Research on Quantitative Model of Royal Jelly Quality by Raman Spectroscopy
CHEN Fan1, LIU Cui-ling1*, CHEN Lan-zhen2, 3, 4*, SUN Xiao-rong1, LI Yi2, 3, 4, JIN Yue2, 3, 4
1. Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University，Beijing 100048, China
2. Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing 100093, China
3. Key Laboratory of Bee Products for Quality and Safety Control, Ministry of Agriculture and Rural Affairs, Beijing 100093, China
4. Laboratory of Risk Assessment for Quality and Safety of Bee Products, Ministry of Agriculture and Rural Affairs, Beijing 100093, China
Abstract：Royal jelly is a natural nutrient health food that has antioxidant, anti-aging, regulate cardiovascular system and immune function. In recent years, royal jelly has been widely applied in food, biomedicine and other fields. Because the collection process of royal jelly is time-consuming and the quality of the royal jelly is difficult to detect and the quality of royal jelly is uneven in the market. It is very important to realize the rapid identification of the quality of royal jelly. Therefore, the content of two components of moisture and protein on the quality of royal jelly is explored in this paper, and the quantitative analysis model of principal component regression (PCR) and partial least squares (PLS) method is established for the moisture and proteir of royal jelly by Raman spectroscopy, and the feasibility of the quantitative analysis of royal jelly is explored. In the experiment, the determination of the moisture and protein chemical vollues in royal jelly adopts the vacuum drying method and kjeldahl method as specified in the national standard of royal jelly. The royal jelly spectrum is collected by the DXR laser confocal microscopy Raman spectrometer. The TQ Analyst analysis software is used to pretreat the full spectrum of royal jelly and establish a quantitative analysis model. Among them, four spectral preprocessing methods includes first derivative, second derivative, Standant Normal Variate Transformation, Multipicative Scatter Correction and Savitsky-Golay, convolution these four spectral preprocessing methods are combined into a variety of different pretreatment methods. A lot of experiments have been carried out on royal jelly samples to find out the best models and treatment methods. The results show that the effect of the quantitative model by using the principal regression method to establish moisture and protein of royal jelly is not ideal. The results of the quantitative model of moisture show that the best spectral processing method of PCR is Savitsky-Golay smoothing (7), but it is only 0.741 3. The coefficient of prediction set is 0.661 6, RMSEC is 0.656, RMSEP is 1.34, and the modeling effect is not ideal. The quantitative model of protein show that the best spectral processing method of PCR is Savitsky-Golay smoothing (7), the coefficient of correction set is 0.675 0, the coefficient of prediction set is 0.566 8, RMSEC is 0.548, RMSEP is 0.957, and the modeling effect is bad. Therefore, the model based on PCR have a certain prediction possibility for the content of moisture and protein in royal jelly, but the modeling effect is poor, the prediction accuracy is low, and the robustness is poor. The PLS method is used to establish quantitative model of moisture and protein, and S-G(7)+second derivative +SNV is the best spectral processing method for moisture of royal jelly, the coefficient of correction set and prediction set are 0.992 7 and 0.948 8, RMSEC and RMSEP are 0.162 and 0.442 respectively. And S-G (7)+first derivative +SNV is the best spectral processing method for protein of royal jelly, and the coefficients of correction set and prediction set are 0.991 6 and 0.879 5, respectively, RMSEC and RMSEP are 0.143 and 0.497, respectively. The modeling effect is ideal. The results show that it is feasible to detect moisture and protein content in royal jelly by Raman spectroscopy combined with partial least squares, and the established quantitative model has good robustness and high prediction accuracy. Through the above experiments, we can conclude that under the influence of unavoidable external factors, the combination of various pretreatment methods can improve the accuracy and robustness of the model. It is more effective than single spectral pretreatment method to correct spectra, and the optimization effect is more obvious. It effectively improves the parameters of the model, and improves the accuracy of model prediction. It is also shown that Raman spectroscopy is feasible for rapid detection of royal jelly quality, and it has high accuracy and speed and has great prospects in the rapid detection of royal jelly quality.
Key words：Royal jelly; Raman spectroscopy; Partial least square method; Principal component regression; Quantitative analysis
陈 繁，刘翠玲，陈兰珍，孙晓荣，李 熠，金 玥. 基于拉曼光谱技术的蜂王浆品质定量模型研究[J]. 光谱学与光谱分析, 2019, 39(02): 471-476.
CHEN Fan, LIU Cui-ling, CHEN Lan-zhen, SUN Xiao-rong, LI Yi, JIN Yue. Research on Quantitative Model of Royal Jelly Quality by Raman Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(02): 471-476.
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