.98，RMSEP为4.47%，RPD为7.16。最后，计算出的检测限（LOD）为令人满意的7.58%，使用首选的简化PLSR模型进行易于辨别的可视化程序产生了掺假情况的令人满意的空间分布。已知分布的对照样品也进行了可视化，以成功证明其有效性。因此，本研究提供了一种替代方法，用于直观快速地检测碎猪肉中肺脏掺假的内脏。
