基于近红外光谱技术快速检测小龙虾中的生物胺

Rapid determination of biogenetic amine in Prokaryophyllus clarkii based on the near infrared spectroscopy

中文关键词:  近红外光谱分析技术  小龙虾  生物胺  偏最小二乘  波长选择

英文关键词:near infrared spectroscopy  Prokaryophyllus clarkii  biogenetic amine  partial least squares regression  variable selection

中文摘要:

      目的 建立近红外光谱法快速检测小龙虾中总生物胺含量的方法。方法 利用近红外光谱仪采集154个不同新鲜程度小龙虾样品的近红外光谱, 使用高效液相色谱技术检测对应样品总生物胺含量; 使用KS (Kennard-Stone)算法将103个样品作为训练集, 51个样品作为预测集。采用多元散射校正(multiplicative scatter correction, MSC)、标准正态变换(standard normal variate, SNV)、小波变换(wavelet transform, WT)和1阶导数(1st)分别对样品的光谱进行处理, 利用训练集样品的光谱和生物浓度建立偏最小二乘回归(partial least squares regression, PLSR)模型, 使用竞争性自适应重加权算法(competitive adaptive reweighted sampling, CARS)进一步选择波长, 对模型进行优化。结果 经过小波变换处理之后的光谱所建立的PLSR模型具有较好的预测结果, CARS方法可以进一步提高模型的预测和解释能力, 预测集生物胺的预测均方根误差(root mean square error of prediction, RMSEP)值和决定系数(r2)分别可达55.74和0.92。结论 基于近红外光谱分析技术快速检测小龙虾总生物胺含量是可行的, 优化后的PLSR模型可以用于评价小龙虾总生物胺含量。

英文摘要:

      Objective To establish a rapid method for the determination of biogenetic amine in Prokaryophyllus clarkii based on the near infrared spectroscopy. Methods The spectra of 154 samples with different freshness degrees were measured and the content of corresponding biogenetic amine were determined by the high performance liquid chromatography. The 154 samples were divided into a calibration and prediction set with 103 and 51 samples through the Kennard-Stone algorithm. The spectra were preprocessed by multiplicative scatter correction (MSC), standard normal variate (SNV), wavelet transform (WT) and 1st derivative, respectively. Then the spectra and bioconcentration of the samples in calibration set were utilized to construct the partial least squares regression (PLSR) models. The competitive adaptive reweighted sampling (CARS) algorithm was used to further select wavelengths and optimize the model. Results A better result could be obtained for the PLSR model constructed by the spectra preprocessed with the wavelet transform and the PLSR model could be further improved through the CARS method. The root mean square error of prediction (RMSEP) and determination coefficient (r2) of biogenetic amine in the prediction set were 55.74 and 0.92, respectively. Conclusion It is feasible to rapidly detect the biogenetic amine in Prokaryophyllus clarkii through the near infrared spectroscopy. The optimized PLSR model can be utilized to evaluate the biogenetic amine in Prokaryophyllus clarkii.