Comparative Study Of Data Mining Techniques For The Authentication Of Organic Grape Juice Based On Icp-Ms Analysis

Keywords

Classification; Data mining; Feature selection; Neural networks; Support vector machines

Abstract

Authenticity is a substantial matter and a current concern of the organic food industry. Organic foods are appreciated by customers because of their benefits to health and friendliness to the environment. However, currently, the most common way for customers to confirm that the organic food they are buying are organic is by certificates and label information, which can be fraudulent. Furthermore, it is interesting to gain insight into organic food composition and visualize which mineral components are fundamental in the differentiation of organic from conventional food. This work addresses these problems using data mining concepts and techniques in a comparative study of organic and conventional food focusing on grape juice, but the proposed methodology can be adapted and employed for analysis of other types of organic food. This article presents a data mining analysis of the elemental composition of 37 grape juice samples collected from different locations in Brazil. The elemental composition of grape juice samples was determined by inductively-coupled plasma-mass spectrometry (ICP-MS). Forty-four elements were determined in the two types of samples, namely organic and conventional grape juice. Special effort was devoted to selecting the variables (elements) that best described each type of grape juice. Predictive models based on support vector machines, neural networks and decision trees were developed to successfully differentiate organic from conventional grape juice samples. We found that, according to the F-score, Chi-square and Random Forest Importance variable selection measures, the components Na, Sn, P, K, Sm and Nd are among the most important variables in the differentiation of organic and conventional grape juice samples. Particularly, the components Na, Sn and K received first, second or third position according to at least two methods. On the other hand, all variable selection methods considered indicated that Ag, Zn, Cr, Be and Pd were among the least important variables for the differentiation of organic and conventional grape juices. SVM yielded an accuracy of 89.18%, both CART and MLP achieved an accuracy of 86.48%.

Publication Date

5-1-2016

Publication Title

Expert Systems with Applications

Volume

49

Number of Pages

60-73

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.eswa.2015.11.024

Socpus ID

84953400121 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84953400121

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