# Dietary Diversity with Dried Moringa Leave Powder and Amla Juice to Increase Haemoglobin level of Adolescent girls of Farm Families

Ajeet Kumar, Kumari Sunita, Y. Prabhabati Devi, Ingudam Bhupenchandra, K. Bhagya Lakshmi, Savita Kumari, Anupama Kumari

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## To cite this version:

Ajeet Kumar, Kumari Sunita, Y. Prabhabati Devi, Ingudam Bhupenchandra, K. Bhagya Lakshmi, et al.. Dietary Diversity with Dried Moringa Leave Powder and Amla Juice to Increase Haemoglobin level of Adolescent girls of Farm Families. Journal of Community Mobilization and Sustainable Development, 2021, 16 (2), pp.619-625. hal-04676951

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# Dietary Diversity with Dried Moringa Leave Powder and Amla Juice to Increase Haemoglobin level of Adolescent girls of Farm Families

Kumari Sunita<fnref n="1" />, Y. Prabhabati Devi<fnref n="2" />*, Ingudam Bhupenchandra<fnref n="3" />, K. Bhagya Lakshmi<fnref n="4" />, Savita Kumari<fnref n="5" />, Anupama Kumari<fnref n="6" /> and Ajeet Kumar<fnref n="7" />

## ABSTRACT

The dried and powdered leave of *Moringa oleifera* can prevent and restore iron reserve because of its high content of protein and micronutrients including iron. This study aimed to assess the effects of *Moringa oleifera* supplementation on haemoglobin level of adolescent girls of farm families to prevent the prevalence of anaemia among future mothers. This was an On-farm trial in which fifty adolescent girls aged 14-16 years participated. They were again divided into five groups each having ten subjects who were treated with different tested technological options to increase haemoglobin level except control group who was on regular daily diet. Group 2, 3, 4 & 5 were given DFS, amla juice, dried powdered moringa leave and moringa leave powder and amla juice both respectively. Among pre-intervention 83 per cent of them are having mild anaemia, 7 per cent of them having moderate anaemia and 10 per cent of them are having normal haemoglobin level and none of them have severe anaemia. In post-test, 35 per cent of them are having normal haemoglobin, 40 per cent of them having mild anaemia and 5 per cent of them are having moderate anaemia and none of them were in severe anaemia among all the selected subjects. In general, correlation study between various anthropometric indices revealed both positive and negative association for a few of the indices evaluated. Cluster analysis of anthropometric variables yielded three distinct clusters: Cluster I (TSF, MUAC, and haemoglobin), Cluster II (Height and weight), and Cluster III (BMI and age). Multivariate principal component analysis applied to anthropometric indicators extracted three principal components that explained 52.48 per cent of the cumulative variability of data for PC1, 26.05 per cent for PC2 and 16.79 per cent for PC3, respectively.

**Keywords:** Anaemia, Haemoglobin, Adolescent, DFS, Amla

## INTRODUCTION

Adolescence, the period between 10-19 years (WHO, 1996) is a journey from the world of the childhood to the world of the adulthood. It is a formative period of time when the maximum amount of physical, behavioural and emotional changes take place as the body matures. As per 2011 estimates, adolescents constitute about 16 per cent of global population with an absolute number of 1.2 billion. More than half of all the adolescents live in Asia. India has the largest adolescent population in the world and the number is 253 million. About one-fifth of India's

population is adolescent. Among them, 72 per cent of adolescent population resides in rural areas.

