Nantongo Judith Ssali, Serunkuma Edwin, Burgos Gabriela, Nakitto Mariam, Kitalikyawe Joseph, Mendes Thiago, Davrieux Fabrice, Ssali Reuben
International Potato Center, Ntinda II Road, Plot 47 PO Box 22274, Kampala, Uganda.
International Potato Center, Lima, Uganda.
Int J Food Sci. 2024 Oct 30;2024:1350090. doi: 10.1155/2024/1350090. eCollection 2024.
In sweet potato and potato, sensory traits are critical for acceptance by consumers, growers, and traders, hence underpinning the success or failure of a new cultivar. A quick analytical method for the sensory traits could expedite the selection process in breeding programs. In this paper, the relationship between sensory panel and instrumental color plus texture features was evaluated. Results have shown a high correlation between the sensory panel and instrumental color in both sweet potato (up to = 0.84) and potato ( > 0.78), implying that imaging is a potential alternative to the sensory panel for color scoring. High correlations between sensory panel aroma and flavor with instrumental color were detected (up to = 0.66), although the validity of these correlations needs to be tested. With instrumental color and texture parameters as predictors, low to moderate accuracy was detected in the machine learning models developed to predict sensory panel traits. Overall, the performance of the eXtreme Gradient Boosting (XGboost) was comparable to the radial-based support vector machine (NL-SVM) algorithm, and these could be used for the initial selection of genotypes for aromas and flavors ( = 0.64-0.72) and texture attributes like moisture or mealiness ( > 50). Among the chemical properties screened in sweet potato, only starch showed a moderate correlation with sensory features like mealiness ( = 0.54) and instrumental color ( = 0.65). From the results, we can conclude that the instrumental scores of color are equivalent to those scored by the sensory panel, and the former could be adopted for quick analysis. Further investigations may be required to understand the association between color and aroma or flavor.
在甘薯和马铃薯中，感官特性对于消费者、种植者和贸易商的接受程度至关重要，因此决定着新品种的成败。一种针对感官特性的快速分析方法可以加快育种计划中的选择过程。本文评估了感官评定小组与仪器颜色及质地特征之间的关系。结果表明，在甘薯（高达 = 0.84）和马铃薯（> 0.78）中，感官评定小组与仪器颜色之间都存在高度相关性，这意味着成像可作为感官评定小组进行颜色评分的潜在替代方法。虽然这些相关性的有效性有待检验，但已检测到感官评定小组的香气和风味与仪器颜色之间存在高度相关性（高达 = 0.66）。以仪器颜色和质地参数作为预测指标，在为预测感官评定小组特征而开发的机器学习模型中，检测到的准确率较低至中等。总体而言，极端梯度提升（XGboost）的性能与基于径向的支持向量机（NL-SVM）算法相当，这些可用于香气和风味（ = 0.64 - 0.72）以及水分或粉质等质地属性（> 50）的基因型初步选择。在甘薯筛选的化学特性中，只有淀粉与粉质（ = 0.54）和仪器颜色（ = 0.65）等感官特征表现出中等相关性。从结果来看，我们可以得出结论，颜色的仪器评分与感官评定小组的评分相当，前者可用于快速分析。可能需要进一步研究以了解颜色与香气或风味之间的关联。
