Fingerprinting black tea: When spectroscopy meets machine learning a novel workflow for geographical origin identification.

Food fraud, along with many challenges to the integrity and sustainability, threatens the prosperity of businesses and society as a whole. Tea is the second most commonly consumed non-alcoholic beverage globally. Challenges to tea authenticity require the development of highly efficient and rapid solutions to improve supply chain transparency. This study has produced an innovative workflow for black tea geographical indications (GI) discrimination based on non-targeted spectroscopic fingerprinting techniques. A total of 360 samples originating from nine GI regions worldwide were analysed by Fourier Transform Infrared (FTIR) and Near Infrared spectroscopy. Machine learning algorithms (k-nearest neighbours and support vector machine models) applied to the test data greatly improved the GI identification achieving 100% accuracy using FTIR. This workflow will provide a low-cost and user-friendly solution for on-site and real-time determination of black tea geographical origin along supply chains.

Rapid metabolic fingerprinting with the aid of chemometric models to identify authenticity of natural medicines: Turmeric, Ocimum, and Withania somnifera study.

Herbal medicines are popular natural medicines that have been used for decades. The use of alternative medicines continues to expand rapidly across the world. The World Health Organization suggests that quality assessment of natural medicines is essential for any therapeutic or health care applications, as their therapeutic potential varies between different geographic origins, plant species, and varieties. Classification of herbal medicines based on a limited number of secondary metabolites is not an ideal approach. Their quality should be considered based on a complete metabolic profile, as their pharmacological activity is not due to a few specific secondary metabolites but rather a larger group of bioactive compounds. A holistic and integrative approach using rapid and nondestructive analytical strategies for the screening of herbal medicines is required for robust characterization. In this study, a rapid and effective quality assessment system for geographical traceability, species, and variety-specific authenticity of the widely used natural medicines turmeric, Ocimum, and Withania somnifera was investigated using Fourier transform near-infrared (FT-NIR) spectroscopy-based metabolic fingerprinting. Four different geographical origins of turmeric, five different Ocimum species, and three different varieties of roots and leaves of Withania somnifera were studied with the aid of machine learning approaches. Extremely good discrimination (R2 > 0.98, Q2 > 0.97, and accuracy = 1.0) with sensitivity and specificity of 100% was achieved using this metabolic fingerprinting strategy. Our study demonstrated that FT-NIR-based rapid metabolic fingerprinting can be used as a robust analytical method to authenticate several important medicinal herbs.

Authentication of organically grown vegetables by the application of ambient mass spectrometry and inductively coupled plasma (ICP) mass spectrometry; The leek case study.

Health conscious and environmentally aware consumers are purchasing more organically produced foods. They prefer organic fruits and leafy vegetables as these are much less likely to have been exposed to contaminants such as pesticides. The detection of fraudulent activity in this area is difficult to undertake, because many chemical plant protection treatments degrade very quickly or can be washed off to remove evidence of their existence. It was found that when combining DART-MS with a compact, inexpensive and robust single quadrupole mass spectrometer, it was possible to differentiate organic from conventional leeks with 93.8% to 100% accuracy. ICP-MS results showed similar performance, with an ability to differentiate conventional from organic leeks with 92.5% to 98.1% accuracy. This study has paved the way for the certification of vegetables as being organically produced. The next step is to create data libraries to support the roll out of the methodologies described.