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目的应用近红外光谱技术建立海参产地区分和胶原蛋白快速检测的方法。方法总计43个海参样品来自大连、福建、连云港、山东4个地区。首先采集样品的近红外光谱图,经过标准正态变量(standard normal variables,SNV)预处理,利用不同定性判别模型对海参产地进行区分。通过分光光度计法测定海参的胶原蛋白含量,利用偏最小二乘法(partial least squares,PLS)、区间偏最小二乘法(interval partial least squares,iPLS)、向后区间偏最小二乘法(backwards interval partial least squares,BiPLS)和联合区间偏最小二乘法(synergy interval partial least squares,Si PLS)建立了海参胶原蛋白含量的预测模型。结果产地区分模型中最小二乘支持向量机(least-squares support vector machine regression,LS-SVM)的识别率最高,校正集识别率为100%,预测集识别率为95.35%;海参胶原蛋白预测模型中BiPLS的预测效果较好,校正集相关系数Rc为0.9002,预测集相关系数Rp为0.8517。结论近红外光谱技术可实现对海参的产地区分和胶原蛋白的快速检测。
Objective To establish a method for the rapid identification of sea cucumber production and collagen by near-infrared spectroscopy. Methods A total of 43 samples of sea cucumber from Dalian, Fujian, Lianyungang, Shandong 4 regions. First of all, the near-infrared spectra of the samples were collected and subjected to standard normal variables (SNV) pretreatment. Different qualitative discriminant models were used to distinguish the origin of sea cucumber. The collagen content of sea cucumber was determined by spectrophotometer. Partial least squares (PLS), interval partial least squares (iPLS), backwards interval partial least squares (backwards interval partial least squares) The least squares (BiPLS) and synergy interval partial least squares (Si PLS) were used to establish the prediction model of collagen content in sea cucumber. Results LS-SVM (LS-SVM) had the highest recognition rate, the recognition rate of the calibration set was 100%, and the recognition rate of the prediction set was 95.35%. The prediction model of the collagen of the sea cucumber The predicted effect of BiPLS is better, the correlation coefficient Rc of the calibration set is 0.9002, and the correlation coefficient Rp of the prediction set is 0.8517. Conclusion Near-infrared spectroscopy can be used to distinguish the origin of sea cucumbers and the rapid detection of collagen.