Adolescent girls are at high risk of anaemia and malnutrition, inadequate nutrition during adolescence can have serious consequences throughout the reproductive years of life and beyond. Very often, in India, girls get married and pregnant even before the growth period is over, thus doubling the risk for anaemia (Biradkar et al., 2012). During this period, iron requirement increases dramatically due to increase in the total blood volume, the increase in the lean body mass and the onset of menses in

<footnote n="1">¹Krishi Vigyan Kendra, Madhopur, West Champaran Bihar (DRPCAU, Pusa Samastipur), Bihar</footnote>

<footnote n="2">²Krishi Vigyan Kendra, Chandel, ICAR-Manipur Centre, Manipur</footnote>

<footnote n="3">³Krishi Vigyan Kendra, Tamenglong, ICAR-Research Complex for NEH Region, Manipur Centre, Manipur</footnote>

<footnote n="4">⁴Krishi Vigyan Kendra, Amadalavalasa, Acharya NG Ranga Agricultural University, Andhra Pradesh</footnote>

<footnote n="5">⁵Krishi Vigyan Kendra Saraiya, Muzzaffarpur, Bihar (DRPCAU, Pusa Samastipur), Bihar</footnote>

<footnote n="6">⁶Directorate of Extension Education, DRPCAU, Pusa Samastipur, Bihar</footnote>

<footnote n="7">⁷Department of Soil Science, SRI, DRPCAU, Pusa Samastipur, Bihar</footnote>

<footnote>*Corresponding author email id: prabhayumnam@rediffmail.com</footnote>
girls. The overall iron requirement increases from a preadolescent level of 0.7-0.9 mg iron / day to as much as 2.2 mg iron/day or perhaps more in heavily menstruating women (Kulkarni *et al.*, 2012). Adolescence is a vulnerable period in human life cycle for the development of micronutrient malnutrition especially iron deficiency or nutritional anaemia (Chaudhary *et al.*, 2008). Iron deficiency has a negative effect on cognitive performance in adolescents (Thomas *et al.*, 2015). The prevalence of anaemia among adolescents is 27 per cent in developing countries, and 6 per cent in developed countries. In world health report World Health Organization (WHO) stated that the world-wide mortality of iron deficiency anaemia is 60,404,000 in 2005. National Family Health survey in 2006 showed that 56 per cent of adolescent girls are anaemic in India. The report showed that 2 billion people over 30 per cent of the world's population are anaemic (WHO, 2014). The pre pregnancy nutritional level of young girls is important as it impacts on the course and outcome of their pregnancy (Upadhye *et al.*, 2017). The important nutrients that need to increase during adolescence include energy, protein, calcium, and iron. Anaemia results from nutritional deficiency of iron, folate, vitamin B12 and some other nutrients. Low intake of iron will also lead to stunting. Iron is one of the micronutrients used for formation of haemoglobin, oxygen transportation, brain development, regulation of body temperature and muscle activity. When the iron is decreased in human body, it is called as iron deficiency. According to World Health Organization (WHO) the haemoglobin level should be 12 g/dl for adolescent girls. When the haemoglobin level becomes less than 12 g/dl is considered as iron deficiency anaemia. The decreased dietary iron intake, poor absorption, worm infestation, increased body demand, menstruation are the major causes of iron deficiency anaemia among adolescent girls. Iron deficiency is the most widespread form of malnutrition in the world, affecting more than 2 million people (Dreyfuss, 2000). Drumstick leaves (*Moringa oleifera*) is reported to contain alkaloids, flavonoids, anthocyanins proanthocyanodin and cinnamates and is highly reputed in traditional system of medicine as a remedy of various ailment. It is a rich source of iron providing 0.85 mg iron/100 g leaf.

Drumstick leaves with its high beta carotene content (19690 mcg/100g) along with vitamin C from lemon juice may have a positive impact in the mobilization of stored iron and increase haemoglobin level. Walczyk *et al*. (2003) reported that vitamin A binds with iron during digestive

process and forms a complex that act as chelating agent thus blocking the effect of hydroxyl radicals present in phytates and polyphenols in flour and tea/coffee respectively. According to the book "the miracle tree", the dried and powdered leaves can prevent malnutrition and restore iron reserves because of its high content of protein and micronutrients including iron. However, scientific studies were lacking to substantiate these statements. Outside of anecdotal evidence of benefits from *Moringa Oleifera*, no clinical trials or randomized studies in human have been completed to show the efficacy of *Moringa Oleifera* leaves consumption on iron deficiency anaemia or malnutrition treatment as reported by Thurber *et al*. (2001).

