Multivariate curve resolution of spectrophotometric data for the determination of artificial food colors
Multivariate curve resolution of spectrophotometric data for the determination of artificial food colors
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DOI：
10.1021/jf800069p
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发表时间：
2008-07-23
影响因子：
6.1
通讯作者：
Kessler, Waltraud
中科院分区：
文献类型：
--
作者：
Lachenmeier, Dirk W.;Kessler, Waltraud
在食品添加剂的分析中，过去的重点放在发展色谱技术，从复杂的基质中分离目标成分。特别是在人造食品色素的情况下，由于不同成分之间的光谱重叠很高，直接分光光度测量被认为缺乏特异性。多变量曲线分辨率（MCR）可用于克服该限制。MCR能够（i）从复杂的光谱特征中提取所涉及的组分的数量，（ii）将所得光谱归因于化合物，以及（iii）在有或没有先验知识的情况下量化单个光谱贡献。我们已经评估了MCR用于苦艾酒中黄色和蓝色食品色素的常规分析。使用校准标准品，我们能够表明，MCR同样执行相比，偏最小二乘回归，但大大改善了预测光谱中包含的化学信息。然后将MCR应用于不同苦艾酒的真实集合。如参考分析所证实的，食品颜色被正确分配，灵敏度为0.93，特异性为0.85。除了人工色素外，该算法还在一些样本中检测到了另一种成分，这种成分可以被分配给叶绿素的天然色素。
In the analysis of food additives, past emphasis was put on the development of chromatographic techniques to separate target components from a complex matrix. Especially in the case of artificial food colors, direct spectrophotometric measurement was seen to lack in specificity due to a high spectral overlap between different components. Multivariate curve resolution (MCR) may be used to overcome this limitation. MCR is able to (i) extract from a complex spectral feature the number of involved components, (ii) attribute the resulting spectra to chemical compounds, and (iii) quantify the individual spectral contributions with or without a priori knowledge. We have evaluated MCR for the routine analysis of yellow and blue food colors in absinthe spirits. Using calibration standards, we were able to show that MCR equally performs as compared to partial least-squares regression but with much improved chemical information contained in the predicted spectra. MCR was then applied to an authentic collective of different absinthes. As confirmed by reference analytics, the food colors were correctly assigned with a sensitivity of 0.93 and a specificity of 0.85. Besides the artificial colors, the algorithm detected a further component in some samples that could be assigned to natural coloring from chlorophyll.