State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang 330047, People's Republic of China.
State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang 330047, People's Republic of China.
Meat Sci. 2021 Oct;180:108559. doi: 10.1016/j.meatsci.2021.108559. Epub 2021 May 21.
With application of PLS regression and SVR, quantitation models of near infrared diffuse reflectance spectroscopy were established for the first time to predict the content of volatile basic nitrogen (TVB-N) content in beef and pork. Results indicated that the best PLS model based on the raw spectra showed an excellent prediction performance with a high value of correlation coefficient at 0.9366 and a low root-mean-square error of prediction value of 3.15, and none of those pretreatment methods could improve the prediction performance of the PLS model. Moreover, comparatively the model obtained by SVR showed inferior quantitative predictive ability (R = 0.8314, RMSEP = 4.61). Analysis on VIP selected wavelengths inferred amino bond containing compounds and lipid may play important roles in the development of PLS models for TVB-N. Results from this study demonstrated the potential of using NIR spectroscopy and PLS for the prediction of TVB-N in beef and pork while more efforts are required to improve the performance of SVR models.
应用偏最小二乘法（PLS）回归和支持向量回归（SVR），首次建立了近红外漫反射光谱定量分析模型，用于预测牛肉和猪肉中挥发性盐基氮（TVB-N）的含量。结果表明，基于原始光谱的最佳 PLS 模型表现出优异的预测性能，相关系数值高达 0.9366，预测值的均方根误差较低，为 3.15，且没有任何预处理方法可以提高 PLS 模型的预测性能。此外，SVR 得到的模型表现出较差的定量预测能力（R=0.8314，RMSEP=4.61）。对 VIP 选择的波长进行分析推断，含氨基键的化合物和脂质可能在 PLS 模型中对 TVB-N 的发展起着重要作用。本研究结果表明，近红外光谱和 PLS 可用于预测牛肉和猪肉中的 TVB-N，但需要进一步努力提高 SVR 模型的性能。
