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This paper (open access) provides a comprehensive overview of emerging non-invasive techniques—such as fluorescence, near-infrared, mid-infrared, and Raman spectroscopy—for assessing meat quality and detecting adulteration.

The key novelty of this review is its integration of bibliometric analysis with a critical evaluation of advanced technologies aligned with the UN Sustainable Development Goals. Within the tabulated lists of published papers, the authors add their own 1-line opinion on the robustness of the underpinning database or chemometrics, and how near the work is to practical application.

The review highlights the potential of hybrid systems that integrate spectroscopy with chemometrics and machine learning to provide accurate, real-time, and sustainable meat authentication solutions. It also highlights research gaps such as the need for multi-adulterant detection models, standardized validation protocols, and open-access spectral databases.

The authors aim to align their commentary on innovation with regulatory and sustainability frameworks, including the UN Sustainable Development Goals.

Photo by Victoria Shes on Unsplash

Fluorescence spectroscopy utilizing benchtop and portable spectrometers with light-emitting diodes (LEDs) as a fixed excitation source has been used as a method for detecting food adulteration in various products, including honey, extra virgin olive oil, tea, and coffee  It is cost-effective, rapid, and sensitive, allowing for intact measurement. LED-based fluorescence spectroscopy is fast, accurate, and cheaper than using a laser.. Recent advancements in semiconductor technology have enabled the delivery of LEDs with commercially available wavelengths ranging from 370 to 470 nm, exhibiting significant light intensity.

In this paper (purchase required), the authors used the technique to develop a classification model to detect ground soy in ground-roasted Arabica coffee, and to differentiate Robusta and Liberica varieties.  The abstract gives no details of the reference samples used to construct or validate the model but it was limited to 2024 season samples harvested in Indonesia.

Photo by Nathan Dumlao on Unsplash

Measuring the profile of trace metals in food is one approach to discriminating its geographic origin.  Analytical tools for trace-metal measurement tend to be laboratory-based with expensive capital equipment.

There has been recent research to develop point-of-use sensors for metal ions using specific chemical binders (often based on the chemistry of human olefaction) with fluorescent markers.  This paper (purchase requires) takes the work a significant step forward.  Their sensor is based upon the principle that different metal ions induce different degrees of aggregation in perylene diimide derivative based supramolecular nanoaggregates.  This enabled the construction of a multi-analyte sensor which they report as having ease of preparation, rapid response, and high sensitivity originating from large specific surface areas.  The authors report that they used the sensor to build a successful classification model of geographic origin for both drinking water and apples.