Pattern Recognition in Dried Milk Droplets Using Lacunarity and Deep Learning

This study introduces a novel method for classifying whole and lactose-free milk and the detection of water adulteration through analyzing dried droplets. The key innovation is the addition of NaCl, which modulates crystallization to enhance structural differentiation and facilitate the classification of milk types and detection of adulteration. Dried droplets of milk containing NaCl concentrations of $0\%$, $2\%$, and $4\%$ were analyzed, revealing distinct morphologies, including amorphous, cross-shaped, and dendritic crystals. These structures were quantitatively characterized using lacunarity to assess their discriminatory power. Two classification approaches were evaluated: one based on lacunarity analysis alone and another incorporating deep learning. Both methods yielded high classification accuracies, with lacunarity achieving $95.04\%\pm 6.66\%$, while deep learning reached $95.22\%\pm 4.47\%$. Notably, the highest performance was obtained with $2\%$ NaCl, where lacunarity reached $97.08\%\pm 2.27\%$ and deep learning $96.88\%\pm 2.8\%$, indicating improved precision and stability. While deep learning demonstrated more consistent performance across test cases, lacunarity alone captured highly discriminative structural features, making it a valuable complementary tool. The integration of NaCl and lacunarity analysis offers a robust and interpretable methodology for ensuring the quality and authenticity of dairy products, particularly in detecting adulteration, where morphological contrast is less evident.