Laser-induced breakdown spectroscopy unified partial least squares regression: An easy and speedy strategy for predicting Ca, Mg and Na content in honey

• Main Authors: Kuan, Wei Se, Ghoshal, Sib Krishna, Abdul Wahab, Roswanira
• 格式: Article
• 出版: Elsevier B.V. 2019
• 主题: QC Physics
• 在线阅读: http://eprints.utm.my/id/eprint/96941/; http://dx.doi.org/10.1016/j.measurement.2018.12.052

We used inductively coupled plasma-optical emission spectrometry (ICP-OES) and laser-induced breakdown spectroscopy (LIBS) united partial least square regression (PLSR) with reference to ICP-OES outcome for the evaluation of Ca, Mg and Na contents in 30 stingless bees’ honeys. The PLSR calibration models were constructed using 25 honeys. Coefficient of determinations (standard error of cross validation) for the calibration and the cross validation assessments of the three individual PLSR models targeted in favour of Ca, Mg and Na were respectively over 0.923 (25.6 mg/kg), 0.950 (15.1 mg/kg), and 0.909 (30.5 mg/kg). Five independent samples were used as validation dataset for testing the legitimacy and repeatability of the proposed strategy. Constructed models predicted the concentrations of Ca, Mg and Na with corresponding averaged percentage errors (relative standard deviations) of 6.71% (4.56%), 27.22% (6.14%) and 6.48% (5.47%). The proposed strategy was shown to be efficient for the stingless bees’ honeys elemental compositions analyses.