Li Liang, Ding Wu
College of Food Science and Engineering, Northwest Agriculture and Forestry University, Yangling 712100, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2010 May;30(5):1238-42.
In order to find out a fast measure method of adulterated milk based on near infrared spectroscopy, milk adulterated with plant butter, vegetable protein and starch was collected respectively. Using Fourier transform near infrared spectroscopy to scan the samples, the spectrum data were obtained. The samples were scanned in the spectral region between 4 000 and 12 000 cm(-1) by FT-NIR spectrometer with an optic fiber of 2 mm path-length and an InGaAs detector. Then all data were analyzed by principal component analysis combined with Fisher line discriminant analysis (FLDA) and partial least squares (PLS). Results show that the accumulative reliabilities of the first six components were more than 99%, so the first six components were applied as FLDA inputs and the values of the type of milk were applied as the outputs. An adulterated milk qualitative discriminant model based on Fisher line discriminant analysis was developed finally. The result indicated that the accuracy of detection of calibration samples is 97.78%. The unknown test samples were tested by this model and the correct identification rate is 94.44%. Partial least square models for detecting the content of material added to raw milk were set up with good veracity. The predictive correlation coefficient (R2) of calibration sets of milk adulterated with plant butter, vegetable protein and starch are 99.08%, 99.96% and 99.39%, respectively, while the root mean square errors of cross validation (RMSECV) of the three calibration sets are 0.304%, 0.013 5% and 0.060%, respectively. The R2 of validation sets of the three kinds of adulterated milk are 98.50%, 99.94% and 98.50%, respectively, while the root mean square errors of prediction (RMSEP) of the three validation sets are 0.323%, 0.028 8% and 0.068%, respectively. All of these suggested that near infrared spectroscopy has good potential for rapid qualitative and quantitative detection of milk adulterated with botanical filling material.
为了探寻一种基于近红外光谱的掺假牛奶快速检测方法,分别采集了掺有植物黄油、植物蛋白和淀粉的掺假牛奶。利用傅里叶变换近红外光谱仪对样品进行扫描,获取光谱数据。采用光程为2 mm的光纤及铟镓砷探测器,通过傅里叶变换近红外光谱仪在4000至12000 cm(-1)光谱区域对样品进行扫描。然后采用主成分分析结合Fisher线性判别分析(FLDA)和偏最小二乘法(PLS)对所有数据进行分析。结果表明,前六个主成分的累积贡献率均大于99%,因此将前六个主成分作为FLDA输入,牛奶类型值作为输出。最终建立了基于Fisher线性判别分析的掺假牛奶定性判别模型。结果表明,校正集样本的检测准确率为97.78%。利用该模型对未知测试样本进行检测,正确识别率为94.44%。同时建立了检测原料乳中掺假物质含量的偏最小二乘模型,准确性良好。掺植物黄油、植物蛋白和淀粉牛奶校正集的预测相关系数(R2)分别为99.08%、99.96%和99.39%,三个校正集的交叉验证均方根误差(RMSECV)分别为0.304%、0.0135%和0.060%。三种掺假牛奶验证集的R2分别为98.50%、99.94%和98.50%,三个验证集的预测均方根误差(RMSEP)分别为0.323%、0.0288%和0.068%。所有这些表明近红外光谱技术在掺植物性填充材料牛奶的快速定性和定量检测方面具有良好的应用潜力。