Comparison of different chemometric methods in quantifying total volatile basic-nitrogen (TVB-N) content in chicken meat using a fabricated colorimetric sensor array
Comparison of different chemometric methods in quantifying total volatile basic-nitrogen (TVB-N) content in chicken meat using a fabricated colorimetric sensor array
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使用制造的比色传感器阵列定量鸡肉中总挥发性碱氮 (TVB-N) 含量的不同化学计量方法的比较
DOI：
10.1039/c5ra25375f
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发表时间：
2016-01-01
期刊：
影响因子：
3.9
通讯作者：
Chen, Quansheng
中科院分区：
文献类型：
--
作者：
Khulal, Urmila;Zhao, Jiewen;Chen, Quansheng
总挥发性盐基氮（TVB-N）含量是评价鸡肉新鲜度的核心指标之一。本研究以化学计量学分析为手段，探讨了利用低成本比色传感器阵列定量测定鸡肉中挥发性盐基氮（TVB-N）含量的可行性。将12种化学响应染料（9种卟啉/金属卟啉和3种pH指示剂）印刷在C2反相硅胶平板上，构建了一种比色传感器阵列，用于鸡肉中挥发性盐基氮（TVB-N）含量的快速、非破坏性定量测定。通过区分暴露于挥发性有机化合物（VOC）之前和之后的传感器阵列的图像来获得每个样品的颜色变化曲线。线性算法;采用偏最小二乘回归（PLSR）和非线性算法、反向传播人工神经网络（BPANN）、自适应Boosting BPANN（BP-AdaBoost）和基于粒子群优化（PSO）的支持向量机回归（SVMR）方法建立TVB-N预测模型。实验结果表明，PSO-SVMR模型的预测精度优于线性模型和经典非线性模型，具有上级的特点。最优PSO-SVMR模型为4个支持向量，Rp为0.8981，RMSEP为5.5255。总体结果是令人鼓舞的应用低成本的比色传感器结合适当的化学计量学方法在家禽业的质量评估，因为它是实用的，非侵入性的，快速和简单。
Total Volatile Basic-Nitrogen (TVB-N) content is one of core measures in evaluating chicken freshness. This study reported the feasibility to quantify Total Volatile Basic-Nitrogen (TVB-N) content in chicken meat by a low cost colorimetric sensor array with the help of chemometric analysis. We fabricated a colorimetric sensor array by printing 12 chemically responsive dyes (i.e. 9 porphyrins/metalloporphyrins and 3 pH indicators) on a C2 reverse silica-gel flat plate for the fast and non-destructive quantitative determination of TVB-N content in chicken. A colour change profile for each sample was obtained by differentiating the image of the sensor array before and after exposure to volatile organic compounds (VOCs). Linear algorithm; partial least squares regression (PLSR) and nonlinear algorithms; back propagation artificial neural network (BPANN), Adaptive Boosting BPANN (BP-AdaBoost) and support vector machine regression (SVMR) methods based on particle swarm optimization (PSO) were used to build the TVB-N prediction model. Experimental results showed that the predictive precision of the PSO-SVMR model was superior to linear and classic non-linear models. The optimum PSO-SVMR model was obtained with 4 support vectors and Rp of 0.8981, RMSEP of 5.5255. The overall results are encouraging for the application of low cost colorimetric sensors combined with an appropriate chemometric method in the poultry industry for quality assessment because it is practical, non-invasive, rapid and simple.