Simca software v 14

The whole flies at 30 days of age were collected, and the sample preparation methods of metabolomics analysis were assessed as reported previously [25 (link)]. Briefly, samples were separated using Agilent 1290 UHPLC (Agilent Technologies, Santa Clara, CA, USA) equipped with the ACQUITY UPLC BEH Amide column (1.7 μm, 2.1 mm × 100 mm), and analyzed by Triple 6600 TOF mass spectrometer (AB Sciex, Concord, Toronto, ON, Canada). Metabolites were identified based on the exact mass of their MS and tandem MS spectra, which were then searched and compared using a laboratory database (Shanghai Applied Protein Technology Co., Ltd., Shanghai, China). The initially processed data were enumerated with SIMCA software (V14.1, Umetrics, Ume, Västerbotten, Sweden) for mode identification following normalization to total peak intensity (by weight of the complete flies). Following the collection of valid data, principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) were applied to differentiate STP from the CT group. Variable importance in projection (VIP) > 1 and p < 0.05 were employed as criteria for screening potential biomarkers, and the KEGG metabolomics pathway analysis was constructed to reveal the most relevant pathway for STP to exert anti-aging effects.

Principal component analysis (PCA) was performed using SIMCA software v.14.1 (Umetrics AB, Umeå, Sweden) [21 (link)]. PCA models were used to explore clustering patterns of observations, trends in the data and outliers. Two samples were removed due to poor data quality. Samples from each breakfast group were modelled separately to identify outliers, resulting in exclusion of 2 of 96 for the CB samples and 6 of 96 for the EHB samples according to Hotellings T2 range (Tcrit 99%) and Distance to Model (DModX) (not exceeding 1.8). In total, 182 samples were included in further data analysis. Two variables were removed from the data set (imidazole (pH indicator)). In total, 294 variables were included in the model for further analysis.

Statistically significant differences between means were determined by analysis of variance (one-way and two-way ANOVA) followed by Tukey’s honestly significant difference (HSD, 5% level) post hoc test. One-way ANOVA was performed to assess the differences between treatments in each year. A two-way ANOVA was performed to evaluate the differences obtained by treatment, year and interaction. These analyses were performed using the JMP statistical software v. Pro 14 (SAS Institute Inc., Cary, NC, USA). To evaluate the relationship between olive fruit and oil phenolic compounds and fatty acids, an orthogonal partial least squares-discriminant analysis (OPLS-DA) was performed using the SIMCA software v. 14.1 (Umetrics, Umea, Sweden). Multiple Factor analysis (MFA) of the fruit and oil phenolic compounds data was performed using XLSTAT (Addinsoft, Anglesey, UK).

All data were expressed as mean ± SD (n = 8). Statistical analysis was carried out by one-way analysis of variance (ANOVA) and the Costat software computer program. Significance values between groups were calculated at p < 0.05. The percentage change versus control (− or +) was calculated according to Motawi et al. [35 (link)], where the negative control was the normal healthy rats, and the positive control was the diabetic rats left untreated.


In addition, correlation was established between investigated biological parameters and phytochemical composition based on regression analysis using Excel 2013 (Microsoft, Redmond, WA, USA). The R² values were calculated for different metabolites versus % improvement in each parameter, following treatment with each sprout extract. As well, PLS analysis was performed using SIMCA software (v. 14.1, Umetrics, Umeå, Sweden).

NMR analysis was performed at 300 K on a Bruker 800 MHz spectrometer equipped with a 5 mm CPTCI ¹H-¹³C/¹⁵N/D Z-gradient cryoprobe, and automated tuning and matching. ¹H NMR spectra were acquired using standard one-dimensional pulse sequence, with water presaturation (noesygppr1d sequence) during both the relaxation delay (RD = 4 s) and mixing time (τ_m = 10 ms). The receiver gain was set to 16, and acquisition time to 2.73 s for all experiments. Each culture media spectrum was acquired using 4 dummy scans, 32 scans and 64 K data-points with a spectral window set to 20 ppm. Prior to Fourier Transformation, each free induction decay (FID) was multiplied by an exponential function corresponding to a line broadening of 0.3 Hz. ¹H NMR spectra were manually phased, baseline corrected and digitized over the range δ −0.5 to 11, and imported into MATLAB (2014a, MathWorks, Natick, U.S.). FIDs were referenced to TSP at δ 0.0 ppm. Spectral regions containing residual water (δ 4.69 to 5.18), TSP (δ −0.50 to 0.77), and noise (δ 10.26 to 11.00) were removed prior to probabilistic quotient normalization^{95 (link)}. Principal component analysis (PCA) was applied to the processed Pareto-scaled NMR data using SIMCA software (v. 14.1; Umetrics, Sweden). This model was validated by a 7-fold cross-validation.