Three non-targeted methods, i.e. 1H NMR, LC-HRMS, and HS-SPME/MS-eNose, combined with chemometrics, were used to classify two table grape cultivars (Italia and Victoria) based on five quality levels (5, 4, 3, 2, 1). Grapes at marketable quality levels (5, 4, 3) were also discriminated from non-marketable quality levels (2 and 1). PCA-LDA and PLS-DA were applied, and results showed that, the MS-eNose provided the best results. Specifically, with the Italia table grapes, mean prediction abilities ranging from 87% to 88% and from 98% to 99% were obtained for discrimination amongst the five quality levels and of marketability/non-marketability, respectively. For the cultivar Victoria, mean predictive abilities higher than 99% were achieved for both classifications. Good models were also obtained for both cultivars using NMR and HRMS data, but only for classification by marketability. Satisfying models were further validated by MCCV. Finally, the compounds that contributed the most to the discriminations were identified.

中文翻译：

---

通过使用1 H NMR，LC-HRMS，MS-eNose和多元统计分析对鲜食葡萄的质量进行评估。

三种非目标方法，即1H NMR，LC-HRMS和HS-SPME / MS-eNose，结合化学计量学，基于五个质量等级（5、4， 3，2，1）。也将可销售质量等级（5、4、3）的葡萄与不可销售质量等级（2和1）区分开。应用PCA-LDA和PLS-DA，结果表明，MS-eNose效果最好。具体而言，对于意大利鲜食葡萄，在五个质量等级和可销售性/不可销售性之间的区分上，获得的平均预测能力分别为87％至88％和98％至99％。对于维多利亚品种，两种分类的平均预测能力均高于99％。还使用NMR和HRMS数据获得了两个品种的良好模型，但仅适用于按适销性进行分类。MCCV进一步验证了令人满意的模型。最后，鉴定出对鉴别最有帮助的化合物。