Laboratory of Chemistry, Department of Food Science and Human Nutrition, Agricultural University of Athens, Athens, Greece.
Erganal Food and Environmental Testing Laboratories, Piraeus, Greece.
J Sci Food Agric. 2021 May;101(7):2994-3002. doi: 10.1002/jsfa.10932. Epub 2020 Dec 15.
Consumers today wish to know the botanical origin of the olive oil they purchase. The objective of the present study was the development of robust chemometric models based on gas chromatography-mass spectrometry (GC-MS) and attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) for the purpose of botanical differentiation of three commercial Greek olive oil cultivars.
Using the solid-phase microextraction technique (SPME), volatile compounds (VC) were obtained and analyzed by GC-MS. Five hydrocarbons and one ester were selected by the forward stepwise algorithm, which best discriminated the olive oil samples. From ATR-FTIR analysis, the spectral regions chosen from the forward stepwise algorithm were associated with CO stretching vibration of the esters of triglycerides and the CH bending vibrations of the CH aliphatic group and double bonds. Application of the supervised methods of linear and quadratic discriminant cross-validation analysis, based on VC data, provided a correct classification score of 97.4% and 100.0%, respectively. Corresponding statistical analyses were used in the mid-infrared spectra, by which 96.1% of samples were discriminated correctly.
ATR-FTIR and SPME-GC-MS techniques in conjunction with the appropriate feature selection algorithm and classification methods proved to be powerful tools for the authentication of Greek olive oil. The proposed methodology could be used in an industrial setting for determination of the botanical origin of Greek olive oil. © 2020 Society of Chemical Industry.
如今的消费者希望了解他们所购买橄榄油的植物来源。本研究的目的是开发基于气相色谱-质谱(GC-MS)和衰减全反射-傅里叶变换红外光谱(ATR-FTIR)的强大化学计量学模型,用于对三种商业希腊橄榄油品种进行植物学区分。
使用固相微萃取技术(SPME)获得挥发性化合物(VC)并通过 GC-MS 进行分析。正向逐步算法选择了五种烃类和一种酯类,它们能最好地区分橄榄油样品。从 ATR-FTIR 分析中,正向逐步算法选择的光谱区域与三酰基甘油酯的酯的 CO 伸缩振动和 CH 脂肪族基团和双键的 CH 弯曲振动有关。基于 VC 数据,应用线性和二次判别交叉验证分析的监督方法,分别提供了 97.4%和 100.0%的正确分类评分。对中红外光谱进行了相应的统计分析,其中 96.1%的样品被正确区分。
ATR-FTIR 和 SPME-GC-MS 技术结合适当的特征选择算法和分类方法被证明是鉴定希腊橄榄油的有力工具。所提出的方法可用于工业领域确定希腊橄榄油的植物来源。© 2020 化学工业协会。