Huang Xiaowei, Zou Xiaobo, Zhao Jiewen, Shi Jiyong, Zhang Xiaolei, Li Zhihua, Shen Lecheng
School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Rd., 212013 Zhenjiang, Jiangsu, China.
School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Rd., 212013 Zhenjiang, Jiangsu, China; Key Laboratory of Modern Agricultural Equipment and Technology, 301 Xuefu Rd., 212013 Zhenjiang, Jiangsu, China.
Meat Sci. 2014 Oct;98(2):203-10. doi: 10.1016/j.meatsci.2014.05.033. Epub 2014 Jun 8.
Yao-meat is a traditional Chinese salted meat. Total volatile basic nitrogen content (TVB-N), total viable bacterial count (TVC), and residual nitrite (RN) level are important indexes of freshness for Yao-meat. This paper attempted the feasibility to determine TVB-N content, TVC and RN level in Yao-meat by a colorimetric sensor array chip. A color change profile for each sample was obtained by differentiating the image of sensor array before and after exposure to Yao-meat's volatile organic compounds. Genetic algorithm partial least squares regression (GA-PLS) was proposed to build the relationship between the TVB-N content, TVC, RN and the color change profiles of sensor array, and to select informative chemically responsive dyes for the three quality parameters. The GA-PLS models were achieved with RTVB-N=0.812, RTVC=0.856, RRN=0.855, in prediction set. This study demonstrated that colorimetric sensory array with GA-PLS algorithm could be used successfully to analyze the quality of Chinese traditional Yao-meat.
肴肉是一种中国传统腌肉。挥发性盐基氮含量（TVB-N）、总活菌数（TVC）和残留亚硝酸盐（RN）水平是肴肉新鲜度的重要指标。本文尝试了用比色传感器阵列芯片测定肴肉中TVB-N含量、TVC和RN水平的可行性。通过区分传感器阵列在暴露于肴肉挥发性有机化合物前后的图像，获得每个样品的颜色变化曲线。提出了遗传算法偏最小二乘回归（GA-PLS）来建立TVB-N含量、TVC、RN与传感器阵列颜色变化曲线之间的关系，并为这三个质量参数选择信息丰富的化学响应染料。在预测集中，GA-PLS模型的RTVB-N = 0.812、RTVC = 0.856、RRN = 0.855。本研究表明，采用GA-PLS算法的比色传感阵列可成功用于分析中国传统肴肉的品质。
