Rapid Detection of Allantoin in Raw Yam by Surface-Enhanced Raman Spectroscopy
10.19756/j.issn.0253-3820.241312
- VernacularTitle:表面增强拉曼光谱结合化学计量学用于生鲜山药中尿囊素含量的快速检测
- Author:
Wei WANG
1
;
Yong-Yu LI
;
Yan-Kun PENG
;
Shao-Jin MA
;
Yue-Xiang ZHANG
;
Kun PENG
Author Information
1. 中国农业大学工学院,北京100083
- Keywords:
Yam;
Allantoin;
Surface-enhanced Raman spectroscopy;
Predictive models
- From:
Chinese Journal of Analytical Chemistry
2024;52(11):1659-1668
- CountryChina
- Language:Chinese
-
Abstract:
Allantoin,as a functional constituent of yam,has an extremely important role in the medical and cosmetic fields. In this study,based on the Raman spectroscopy detection system constructed in the laboratory,the Raman spectra of the powder of allantoin standard and the surface-enhanced Raman spectra of the allantoin extract of fresh yam were analyzed,and the surface-enhanced Raman characteristic displacements of allantoin in raw yam were determined to be 644,1027 and 1398 cm-1. The effects of the adsorption time of allantoin and silver sol and the thickness of the yam on the intensity of Raman feature displacement were investigated,and a method was established to directly obtain the surface-enhanced Raman feature information of allantoin in fresh yam. Based on this method,the surface-enhanced Raman spectra of 32 raw yams were collected,and the Raman feature displacements of allantoin at 644,1027 and 1398 cm-1 were established by unary linear regression (ULR),multivariable linear regression (MLR),and partial least squares regression (PLSR). The results showed that the MLR model was the most effective,with the validation set coefficient of determination (R2V) of 0.93 and the root mean square error of validation (RMSEV) of 0.35 mg/g. However,the allantoin feature shift was susceptible to the changes of solution polarity and substrate,which led to a certain shift of the feature shift affecting the accuracy of the detection,and the quantitative prediction model of PLSR using the full-waveband Raman spectroscopy would improve the model's Robustness. The random frog (RF)-PLSR quantitative prediction model of allantoin was established based on the RF algorithm to screen the feature variables,and the R2V was increased to 0.96,and the RMSEV was reduced to 0.26 mg/g. The model was externally validated using ten raw yam samples which were not involved in the modeling,and the absolute value of maximum residual was 0.74 mg/g. The method could realize the rapid quantitative detection of allantoin content in raw fresh yam,and provided new ideas and technical references for the direct rapid quantitative detection of allantoin in agricultural products.
