Spss v18

The experimental data were presented as mean ± SD. Statistical significance was assessed by ANOVA test and paired-sample t-test was adopted to test normal distribution by SPSS v18.0 (SPSS, Inc., Chicago, IL, USA). In all experiments, confidence level was set at 95% to determine the significance of difference (p < 0.05).
Pearson correlation coefficients were calculated with SPSS v18.0 (IBM SPSS, USA). The correlation heat-map was built and optimized with HemI software (Deng et al., 2014 (link)). The correlation coefficient is always between −1 and +1. The closer the correlation is to ±1, the closer to a perfect linear relationship.
The results of a PCA are usually discussed in terms of component scores. Consider a data matrix, X, where each of the n rows represents different samples, and each of the p-columns gives the results tested factors. A set of p-dimensional vectors of loadings (α_ij) map each row vector (x_i) of X to a new vector of the principal component scores (F_j), given by F_j = α_1jx₁+α_2jx₂+…+α_ijx_i, for i = 1, 2, …, n, j = 1, 2, …, m. The full principal components score (F) decomposition of X can therefore be given as F = ρ₁F₁+ρ₂F₂+…ρ_jF_j, where ρ_j is the jth eigenvector of F_j (Abdi and Williams, 2010 (link)).

The findings have been represented in terms of means ± standard error mean (SEM). A comprehensive statistical analysis, encompassing one-way analysis of variance (ANOVA) and subsequent Scheffe post hoc examination, was employed to scrutinize disparities among the animal groups. All statistical computations were carried out using SPSS v.18.0 (SPSS Inc., Chicago, IL, USA), with statistical significance established at a threshold of * p < 0.05, ** p < 0.01, *** p < 0.001, ^#p < 0.05, and ^##p < 0.01. The study sample size and statistical power were calculated using the SPSS v.18.0 software analysis program. The sample size in all groups achieves a power of above 80% and an alpha value of 5%. For CMG and histological analysis, 8 animals/group (n = 32) yielded a statistical power of 0.98; Western blot, 5 animals/group (n = 20), showed a statistical power of 0.91

All data are expressed here as the mean ± SD (standard deviation). Statistical analysis of the above data relied on one-way analysis of variance (ANOVA), followed by Duncan’s multiple test, to identify significant differences among samples of blueberry grown at different locations, using SPSS v18.0 (SPSS Inc., Chicago, USA). An alpha level of p < 0.05 or less was considered to be significant.
Correlation analysis was performed among phytochemical conpounds and sensory properties using SPSS v18.0 (SPSS Inc., Chicago, USA). The correlation coefficients were analyzed by Pearson method, and significance was tested by two-tailed method. An alpha level of p < 0.05 or less was considered to be significant.
In order to test for similarities among blueberries sampled from different locations, principal component analysis (PCA) was applied to the data set. Locations were inserted in rows and response variables including phytochemical properties, and sensory values were placed in columns. The PCA analysis was implemented in the Origin2019 software (Massachusetts, USA).