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This paper (purchase required) reports the use of a portable optical sensor (Multi-Spectral Imaging) to build a classification model for detecting milk adulteration. This encompassed mixtures of milk from different species (cow, goat, and sheep), as well as dilution of cow’s milk with water. The study's scope also included milk with diverse heat treatments, fat content, and commercial brands.

The authors report that discriminant analyses provided reliable predictive models, with Accuracy and Cohen's Kappa values ranging between 0.80 and 1. In quantitative studies, the quantification of milk mixtures at a minimum percentage interval of 10% was detected with Mean Absolute Error (MAE) values between 0.14 and 0.05, and 0.03 for cow's milk adulterated with water at adulteration levels of 5%.

The authors conclude that the portability of these instruments adds a significant advantage by enabling on-site and real-time determination and quantification of milk adulteration.

This study (open-access author’s link available until February 14, with thanks to Michele Suman for sharing) reports the development and validation of a non-targeted classification method for authenticity of dried oregano leaves by atmospheric pressure matrix-assisted laser desorption ionization mass spectrometry (AP-MALDI-MS).

The model was trained on 23 authentic oregano samples (sourced from a reputable company with full supply chain traceability - originated from Italy, France, Turkey, or Albania, harvested between 2019 and 2022) along with five pure adulterants (dried leaves of savory (Satureja montana), myrtle (Lagerstroemia indica), sumac leaves (Rhus coriaria), strawberry tree (Arbutus unedo), and olive tree (Olea europaea)), plus sixteen adulterated oregano samples, intentionally mixed with the above mentioned adulterants at ranges between 5 % and 60 %.

The most abundant signals were characterized by collision induced dissociation and library search, the spectral data were submitted to statistical analysis. A basal inquiry of the data by partial least squared discriminant analysis (PLS-DA) was carried out for the simple assessment of the discrimination capabilities of the ± AP-MALDI-MS signatures. The researchers then constructed two distinct random forest (RF) classifiers using the positive and negative most informative ions teased out by recursive feature elimination from the training sets. The aforementioned most significant variables (m/z values) were also merged by mid-level data fusion and used to build a third RF classifier.

They report that the cross-validations of the three RF classifiers achieved good outcomes as demonstrated by the satisfactory values of overall accuracy (84.9 %, 92.1 %, and 92.8 %, respectively). The three RF classifiers were tested on the hold-out data, which revealed reliable classifier performances (accuracy 80.1 %, 87.0 %, and 85.4 %).

Photo by 360floralflaves on Unsplash

This FSA project develops a conceptual framework for modelling and capturing the full range of costs attributed to food crime on UK society, along with an assessment of the availability of data that would be necessary to produce this model.

An economic framework was developed for estimating the economic cost of food crime which uses:

1. Victim costs: Direct economic losses suffered by crime victims, including medical care costs and lost earnings.
2. Criminal justice system costs: Costs of anti-food crime activities, legal and adjudication services, and corrections programs including incarceration.
3. Crime career costs: Opportunity costs associated with the criminal’s choice to engage in illegal rather than legal and productive activities.
4. Intangible costs: Indirect losses suffered by crime victims, including pain and suffering, decreased quality of life, and psychological distress.
5. Market costs: Loss of profits for genuine firms.

Analysis was also conducted to assess how these costs can be calculated given available data sources. Finally, an assessment of the possibility of applying machine learning or other tools to build algorithms to calculate the costs was carried out.

The findings of this project will eventually be used in a phase 2 of the work which will look to build a model to provide preliminary estimates of the cost of food crime to UK society. 

Read full report.