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This review (open access) covers the main approaches for verifying tea traceability, including sensory analysis, stable isotope ratio analysis (SIRA), mineral element fingerprints, spectroscopy and mass spectrometry metabolomics, and emerging sensors.  The authors then discuss in some detail why some of these approaches are more – or less – applicable for different types of tea.

They argue that origin traceability of tea is not merely an analytical chemistry issue. The essential differences in the processing techniques of the six major tea types (green, white, yellow, oolong, black, and dark)  determine that the applicability boundaries of the same traceability technology may vary significantly among different tea types.

• The high-temperature kill-green process in green tea deactivates the polyphenol oxidase activity, preserving the original chemical fingerprint of the fresh leaves to the greatest extent.
• White tea undergoes the least processing intervention, and theoretically has the best traceability.
• The impact of the micro-fermentation process on the chemical profile of yellow tea remains to be systematically evaluated.
• Oolong tea’s withering and roasting processes result in a fermentation degree ranging from 10% to 70%, making the complexity of the chemical fingerprint’s interference from processing the most among the six major tea types.
• The full fermentation process in black tea converts a large amount of catechins into theaflavins and thearubigins, deeply reshaping the primary metabolite profile.
• The post-fermentation of dark tea, driven by microbial communities, triggers biochemical transformations that can last for months or even years, with the origin signal highly intertwined with factors such as the degree of fermentation, aging years, and tree age

Photo by 五玄土 ORIENTO on Unsplash

Authenticity tests for coffee tend to focus on the variety (Arabica vs Rustica) or adulteration of roasted ground coffee (e.g. with chicory).  There has been relatively little focus on authenticating the origin of green beans, for example to underpin Fair Trade traceability.

Proteomics has previously shown differences among cultivars.  This paper (subscription required) built on previous studies that had showed that long-term adaptation to a distinct climate (associated with the geographical location), are likely to significantly affect various metabolic processes and thus protein profiles.  Most proteins in beans are likely to be enzymes, such as oxidases and peroxidases. Previous researchers had identified 531 proteins in C. arabica cultivars in high-altitude African and low-altitude South American samples. Further analysis pointed out that only a few proteins were significantly different between them, plausibly corresponding to the concentration of certain compounds (e.g., flavonoids) alongside the adaptation to the environmental niches (e.g., colder climate or predominant pathogens). Post-harvest processing modifies proteomic profile.

This study used a combination of proteomic profiling with linear discriminant analysis for the classification of the geographical origin of green specialty coffee beans from well-known harvesting regions in Central America, South America, Africa, and Asia. Out of 1596 identified proteins, the authors selected the top 30 target markers ranked by ANOVA. They report that the model's prediction performance using leave-one-out cross-validation reached 85.3 %, with the lowest accuracy in the prediction rate for Asian samples. Model performance and prediction sensitivity to random states were tested using 5-fold cross-validation. After 20 iterations, the model performance slightly decreased to 84.0 %. Specificity and sensitivity confirmed that the model appears to be reliable at distinguishing Asian and African samples.

Photo by wisnu dwi wibowo on Unsplash

In this paper (purchase required) a reversed-phase thin-layer chromatography (RP-TLC) method was developed to quantify the flavour enhancer monosodium glutamate (E-621) in various food samples following ultrasound-assisted extraction.

RP-TLC layers were precoated with RP-18 modified silica gel 60 F_254s. The mobile phase was ethanol‒water‒isopropanol (5:2.5:2.5, V/V).  The method used densitometric detection at 570 nm with an R_F value of 0.57.

The authors report that Monosodium glutamate (MSG) showed linearity in 500–1500 ng/spot range. RP-TLC separation was optimized, and the most influential and interacting parameters were identified using central composite design. Validation study was performed as per the International Council for Harmonisation (ICH) recommendations, which demonstrated all the parameters are within acceptable limits. MSG showed recovery of 93.12–101.42% among various food samples.

This test method was specifically designed to be sustainable and “green”, both in the extraction technology and the solvents used.  Its development used Analytical Quality by Design (AQbD) methodology, and various sustainability metrics are reported as favourable.

The authors conclude that the method provides a fast and environmentally friendly alternative to traditional procedures used to analyse MSG in foods.