Sánchez-López E, Sánchez-Rodríguez M I, Marinas A, Marinas J M, Urbano F J, Caridad J M, Moalem M
Organic Chemistry Department, Campus de Excelencia Internacional CeiA3, University of Córdoba, Campus de Rabanales, Marie Curie Building, E-14014 Córdoba, Spain.
Statistics and Business Department, University of Córdoba, Avda. Puerta Nueva, s/n, E-14071 Córdoba, Spain.
Talanta. 2016 Aug 15;156-157:180-190. doi: 10.1016/j.talanta.2016.05.014. Epub 2016 May 3.
Authentication of extra virgin olive oil (EVOO) is an important topic for olive oil industry. The fraudulent practices in this sector are a major problem affecting both producers and consumers. This study analyzes the capability of FT-Raman combined with chemometric treatments of prediction of the fatty acid contents (quantitative information), using gas chromatography as the reference technique, and classification of diverse EVOOs as a function of the harvest year, olive variety, geographical origin and Andalusian PDO (qualitative information). The optimal number of PLS components that summarizes the spectral information was introduced progressively. For the estimation of the fatty acid composition, the lowest error (both in fitting and prediction) corresponded to MUFA, followed by SAFA and PUFA though such errors were close to zero in all cases. As regards the qualitative variables, discriminant analysis allowed a correct classification of 94.3%, 84.0%, 89.0% and 86.6% of samples for harvest year, olive variety, geographical origin and PDO, respectively.
特级初榨橄榄油(EVOO)的认证是橄榄油行业的一个重要课题。该行业的欺诈行为是影响生产者和消费者的一个主要问题。本研究分析了傅里叶变换拉曼光谱(FT-Raman)结合化学计量学处理预测脂肪酸含量(定量信息)的能力,以气相色谱法作为参考技术,并根据收获年份、橄榄品种、地理来源和安达卢西亚原产地保护认证(PDO)(定性信息)对不同的特级初榨橄榄油进行分类。逐步引入了总结光谱信息的偏最小二乘法(PLS)成分的最佳数量。对于脂肪酸组成的估计,最低误差(拟合和预测方面)对应于单不饱和脂肪酸(MUFA),其次是饱和脂肪酸(SAFA)和多不饱和脂肪酸(PUFA),不过在所有情况下这些误差都接近于零。至于定性变量,判别分析对收获年份、橄榄品种、地理来源和PDO的样本分别实现了94.3%、84.0%、89.0%和86.6%的正确分类。