牛油果油是从牛油果果肉中提炼的一种新型植物油，由于其价格昂贵和大众认知度有限，市场上很 可能会出现制假贩假的现象。为实现快速、无损和高通量的检测需求，本文提出一种利用一维卷积神经网络 模型和原位显微拉曼光谱定量分析牛油果油的检测方法。采用菜籽油和葵花油的混合物作为牛油果油掺假 的主要成分，并使用原位显微拉曼光谱技术检测了纯植物油和混合油品的光谱，分析并解译了牛油果油的拉 曼光谱特征谱峰的化学信息，通过协方差和相关系数遴选了与牛油果油浓度变化存在协同性和相关性的光谱 信息，并将其作为网络模型的输入。构建的一维卷积神经网络模型在测试集中预测效果良好，总体的R²> 0.915,RMSR<0.0755 。 基于一维卷积神经网络模型结合原位显微拉曼光谱技术预测牛油果油掺伪浓度的 检测方法可行性较好，满足市场应用的检测需求，该成果对于规范国内的牛油果油市场，加快市场监督的职能 性管理具有重要的价值。

Avocado oil is a new vegetable oil extracted from avocado pulp.Because of its high price and limited public awareness,it is likely to produce and sell fake products.To meet the requirements of rapid,non-destructive,and high-throughput detection,this paper proposes a detection method for avocado oil by using a one-dimensional convolutional neural network model and in-situ micro Raman spectroscopy.The mixture of rapeseed oil and sunflower oil was used as the main component of avocado oil adulteration,and the spectra of pure vegeta- ble oil and mixed oil were detected by in-situ micro Raman spectroscopy technology.The chemical information of the Raman spectrum characteristic peaks of avocado oil was analyzed and interpreted.The spectral information with synergy and correlation with Avocado concen- tration changes was selected through the covariance difference and correlation coefficient, and it was used as the input of the network model.The one-dimensional convolutional neural network  model  has  a  good  prediction  effect  in  the  test  set,with  overall  R²>0.915  and  rmsr <0.0755.The  detection  method  is  based  on  a  one-dimensional  convolutional  neural  network model  combined  with  in-situ  micro  Raman  spectroscopy  technology  to  predict  the  adultera- tion  concentration  of  avocado  oil,which  is  feasible  and  meets  the  detection  requirements  of market  applications.The  results  have  significant  value  for  standardizing  the  domestic  avoca- do  market  and  accelerating  the  functional  management  of market  supervision.