Spss v 9

Dose response curves were generated by using cumulative mortality in each concentration within the specific time period. Percentage of inhibition of emergence (IE%) was calculated using the following equation.
$\%\text{IE}=100-\left(\text{T}/\text{C}\ast 100\right)$
where T is the number of emerged adults in tested units, C is the number of emerged adults in control unit^{5 ,26 , 39 (link)}.
Mortality and IE data obtained at each concentration was subjected to probit-regression analysis to determine LC₅₀ and LC₉₀ values and, IE₅₀ and IE₉₉ values using SPSS (v.9.0) program for Windows.
Residual effect in semi-field data was analyzed by one-way ANOVA. Kruskal–Wallis test was employed to compare adult emergence inhibition and adult emergence rates across all stand-alone and combination treatments. This non-parametric test was well-suited for comparing medians, particularly in cases where a non-normal distribution was observed, a condition not accommodated by the ANOVA test. A significance level of p < 0.05 was considered statistically significant.

One-way analysis of variance (ANOVA) was used for BF and SM for physicochemical and volatile compound data. One-way ANOVA was followed by a post hoc Tukey honest significance (HSD) if a significant effect was found (p < 0.05). A Student t-test (p < 0.05) was performed to test for differences in physicochemical and volatile compound data between investigated muscles at the same processing stage. The general linear model (GLM) was used to estimate the effects of the factors (muscle (M); phase of production (P) and M × P) on the studied parameters (dependent variables). GLM test was performed for a significance level p < 0.05. All statistics were generated using the statistical program SPSS v.9.0 (SPSS inc., Chicago, IL, USA).

Continuous and normally distributed variables are presented as arithmetic means and standard error of the mean (SEM). The significance of data differences (P≤0.05) was assessed using a nonparametric t-test in GraphPad Prism 7 (Graph Pad Software; La Jolla, CA, USA). The statistical analysis of beta diversity used the nonparametric “Adonis” method in the “vegan” package of the QIIME-incorporated version of the R software. Correlation analyses used Spearman’s rank correlation tests. Statistical analyses were performed in SPSS v.9.0 (SPSS Inc.; Chicago, IL, USA).