Liu Changhong, Liu Wei, Lu Xuzhong, Chen Wei, Yang Jianbo, Zheng Lei
School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China.
Intelligent Control and Compute Vision Lab, Hefei University, Hefei 230601, China.
Food Chem. 2016 Mar 15;195:110-6. doi: 10.1016/j.foodchem.2015.04.145. Epub 2015 May 6.
Colour and moisture content are important indices in quality monitoring of dehydrating carrot slices during dehydration process. This study investigated the potential of using multispectral imaging for real-time and non-destructive determination of colour change and moisture distribution during the hot air dehydration of carrot slices. Multispectral reflectance images, ranging from 405 to 970 nm, were acquired and then calibrated based on three chemometrics models of partial least squares (PLS), least squares-support vector machines (LS-SVM), and back propagation neural network (BPNN), respectively. Compared with PLS and LS-SVM, BPNN considerably improved the prediction performance with coefficient of determination in prediction (RP(2))=0.991, root-mean-square error of prediction (RMSEP)=1.482% and residual predictive deviation (RPD)=11.378 for moisture content. It was concluded that multispectral imaging has an excellent potential for rapid, non-destructive and simultaneous determination of colour change and moisture distribution of carrot slices during dehydration.
颜色和水分含量是脱水胡萝卜片干燥过程中质量监测的重要指标。本研究探讨了利用多光谱成像技术实时无损测定胡萝卜片热风干燥过程中颜色变化和水分分布的潜力。采集了405至970nm范围内的多光谱反射图像，并分别基于偏最小二乘法（PLS）、最小二乘支持向量机（LS-SVM）和反向传播神经网络（BPNN）三种化学计量学模型进行校准。与PLS和LS-SVM相比，BPNN显著提高了预测性能，预测决定系数（RP(2)）=0.991，预测均方根误差（RMSEP）=1.482%，水分含量的剩余预测偏差（RPD）=11.378。得出结论，多光谱成像在快速、无损和同时测定胡萝卜片干燥过程中的颜色变化和水分分布方面具有优异的潜力。
