...
首页> 外文期刊>European Food Research and Technology >A comparison and joint use of mid infrared and fluorescence spectroscopic methods for differentiating between manufacturing processes and sampling zones of ripened soft cheeses
【24h】

A comparison and joint use of mid infrared and fluorescence spectroscopic methods for differentiating between manufacturing processes and sampling zones of ripened soft cheeses

机译:中红外和荧光光谱法的比较和联合使用,以区分成熟的软干酪的制造过程和采样区域

获取原文
获取原文并翻译 | 示例

摘要

Ten traditional M1 (n = 5) and M2 (n = 5) soft cheeses produced from raw milk, and five other stabilised M3 (n = 5) cheeses manufactured from pasteurised milk, were studied using mid infrared (MIR) and front face fluorescence (FFFS) spectroscopies. MIR (3000–900 cm?1), tryptophan (excitation: 290 nm, emission: 305-450 nm), 400-640 emission spectra (excitation: 380 nm) and vitamin A (excitation: 280–350 nm, emission: 410 nm) spectra were recorded at two sampling zones (external (E) and central (C)) of the investigated cheeses. When the factorial discriminant analysis (FDA) was applied to the MIR spectra, the classification was not satisfactory. With tryptophan fluorescence spectra, correct classification of 94.4 and 69.4% was observed for the calibration and validation spectra, respectively. Better classification was obtained using vitamin A fluorescence spectra, since 91.8 and 80.6% of the calibration and validation spectra, respectively, were correctly classified. When the first five principal components (PCs) of the PCA extracted from each data set were pooled into a single matrix and analysed by FDA, the classification was considerably improved, obtaining a percentage of correct classification of 100 and 91.7% for the calibration and validation samples, respectively. It was concluded that concatenation of the physico-chemical and spectroscopic data sets is an efficient technique for the identification of soft cheese varieties.
机译:使用中红外(MIR)和正面荧光技术研究了十种由生奶生产的传统M1(n = 5)和M2(n = 5)软奶酪,以及另外五种由巴氏消毒的牛奶制造的稳定化M3(n = 5)奶酪。 (FFFS)光谱学。 MIR(3000-900 cm?1 ),色氨酸(激发:290 nm,发射:305-450 nm),400-640发射光谱(激发:380 nm)和维生素A(激发:280-350 nm) ,发射:410 nm)的光谱记录在研究奶酪的两个采样区(外部(E)和中央(C))。当将因子判别分析(FDA)应用于MIR光谱时,分类不令人满意。使用色氨酸荧光光谱,对于校准和验证光谱,分别观察到正确分类的94.4%和69.4%。使用维生素A荧光光谱可获得更好的分类,因为分别正确校准了91.8和80.6%的校准和验证光谱。当将从每个数据集中提取的PCA的前五个主要成分(PC)汇总到一个矩阵中并通过FDA分析时,分类得到了很大改善,获得了100%和91.7%的正确分类百分比用于校准和验证样本。结论是,理化和光谱数据集的组合是识别软干酪品种的有效技术。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号