Authentication of dietary supplements through Nuclear Quadrupole Resonance (NQR) spectroscopy
Authentication of dietary supplements through Nuclear Quadrupole Resonance (NQR) spectroscopy
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DOI：
10.1111/ijfs.13892
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发表时间：
2018-12-01
影响因子：
3.3
通讯作者：
Bhunia, Swarup
中科院分区：
文献类型：
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作者：
Masna, Naren Vikram Raj;Zhang, Fengchao;Bhunia, Swarup
随着行业的发展，许多产品通过贴错标签、重新命名和虚假广告来掺假的做法正在变得普遍。现有的分析解决方案通常需要大量的样品制备，或者在检测不同类型的完整性问题方面受到限制。提出了一种新的基于核四极共振（NQR）光谱的定量、非侵入性和非破坏性认证方法。它对产品固体化学结构的微小偏差很敏感，这种偏差会改变NQR信号的性质。这些特征对于不同的制造商来说是独一无二的，从而产生了特定于制造商的水印。我们表明，来自不同制造商的名义上相同的膳食补充剂可以根据NQR光谱的特征进行准确分类。具体来说，我们使用一种基于机器学习的分类，称为支持向量机（svm）来验证被测产品的真实性。该方法已经在三种使用半定制硬件的产品上进行了评估，并显示出令人满意的结果，典型的分类准确率超过95%。
As the industry grows, adulteration of many products by mislabelling, re-branding and false advertising is becoming prevalent practice. Existing solutions for analysis often require extensive sample preparation or are limited in terms of detecting different types of integrity issues. We describe a novel authentication method based on Nuclear Quadrupole Resonance (NQR) spectroscopy which is quantitative, non-invasive and non-destructive. It is sensitive to small deviation in the solid-state chemical structure of a product, which changes the NQR signal properties. These characteristics are unique for different manufacturers, resulting in manufacturer-specific watermarks. We show that nominally identical dietary supplements from different manufacturers can be accurately classified based on features from NQR spectra. Specifically, we use a machine learning-based classification called support vector machines (SVMs) to verify the authenticity of products under test. This approach has been evaluated on three products using semi-custom hardware and shows promising results, with typical classification accuracy of over 95%.