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This study (open access) builds on a previously-published proof of concept.  The authors are working towards producing a hand-held multi-mode scanner (combining fluorescence, visible, NIR, and short-wave IR spectroscopy) to support species verification of white fish fillets in business-to-business supply (currently reliant, largely, on visual recognition by experienced traders).

The explain that one of the key challenges in using machine learning for fish species identification is managing the large number of classes, as the variety of fish species is extensive. In their previous research, they introduced a novel multi-mode, highly multi-class machine learning framework based on a hierarchy of dispute models. This approach involved training a global model, and then recognizing groups of classes that have feature subspaces too similar for effective single-stage classification. By partitioning the overall space into smaller, distinct subspaces, they trained specialized models that are more tailored to these specific subsets of the dataset. In practice, the global model initially classified a sample to determine the appropriate subspace, while the dispute model then identified the precise species within that subspace.

The objective of this latest study was to apply this approach data acquired with the multi-mode handheld spectroscopy device. Tissue spectra were acquired at 25 positions on 68 fillets from 11 species, in both frozen and thawed states.

They report that feature-level fusion across the four spectroscopy modes enabled higher classification accuracy than any single mode alone. A global machine-learning model classified all species with 85 ± 2.8 %, while specialized dispute models for commonly misclassified species improved performance to 90 % ± 6.1 %. Individual models for thawed and frozen fillets achieved 90 ± 6.0 % and 90 ± 5.4 %, respectively, with dispute models in the thawed dataset increasing accuracy to 93 ± 4.3 %.

They conclude that their results demonstrate that portable multi-mode spectroscopy, combined with machine learning, can provide a fast, non-destructive and reliable tool for on-site fish species identification.

In this study (open access) researchers developed and piloted a single in-line sensor to classify yoghurt as either sheep, goat or milk origin and simultaneously check viscosity and pH Quality Attribute Specifications.  Their goal is a rapid in-line sensor that incorporates automated decision making, for routine use in the dairy industry.

Their reference dataset was sourced directly from two reputable Spanish companies and included both pasteurised and UHT yoghurts.

They found that the animal origin of milk could be predicted by building models based on the spectral data between 400 and 600 nm whilst viscosity and pH could be predicted by building models based on the spectral data between 800 and 1800 nm. To identify the animal origin of milk, they used Partial Least Squares-Discriminant Analysis (PLS-DA), achieving 100 % accuracy (95 % confidence interval). The model used to predict pH and viscosity was built with Partial Least Squares Regression (PLSR). The predictive power was generally very good (MSE=0.04–0.06; R²=0.94–0.96; MAE=0.16–0.17).

They conclude that their study demonstrates that the proposed spectroscopic method offers a more efficient approach for the simultaneous prediction of pH, viscosity, and milk origin in yogurt compared to existing methods, that require separate and slower analyses. Further work still needs to be carried out to optimize the model and achieve real-time monitoring that enables automated decision-making.

[picture – from the publication]

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

Animal origin food products, including fish and seafood, meat and poultry, milk and dairy foods, and other related products play significant roles in human nutrition. However, fraud in this food sector frequently occurs, leading to negative economic impacts on consumers and potential risks to public health and the environment. Therefore, the development of analytical techniques that can rapidly detect fraud and verify the authenticity of such products is of paramount importance.

Traditionally, a wide variety of targeted approaches, such as chemical, chromatographic, molecular, and protein-based techniques, among others, have been frequently used to identify animal species, production methods, provenance, and processing of food products. Although these conventional methods are accurate and reliable, they are destructive, time-consuming, and can only be employed at the laboratory scale. On the contrary, alternative methods based mainly on spectroscopy have emerged in recent years as invaluable tools to overcome most of the limitations associated with
traditional measurements. The number of scientific studies reporting on various authenticity issues investigated by vibrational spectroscopy, nuclear magnetic resonance, and fluorescence spectroscopy has increased substantially over the past few years, indicating the tremendous potential of these techniques in the fight against food fraud.

This manuscript reviews the state-of-the-art research advances since 2015 regarding the use of analytical methods applied to detect fraud in food products of animal origin, with particular attention paid to spectroscopic measurements coupled with chemometric analysis. The opportunities and challenges surrounding the use of spectroscopic techniques and possible future directions are also be discussed.

Read full paper here.

Food fraud and adulteration is a major concern in terms of economic and public health.Multivariate methods combined with spectroscopic techniques have shown promise as a novel analytical strategy for addressing issues related to food fraud that cannot be solved by the analysis of one variable, particularly in complex matrices such distilled beverages.

This review describes and discusses different aspects of whisky production, and recent developments of laboratory, in field and high throughput analysis. In particular, recent applications detailing the use of vibrational spectroscopy techniques combined with data analytical methods used to not only distinguish between brand and origin of whiskey but to also detect adulteration are presented.

Read open access paper here.