期刊
FOOD CHEMISTRY
卷 197, 期 -, 页码 1191-1199出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.foodchem.2015.11.084
关键词
Chicken spoilage; Hyperspectral imaging (HSI); ACO algorithm; Wavelength selection; Texture analysis
资金
- National Natural Science Foundation of China [31371770]
Hyperspectral imaging (HSI) system has been used to assess the chicken quality in this work. Principle component analysis (PCA) and Ant Colony Optimization (ACO) were comparatively used for data dimension reduction. First, we selected 5 dominant wavelength images from chicken hypercube using PCA and ACO. Then, 6 textural variables based on statistical moments were extracted from each dominant wavelength image, thus totaling to 30 variables. Next, we selected the classic back propagation artificial neural network (BPANN) algorithm for modeling. Experimental results showed the performance of ACO-BPANN model is superior to that of PCA-BPANN model, and the optimum ACO-BPANN model was achieved with RMSEP = 6.3834 mg/100 g and R = 0.7542 in the prediction set. Our work implies that HSI integrating spectral and spatial information has a high potential in quantifying TVB-N content of chicken in rapid and non-destructive manner, and ACO has superiority in dimension reduction of hypercube. (C) 2015 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据