College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China.
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi, 712100, China.
J Dairy Sci. 2022 Nov;105(11):8638-8649. doi: 10.3168/jds.2022-21969. Epub 2022 Aug 31.
The nonhomogeneity of bovine colostrum leads to strong scattering effects for electromagnetic waves, which affects the application of electromagnetic spectroscopy in detecting colostrum. This work aimed to compare the performance of near-infrared spectroscopy (NIRS) and dielectric spectroscopy (DS) in quantitatively predicting the content of mature milk as an adulterant in colostrum. The near-infrared spectra in the range of 833 to 2,500 nm and the dielectric spectra in the range of 20 to 4,500 MHz of 150 adulterated colostrum samples containing 0 to 50% mature milk were analyzed. The different proportions of mature milk in colostrum significantly changed near-infrared and dielectric spectra. The principal component analysis score plot showed that both NIRS and DS could identify the proportion of mature milk in colostrum, but the 2 methods had different characteristics. Linear partial least squares regression and nonlinear least squares support vector machine (LSSVM) models based on near-infrared and dielectric spectra were established to identify doping proportions. The results showed that DS had better identification performance with a root-mean-square error of prediction of 4.9% and a residual prediction deviation of 3.441 by successive projection algorithm LSSVM, whereas NIRS was relatively weak with a root-mean-square error of prediction of 7.3% and a residual prediction deviation of 2.301 by full-spectra LSSVM. This work provides important insights for the quantitative prediction of nonhomogeneous liquid food by DS.
牛初乳的非均质性导致电磁波产生强烈的散射效应,这影响了电磁光谱学在检测牛初乳中的应用。本工作旨在比较近红外光谱(NIRS)和介电光谱(DS)在定量预测牛初乳中成熟乳作为掺杂物的含量方面的性能。分析了 150 个掺假牛初乳样品的近红外光谱(范围为 833 至 2500nm)和介电光谱(范围为 20 至 4500MHz),其中含有 0 至 50%的成熟乳。牛初乳中成熟乳的不同比例显著改变了近红外和介电光谱。主成分分析得分图表明,NIRS 和 DS 均可识别牛初乳中成熟乳的比例,但 2 种方法具有不同的特点。基于近红外和介电光谱的线性偏最小二乘回归和非线性最小二乘支持向量机(LSSVM)模型被建立以识别掺杂比例。结果表明,DS 通过连续投影算法 LSSVM 具有更好的识别性能,预测均方根误差为 4.9%,残差预测偏差为 3.441,而 NIRS 相对较弱,全谱 LSSVM 的预测均方根误差为 7.3%,残差预测偏差为 2.301。这项工作为 DS 对非均相液体食品的定量预测提供了重要的见解。