current literature. We have observed that average zinc intake (3.691.2 mg/d) of the girls was low, around 40% of the Indian RDI for zinc (66), with almost all girls having their zinc intake below RDI. Plasma zinc levels were low (B0.7 mg/l) (41) in 72.4%, and erythrocyte zinc levels were low (B8 mg/g of packed cells) (56) in 23.6% of the girls. Prevalence of anemia (Hb B12 g/dl) was 27.2%, and 26.6% girls had low serum ferritin lpar;B12 mg/l) (67). Plasma retinol was below the normal cutoff of 200 mg/l (68) in 65.4% girls. Plasma vitamin C level of 10.8% girls was below the normal cut- off of 0.2 mg/dl (69 p. 534), and 67.6% of the girls had low plasma vitamin C status. This high prevalence of micro- nutrient deficiencies confirmed inadequate dietary in- takes of micronutrients by the girls from the study group. Little information is available on the measurement of cognitive performance in adolescents. Paper I described the cognitive performance and assessed its relation with status of zinc and other micronutrients (60). The data on cognitive scores in our girls indicated that 60% of the girls were slow in simple reaction tasks, 71.5% of the girls had their RPM scores below 50th percentile and memory deficits were seen in 44% girls (60). Reaction time and RPM percentiles of majority of the girls were below the normal range, indicating deficits in the cognitive performance of the girls. To explore the relative significance of dietary nutrient intakes with cognitive performance, weighted multiple regression adjusted for energy intake was carried out. The analysis revealed that intakes of b-carotene, iron and zinc were significant predictors for SRT (p B0.01). Other cognitive scores did not exhibit significant association with micronutrient intakes. Further, our study also examines association of biomarkers of iron and zinc with cognitive scores of the girls. We have noted that plasma zinc and erythrocyte zinc were negatively corre- lated with SRT and RRT and positively with Memory and RPM (p B0.05), whereas Hb was significantly correlated with RRT (p B0.05) in girls with iron deficiency anaemia. Taste RTS was found to be high in 45.2% girls, indicating impaired taste function. RTS showed significant negative correlation with erythrocyte zinc (r 0.25, p B0.05), Zinc status and its association with health of Indian adolescent implying that lower the zinc status higher would be the salt concentration for taste perception. Thus, our data indicated high prevalence of micronu- trient deficiencies including zinc in adolescent girls. Moreover, we observed improvement in cognitive perfor- mance and taste perception with increased blood zinc levels in the girls. Therefore, an attempt has been made to develop zinc-rich recipes to improve total and bio- available zinc intake of the girls. Diet patterns of adolescent girls and food-based approach for improving zinc intake Description of diet patterns In paper II (61), dietary food intakes on three random days by 24-hour recall were analysed for investigating prevailing diet patterns in the girls. It was observed that the girls were consuming rice-legumes meals, wheat- legumes/vegetables, mixed meals or bakery items. In all, the consumption of 86 different food items was reported by the girls during the 3 days’ diet recall. Of these, 19 foods were different cereals preparations comprising rice, wheat or millets,; 16 were legume preparations; seven different bakery products; five leafy vegetables; 14 other vegetables; 13 fried foods; six fruits; and six milk and milk products. These foods were categorised into 12 food groups based on their major ingredients, such as rice, whole wheat, pearl millet, sorghum, bakery products, fried foods, sprouts, un-sprouted legumes, leafy vegeta- bles, other vegetables, milk and milk products and fruits. For example, plain rice, rice flakes and puffed rice were categorised under the Rice food group, or foods such as unleavened pancakes (chapatti, paratha, roti) were cate- gorised under the Wheat group. Food intake data from 12 food subgroups were used to identify diet patterns of the girls. Principal component analysis was performed to identify diet patterns using orthogonal rotation method (varimax). Food subgroups with factor loading 0.3, eigenvalues and percentage of variance explained by each of the major dietary patterns identified in adolescent girls is given in Table 1. The factor loading indicated the degree to which the girls’ diet conformed to one of the dietary patterns identified. Five patterns with eigenvalues greater than one Table 1. Food subgroups, eigenvalues and percentage of variance explained by major dietary patterns identiﬁed adolescent girls Diet patterna Rice meal Snacks-type meal Wheat based meal Pearl millet meal Sorghum meal aExtraction method: Principle component analysis. bVarimax- rotated factor loading for the food subgroups. Citation: Glob Health Action 2012, 5: 7353 - DOI: 10.3402/gha.v5i0.7353 Food subgroups (factor loadingb) Eigenvalues Variance explained (%) Rice (0.794), un-sprouted legumes (0.768) Fried foods (0.781), Bakery products (0.336), fruits (0.764) Whole wheat (0.457, milk (0.604), sprouts (0.300) Pearl millet (0.300), sprouts (0.535), leafy vegetable (0.444) Sorghum (0.859), leafy vegetable (0.449) 1.67 1.40 1.32 1.16 1.06 13.93 11.70 11.04 9.70 8.83 5 (page number not for citation purpose)