Spss modeler v 18

Continuous variables were expressed as mean±SD and counting variables were expressed in terms of frequency. Student’s t-test and signed-rank tests were used to test the difference between paired quantitative data. T-test and general linear models for analysis of variance were used as parameter test approaches. Non-parametric tests were implemented in Wilcoxon rank-sum analysis and Kruskal-Wallis test. If the data were normally distributed and the variances were equal, an appropriate parameter test was used, otherwise, a non-parametric test was used to realize hypothesis testing. Spearman correlation analysis was used as correlation analysis approach between two continuous variables. Multiple linear regression was used for multivariate analysis, and during which process variance inflation factor and standardized estimate (SE) were used to assess the multicollinearity risk and the weight of the affecting to the dependent variable.
Excel 2016 was used to summarize the data. Data cleaning and modeling were completed using IBM SPSS Modeler V.18.0 software. Variable screening, hypothesis testing, and regression analysis were performed using SAS V.9.21 (SAS Institute). The figure in the sample size verification section was plotted using Prism for Windows 6 software (GraphPad Software).

The isotope ratios of ¹³C/¹²C in honey and protein are expressed as δ¹³C in units of ‰. Corrected values were obtained according to equation (2): $${\delta }^{13}\mathrm{C}(\mathrm{‰})=\left(\frac{R_{means(sample)}-R_{means(Std1)}}{R_{means(Std2)}-R_{means(Std1)}}\right)\times (R_{true(Std2)}-R_{true(Std1)})+R_{true(Std1)}$$ where R = ¹³C/¹²C, R_{means (sample)} is the measured ¹³C/¹²C ratios of the honey or protein samples, R_{means (Std 1)} and R_{means (Std 2)} are the measured ¹³C/¹²C ratios of the standards USGS 24 graphite and NBS 22 oil, respectively; R_{true (Std 1)} and R_{true (Std 2)} are the true ¹³C/¹²C ratios for USGS 24 graphite and NBS 22 oil, which are −16.05‰ and −30.03‰, respectively.
Data were analysed using GraphPad Prism 7 for boxplot presentation with Tukey whiskers and a one-way ANOVA test. Multivariate approaches were used to investigate the feasibility of characterising the geographic origin of authentic commercial honey samples based on the measured variables of δ¹³C_honey and δ¹³C_protein together with trace elements of Al, Ba, B, Ca, Cu, Fe, Mg, Mn, Ni, P, K, Rb, Na, Sr, Sn and Zn. Principal component analysis and canonical discriminant analysis were carried out using IBM SPSS v22.0, and classification analyses using C5.0 model were implemented via IBM SPSS Modeler v18.0. Concentrations of trace elements with