Correlations between quality attributes determined by destructive and non-destructive analysis methods are being investigated to enable quantification and prediction of internal quality characteristics without the need for destructive techniques. Our study correlated sensory and physicochemical attributes of 'Rama Forte' persimmons treated for astringency removal with 70 % CO2 18 h or 1.70 mL·Kg-1 ethanol 6 h, to establish predictive models for destructive analytical methods based on non-destructive ones. Physicochemical (skin color, firmness, soluble tannins and astringency index) and sensory analyses (color, translucency, aroma, flavor, sweetness, bitterness, astringency, firmness, juiciness and crispness) were carried out daily. Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and regression analysis by Partial Least Squares (PLS) were applied to obtain prediction models. Two models based on fruit translucency (non-destructive) were obtained for persimmons treated with CO2, for flesh firmness, and for color index prediction. A model based on sensory astringency (destructive) was developed to predict the astringency index for ethanol treatment. The models show a reliable fit, particularly in predicting flesh firmness by using the translucency of 'Rama Forte' fruit treated with CO2. Using the translucency scale and the prediction model, it is possible to establish the maximum period for logistical steps to reduce losses and waste in the persimmon chain. The low correlation between sensory astringency and soluble tannins content points to other possible compounds involved in the perception of astringency. Identifying these compounds will enable advances in the development of predictive models for quality attributes and shelf life of astringent persimmons.

中文翻译：

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'Rama Forte' 柿子的感官和物理化学属性之间的预测建模和相关性


正在研究由破坏性和非破坏性分析方法确定的质量属性之间的相关性，以便能够在不需要破坏性技术的情况下量化和预测内部质量特征。本研究将 'Rama Forte' 柿子与 70% CO2 18 h 或 1.70 mL·Kg-1 乙醇 6 h 的枿子的感官和理化特性相关联，以建立基于非破坏性分析方法的破坏性分析方法的预测模型。每天进行理化 （肤色、硬度、可溶性单宁和涩味指数） 和感官分析 （颜色、半透明性、香气、风味、甜味、苦味、涩味、硬度、多汁性和脆度）。采用主成分分析 （PCA） 、偏最小二乘判别分析 （PLS-DA） 和偏最小二乘法 （PLS） 回归分析获得预测模型。获得了两个基于水果半透明性（非破坏性）的模型，用于 CO2 处理的柿子、果肉硬度和颜色指数预测。开发了一种基于感觉涩味 （destructive） 的模型来预测乙醇处理的涩味指数。这些模型显示出可靠的拟合，特别是在通过使用经 CO2 处理的 “Rama Forte” 水果的半透明性来预测果肉硬度时。使用半透明量表和预测模型，可以确定物流步骤的最大期限，以减少柿子链中的损失和浪费。感觉涩味和可溶性单宁含量之间的低相关性表明其他可能的化合物参与对涩味的感知。 鉴定这些化合物将有助于开发柿子质量属性和保质期的预测模型。