Spss 16.0 statistical software

All data are presented as the mean ± standard deviation and were analyzed using SPSS 16.0 statistical software (SPSS, Inc., Chicago, IL, USA). Statistical differences between groups were analyzed with non-parametric methods (Mann-Whitney U-test for unpaired data). The χ² test was used to estimate mRNA values. P<0.05 was considered to indicate a statistically significant difference.

Response surface methodology was adopted in the design of experimental combinations. The central composite rotatable design consisted of four numerical independent variables. The variables had values of: X₁ (percentage of FDDG) = 0, 5, 10, 15, and 20; X₂ (barrel and die temperature) = 100, 110, 120, 130, and 140°C; X₃ (screw speed) = 100, 125, 150, 175, and 200 rpm; and X₄ (percentage of moisture content) = 14, 15.5, 17, 18.5, and 20 (Table 2). The total number of observations were 27 with three replicates at the design center. The experimental design and the codes for the processing variables have been reported in Table 3. The responses studied were ER, BD, color parameters, and overall color changes, WAI, WSI, and total dietary fiber (TDF). Analysis of variance (ANOVA) (Tables 6–7, 8, 9) was performed to determine the goodness of the fit and the significance of the effects of each factor on the responses. Statistical analysis was conducted using Design Expert 8.0.7.1 (Statease, Minneapolis, MN). Pearson's correlation coefficient (R) was also applied to establish specific correlations using SPSS (16.0) statistical software.

Statistical analyses were performed utilizing SPSS 16.0 statistical software. All experiments were effectuated in triplicate, and the results are expressed as the mean ± standard deviation (SD). The difference between mean values was determined by Pair-Sample t-Test at an α-level of 5%.

Data are reported as the mean ± standard deviation (SD), and all tests were performed using the SPSS 16.0 statistical software program (SPSS, Chicago, IL, USA). A two-way ANOVA model with shading and provenance as the main fixed factors plus a shading × provenance interaction term, followed by Tukey’s multiple-range test, was performed for each leaf characteristic, flavonoid concentration, plant growth, and flavonoid accumulation per plant. Relationships be-tween leaf characteristics, individual environmental factors (solar radiation, air temperature, and RH values) and leaf flavonoid concentration were evaluated using the Pearson’s correlation analysis. The data were tested for normality (Shapiro–Wilk normality test) before analysis of variance. All statistical analyses were performed at a 95 % confidence level.

All data were analyzed using SPSS 16.0 statistical software (Version 16.0, SPSS Inc., Chicago, IL, US). Results were expressed as means ± SD. Statistical comparisons between two groups used a Mann-Whitney non-parametric U test, whereas comparisons between the same individual used a Wilcoxon’s matched-pairs t test. The Kruskal-Wallis H nonparametric test was used for multiple comparisons between different groups. The Spearman’s rank correlation test was used to assess the relationship of immune factors and clinical characters. Two-sided P < 0.05 was considered statistically significant.