Metabolomic selection for enhanced fruit flavor.
Metabolomic selection for enhanced fruit flavor.
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DOI：
10.1073/pnas.2115865119
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发表时间：
2022-02-15
影响因子：
11.1
通讯作者：
Resende MFR Jr
中科院分区：
文献类型：
--
作者：
Colantonio V;Ferrão LFV;Tieman DM;Bliznyuk N;Sims C;Klee HJ;Munoz P;Resende MFR Jr
消费者通常认为在花园里种植的传家宝水果品种比在杂货店购买的商业品种更美味。虽然植物育种者历来专注于改善生产者导向的性状，如产量，但消费者导向的性状，如风味，经常被忽视。这在一定程度上是由于与测量风味的感官知觉相关的困难。在这里，我们将联合收割机水果化学成分和消费者感官面板信息结合起来，训练机器学习模型，可以从化学成分中预测水果的味道。通过增加风味评估的吞吐量，这些模型将帮助植物育种者在育种过程中更早地整合风味，并有助于设计具有特殊风味特征的品种。虽然它们是全球美食的主食，但随着时间的推移，许多商业水果品种已经变得越来越不美味。由于与风味表型相关的成本和困难，育种计划长期以来一直在选择这种复杂的性状方面受到挑战。为了解决这个问题，我们利用不同番茄和蓝莓品种的目标代谢组学及其相应的消费者小组评级来创建统计和机器学习模型，可以预测水果风味的感官感知。使用这些模型，育种计划可以评估大量基因型的风味评级，以前受到消费者感官小组低通量的限制。通过10倍交叉验证评估了预测消费者喜欢，甜，酸，鲜味和风味强度评级的能力，并评估了18种不同模型的准确性。大多数属性的预测精度都很高，范围从0.87的酸味强度在蓝莓使用XGBoost到0.46的整体喜欢在番茄使用线性回归。此外，性能最好的模型用于推断对每个风味属性贡献最大的风味化合物（糖、酸和挥发物）。我们发现，总体喜欢分数的方差分解估计，42%和56%的方差分别由番茄和蓝莓中的挥发性有机化合物解释。我们期望这些模型将使风味作为育种目标的早期纳入，并鼓励选择和释放更美味的水果品种。
Consumers often regard heirloom fruit varieties grown in the garden as more flavorful than commercial varieties purchased at the grocery store. While plant breeders have historically focused on improving producer-orientated traits such as yield, consumer-oriented traits such as flavor have regularly been neglected. This is, in part, due to the difficulty associated with measuring the sensory perceptions of flavor. Here, we combine fruit chemical and consumer sensory panel information to train machine learning models that can predict how flavorful a fruit will be from its chemistry. By increasing the throughput of flavor evaluations, these models will help plant breeders to integrate flavor earlier in the breeding pipeline and aid in the design of varieties with exceptional flavor profiles. Although they are staple foods in cuisines globally, many commercial fruit varieties have become progressively less flavorful over time. Due to the cost and difficulty associated with flavor phenotyping, breeding programs have long been challenged in selecting for this complex trait. To address this issue, we leveraged targeted metabolomics of diverse tomato and blueberry accessions and their corresponding consumer panel ratings to create statistical and machine learning models that can predict sensory perceptions of fruit flavor. Using these models, a breeding program can assess flavor ratings for a large number of genotypes, previously limited by the low throughput of consumer sensory panels. The ability to predict consumer ratings of liking, sweet, sour, umami, and flavor intensity was evaluated by a 10-fold cross-validation, and the accuracies of 18 different models were assessed. The prediction accuracies were high for most attributes and ranged from 0.87 for sourness intensity in blueberry using XGBoost to 0.46 for overall liking in tomato using linear regression. Further, the best-performing models were used to infer the flavor compounds (sugars, acids, and volatiles) that contribute most to each flavor attribute. We found that the variance decomposition of overall liking score estimates that 42% and 56% of the variance was explained by volatile organic compounds in tomato and blueberry, respectively. We expect that these models will enable an earlier incorporation of flavor as breeding targets and encourage selection and release of more flavorful fruit varieties.
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影响因子：
3.7
作者：
Goldansaz SA;Guo AC;Sajed T;Steele MA;Plastow GS;Wishart DS
通讯作者：
Wishart DS
影响因子：
3.3
作者：
BRUHN, CM;FELDMAN, N;WILLIAMSON, E
通讯作者：
WILLIAMSON, E
影响因子：
3.3
作者：
de los Campos, Gustavo;Naya, Hugo;Cotes, Jose Miguel
通讯作者：
Cotes, Jose Miguel
影响因子：
27.5
作者：
Goulet, Charles;Kamiyoshihara, Yusuke;Klee, Harry J.
通讯作者：
Klee, Harry J.
影响因子：
1.9
作者：
Eggink, P. M.;Maliepaard, C.;Visser, R. G. F.
通讯作者：
Visser, R. G. F.