基于近红外光谱的生鲜猪肉新鲜度实时评估

Real-time assessment of pork freshness based on visible/near infrared spectroscopy

中文关键词:  生鲜猪肉  近红外光谱  新鲜度  挥发性盐基氮  pH值

英文关键词:pork  visible/near infrared spectroscopy  freshness  total volatile basic nitrogen  pH

中文摘要:

      目的 运用近红外光谱对生鲜猪肉新鲜度进行实时评估。方法 利用多通道可见近红外光谱系统, 获取了猪肉表面380~1080 nm波长范围内的漫反射光谱数据, 采用多元散射校正(MSC)和变量标准化(SNV)的预处理方法, 然后使用偏最小二乘回归建立猪肉新鲜度的预测模型, 进而对猪肉新鲜度进行评价。结果 采用变量标准化处理后的偏最小二乘回归模型相对比较稳定, 建模效果比较好。对挥发性盐基氮 (TVB-N)的验证集的相关系数达到0.91, 对pH值的验证集的相关系数达到0.93。最后利用该模型对猪肉新鲜度进行评定, 评定准确率达92.9%。结论 实验中运用多点的测量方式提高了近红外检测的精度和稳定性, 对于实时检测评估生鲜猪肉的新鲜度有很大的潜力。

英文摘要:

      Objective To assess pork freshness using visible/near infrared spectroscopy. Methods Diffuse reflectance spectroscopy data were obtained from the pork surface of 380~1080 nm by use of multi-chan?nel visible near-infrared spectroscopy system. The multiplicative scattering correction (MSC) and standard normal variables (SNV) were carried out as pre-treatment methods of the spectral data. The prediction model of pork freshness was then established using partial least squares regression, so as to evaluate the freshness of pork. Results The partial least squares regression model after SNV treatment was relatively stable, and the performance was better. The correlation coefficient of total volatile basic nitrogen (TVB-N) and pH value were 0.91 and 0.93 respectively. The accuracy rate of pork freshness assessment was 92.9% by this model. Conclusion The use of multi-point measurements can improve the accuracy and stability of the near-infrared detection, there is a great potential for real-time assessment of pork freshness by visible/near infrared spectroscopy.