Grape is the most consumed nectar in Brazil and a relatively expensive beverage. Therefore, it is susceptible to fraud by substitution with other less expensive fruit juices. Adulterations of grape nectars by the addition of apple juice, cashew juice, and mixtures of both were evaluated by using low-field nuclear magnetic resonance (LF-NMR) and supervised multivariate classification methods. Two different approaches were investigated using one-class (only unadulterated samples (UN) were modeled) and multiclass (three classes were modeled: UN, adulterated with cashew (CAS), and adulterated with apple (APP)) strategies. For the one-class approach, soft independent modeling of class analogy (SIMCA), one-class partial least squares (OCPLS), and data-driven SIMCA (DD-SIMCA) models were built. For the multiclass approach, partial least squares discriminant analysis (PLS-DA) and multiclass SIMCA models were built. The results obtained demonstrated good performances by all the one-class methods with efficiency rates higher than 93%. For the multiclass approach, the classification of samples containing only one type of adulterant presented efficiencies higher than 90% and 97% using SIMCA and PLS-DA, respectively. The classification of samples containing blends of two adulterants was satisfactory for the CAS class, but not for the APP class when applying PLS-DA. Nevertheless, multiclass SIMCA did not provide satisfactory predictions for either of these two classes.

中文翻译：

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低场核磁共振检测葡萄花蜜掺假的不同多元分类方法的比较

葡萄是巴西最消耗的花蜜，也是一种相对昂贵的饮料。因此，它很容易被其他较便宜的果汁替代而欺诈。通过使用低场核磁共振（LF-NMR）和有监督的多元分类方法，评估了通过添加苹果汁，腰果汁以及两者的混合物掺入的葡萄花蜜的掺假情况。使用一类（仅对未掺杂样品（UN）进行建模）和多类（对三类进行建模：UN，腰果（CAS）掺杂和苹果（APP）掺杂）策略研究了两种不同的方法。对于一类方法，建立了类比的软独立建模（SIMCA），一类偏最小二乘（OCPLS）和数据驱动的SIMCA（DD-SIMCA）模型。对于多类方法，建立了偏最小二乘判别分析（PLS-DA）和多类SIMCA模型。所有一类方法均显示出良好的性能，效率高于93％。对于多类方法，使用SIMCA和PLS-DA分别对仅包含一种掺杂物的样品进行分类，其效率分别高于90％和97％。包含两种掺杂物的混合物的样品分类对于CAS类而言是令人满意的，但在应用PLS-DA时对于APP类而言是令人满意的。但是，多类SIMCA不能为这两类中的任何一种提供令人满意的预测。对于多类方法，使用SIMCA和PLS-DA分别对仅包含一种掺杂物的样品进行分类，其效率分别高于90％和97％。包含两种掺杂物的混合物的样品分类对于CAS类而言是令人满意的，但在应用PLS-DA时对于APP类而言是令人满意的。但是，多类SIMCA不能为这两类中的任何一种提供令人满意的预测。对于多类方法，使用SIMCA和PLS-DA分别对仅包含一种掺杂物的样品进行分类，其效率分别高于90％和97％。包含两种掺杂物的混合物的样品分类对于CAS类而言是令人满意的，但在应用PLS-DA时对于APP类而言是令人满意的。但是，多类SIMCA不能为这两类中的任何一种提供令人满意的预测。