机构地区:[1]中国农业科学院果树研究所/农业部果品质量安全风险评估实验室(兴城)/农业部果品及苗木质量监督检验测试中心(兴城),辽宁兴城125100
出 处:《中国农业科学》2015年第14期2796-2805,共10页Scientia Agricultura Sinica
基 金:国家公益性行业(农业)科研专项(200903043);国家农产品质量安全风险评估计划(GJFP2014002;GJFP2015002);中国农业科学院科技创新工程
摘 要:【目的】筛选适宜的苹果风味评价指标,为构建苹果风味评价指标体系和苹果风味科学评价奠定基础。【方法】测定132个苹果样品的6项风味指标:用酸度计测定p H,用全糖仪测定可溶性固形物含量,用斐林试剂滴定法测定可溶性糖含量,用指示剂滴定法测定可滴定酸含量,用口尝鉴评法进行风味鉴评,固酸比用可溶性固形物含量与可滴定酸含量的比值表示,糖酸比用可溶性糖含量与可滴定酸含量的比值表示。利用水平分析、回归分析、聚类分析、因子分析等数理统计分析方法,明确各指标的水平及其分布、指标间的变化趋势和定量关系,筛选出适宜的苹果风味评价指标,并进行苹果风味评价与分类。【结果】(1)苹果品种间p H、可溶性固形物含量和可溶性糖含量差异均较小,变异系数分别为8.5%、10.7%和10.6%,可滴定酸含量、固酸比和糖酸比差异均很大,变异系数分别高达45.1%、64.9%和66.5%,可溶性固形物含量和可溶性糖含量均服从正态分布,概率值P分别达0.6241和0.6298,p H、可滴定酸含量、固酸比和糖酸比均呈偏态分布。(2)p H、固酸比和糖酸比均与可滴定酸含量呈极显著负相关,相关系数分别为-0.8810、-0.8117和-0.8116;p H与固酸比和糖酸比间、可溶性糖含量与可溶性固形物含量间、糖酸比与固酸比均呈极显著正相关,相关系数分别为0.8650、0.8507、0.8794和0.9959。(3)p H与可滴定酸含量存在极显著的对数函数变化趋势,其决定系数R2为0.8522,可溶性固形物含量与可溶性糖含量、糖酸比与固酸比均存在极显著的线性变化趋势,相关系数R分别为0.8793和0.9959;固酸比和糖酸比均与可滴定酸含量存在极显著的幂函数变化趋势,其决定系数R2分别为0.9590和0.9638。(4)研究的6项苹果风味指标中,除可滴定酸含量外,其余5项指标均存在关于其他5项指标的多元线性模型,各方程的决定系数R2均接近�【Objective】 The aim of this study is to screen scientific evaluation indexes for apple taste. 【Method】 Six indexes of 132 apple samples were determined, including p H determined by acidity meter, total soluble solid(TSS) determined by brix meter, soluble sugar(SS) determined with the fehling reagent titration, titratable acidity(TA) determined with the indicator titration method, the taste of each sample was determined by tasting, the ratio of total soluble solid to titratable acidity(RTT), and the ratio of soluble sugar to titratable acidity(RST). Statistical methods(such as level analysis,regression analysis, cluster analysis, and factor analysis) were used to clarify the level of each index and its distribution, to clarify the variation tendency between indexes and the quantitative relationships between/among indexes, and to screen appropriate indexes for evaluating and classifing apple taste. 【Result】 The variances of three indexes(p H, TSS and SS) were all small, with the variable coefficient of 8.5%, 10.7% and 10.6%, respectively, while the variances of other three indexes(TA, RTT, and RST) were all big, with the variable coefficient of 45.1%, 64.9% and 66.5%. Both TSS and SS distributed normally, with the probability values of 0.6241 and 0.6298. p H, TA, RTT and RST distributed unnormally. p H, RTT and RST all had a significant negative correlation with TA, with the correlation coefficient of-0.8810,-0.8117 and-0.8116. Both RTT and RST had a significant positive correlation with p H, so did SS with TSS, and RST with RTT. p H had a significant logarithmic-function concerning TA with a determination coefficient of 0.8522. Between TSS and SS, as well as between RTT and RST, there was a significant linear function. RTT as well as RST had a significant power function concerning TA. Each of the six indexes mentioned above, except TA(with low fitting precision and low prediction accuracy), had a multivariate linear model concerning the other five indexes with the me
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