如果不切开肉豆蔻并仔细检查,内部发霉的肉豆蔻很难感知,这对公众健康构成了重大风险。目前,宏观鉴定和色谱分析用于确定肉豆蔻是否发霉。然而,前者依靠人类小组,具有主观性和经验依赖性的缺点,而后者通常是耗时的并且需要有机溶剂。因此,迫切需要开发一种快速可行的方法来评价肉豆蔻的质量和预测霉变。在这项研究中,利用电子鼻反应结合化学谱分析分析了五组不同发霉程度的肉豆蔻样品的质量和气味特征。主要理化指标,例如α-pine烯的水平,β-pine烯,elemicin,和脱水-二异丁香酚,决心。结果表明,α-pine烯的含量,β-pine烯,随着储存时间的延长,埃利米星的变化明显。通过使用电子鼻和HS-GC-MS技术来评估肉豆蔻样品的整体气味特征,发现挥发性有机化合物(VOC)的产生,如氨/有机胺,一氧化碳,乙醇,还有硫化氢,以及肉豆蔻本身的萜烯和苯基丙烯成分的变化,可能是气味变化的物质基础。根据电子鼻数据结合不同的机器学习算法,建立的肉豆蔻霉变程度定性分类模型的准确率高于90%。建立了预测化学成分含量的定量模型,和基于BP神经网络(BPNN)的模型,支持向量机(SVM),随机森林算法(RF)在预测这些化学成分的浓度方面均表现出良好的性能,除了脱水-二异丁香酚。BPNN根据电子鼻的反应有效地预测了肉豆蔻的储存时间,训练集的RMSE和R2为0.268和0.996,测试集的0.317和0.993,分别。结果表明,电子鼻(E-nose)的反应与肉豆蔻的内部质量具有高度相关性。这项工作提出了一种快速,无损的肉豆蔻质量评估方法,对肉豆蔻中不同发霉程度的判别具有较高的准确性,有利于早期发现和预警发霉现象。
Internal mildewed nutmeg is difficult to perceive without cutting the nutmeg open and examining it carefully, which poses a significant risk to public health. At present, macroscopic identification and chromatographic analysis are applied to determine whether nutmeg is moldy or not. However, the former relies on a human panel, with the disadvantages of subjectivity and empirical dependence, whilst the latter is generally time-consuming and requires organic solvents. Therefore, it is urgent to develop a rapid and feasible approach for evaluating the quality and predicting mildew in nutmeg. In this study, the quality and odor characteristics of five groups of nutmeg samples with different degrees of mildew were analyzed by using the responses of an electronic nose combined with chemical profiling. The main physicochemical indicators, such as the levels of α-pinene, β-pinene, elemicin, and dehydro-di-isoeugenol, were determined. The results revealed that the contents of α-pinene, β-pinene, and elemicin changed significantly with the extension of storage time. Through the use of an electronic nose and HS-GC-MS technology to assess the overall odor characteristics of nutmeg samples, it was found that the production of volatile organic compounds (VOCs) such as ammonia/organic amines, carbon monoxide, ethanol, and hydrogen sulfide, as well as changes in the terpene and phenylpropene components of the nutmeg itself, may be the material basis for the changes in odor. The accuracy of the qualitative classification model for the degree of mildew in nutmeg was higher than 90% according to the electronic nose data combined with different machine learning algorithms. Quantitative models were established for predicting the contents of the chemical components, and models based on a BP neural network (BPNN), the support vector machine (SVM), and the random forest algorithm (RF) all showed good performance in predicting the concentrations of these chemical components, except for dehydro-di-isoeugenol. The BPNN performed effectively in predicting the storage time of nutmeg on the basis of the E-nose\'s responses, with an RMSE and R2 of 0.268 and 0.996 for the training set, and 0.317 and 0.993 for the testing set, respectively. The results demonstrated that the responses of the electronic nose (E-nose) had a high correlation with the internal quality of nutmeg. This work proposes a quick and non-destructive evaluation method for the quality of nutmeg, which has high accuracy in discriminating between different degrees of mold in nutmeg and is conducive to early detection and warning of moldy phenomena.