京郊鲜食杏白利糖度的便携式光谱快速无损检测方法研究

Research on the Brix content of fresh Armeniaca in suburbs of Beijing based on portable spectroscopic rapid non-destructive detection method

中文关键词:  鲜食杏  白利糖度  便携式  无损检测  偏最小二乘回归

英文关键词:fresh Armeniaca  Brix  portable  non-destructive detection  partial least squares regression

中文摘要:

      目的 建立京郊鲜食杏白利糖度的定量分析预测模型, 实现对京郊鲜食杏品质的快速无损检测。方法 使用便携式近红外光谱仪采集900~1700 nm下鲜食杏的漫反射光谱信息, 使用多元散射校正(multiplicative scatter correction, MSC)、标准正态变量变换(standard normal variable transformation, SNV)和Savitzky-Golay卷积平滑(Savitzky-Golay smooth, S-G)对原始光谱数据进行预处理, 使用Kennard-Stone (K-S)算法以3:1比例将样本集划分成校正集和预测集, 利用竞争自适应重加权采样(competitive adaptive reweighted sampling, CARS)算法和连续投影算法(successive projections algorithm, SPA)对光谱进行特征波长筛选, 使用偏最小二乘回归(partial least squares regression, PLSR)算法建立京郊鲜食杏白利糖度的预测模型。结果 以MSC+S-G+CARS+PLSR算法建立的北京鲜食杏的白利糖度预测模型取得较好的预测精度, 模型的校正集均方根误差、校正集相关系数、预测集均方根误差、预测集相关系数分别为0.3502、0.9747、0.4698、0.9616。结论 基于便携式近红外光谱技术所采集数据构建的京郊鲜食杏白利糖度预测模型准确性较高, 可以快速准确检测鲜食杏白利糖度, 从而实现对鲜食杏品质的快速无损检测, 为鲜食杏的品质检测提供了理论依据和方法指导。

英文摘要:

      Objective To establish a quantitative analysis and prediction model for the Brix content of fresh Armeniaca in the suburbs of Beijing, realize rapid non-destructive testing of the quality of fresh Armeniaca in the suburbs of Beijing. Methods Diffuse reflectance spectral information of fresh Armeniaca in the suburbs of Beijing was collected by a portable near-infrared spectrometer. The raw spectral data were preprocessed using multiplicative scatter correction (MSC), standard normal variable transformation (SNV), and Savitzky-Golay smooth (S-G). The sample set was divided into calibration set and prediction set according to the ratio of 3:1 using Kennard-Stone algorithm. The characteristic wavelengths of the spectrum were selected by the competitive adaptive reweighted sampling (CARS) algorithm and the successive projections algorithm (SPA). A prediction model of Beijing fresh Armeniaca Brix was established using the partial least squares regression (PLSR) algorithm. Results The prediction model for the Brix content of fresh Armeniaca in Beijing suburbs established by the MSC+S-G+CARS+PLSR algorithm had better prediction accuracy, and the root mean square of calibration, correlation coefficient of calibration, root mean square of prediction, and correlation coefficient of prediction of the model was respectively 0.3502, 0.9747, 0.4698, and 0.9616. Conclusion The prediction model of the Brix content of fresh Armeniaca in suburban Beijing constructed based on the data of the portable spectrometer has high accuracy, which can quickly and accurately detect the Brix content of fresh Armeniaca, and can realize rapid and non-destructive testing of the quality of fresh Armeniaca. The theoretical basis and method guidance are provided for the quality detection of fresh Armeniaca.