Statistica 13

The data were analyzed as a completely randomized design using a 2-way ANOVA concerning the time of sous-vide cooking (4, 6, and 12 h) and temperature of sous-vide cooking (60°C, and 80°C) as a factorial design (3 × 2) (written in the table as a-f), according to the following linear model: Y_ij = μ + A_i + B_j + (AB)_ij + e_ij, where Y_ij = value of trait (the dependent variable); μ = overall mean; A_j = effect of time of sous-vide cooking; B_j = effect of temperature of sous-vide cooking; (AB) = interaction, and e_ij = random observation error, using Statistica®13.3 software (Statsoft Inc., 2019 ). Moreover, it was made also 2-way ANOVA concerning the heat treatment (time x temperature) of sous-vide cooking and kind of meat (with and without skin) as a factorial design (6 × 2) (written in the tables as x-y), according to the following linear model: Y_ij = μ + A_i + B_j + (AB)_ij + e_ij, where Y_ij = value of trait (the dependent variable); μ = overall mean; A_j = effect of heat treatment (time × temperature); B_j = effect of kind of meat (with skin and without skin); (AB) = interaction, and e_ij = random observation error, using Statistica 13.3 software (Statsoft Inc., 2019 ). The statistical significance of the differences between the averages of the groups was calculated using Tukey's test and was at a level of P≤0.05. The tables present the average values and their standard deviation.

Statistical analysis was performed in Statistica 13.3 software (StatSoft Power Solutions, Inc./Dell, Round Rock, TX, USA). Analyses include Pearson-correlation tests (between DPPH, FRAP, ORAC, TPC and TFC) and principal component analysis. Principal component analysis was performed in the matrix correlation model. DPPH, FRAP, ORAC, TPC, and TFC were used as variables. All data was normalized before PCA.
Comparison and classification of propolis chemical compositions were performed by cluster analysis based on Pearson correlation and complete linkage clustering by Statistica 13.3 software (StatSoft Power Solutions, Inc./Dell, Round Rock, TX, USA). Matrix consisted of normalized UV peak areas (see Section 2.3).

All tests and experiments were conducted three times, and the results are presented as the average value ± the standard deviation (SD). Statistical analyses of the data were carried out using the Statistica 13.1 software (StatSoft Polska, Krakow, Poland). A significance level of p < 0.05 was used, and the analysis involved employing a one-way analysis of variance along with Tukey’s post hoc test [76 ].
The impact of the addition of oyster mushrooms on the antioxidant activity, oxidative changes, and selected sensory evaluation parameters in the final product was examined using Principal Components Analysis (PCA). The PCA was used for groups in which individual characteristics were most strongly correlated. The Statistica 13.1 program (StatSoft Polska, Krakow, Poland) was used for execution.

All tests and experiments were conducted three times, and the results are presented as the average value ± the standard deviation (SD). Statistical analyses of the data were carried out using the Statistica 13.1 software (StatSoft Polska, Krakow, Poland). A significance level of p < 0.05 was used, and the analysis involved employing a one-way analysis of variance along with Tukey’s post hoc test [76 ].
The impact of the addition of oyster mushrooms on the antioxidant activity, oxidative changes, and selected sensory evaluation parameters in the final product was examined using Principal Components Analysis (PCA). The PCA was used for groups in which individual characteristics were most strongly correlated. The Statistica 13.1 program (StatSoft Polska, Krakow, Poland) was used for execution.

All results were expressed as the mean values ± standard deviation. Statistical analysis was performed using software STATISTICA 13.1 (StatSoft Inc., Tulsa, OK, USA). Analysis of the variance (ANOVA) and Fisher’s least significant difference (LSD) with the significance defined at p < 0.05 were used for data analysis.
For grouping the data obtained by DART-TOF/MS analysis according to the m/z for a given abundance threshold, a macro was created in Excel 2016 (Microsoft). A detailed description of the work in the macro is described elsewhere [65 (link)]. The created table was ranked according to m/z descending, and thus, the assembled matrix was used for the principal component analysis (PCA) using STATISTICA 13.1 (StatSoft, Inc.). For statistical processing using PCA, 1000 of the most significant masses were taken in both positive and negative modes. The threshold (minimum abundance) for masses in positive mode was 5000 and in negative mode was 70,000.

The software Statistica 13.4 (Statsoft Inc.) and GraphPad Prism (version 8.03, for Windows, GraphPad Software, La Jolla, CA, United States) were used for all statistical analysis. Data were tested for normality and homogeneity of variance using a Kolomogorov-Smirnov test and Shapiro-wilk test, respectively. Data from protein DH% from enzyme extract standardisation were transformed to arcsin (x^1/2) before statistical analysis. Enzymes dilution (DH%) data were subjected to two way full-factorial ANOVA followed by Duncan’s Multiple Range Test at significance level of 95% using SAS 9.4 (SAS Institute Inc.) software for Windows (SAS, 2013, Institute, Cary NC). In vitro amino acid solubility data were subjected to two-way analysis of variance (ANOVA) with diet and enzyme effect as two factors. One-way ANOVA was performed to analyze any significant difference between diets for each enzyme, followed by Tukey’s multiple comparison. The in vivo digestibility data were analysed using t-test to compare between test diet and reference diet. For all statistical tests, p-values < 0.05 were considered significant, and all the results are expressed as mean ± standard deviation. Figures and graphs were obtained by using GraphPad Prism (version 8.03, for Windows, GraphPad Software, La Jolla, CA, United States).