Amla is a rich source of vitamin C, it provides 1gm of vitamin C per 100 ml fresh juice and requisite for the synthesis of collagen, which is liable for keeping the cells of the body together. It increases the red blood cell count and helps to promote good absorption of iron. Numerous experimental evidences have shown that amla fruit possess antioxidant, hepatoprotective, hypocholesterolemic and anti-inflammatory activities. *Emblica officinalis* is a natural, efficacious, an antioxidant is the richest natural source of Vitamin C. Numerous studies conducted on *Emblica officinalis* fruit suggest that it has anti-viral properties and also functions as an anti-bacterial and anti-fungal agent. A clinical study on patients with pulmonary tuberculosis showed that the vitamin C contained in *Emblica officinalis* was better assimilated than synthetic vitamin C. Further research of contemporary and traditional medical literature indicated that *Emblica officinalis* either in combination with other herbs or alone has been useful in the amelioration of colds, warts, skin afflictions, influenza, anaemia, diabetes, lung conditions, elevated cholesterol and as an immune restorative in cancer conditions. Agrawal (1998) stated that vitamin C should be added in supplementary feeding of children with other nutrients such as calories, protein, iron, and vitamin B12 because it helps to increase the iron absorption and thus vitamin C promotes the formation of haemoglobin in children with much faster rate.

Double Fortified Salt presents one of the most cost-effective opportunities to deliver two of the most critical micronutrients for mental capacity, maternal and infants' survival and human productivity, iodine and iron to human beings through their diet. Efficacy of DFS in improving haemoglobin and in reducing IDA among school-age children is mixed and unclear. Some recent studies
conducted in India were unable to find a significant impact of DFS on anaemia. Therefore, considering the important role played by adolescent girls as future mother the present trial was planned with the objective to assess the efficacy of feeding of low-cost dietary supplements moringa powder, amla juice and double fortified salt (DFS) on the haematological indices of the adolescent girls of farm families.

## MATERIALS AND METHODS

**Selection of sample:** Fifty adolescent girls between the age group of 12-16 years were selected from Rampurwa and Madhopur Majhaulia West Champaran village by purposive random sampling technique for on farm trial at Krishi Vigyan Kendra Madhopur West Champaran during 2016-18.

**Collection of study materials:** Fresh, healthy, good quality moringa leaves were collected from the moringa trees grown in and around KVK campus, Madhopur, West Champaran. Amla juice of reputed brand and fortified salt were purchased from local market.

**Preparation of moringa powder:** Drumstick leaves were cleaned, washed, dry roasted and then finely powdered. Leaves so processed retain their original green colour and the processed powder was stored in sterilised bottle. Flow chart for the preparation of moringa powder is shown in Figure 1.

**Method of feeding with different dietary treatments:** The samples were again equally divided into five groups

<figure><img src="image_3.png" /><figcaption>Figure 1: Flow Chart for the preparation of moringa powder</figcaption></figure>

of ten adolescent girls each. The first group served as control which was on normal daily diet while the second group was fed with double fortified salt the third group was given amla juice @ 2 table spoon providing about 60 mg vitamin C per day the fourth group received moringa leave powder thrice in a week @ 2 table spoon per day while the fifth group took amla juice @ 2 Tbs and in addition to dried moringa leave powder in the same ratio as stated above. The feeding trial was conducted for a period of three months. Data collection was done at an interval of fifteen days. Base line data was collected on the socioeconomic status, clinical symptoms of anaemia, anthropometry (weight, height, Triceps Skin Fold, Mid Upper Arm Circumference and Body Mass Index as well as dietary pattern. As the respondents belonged to the same socio-economic status so the dietary pattern was almost comparable. The method of feeding with different dietary treatments is shown in Table 1.

