Borràs Eva, Mestres Montserrat, Aceña Laura, Busto Olga, Ferré Joan, Boqué Ricard, Calvo Angels
Instrumental Sensometry Group, Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Campus Sescelades, 43007 Tarragona, Spain.
Instrumental Sensometry Group, Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Campus Sescelades, 43007 Tarragona, Spain.
Food Chem. 2015 Nov 15;187:197-203. doi: 10.1016/j.foodchem.2015.04.030. Epub 2015 Apr 18.
Mid-infrared (MIR) spectra (4000-600 cm(-1)) of olive oils were analyzed using chemometric methods to identify the four main sensorial defects, musty, winey, fusty and rancid, previously evaluated by an expert sensory panel. Classification models were developed using partial least squares discriminant analysis (PLS-DA) to distinguish between extra-virgin olive oils (defect absent) and lower quality olive oils (defect present). The most important spectral ranges responsible for the discrimination were identified. PLS-DA models were able to discriminate between defective and high quality oils with predictive abilities around 87% for the musty defect and around 77% for winey, fusty and rancid defects. This methodology advances instrumental determination of results previously only achievable with a human test panel.
使用化学计量学方法分析橄榄油的中红外（MIR）光谱（4000 - 600 cm(-1)），以识别先前由专业感官评定小组评估的四种主要感官缺陷，即霉味、酒味、陈腐味和酸败味。使用偏最小二乘判别分析（PLS-DA）开发分类模型，以区分特级初榨橄榄油（无缺陷）和低质量橄榄油（有缺陷）。确定了造成这种鉴别的最重要光谱范围。PLS-DA模型能够区分有缺陷和高质量的油，对于霉味缺陷的预测能力约为87%，对于酒味、陈腐味和酸败味缺陷的预测能力约为77%。这种方法推动了以前只能通过人工测试小组才能获得的结果的仪器测定。
