Mai Ruijie, Xue Shuo, Ren Jingnan, Fan Gang, Yang Jinchu, Huang Linhua, Li Guijie, Cheng Yujiao, Wang Qiuling, Yang Yongfeng, Huang Zhenzhen, Feng Yingjie, Liu Wenzhao, Yu Aiqun, Feng Jian
Key Laboratory of Environment Correlative Dietology, Ministry of Education, College of Food Science and Technology, Huazhong Agricultural University, Wuhan, China.
Technology Center, China Tobacco Henan Industrial Co., Ltd., Zhengzhou, China.
Front Nutr. 2025 May 21;12:1557934. doi: 10.3389/fnut.2025.1557934. eCollection 2025.
This study revealed the mechanism of improvement in sensory evaluation of citrus juices after pasteurization by the natural cyclodextrin (-CD), and developed a prediction model for encapsulation based on conventional physicochemical indicators. The results of gas chromatography-mass spectrometry indicated that the off-flavor in citrus juice after pasteurization were mainly caused by -terpineol, terpinen-4-ol and carvone, and the addition of -CD could effectively reduce the content of these compounds. The inclusion complexes of -CD and off-flavor compounds were characterized by formation constants, scanning electron microscope, X-ray diffraction, Fourier transform infrared spectroscopy, and thermogravimetric analyses, followed by molecular docking to show the possible conformations of -CD and off-flavor compounds to form 1:1 inclusion complex by hydrophobic interaction, van der Waals forces and hydrogen bonding. Multiple prediction models were constructed and evaluated by deep learning using basic physicochemical indicators such as sucrose content, citric acid content, pH, temperature and storage conditions as input variables, and peak area of off-flavor compounds as output layer. The results showed that the Multilayer Perceptron Model had great potential in predicting the cyclodextrin embedding effect.
本研究揭示了天然环糊精(β-CD)对巴氏杀菌后柑橘汁感官评价改善的机制,并基于传统理化指标建立了包封预测模型。气相色谱 - 质谱结果表明,巴氏杀菌后柑橘汁中的异味主要由α-松油醇、萜品-4-醇和香芹酮引起,添加β-CD可有效降低这些化合物的含量。通过形成常数、扫描电子显微镜、X射线衍射、傅里叶变换红外光谱和热重分析对β-CD与异味化合物的包合物进行了表征,随后进行分子对接以显示β-CD与异味化合物通过疏水相互作用、范德华力和氢键形成1:1包合物的可能构象。使用蔗糖含量、柠檬酸含量、pH值、温度和储存条件等基本理化指标作为输入变量,异味化合物的峰面积作为输出层,通过深度学习构建并评估了多个预测模型。结果表明,多层感知器模型在预测环糊精包埋效果方面具有很大潜力。