<table><caption>Table 1: Dietary diversification with the different low-cost resource available</caption><thead><tr><th>Technological Intervention options</th><th></th></tr></thead><tbody><tr><td>T1</td><td>Regular daily diet</td></tr><tr><td>T2</td><td>Regular diet prepared with DFS</td></tr><tr><td>T3</td><td>Regular Diet with amla juice</td></tr><tr><td>T4</td><td>Regular diet with moringa powder</td></tr><tr><td>T5</td><td>Regular diet with moringa powder & amla juice</td></tr></tbody></table>

**Biochemical estimation:** Haematological indices were assessed of all the participants by pathologist by finger prick method at the start of the trial and after completion of the trial at ninety days. Other parameters weight, height, TSF, MUAC were recorded after every fifteen days intervals.

**Data analysis and visualization:** Means of three replicates and standard deviations of the means were calculated for all the sensory parameters. Statistical analysis was performed by Windows-based SPSS version 22.0. Multiple comparisons of means of sensory parameters were made using Duncan's Multiple Range Test. Pearson's correlation coefficient analysis was performed to evaluate relationships between the sensory parameters under investigations using the same statistical package. The 5 per cent probability level is regarded as statistically significant. Pearson's correlation coefficient analysis, cluster analysis and regression were computed to identify the relationship between the sensory parameters. Cluster analysis (CA) helps
in grouping objects (cases) into classes (clusters) on the basis of similarities within a class and dissimilarities between different classes. The results of CA help in interpreting the data and indicate patterns of clustering. Using R studio, the principal component analysis (PCA) of all the data was used to extract the prominent principal components from the entire data set.

## RESULTS AND DISCUSSION

Among pre-test 83 per cent of them are having mild anaemia, 7 per cent of them having moderate anaemia and 10 per cent of them are having normal haemoglobin level and none of them have severe anaemia. In post-test, 35 per cent of them are having normal haemoglobin, 40 per cent of them having mild anaemia and 5 per cent of them are having moderate anaemia and none of them were in severe anaemia among all the selected subject. Statistically there was a significant difference between before and after intervention with different technological options. Base line data revealed that there was no significant difference in the haemoglobin level initially among five selected groups of adolescent girls. The mean age of the four groups was 14.2±1.07 years in control group while it was 13.7±1.27, 13.8±0.82, 14±0.82, 13.9±0.86, 13.95±0.86 years in other four groups respectively. The range and mean values for other anthropometric indices are depicted in the graph which showed that a significant increase was found in haemoglobin level of the group fed with moringa leave powder and amla juice both however other groups

did not show any significant increase in haemoglobin level. But, changes in haemoglobin level were found in all treated groups. Deshmukh *et al.* (2008) evaluated the effectiveness of a weekly regimen among slum and tribals of Nasik district, Maharashtra, India. The study was conducted to reveal the improvement of consuming vitamin C foods to improve the haemoglobin levels. The participants from Baroda city were given a mixed diet in a hotel with vitamin C rich guava, citrus fruits, lemon juice. At the end of nine months, interventional trial, there was a very significant rise in haemoglobin. However, efficacy of Double Fortified Salt in improving haemoglobin and in reducing Iron Deficiency Anaemia (IDA) among school-age children is mixed and unclear. Two recent studies conducted in India were unable to find a significant impact of DFS on anaemia. (www.ideasforindia). Results indicated that the DFS intervention in the mid-day meal preparation had a positive impact on haemoglobin levels and significantly reduced iron-deficient anaemia among second-grade children in Bihar.

**Correlation analysis:** Pearson's correlation coefficient analysis of anthropometric indices (parameters) revealed the existence of comparatively fewer correlation with each other. Results (Table 2) showed the existence of a significant negative correlation with age and haemoglobin content of the subject (p < 0.05 and r = -0.829) and also with MUAC (p < 0.05 and r = -0.909). On contrary, TSF exhibited a significant positive correlation with haemoglobin content (p < 0.05 and r = 0.855). Similarly,

