期刊
MOLECULES
卷 22, 期 8, 页码 -出版社
MDPI AG
DOI: 10.3390/molecules22081339
关键词
environmental adulteration; geographical adulteration gas chromatography; mass spectrometry; predictive chemometric modelling
Standard raw material test methods such as the ISO Standard 11024 are focused on the identification of lavender oil and not the actual class/quality of the oil. However, the quality of the oil has a significant effect on its price at market. As such, there is a need for raw material tests to identify not only the type of oil but its quality. This paper describes two approaches to rapidly identifying and classifying lavender oil. First, the ISO Standard 11024 test method was evaluated in order to determine its suitability to assess lavender oil quality but due to its targeted and simplistic approach, it has the potential to miss classify oil quality. Second, utilizing the data generated by the ISO Standard 11024 test methodology, an untargeted chemometric predicative model was developed in order to rapidly assess and characterize lavender oils (Lavandula angustifolia L.) for geographical/environmental adulteration that impact quality. Of the 170 compounds identified as per the ISO Standard 11024 test method utilizing GC-MS analyses, 15 unique compounds that greatly differentiate between the two classes of lavender were identified. Using these 15 compounds, a predicative multivariate chemometric model was developed that enabled lavender oil samples to be reliably differentiated based on quality. A misclassification analysis was performed and it was found that the predictions were sound (100% matching rate). Such an approach will enable producers, distributers, suppliers and manufactures to rapidly screen lavender essential oil. The authors concede that the validation and implementation of such an approach is more difficult than a conventional chromatographic assay. However, the rapid, reliable and less problematic screening is vastly superior and easily justifies any early implementation validation difficulties and costs.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据