荧光相关光谱测定溶液中胭脂红的含量

Detection of Carmine in Solution by Fluorescence Correlation Spectroscopy

使用FLS920P型荧光光谱仪测量了20个合成色素胭脂红溶液样本的荧光发射谱,实验表明:胭脂红的最佳激发波长为300nm,在此波长激发光下,荧光峰值波长为440nm。同时测量相同条件下超纯水的光谱数据作为参考光谱,进行与胭脂红溶液光谱数据的相关计算,构建以浓度为外扰的荧光相关光谱。采用sym8小波函数4尺度降噪,将降噪后的同步相关光谱数据、自相关光谱数据应用偏最小二乘回归(PLSR)算法进行预测,建立溶液中胭脂红含量的定量模型,结果表明:采用同步相关光谱建模的预测相关系数为99.863%,预测均方根误差为0.414μg·mL^(-1);而采用自相关光谱建模的预测相关系数为99.940%,预测均方根误差为0.303μg·mL^(-1)。对比可知,自相关光谱数据有效地避免了信息冗余,预测结果更为可靠。该方法无需样本处理,操作简单,为食品安全检测提供了一种新的思路。

20 samples of Carmine solution with different mass concentration were prepared and the emission spectra were meas-ured by FLS920P fluorescence spectrometer .Experimental results showed that the optimum excitation wavelength and emission wavelength of Carmine solution were 300 and 440 nm respectively .The spectral data of ultrapure water were measured under the same condition and it was selected as the reference spectrum .The two-dimensional fluorescence correlation spectra were calculat-ed under the perturbation of concentration .Sym8 wavelet based on the four-scale was selected for denoising .Partial least squares regression （PLSR） predictive models were built by using the synchronous correlation spectral data and auto correlation spectral data after the noise reduction .When Partial least square regression model was used combined with synchronous correlation spec-tral data for predicting the carmine contents in prediction set ,the root mean square errors of prediction （RMSEP） was 0.414μg · mL -1 and the coefficient correlation of actual values and predicted values was 99.863% .However ,the model based on the Partial least squares regression model and auto correlation spectral data was better .The coefficient correlation of prediction set reached to 99.863% ,and the root mean square errors of prediction （RMSEP） was 0.303 μg ·mL -1 .As can be seen from the results ,the data of the autocorrelation spectra can effectively avoid information redundancy ,and the effect is more reliable .The method simple operation does not rely on sample separation , ,and can provide a new way of thinking for food safety testing .