<figure><img src="image_4.png" /><figcaption>**Figure 2: Allocation of different anthropometric indices due to implications of treatment effects**</figcaption></figure>
<table><caption>Table 2: Pearson's correlation coefficient (r) matrix between different anthropometric indices</caption><thead><tr><th></th><th>Age</th><th>Weight</th><th>Height</th><th>Haemoglobin</th><th>BMI</th><th>TSF</th><th>MUAC</th></tr></thead><tbody><tr><td>Age</td><td><strong>1.000</strong></td><td>0.338</td><td>-0.390</td><td><strong>-0.829</strong></td><td>0.294</td><td>-0.763</td><td><strong>-0.909</strong></td></tr><tr><td>Weight</td><td>0.338</td><td><strong>1.000</strong></td><td>0.565</td><td>-0.183</td><td>-0.014</td><td>-0.076</td><td>-0.123</td></tr><tr><td>Height</td><td>-0.390</td><td>0.565</td><td><strong>1.000</strong></td><td>0.103</td><td>-0.530</td><td>0.199</td><td>0.351</td></tr><tr><td>Haemoglobin</td><td><strong>-0.829</strong></td><td>-0.183</td><td>0.103</td><td><strong>1.000</strong></td><td>0.005</td><td><strong>0.855</strong></td><td><strong>0.942</strong></td></tr><tr><td>BMI</td><td>0.294</td><td>-0.014</td><td>-0.530</td><td>0.005</td><td><strong>1.000</strong></td><td>0.334</td><td>-0.313</td></tr><tr><td>TSF</td><td>-0.763</td><td>-0.076</td><td>0.199</td><td><strong>0.855</strong></td><td>0.334</td><td><strong>1.000</strong></td><td>0.742</td></tr><tr><td>MUAC</td><td><strong>-0.909</strong></td><td>-0.123</td><td>0.351</td><td><strong>0.942</strong></td><td>-0.313</td><td>0.742</td><td><strong>1.000</strong></td></tr></tbody></table>

The correlation coefficient (r) values are significantly positive at *p* < 0.01 (Boldfaced italics) and *p* < 0.05 (Bold) levels of probability (2-tailed); Boldfaced blue-coloured fonts indicate significantly negative correlation at *p* < 0.05 levels of probability (2-tailed);

The correlation coefficient (r) values correspond directly to the colour codes from green to yellow and red, respectively

MUAC was found to correlate significantly and positively with (*p* < 0.01 and *r* = 0.942). Apart from the aforementioned correlation between the parameters, the remaining parameters did not show any significant correlation amongst them.

**Cluster analysis:** Results regarding the cluster analysis (Figure 3) of human health parameters elucidated that 3 distinct cluster are generated from the reiteration of health parameters. The distinct parameters generated are red coloured Cluster I (TSF, MUAC and haemoglobin), blue coloured Cluster II (Height and weight) and green coloured Cluster III (BMI and age). The clusters were formed based on the close affinity and similarities in respect of their mode of function, correlation, grouping etc. In respect of Cluster I, it can be inferred from correlation table that haemoglobin was found to correlate significantly and positively with both MUAC and TSF. Likewise, in case of Cluster II weight and height had a perfect correlation (*r* =1) indicating that with increase in height weight automatically increases due to expansion in volume.

**Principal component analysis:** Results of PCA (Table 3 and Figure 4) on different anthropometric indices extracted three principal components (PC) with eigenvalues > 1 (Kaiser, 1960) explaining cumulatively 85.37 per cent of the total variances present in the original dataset. The extracted three PC's explained the variation to the tune of

52.48 for PC1, 26.05 for PC2 and 16.79% for PC3, respectively, of the total variability of data. The loading plot of biplot (Figure 4) showed that PC1 had large positive loadings on haemoglobin content of human body and TSF; and these parameters are highly correlated to each other as the angle amid the variables of 0 or 180° divulges a correlation of 1 or -1, respectively (Kohler and

