School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China.
School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Food Refrigeration and Computerized Food Technology, University College Dublin, National University of Ireland, Agriculture and Food Science Centre, Belfield, Dublin 4, Ireland.
Food Chem. 2018 May 15;248:119-127. doi: 10.1016/j.foodchem.2017.12.050. Epub 2017 Dec 14.
Near-infrared (NIR) spectra contain abundant data, heterospectral two-dimensional correlation (H2D-CS) analysis offers a good way to interpret these data. For the first time, H2D-CS was used to correlate the NIR hyperspectral imaging (HSI) data with mid-infrared spectra and to identify feature-related wavebands for developing models for monitoring the oxidative damage of pork myofibrils during frozen storage. The HSI images were acquired at frozen state without thawing and the oxidative damage of myofibrils was assessed by carbonyl content. Results showed that the simplified PLSR model based on H2D-CS identified feature wavebands obtained determination coefficient in prediction (R) of 0.896 and root mean square error in prediction (RMSEP) of 0.177 nmol/mg protein, which was better than the partial least square regression (PLSR) model based on full wavebands (R = 0.856, RMSEP = 0.209 nmol/mg protein). Therefore, H2D-CS was effective in selecting feature-related wavebands of NIR HSI.
近红外（NIR）光谱包含丰富的数据，异谱二维相关（H2D-CS）分析为解释这些数据提供了一种很好的方法。首次将 H2D-CS 用于将 NIR 高光谱成像（HSI）数据与中红外光谱相关联，并确定特征相关波段，以开发用于监测冷冻储存过程中肌原纤维氧化损伤的模型。HSI 图像在不解冻的冷冻状态下获取，通过羰基含量评估肌原纤维的氧化损伤。结果表明，基于 H2D-CS 的简化偏最小二乘回归（PLSR）模型确定的特征波段在预测（R）中的决定系数为 0.896，预测均方根误差（RMSEP）为 0.177 nmol/mg 蛋白，优于基于全波段的偏最小二乘回归（PLSR）模型（R=0.856，RMSEP=0.209 nmol/mg 蛋白）。因此，H2D-CS 有效地选择了 NIR HSI 的特征相关波段。
