On the utilization of deep and ensemble learning to detect milk adulteration.

ABSTRACT:

Background

Fraudulent milk adulteration is a dangerous practice in the dairy industry that is harmful to consumers since milk is one of the most consumed food products. Milk quality can be assessed by Fourier Transformed Infrared Spectroscopy (FTIR), a simple and fast method for obtaining its compositional information. The spectral data produced by this technique can be explored using machine learning methods, such as neural networks and decision trees, in order to create models that represent the characteristics of pure and adulterated milk samples.

Results

Thousands of milk samples were collected, some of them were manually adulterated with five different substances and subjected to infrared spectroscopy. This technique produced spectral data from the milk samples composit

SUBMITTER: Neto HA 

PROVIDER: S-EPMC6615233 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature