ABSTRACT Total volatile basic nitrogen (TVB-N) level rapid evaluation on chicken meat based on gas sensor array (GSA) technique was studied in this paper. GSA responses to chicken meat stored at 4°C were examined for 5 days. TVB-N content was synchronously measured by chemical examination. Principal component analysis (PCA) and non-linear double-layered cascaded serial stochastic resonance (DCSSR) were utilized for measurement data analysis. TVB-N examination results suggested that chicken meat stored for more than 3 days was not fresh. PCA showed poor discrimination abilities, while DCSSR signal-to-noise ratio (SNR) quantitatively characterized the freshness of all samples. Chicken meat TVB-N forecasting model was developed by non-linear fitting between SNR eigenvalues and TVB-N values. The predicting model was constructed. Validation experiment results demonstrated that the forecasting accuracy of the developed model reached 93.3%.

中文翻译：

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考虑总挥发性碱性氮的气体传感器阵列和信号分析快速评估鸡肉新鲜度

摘要 本文研究了基于气体传感器阵列（GSA）技术的鸡肉总挥发性碱性氮（TVB-N）水平快速评估。对在 4°C 下储存的鸡肉的 GSA 反应进行了 5 天的检查。TVB-N含量通过化学检测同步测定。主成分分析 (PCA) 和非线性双层级联串行随机共振 (DCSSR) 被用于测量数据分析。TVB-N检测结果表明，存放3天以上的鸡肉不新鲜。PCA 显示出较差的辨别能力，而 DCSSR 信噪比 (SNR) 定量表征所有样品的新鲜度。鸡肉TVB-N预测模型是通过SNR特征值和TVB-N值之间的非线性拟合开发的。构建了预测模型。