Mohammadian Amirhossein, Barzegar Mohsen, Mani-Varnosfaderani Ahmad
Department of Food Science and Technology Tarbiat Modares University Tehran Iran.
Department of Analytical Chemistry Tarbiat Modares University Tehran Iran.
Food Sci Nutr. 2021 Mar 24;9(6):3026-3038. doi: 10.1002/fsn3.2260. eCollection 2021 Jun.
The lime juice is one of the products that has always fallen victim to fraud by manufacturers for reducing the cost of products. The aim of this research was to determine fraud in distributed lime juice products from different factories in Iran. In this study, 101 samples were collected from markets and also prepared manually and finally derived into 5 classes as follows: two natural classes (, ), including 17 samples, and three reconstructed classes, including 84 samples (made from Spanish concentrate, Chinese concentrate, and concentrate containing adulteration compounds). The lime juice samples were freeze-dried and analyzed using FT-IR spectroscopy. At first, principal component analysis (PCA) was applied for clustering, but the samples were not thoroughly clustered with respect to their original groups in score plots. To enhance the classification rates, different chemometric algorithms including variable importance in projection (VIP), partial least square-discriminant analysis (PLS-DA), and counter propagation artificial neural networks (CPANN) were used. The best discriminatory wavenumbers related to each class were selected using the VIP-PLS-DA algorithm. Then, the CPANN algorithm was used as a nonlinear mapping tool for classification of the samples based on their original groups. The lime juice samples were correctly designated to their original groups in CPANN maps and the overall accuracy of the model reached up to 0.96 and 0.87 for the training and validation procedures. This level of accuracy indicated the FT-IR spectroscopy coupled with VIP-PLS-DA and CPANN methods can be used successfully for detection of authenticity of lime juice samples.
酸橙汁一直是制造商为降低产品成本而进行欺诈的受害者之一。本研究的目的是确定伊朗不同工厂生产的市售酸橙汁产品中的欺诈行为。在这项研究中，从市场收集了101个样品，并手动制备，最终分为以下5类：两个天然类（，），包括17个样品，以及三个重构类，包括84个样品（由西班牙浓缩汁、中国浓缩汁和含有掺假化合物的浓缩汁制成）。将酸橙汁样品冷冻干燥并使用傅里叶变换红外光谱（FT-IR）进行分析。首先，应用主成分分析（PCA）进行聚类，但在得分图中，样品并未按照其原始类别进行彻底聚类。为了提高分类率，使用了不同的化学计量学算法，包括投影变量重要性（VIP）、偏最小二乘判别分析（PLS-DA）和反向传播人工神经网络（CPANN）。使用VIP-PLS-DA算法选择与每个类别相关的最佳判别波数。然后，将CPANN算法用作非线性映射工具，根据样品的原始类别对其进行分类。在CPANN图中，酸橙汁样品被正确地归为其原始类别，并且该模型在训练和验证过程中的总体准确率分别达到了0.96和0.87。这种准确率表明，傅里叶变换红外光谱结合VIP-PLS-DA和CPANN方法可以成功用于检测酸橙汁样品的真实性。
