Title

Determination Of Trace Elements In Bovine Semen Samples By Inductively Coupled Plasma Mass Spectrometry And Data Mining Techniques For Identification Of Bovine Class

Keywords

Bovine semen; Data mining; Inductively coupled plasma mass spectrometry (ICP-MS); Mineral

Abstract

The reproductive performance of cattle may be influenced by several factors, but mineral imbalances are crucial in terms of direct effects on reproduction. Several studies have shown that elements such as calcium, copper, iron, magnesium, selenium, and zinc are essential for reproduction and can prevent oxidative stress. However, toxic elements such as lead, nickel, and arsenic can have adverse effects on reproduction. In this paper, we applied a simple and fast method of multi-element analysis to bovine semen samples from Zebu and European classes used in reproduction programs and artificial insemination. Samples were analyzed by inductively coupled plasma spectrometry (ICP-MS) using aqueous medium calibration and the samples were diluted in a proportion of 1:50 in a solution containing 0.01% (vol/vol) Triton X-100 and 0.5% (vol/vol) nitric acid. Rhodium, iridium, and yttrium were used as the internal standards for ICP-MS analysis. To develop a reliable method of tracing the class of bovine semen, we used data mining techniques that make it possible to classify unknown samples after checking the differentiation of known-class samples. Based on the determination of 15 elements in 41 samples of bovine semen, 3 machine-learning tools for classification were applied to determine cattle class. Our results demonstrate the potential of support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF) chemometric tools to identify cattle class. Moreover, the selection tools made it possible to reduce the number of chemical elements needed from 15 to just 8. © 2012 American Dairy Science Association.

Publication Date

12-1-2012

Publication Title

Journal of Dairy Science

Volume

95

Issue

12

Number of Pages

7066-7073

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.3168/jds.2012-5515

Socpus ID

84869501798 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84869501798

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