A highly significant ( p < 0.0001) increase in droplet size was detected for (F1) after 60 days of storage, increasing from 1099.0 ± 33.0 nm to 1609.0 ± 35.1 nm.
Excerpts
Equation (5) was extremely significant ( p < 0.0001) and the model was ideal ( F = 29.86).
Correlation analyses ( Figure 1 ) further showed a highly significant negative correlation between pH and total acidity ( p < 0.0001), confirming their strong coupling throughout the distillation process.
This response is well described by a quadratic model (Equation (6)), which was highly significant according to ANOVA ( p < 0.0001, Table S1 ).
However, purge loss in cured pork sausages was highly significant with the addition of ligands ( p < 0.0001; Table 2 ), with cysteine treatment showing higher purge loss than histidine and nicotinamide treatments ( p < 0.05; Table 3 ).
The regression model was highly significant (F = 175.47, p < 0.0001), while the lack of fit was not significant ( p = 0.3775), indicating that the fitted quadratic model adequately described the experimental data.
Gender was highly significant for all tested beverages ( p < 0.0001), with women consuming fewer alcoholic beverages than men.
Genetic was a highly significant ( p < 0.0001) source of variation for starch content, starch granule distribution and for all molecular features of its components ( Table 2 ).
As shown in Table S10 , the model had F = 10.65 and was highly significant ( p < 0.0001), while the lack of fit was not significant ( p = 0.0652 > 0.05).
It can be seen from Table 3 that the model was extremely significant (F = 8.89, p < 0.0001).
By expressing the proximal composition of the meat on a dry basis (DM), the highly significant effect on the ash content was confirmed ( Table 5 ; p < 0.0001).
Significance analysis of regression coefficients reveals the linear term A (trehalose) is highly significant ( p < 0.0001), B (lactose) is significant ( p < 0.05), and C (skim milk powder) is highly significant ( p < 0.01).
The correlations between R-values for filaments of different sizes, as well as between gratings of different sizes, were positive and highly significant ( p < 0.0001).
Analysis of variance (ANOVA) and significance testing ( Table S6 ) indicate both regression models are highly significant ( p < 0.0001) with a non-significant lack of fit.
One-way ANOVA revealed highly significant differences among extraction methods for all phytochemical and antioxidant parameters ( p < 0.0001; Table 2 ), confirming that extraction technology is a critical determinant of bioactive compound recovery from blackthorn berries [ 29 ].
The difference was highly significant ( p < 0.0001).
In the case of examining the content of 5 elements in 10 types of nuts, the discrimination was highly significant (Wilks’ Lambda: 0.004, F = 29.995, p < 0.0001).
Statistical tests, including the likelihood ratio and Pearson chi-square, were highly significant ( p < 0.0001).
The established regression model was highly significant ( p < 0.0001), the misfit term was not significant ( p > 0.05), and the correlation coefficient of the model, R 2 = 0.9909, and the corrected correlation coefficient, R 2 adj = 0.9791, indicated that the model was well fitted and the experimental error was small ( Table 2 ).
The model is extremely significant ( p < 0.0001) following the data processing and analysis, suggesting that the multiple regression equation accurately fits the experimental data and is statistically significant.
After regression analysis was performed, the quadratic polynomial regression equation describing the effects of CAR/GEL mass ratio (A), CS concentration (B), and GSA/CUR dosage (C) on the comprehensive score (Y) was obtained as follows: (11) Y = 0.66 − 0.041A + 0.019B + 0.036C + 0.061AB + 0.075AC + 0.027BC − 0.054A 2 − 0.091B 2 − 0.054C 2 Analysis of variance (ANOVA) for the response surface methodology (RSM) experiments ( Table 7 ) revealed the following: The regression model was highly significant ( p = 0.0002).
The Chao1 index showed highly significant differences among groups ( p = 0.00025), with the HFD group exhibiting markedly reduced microbial richness compared with the ND group.
The ANOVA Welch test demonstrated that there were highly significant differences between these total BA values ( Table 3 ), as expected ( p = 2.84 × 10 −4 ); such differences were largely explicable by those observed between the cheese and wine/vinegar product classifications investigated.
In particular, positions 2 and 3 show a pronounced decrease in the estimated latent score (approximately −0.76 and −1.34, respectively), both highly significant ( p = 3.6 × 10 −4 and p = 1.18 × 10 −7 ).
For this group, Bartlett’s test indicated the adequacy of the data to apply FA with a p -value highly significant ( p < 0.0005), leading to the rejection of the null hypothesis H0: The correlation matrix is equal to the identity matrix.