Dipartimento di Ingegneria Civile, Ambientale, del Territorio, Edile e di Chimica (DICATECh), Politecnico di Bari, via Orabona 4, I-70125 Bari, Italy.
Dipartimento di Ingegneria Elettrica e dell'Informazione, Politecnico di Bari, via Orabona 4, I-70125 Bari, Italy; Innovative Solutions S.r.l. - Spin Off del Politecnico di Bari, zona H 150/B, I-70015 Noci, BA, Italy.
Food Chem. 2020 Dec 1;332:127339. doi: 10.1016/j.foodchem.2020.127339. Epub 2020 Jun 18.
Non-targeted NMR-based approach has received great attention as a rapid method for food product authenticity assessment. The availability of a database containing many comparable NMR spectra produced by different spectrometers is crucial to develop functional classifiers able to discriminate rapidly the commodity class of a given food product. Nevertheless, variability in spectrometer features may hamper the production of comparable spectra due to inherent variations in signal resolution. In this paper, we report on the development of a class-discrimination model for grape juice authentication by application of non-targeted NMR spectroscopy. Different approaches for the pre-treatment of data will be described along with details about the model validation. The developed model performed excellently (95.4-100% correct predictions) even when it was tested against 650 spectra produced by 65 spectrometers with different configurations (magnetic field strength, manufacturer, age). This study may boost the use of non-targeted NMR methods for food control.
基于非靶向 NMR 的方法作为一种快速的食品真实性评估方法受到了极大的关注。拥有一个包含许多由不同光谱仪产生的可比 NMR 光谱的数据库对于开发能够快速区分给定食品商品类别的功能分类器至关重要。然而，由于信号分辨率的固有变化，光谱仪特征的可变性可能会阻碍可比光谱的产生。在本文中，我们通过应用非靶向 NMR 光谱法报告了一种用于葡萄汁鉴别的分类判别模型的开发。将描述不同的数据预处理方法，并详细介绍模型验证。即使在对来自 65 个具有不同配置（磁场强度、制造商、年龄）的光谱仪的 650 个光谱进行测试时，所开发的模型也表现出色（95.4-100%的正确预测）。这项研究可能会促进非靶向 NMR 方法在食品控制中的应用。
