P Padmasri, Sathya Bama B
As milk adulteration and contamination continue to raise a serious threat to public health, there is a growing demand for rapid, reliable, and non-destructive techniques for milk quality assessment in the dairy industry. This comprehensive review provides recent advancements in non-invasive spectroscopic approaches, with a particular focus on near infrared (NIR) spectroscopy and visible-NIR hyperspectral imaging (Vis-NIR-HSI) for detecting both adulterants and contaminants in milk. Combined with chemometrics and machine learning techniques, these approaches can be used as effective qualitative and quantitative methods. The complete analytical workflow is examined in-depth with the significance of preprocessing strategies on spectral quality, the requirements of wavelength selection, and the roles of targeted and non-targeted chemometric models on classification and regression tasks. Finally, this review addresses existing limitations, including insufficient databases and the complexities of multi-adulterant detection, as well as highlights the emerging trends as future opportunities in multimodal data fusion, Internet of Things (IoT), and deep learning to support real-time monitoring for milk. The purpose of this review is to facilitate the practical, large-scale adoption of robust, non-destructive NIR and Vis-NIR-HSI tools for milk authentication.