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Characterisation of PDO olive oil Chianti Classico by non-selective (UV-visible, NIR and MIR spectroscopy) and selective (fatty acid composition) analytical techniques

机译:PDO橄榄油Chianti Classico的非选择性(紫外可见,NIR和MIR光谱)和选择性(脂肪酸组成)分析技术表征

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An authentication study of the Italian PDO (protected designation of origin) extra virgin olive oil Chianti Classico was performed; UV-visible (UV-vis), Near-Infrared (NIR) and Mid-Infrared (MIR) spectroscopies were applied to a set of samples representative of the whole Chianti Classico production area.The non-selective signals (fingerprints) provided by the three spectroscopic techniques were utilised both individually and jointly, after fusion of the respective profile vectors, in order to build a model for the Chianti Classico PDO olive oil.Moreover, these results were compared with those obtained by the gas chromatographic determination of the fatty acids composition.In order to characterise the olive oils produced in the Chianti Classico PDO area, UNEQ(unequal class models) and S1MCA (soft independent modelling of class analogy) were employed both on the MIR, NIR and UV-vis spectra, individually and jointly, and on the fatty acid composition.Finally, PLS (partial least square) regression was applied on the UV-vis, NIR and MIR spectra, in order to predict the content of oleic and linoleic acids in the extra virgin olive oils.UNEQ, SIMCA and PLS were performed after selection of the relevant predictors, in order to increase the efficiency of both classification and regression models.The non-selective information obtained from UV-vis, NIR and MIR spectroscopy allowed to build reliable models for checking the authenticity of the Italian PDO extra virgin olive oil Chianti Classico.
机译:对意大利PDO(原产地标记)特级初榨橄榄油Chianti Classico进行了鉴定研究。紫外可见光谱(UV-vis),近红外光谱(NIR)和中红外光谱(MIR)被应用于代表整个Chianti Classico生产区域的一组样品中,由样品提供的非选择性信号(指纹)。将各自的谱图矢量融合后,分别或联合使用了三种光谱技术,以建立Chianti Classico PDO橄榄油的模型。此外,将这些结果与通过气相色谱测定脂肪酸得到的结果进行了比较为了表征在Chianti Classico PDO地区生产的橄榄油,分别在MIR,NIR和UV-vis光谱上分别使用UNEQ(不等分类模型)和S1MCA(类比分析的软独立模型)。最后,对紫外可见光谱,近红外光谱和中红外光谱进行PLS(偏最小二乘)回归分析,以预测超滤膜中油酸和亚油酸的含量。初榨橄榄油.UNEQ,SIMCA和PLS在选择相关的预测变量后进行,以提高分类和回归模型的效率。从UV-vis,NIR和MIR光谱获得的非选择性信息可以建立可靠的用于检查意大利PDO特级初榨橄榄油Chianti Classico真伪的型号。

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