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.
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