Laboratory of Food Chemistry, Department of Chemistry, University of Ioannina, 45110 Ioannina, Greece.
CP FoodLab Ltd., 25 Polyfonti Str. P.O. Box: 28729, Strovolos- Nicosia 2082, Cyprus.
Molecules. 2020 Jul 31;25(15):3507. doi: 10.3390/molecules25153507.
Seventy-eight graviera cheese samples produced in five different regions of Greece were characterized and discriminated according to geographical origin. For the above purpose, pH, titratable acidity (TA), NaCl, proteins, fat on a dry weight basis, ash, fatty acid composition, volatile compounds, and minerals were determined. Both multivariate analysis of variance (MANOVA) and linear discriminant analysis (LDA) were applied to experimental data to achieve sample geographical discrimination. The results showed that the combination of fatty acid composition plus minerals provided a correct classification rate of 89.7%. The value for the combination of fatty acid compositions plus conventional quality parameters was 94.9% and for the combination of minerals plus conventional quality parameters was 97.4%. When cheeses of the above five geographical origins were combined with previously studied graviera cheeses from six other geographical origins collected during the same seasons in Greece, the respective values for the discrimination of geographical origin of all eleven origins were 89.3% for conventional quality parameters plus minerals; 94.0% for conventional quality parameters plus fatty acids; 94.1% for minerals plus fatty acids; and 95.2% for conventional quality parameters plus minerals plus fatty acids. Such high correct classification rates demonstrate the robustness of the developed statistical model.
78 个在希腊五个不同地区生产的 Graviera 奶酪样本根据地理来源进行了特征描述和区分。为此，测定了 pH 值、可滴定酸度 (TA)、NaCl、蛋白质、干重基础上的脂肪、灰分、脂肪酸组成、挥发性化合物和矿物质。应用多元方差分析 (MANOVA) 和线性判别分析 (LDA) 对实验数据进行分析，以实现样品的地理区分。结果表明，脂肪酸组成加矿物质的组合提供了 89.7%的正确分类率。脂肪酸组成加常规质量参数的组合值为 94.9%，矿物质加常规质量参数的组合值为 97.4%。当将来自上述五个地理来源的奶酪与在同一季节从希腊的另外六个地理来源收集的先前研究的 Graviera 奶酪进行组合时，所有十一个来源的地理来源区分的各自值为常规质量参数加矿物质的 89.3%；常规质量参数加脂肪酸的 94.0%；矿物质加脂肪酸的 94.1%；常规质量参数加矿物质加脂肪酸的 95.2%。如此高的正确分类率证明了所开发的统计模型的稳健性。
