Nondestructive quantifying total volatile basic nitrogen (TVB-N) content in chicken using hyperspectral imaging (HSI) technique combined with different data dimension reduction algorithms
Nondestructive quantifying total volatile basic nitrogen (TVB-N) content in chicken using hyperspectral imaging (HSI) technique combined with different data dimension reduction algorithms
复制标题
利用高光谱成像（HSI）技术结合不同数据降维算法无损定量鸡肉中总挥发性碱氮（TVB-N）含量
DOI：
10.1016/j.foodchem.2015.11.084
复制
发表时间：
2016-04-15
期刊：
影响因子：
8.8
通讯作者：
Chen, Quansheng
中科院分区：
文献类型：
--
作者：
Khulal, Urmila;Zhao, Jiewen;Chen, Quansheng
本工作采用高光谱成像（HSI）系统来评估鸡肉品质。比较了主成分分析法和蚁群算法在数据降维中的应用。首先，我们选择了5个主波长的图像从鸡肉超立方体使用PCA和ACO。然后，从每个主波长图像中提取6个基于统计矩的纹理变量，从而总共30个变量。接下来，我们选择了经典的反向传播人工神经网络（BPANN）算法进行建模。实验结果表明，ACO-BPANN模型的预测性能上级PCA-BPANN模型，在预测集上得到最优模型，RMSEP = 6.3834 mg/100 g，R = 0.7542。研究结果表明，HSI结合光谱和空间信息，在快速、无损地定量测定鸡肉TVB-N含量方面具有很大潜力，而ACO在超立方体降维方面具有优势。(C)2015爱思唯尔有限公司版权所有。
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.