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作 者:崔亮[1] 占军贵 贺常涛 CUI Liang;ZHAN Jungui;HE Changtao(Jiangsu Food&Pharmaceutical Science College,Huaian 223003,China;College of Artificial Intelligence,Nanjing Agricultural University,Nanjing 210031,China;School of Food Science and Technology,Jiangnan University1,Wuxi 214122,China)
机构地区:[1]江苏食品药品职业技术学院,江苏淮安223003 [2]南京农业大学人工智能学院,江苏南京210031 [3]江南大学食品学院,江苏无锡214122
出 处:《光散射学报》2025年第1期129-135,共7页The Journal of Light Scattering
基 金:国家自然科学基金(32072254);江苏省科学技术厅(BY20230995)。
摘 要:核桃油是一种营养丰富且价格较高的坚果植物油,使用较便宜的油掺入核桃油是目前核桃油掺假的主要手段之一。为实现一种快速高效的核桃油掺假的定量分析方法和检测技术,本文提出采用紫外LED荧光光谱结合卷积神经网络算法预测核桃油与葵花油的混合浓度。首先,制备了一系列核桃油和葵花油混合样本,并通过紫外LED激发混合油样的荧光光谱,采用EMD-PSO优化阈值算法可去除了荧光光谱中的噪声信息,理论计算并分析了荧光光谱的叠加谱峰,建立了混合油样的荧光光谱数据库。然后,基于荧光光谱数据库构建了预测混合油样中核桃油浓度的卷积神经网络模型。实验结果表明,本文提出的检测技术不仅表征了两种植物油的荧光光谱差异,基于卷积神经网络的定量分析方法在预测混合浓度方面也具有良好的准确性和稳定性,模型对测试集预测的R 2和平均误差分别为0.9853和0.0783。总之,本研究为快速、非破坏性地检测核桃油与葵花油的掺伪浓度提供了一种新的方案,有望在食品和油脂行业中得到广泛应用。Walnut oil is a kind of nutty vegetable oil with rich nutrition and high prices.Using cheaper oil mixed with walnut oil is one of the primary means of adulteration.To achieve a rapid and efficient quantitative analysis method and detection technology of walnut oil adulteration,this paper proposes to predict the mixed concentration of walnut oil and sunflower oil using the UV LED fluorescence spectrum combined with a convolutional neural network algorithm.Firstly,a series of mixed samples of walnut oil and sunflower oil were prepared,and the fluorescence spectra of the mixed oil samples were excited by UV LED.The noise information in the fluorescence spectra was removed by using the EMD-PSO optimization threshold algorithm.The superposition spectral peaks of the fluorescence spectra were calculated and analyzed theoretically,and the fluorescence spectra database of the mixed oil samples was established.Then,a convolutional neural network model was constructed based on the fluorescence spectrum database to predict the concentration of walnut oil in mixed oil samples.The experimental results show that the detection technology proposed in this paper not only characterizes the difference in the fluorescence spectra of the two vegetable oils but also has good accuracy and stability in predicting the mixed concentration based on a convolutional neural network.The R 2 and ME predicted by the model for the test set are 0.9853 and 0.0783,respectively.In summary,this study provides a new approach for rapid and non-destructive detection of the adulteration concentration of walnut oil and sunflower oil,which is expected to be widely applied in the food and oil industry.
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