Sentence examples for vegetable data from inspiring English sources

Figure 4 Biplot of PC1 and PC2 for vegetable data grouped on location, used PCA2-plant model.

(PNG 73 KB) 13065_2012_542_MOESM2_ESM.png Additional file 2: Figure 4A: Biplot of PC1 and PC2 for vegetable data grouped on location, used PCA1-plant model.

Fruit and vegetable data were then compared to the 2007-08 National Health Survey [ 31] and reported as 95% confidence intervals for the difference in proportions.

Fruit and vegetable data were collected in 185 food stores; one convenience store was excluded because of refusal for an in-store survey of food items [ 65].

Figure 3 Scree plot and PC loadings of vegetables data in PCA2-plant model.

(PNG 102 KB) 13065_2012_542_MOESM3_ESM.png Additional file 3: Figure 5A: Biplot of PC1 and PC2 for vegetables data grouped on vegetables species, used PCA3-plant model supplemented with agrochemical parameters pH and clay content.

This model was computed also only with heavy metals contents of vegetables and its PC1 and PC2 biplot is presented in Figure 5. Figure 5 Biplot of PC1 and PC2 for vegetables data grouped on vegetables species, used PCA3-plant model.

There was no significant difference in EI/BMR ratios across quintiles of fibre or total fruit and vegetables (data not shown).

Jam consumption was correlated with wholemeal/wholegrain bread but not with white bread, and salad dressing was correlated with salad vegetables (data not shown).

The most commonly eaten raw cruciferous vegetables (data not shown) were mustard/horseradish (11.7% of participants) and cabbage (e.g., coleslaw; 4.4% of participants).

Nevertheless, our categorization was sufficient to differentiate cities as high, mid-high, middle and low consumption of fruits and vegetables (data not shown in tables).