CAJ | 학술논문

为了量化评价夏橙化渣程度，利用仪器检测的指标建立夏橙感官化渣程度的预测模型，采集了湖北宜昌秭归的9种夏橙总计270个样品，首先测定了影响夏橙样本化渣程度的粗纤维成分含量，进行了夏橙化渣程度的感官评定分析，再利用质构仪的压缩试验、质地剖面TPA（texture profile analysis）试验、剪切试验模拟了口腔咀嚼果肉的过程。结果表明，粗纤维成分含量与质构参数之间存在显著相关性，质构参数与感官化渣程度之间的相关关系也十分显著，说明夏橙质构特性可以表征果肉的化渣性。进一步采用主成分回归分析法，以仪器测得的质构特征值为自变量，感官化渣程度为因变量进行回归分析，得到具有统计学意义决定系数R2为0.73的预测模型。由此表明，基于质构特性建立的夏橙化渣程度评价模型在一定程度上可以准确地评价夏橙的化渣程度，利用质构特性取代感官评定评价夏橙化渣程度是可行的，该研究可为夏橙化渣程度的检测提供参考。
위료양화평개하등화사정도，이용의기검측적지표건립하등감관화사정도적예측모형，채집료호북의창자귀적9충하등총계270개양품，수선측정료영향하등양본화사정도적조섬유성분함량，진행료하등화사정도적감관평정분석，재이용질구의적압축시험、질지부면TPA（texture profile analysis）시험、전절시험모의료구강저작과육적과정。결과표명，조섬유성분함량여질구삼수지간존재현저상관성，질구삼수여감관화사정도지간적상관관계야십분현저，설명하등질구특성가이표정과육적화사성。진일보채용주성분회귀분석법，이의기측득적질구특정치위자변량，감관화사정도위인변량진행회귀분석，득도구유통계학의의결정계수R2위0.73적예측모형。유차표명，기우질구특성건립적하등화사정도평개모형재일정정도상가이준학지평개하등적화사정도，이용질구특성취대감관평정평개하등화사정도시가행적，해연구가위하등화사정도적검측제공삼고。
To evaluate Valencia orange mastication degree and establish a model for predicting the sensory mastication of Valencia orange quantitatively, nine kinds of Valencia orange, the number of each kind was 30, a total of 270 Valencia orange samples were collected. The research measured the content of crude fiber, sensory attributes, and textural properties of Valencia orange. Sensory evaluation was performed by a panel including eight trained people, and the average score of flesh attributes, residue, and mastication was recorded. Simulating the process of chewing the flesh, compression experiments, TPA tests, and shear tests were performed to analyze the textural properties. The averages and the standard deviations of the three tests were calculated. Statistically, differences were found for crude fiber content among all the cultivars of Valencia oranges. The crude fiber content of Frost and Campbell was significantly higher than others in the condition of significance levels (p<0.05). The results of the sensory evaluation showed that the variation coefficient of flesh attributes, residue, and mastication was 0.4 or higher. The other textural indicators were significantly different, except for flexibility and adhesion in texture trails. Simple correlation analysis was performed between sensory evaluation, crude fiber content, and texture property parameters using SPSS software. The results indicated that the mastication degree and texture properties showed a significant correlation. Compression resistance, elastic modulus, hardness, springiness, shear force, and shear work were selected as a texture index to build a model. Collinearity diagnostics and principal component analysis were performed to eliminate collinearity, which was caused by a quite high correlation between textural indices. According to the feature vector of principal component and scores of each texture property parameter, the best subgroup of principal component factors was selected to build the regression model. Then, with principal component factor scores as independent variables, and the standardized sensory scores as dependent variables, the regression analysis was used to establish a multiple linear regression equation. The determination coefficient (R2) of the model was equal to 0.73. The performances of the model was calibrated by validation of set data. Concerning the validation set, the determination coefficient (R2) and prediction standard deviation (S.E.P) were respectively equal to 0.85 and 0.17. The results presented demonstrated the greater potential of the texture properties for prediction of mastication degree of Valencia orange, which mean that the evaluation model of fruit mastication based on texture properties could accurately evaluate the mastication of Valencia orange. It was feasible by using textural properties to evaluate the fruit mastication of Valencia orange, instead of sensory evaluation. The research provided a reference for evaluating the mastication of Valencia orange.