基于中波近红外光谱构建生鲜鸡肉胆固醇定量分析模型

通过采集市售生鲜鸡肉样品在中波近红外区的光谱信息，采用偏最小二乘法构建胆固醇定量分析模型，并评价模型的预测准确性。在建模过程中，讨论异常样品剔除与组合预处理方法等优化措施对模型的影响。参模样品经2 次异常样本剔除并以SG（Savitzky-Golay）一阶导数、SG平滑及去趋势校正进行预处理后，获得了最佳生鲜鸡肉胆固醇定量分析模型，其中：校正标准差（standard error of calibration，SEC）为4.534 8、校正集相关系数为0.919 7、预测标准差（standard error of prediction，SEP）为7.437 5、验证集相关系数为0.812 0、范围误差比为2.844 7、相对预测标准差为9.44%、主因子数为5、SEP/SEC为1.640 1。对检验集样品预测的结果表明，基于中波近红外光谱构建的胆固醇定量分析模型的预测性能较好（P＞0.05），特别是在60～100 mg/100 g含量区间，可应用于对市售生鲜鸡肉及产品胆固醇的检测。

Establishment of Quantitative Analysis Model of Chicken Cholesterol Based on Medium-Wavelength Near-Infrared Spectroscopy

In this research spectral information for commercially available chicken breast samples was extracted in the
medium wavelength region of near-infrared (NIR) and used for establishing a quantitative analysis model of cholesterol
concentration using partial least squares (PLS) regression, and the prediction accuracy of the model was analyzed. The
modeling was investigated with respect to outlier elimination and optimized combined use of various spectral preprocessing
methods. The optimum quantitative analysis model was achieved with two cycles of outlier elimination and the combined
use of SG first derivative, SG smoothing and De-trending for spectral preprocessing. The model parameters standard error of
calibration (SEC), correlation coefficient of calibration set (Rc), standard error of prediction (SEP), correlation coefficient of validation
set (Rp), ratio of performance to standard deviate (RPD), relative standard error of prediction (RSEP), the number of principal factors (PF)
and SEP/SEC were 4.534 8, 0.919 7, 7.437 5, 0.812 0, 2.844 7, 9.44%, 5, and 1.640 1, respectively. The experimental results showed
that the model had better prediction accuracy in test set (P > 0.05), especially in the range of 60–100 mg/100 g, and could be applied to
analyze the cholesterol concentration of commercially available fresh chicken and chicken products.