College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, PR China.
College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, PR China; Food Refrigeration and Computerised Food Technology, Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland.
Food Chem. 2015 May 15;175:417-22. doi: 10.1016/j.foodchem.2014.11.161. Epub 2014 Dec 5.
In this study, the potential of hyperspectral imaging (HSI) for predicting hydroxyproline content in chicken meat was investigated. Spectral data contained in the hyperspectral images (400-1000 nm) of chicken meat was extracted, and a partial least square regression (PLSR) model was then developed for predicting hydroxyproline content. The model yielded acceptable results with regression coefficient in prediction (Rp) of 0.874 and root mean error squares in prediction (RMESP) of 0.046. Based on the eight optimal wavelengths selected by regression coefficients (RC) from the PLSR model, a new RC-PLSR model was built and good results were shown with high Rp of 0.854 and low RMSEP of 0.049. Finally, distribution maps of hydroxyproline were created by transferring the RC-PLSR model to each pixel in the hyperspectral images. The results demonstrated that HSI has the capability for rapid and non-destructive determination of hydroxyproline content in chicken meat.
本研究旨在探讨高光谱成像（HSI）在预测鸡肉中羟脯氨酸含量方面的潜力。从鸡肉的高光谱图像（400-1000nm）中提取光谱数据，并建立偏最小二乘回归（PLSR）模型以预测羟脯氨酸含量。该模型的预测回归系数（Rp）为 0.874，预测均方根误差平方（RMESP）为 0.046，结果可接受。基于 PLSR 模型中回归系数（RC）选择的八个最佳波长，建立了一个新的 RC-PLSR 模型，结果显示 Rp 较高（0.854），RMSEP 较低（0.049）。最后，通过将 RC-PLSR 模型转换到高光谱图像的每个像素，创建了羟脯氨酸的分布图。结果表明，HSI 具有快速、无损测定鸡肉中羟脯氨酸含量的能力。
