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 composition, which were used for training different machine learning algorithms, such as deep and ensemble decision tree learners. The proposed method is used to predict the presence of adulterants in a binary classification problem and also the specific assessment of which of five adulterants was found through multiclass classification. In deep learning, we propose a Convolutional Neural Network architecture that needs no preprocessing on spectral data. Classifiers evaluated show promising results, with classification accuracies up to 98.76%, outperforming commonly used classical learning methods. Conclusions The proposed methodology uses machine learning techniques on milk spectral data. It is able to predict common adulterations that occur in the dairy industry. Both deep and ensemble tree learners were evaluated considering binary and multiclass classifications and the results were compared. The proposed neural network architecture is able to outperform the composition recognition made by the FTIR equipment and by commonly used methods in the dairy industry. Electronic supplementary material The online version of this article (10.1186/s13040-019-0200-5) contains supplementary material, which is available to authorized users.

Development of Spectroscopic Sensor System for an IoT Application of Adulteration Identification on Milk Using Machine Learning

“…whereas other techniques are able to achieves only less than 100%. • The proposed system able to detect five different adulterants with 100% of accuracy whereas, mostly other methods able to detect one or more adulterants.even [29] able to detect six adulterant, whose accuracy only 98.76% • Our developed system is portable, rapid, non-destructive and less expensive when compared to other systems.…”

“…In [29] the milk adulterant testing using the Fourier Transform spectroscopy spectral analysis was carried out for five adulterants. Though it is non-non-destructive and rapid method, the experiments were tested in the lab set up and the equipment was expensive, non-portable and of higher wavelength region.…”

A Service-based Joint Model Used for Distributed Learning: Application for Smart Agriculture

“…Whereas, in the FSVRG method, one full gradient computation is performed centrally, followed by many distributed stochastic updates over the distributed clients. More details about the D-2 dataset can be found in [48]. So, to set up similar settings as the FL-NNPLS method (i.e., 712/5 ≈ 140 samples per client), with the the FedAvg method, total seven clients were considered (960/7 ∼ 140 samples per client) and only 5 of them were contributed to the model updating process in each updating cycle.…”

Ensemble Learning Models for Food Safety Risk Prediction

“…In authenticity identification, the proposed method performed significantly better than single classification methods, such as partial least squares-discriminant analysis (PLS-DA), artificial neural network (ANN), and support vector machine (SVM). Neto et al [17] applied deep learning and ensemble machine learning techniques to milk spectral data to predict common fraudulent milk adulterations that occur in the dairy industry. The proposed method outperformed both common single learning algorithms and Fourier-transform infrared spectroscopy (FTIR), which is a common technique used to determine sample composition in the dairy industry.…”