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The authors of this study (purchase required) report that they systematically separated and authenticated the triacylglycerols composition of milks from holstein cattle, goats, mongolian horses, bactrian camels, yaks and buffaloes,  using supercritical fluid chromatography coupled to high-resolution mass spectrometry (SFC-Q-TOF-MS). Subsequently, the fingerprinting of triacylglycerols from different livestock milks was modelled using chemometric methods. The results showed that the statistical grouping of different livestock milks was consistent with the species taxonomy, and the accuracy of internal as well as external validation was satisfactory.

They conclude that this work not only provides an innovative strategy for authentic traceability of livestock milk, but also offers potential for the establishment of nutritional databases.

Photo by Polina Kuzovkova on Unsplash

A2 cows’ milk is from selective breeds that produce a higher ratio of the A2/A1 form of β-casein in the milk.  It is sold mostly in Australia, New Zealand, China, and the United States and commands a price premium over conventional milk.  Authenticity testing has been difficult, typically requiring genetic techniques.

In this study (purchase required) the researchers piloted portable NIR to differntiate A2 milk versusnon-A2 milk and their mixtures using a portable NIR spectrometer.  They built a 1-class classification model.  63 samples of whole A2 milk were selected (authentic set), and 40 samples (fraudulent set) composed of non-A2 milk and mixtures in 3 different proportions (10, 25, and 50% v/v) of non-A2 milk in A2 milk. The abstract gives no further details of the reference samples in terms of production systems or seasonality.  For spectra collection, a MicroNIR was used.

Full data were pre-processed using different methods, but they found the most effective approach was the combination of the first derivative with Savitzky-Golay smoothing and Standard Normal Variate (SNV). A Data-driven Soft Independent Modeling of Class Analogy (DD-SIMCA) was applied. Using the Kennard-Stone algorithm, the authentic samples were split into two sets (45 for calibration and 20 for external validation). The non-A2 and fraudulent samples were added to the external validation set, and the model’s performance was evaluated using the metrics of sensitivity, specificity, accuracy, and precision.

They report that the DD-SIMCA model, utilizing 2 PCs, showed 100% results in all metrics, indicating no errors in the recognition of authentic samples.

They conclude that the model is suitable for use with portable equipment. Additionally, this fast and non-invasive technique can be optimized for applications in industrial management, food control, and A2 product authentication.

In this paper (open access) the authors demonstrate an optical, contactless method to discriminate different types of commercial milk (whole, partially skimmed and skimmed) and identify its adulteration with water and 12.5% water-glucose solution.  This adulterant was selected since it exhibits a refractive index comparable to that of whole milk, rendering such adulteration unnnoticed when performing a routine quality test based on refractive index measurements.

The prototype sensor employs a CMOS digital camera to acquire speckle pattern images generated by shining the beam of a red semiconductor laser onto milk samples placed in a plastic cuvette. The collected data are then analyzed to extract informative parameters, such as the average intensity and the speckle grain size.

The authors report that the system can distinguish between different types of milk and detect diluted samples with both water and glucose.

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.

The European Parliament and Council agreed to review and strengthen the existing marketing standards applicable to honey, fruit juices, jams and milk. The so-called Breakfast Directives lay down common rules on the composition, sales names, labelling and presentation of these products to ensure their free movement within the internal market and help consumers make informed choices.

The revised Directives agreed upon by the co-legislators will introduce the following changes:

• Mandatory origin labelling for honey:  the countries of origin in honey blends will have to appear on the label in descending order with the percentage share of each origin. Member States will have the flexibility to require percentages for the four largest shares only when they account for more than 50% of the blend. The Commission is empowered by the co-legislators to introduce harmonised methods of analysis to detect honey adulteration with sugar, a uniform methodology to trace the origin of honey and criteria to ascertain that honey is not overheated when sold to the final consumer. A Platform will be set up to advise the Commission on those matters. This will limit fraudulent practices and increase the transparency of the food chain.

• Innovation and market opportunities for fruit juices in line with new consumers demands: Three new categories will become available: ‘reduced-sugar fruit juice‘, ‘reduced-sugar fruit juice from concentrate‘ and ‘concentrated reduced-sugar fruit juice‘. This way consumers can choose a juice with at least 30% less sugars. It will be possible for fruit juices to indicate on their labels that “fruit juices contain only naturally occurring sugars” to clarify that, contrary to fruit nectars, fruit juices cannot by definition contain added sugars – a feature that most of the consumers are not aware of.

• Higher mandatory fruit content in jams: an increase of the minimum fruit content in jams (from 350 to 450 grams per kilo) and in extra-jams (from 450 to 500 grams per kilo) will improve the minimum quality and reduce the sugar content of these products for EU consumers. Member States will be allowed to authorise the term ‘marmalade' as a synonym of ‘jam', to take into account of the name commonly used locally for these products. The term “marmalade” was authorised until now only for citrus jams.

• Simplified labelling for milk: the distinction between ‘evaporated' and ‘condensed' milk will be removed, in line with the Codex Alimentarius standard. Lactose-free dehydrated milk will also be authorised.

The political agreement reached by the European Parliament, Council and Commission is now subject to formal approval by the co-legislators. From entry into force 20 days after publication of the final text, Member States will have 18 months to transpose the new provisions into national law and 6 more months before it applies throughout the European Union.

Read full press release.

Authorities in the Khyber Pakhtunkhwa province of Pakistan are using a mobile testing laboratory to check the authenticity of milk sold in local shops. 

They have recently identified and destroyed more than 2,000 litres of milk diluted with water or adulterated with other chemicals and have closed a number of dairy shops.

Read full article.

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.

 AOAC International's Food Authenticity Task Force has developed standard method performance requirements (SMPR) for targeted and non-targeted food authenticity methods. SMPR set minimum performance criteria that food authenticity testing methods for milk, honey and olive oil need to fulfil. 

Further information was provided in a recent free-of-charge webinar, which can be viewed on registration.