Carpino S, Acree T E, Barbano D M, Licitra G, Siebert K J
Consorzio Ricerca Filiera Lattiero-Casearia, 97100 Ragusa, Italy.
J Agric Food Chem. 2002 Feb 27;50(5):1143-9. doi: 10.1021/jf0112419.
Ragusano cheeses were produced in duplicate from milk collected from pasture-fed and total mixed ration (TMR)-fed cattle at four time intervals. The cheeses were subjected to chemical analysis, conventional sensory testing, and gas chromatography-olfactometry (GCO). Data from each type of analysis were examined by principal component and factor analysis and by pattern recognition (SIMCA) to see if sufficient information for classification into pasture-fed and TMR-fed groups was contained therein. The results clearly indicate that there are significant differences in sensory panel and chemical analysis results between the two cheeses. The data were also examined to see if models of sensory responses as a function of analytical or GCO results or both could be constructed with the modeling technique partial least-squares regression (PLS). Strong PLS models of some sensory responses (green and toasted odor; salt, pungent, bitter, and butyric sensations; and smooth consistency) were obtained.
拉古萨诺奶酪分两组制作,原料奶分别取自以牧草喂养和全混合日粮(TMR)喂养的奶牛,共采集四次。对奶酪进行化学分析、传统感官测试和气相色谱 - 嗅觉测定法(GCO)。通过主成分分析、因子分析和模式识别(SIMCA)对各类分析数据进行检验,以确定其中是否包含足以将奶酪分为牧草喂养组和TMR喂养组的信息。结果清楚表明,两种奶酪在感官评定和化学分析结果上存在显著差异。还对数据进行检验,看能否使用偏最小二乘回归(PLS)建模技术构建作为分析结果或GCO结果或两者函数的感官反应模型。获得了一些感官反应(青草味和烤香味;咸味、刺激性、苦味和丁酸味;以及顺滑质地)的强PLS模型。