柑橘化渣性量化指标参数优化及模型建立

    Parameter optimization and modeling for quantitative indicators of citrus mastication

    • 摘要: 化渣性是反映柑橘质地特性的主要参数之一,为了客观、定量地评价柑橘的化渣性,该研究通过分析柑橘感官评分和化渣性定量评价指标之间的相关性,确定了化渣性定量评价指标及其基于质构特性的检测方法;进一步开展单因素和响应面试验,筛选最优参数组合,建立化渣性等级评价模型。结果表明,感官评分与纤维素含量、囊衣厚度等5种指标呈显著负相关(P<0.05),其中囊衣厚度与感官评分相关系数最高(−0.92),可作为化渣性定量评价指标;囊衣厚度与最大剪切力、穿刺力等5种质构指标呈显著正相关,与最大剪切力相关系数最高(0.83),确定最大剪切力为囊衣厚度的检测指标;以平口双刀、刀片厚度2mm、剪切速率180mm/min、剪切瓣数2为测试参数时,所测最大剪切力与囊衣厚度相关系数最高,为0.942;所建模型的回归方程为Y=−0.0099X2+2.695X−2.954,R2=0.951;“化渣性指数”在0~1范围内划分为6个等级,指数越高则化渣性越好,预测准确率为91.43%。研究可为准确客观地评价柑橘化渣性提供技术参考。

       

      Abstract: Mastication is one of the most important indexes to measure the taste quality of citrus. Most sensory evaluations are time-consuming and laborious at present. Some research is also related to the mastication of citrus that directly quantify sensory using texture characteristics. Particularly, there is the impact of citrus quality indicators on mastication properties. However, it is still lacking on the varieties and accuracy of models to quantify the mastication of citrus fruits. In this study, the convenient and accurate evaluation was proposed for the mastication of multi-batch citrus samples. 12 types of citrus fruits were selected to combine with the correlation analysis. The sensory evaluation was also used to screen the cell wall components, ultrastructure and thickness of segment membrane, in order to determine the quantitative indicators of mastication. Some indicators were then obtained for the quantitative evaluation of mastication using texture testing. Single-factor experiments and response surface analysis were carried out, where the factors were taken as the occlusal shearing with different tool types, blades thicknesses, shear rates and the number of shear petals. A combination of tool and test was selected for the optimal parameters. A regression model was then established to increase the number of citrus samples by 26. The mastication was finally classified via discriminant analysis. The results showed that the sensory scores were significantly negatively correlated with four indicators, including cellulose content, the thickness of segment membrane, number of capsule cell layers and original pectin content (P <0.01). There was a significant negative correlation between hemicellulose content and sensory average (P <0.05). While there was no significant correlation between water-soluble pectin and sensory average. Among them, a quantitative evaluation index of mastication was taken as the highest correlation coefficient (-0.92) with the thickness of segment membrane. The correlation analysis between various texture indicators and the thickness of segment membrane showed that the maximum shear force and shear work were significantly positively correlated with the capsule thickness, while the puncture force, the maximum shear stress, hardness, and adhesiveness were significantly positively correlated with the thickness of segment membrane. There was no significant correlation between chewability and the thickness of segment membrane. The maximum shear force shared the highest correlation coefficient with the thickness of segment membrane, which was 0.83 as a detection indicator; According to the single-factor experiments, the correlation coefficients between the thickness of segment membrane and the maximum shear force were basically presented a trend of first increasing and then decreasing, as the blade thickness, the shear rate, and the number of petals increased; When the correlation coefficient between the maximum shear force and the thickness of segment membrane was the highest (0.942), the blade thickness was 2mm, the shear rate was 180mm /min, the number of shear petals was 2, and the tool was a flat cutting double blade. The regression equation was obtained for the maximum shear force-thickness of segment membrane model, Y=-0.0099X2+2.695X-2.954, R2=0.951. The mastication index was divided into six grades in a range of 0-1, where the higher the index was, the better the mastication was; The verification test was conducted to classify the citrus mastication. 35 citrus fruits were randomly selected from the samples of the first and second batches. The prediction accuracy of improved model was 91.43%, indicating the high accuracy for the requirements. A quantitative evaluation index was obtained for the mastication and the detection on citrus fruits using texture characteristics. A set of quantitative evaluation and grading standards were achieved for the citrus mastication, according to the optimal parameters. The finding can also provide a strong reference to accurately and conveniently evaluate citrus mastication.

       

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