Milk adulteration identification using hyperspectral imaging and machine learning

score, kappa, and Matthew's correlation coefficient metrics showcase the effectiveness of the proposed pipeline, outclassing numerous state-of-the-art approaches with a validation accuracy of 100%. In conclusion, this study established a multiclass model capable of detecting milk adulterant behavior, showing significant practical application for milk quality assessment.