<figure><img src="image_5.png" /><figcaption>Figure 3: Hierarchical cluster analysis of different anthropometric indices</figcaption></figure>
<table><caption>Table 3: Results depicting the loading of principal component analysis of different anthropometric indices</caption><thead><tr><th>Principal components</th><th></th><th>PC 1</th><th>PC 2</th><th>PC 3</th></tr></thead><tbody><tr><td rowspan="3">Initial Eigenvalues</td><td>Total</td><td>3.67</td><td>1.82</td><td>1.18</td></tr><tr><td>Percent of variance</td><td>52.48</td><td>26.05</td><td>16.79</td></tr><tr><td>Cumulative (%)</td><td>52.48</td><td>78.53</td><td>95.32</td></tr><tr><td rowspan="3">Rotation Sums of Squared Loadings</td><td>Total</td><td>3.62</td><td>1.54</td><td>1.52</td></tr><tr><td>Percent of variance</td><td>51.64</td><td>21.99</td><td>21.69</td></tr><tr><td>Cumulative (%)</td><td>51.64</td><td>73.63</td><td>95.32</td></tr></tbody></table>

### Factor loadings

<table><thead><tr><th rowspan="2">Eigen vectors<sup>b</sup></th><th colspan="3">Principal components<sup>a</sup></th></tr><tr><th>PC 1</th><th>PC 2</th><th>PC 3</th></tr></thead><tbody><tr><td>Age</td><td>-0.923</td><td>0.315</td><td>0.101</td></tr><tr><td>Weight</td><td>-0.184</td><td>0.083</td><td><strong>0.952</strong></td></tr><tr><td>Height</td><td>0.269</td><td>-0.515</td><td><strong>0.761</strong></td></tr><tr><td>Haemoglobin</td><td><strong>0.962</strong></td><td>0.052</td><td>-0.073</td></tr><tr><td>BMI</td><td>-0.018</td><td><strong>0.988</strong></td><td>-0.072</td></tr><tr><td>TSF</td><td><strong>0.920</strong></td><td>0.341</td><td>0.107</td></tr></tbody></table>

**Extraction Method:** Principal Component Analysis; **Rotation Method:** Varimax with Kaiser Normalization

<figure><img src="image_6.png" /><figcaption>Figure 4: Two dimensional graphical biplot illustrating biplot and score plot of different anthropometric indices (where, MUAC-Mid Upper Arm Circumference and TSF-Triceps Skinfold Thickness</figcaption></figure>

Luniak, 2005). Similarly, PC2 exhibited large positive loading on BMI. Likewise, PC3 exhibited high loadings on weight and height respectively. In respect of score plot, it can be inferred that T2 located on the fourth quadrant of score plot has high influence on haemoglobin and TSF followed by effect of T3 on the aforesaid parameters.

Similarly, T5 positioned on first quadrant had high impact on MUAC and human height; and T4 (second quadrant) on weight of human being. Age and BMI were influenced by T1 (third quadrant) and these two parameters almost had negative correlation with rest of other parameters under investigations.

<footnote><sup>a</sup>Boldface factor loadings are considered highly weighted; <sup>b</sup>Rotation converged in 5 iterations</footnote>
## CONCLUSION

As the strategies to prevent malnutrition and anaemia are expensive for rural population in developing countries, local approaches are commonly used. Thus, edible plants like *Moringa oleifera* leaves should be used to fight this burden. Weekly iron supplementation was as effective as daily iron supplementation in raising Hb levels in intervention group. Thus the present study recommends the prospects of more aggressive introduction and utilization of moringa by farm families. It may be worthwhile for industry to take up production of moringa leave powder for nutritional securities as well as for income generation.

## REFERENCES

Agarwal, A. 1998. Impact of vitamin C supplementation on utilization of calories, protein and iron on anaemia of PEM. *The Indian Journal of Nutrition and Dietetics*, 35: 14-19.

Biradkar, S. 2012. Prevalence of anaemia among adolescent girls: A one year cross sectional study. *Journal of Clinical and Diagnostic Research* (Supplee-1) 6(3): 372-377.

Chaudhary, S. and V. Dhage. 2008. A study of anaemia among adolescent females in urban area of Nagpur. *Indian Journal of Community Medicine*, 33(4): 243-245.

Deshmukh, P.R.; B.S. Garg and M.S. Bharambe. 2008. Effectiveness of weekly supplementation of iron to control anaemia among adolescent girls of Nashik, Maharashtra, India. *Journal of Health, Population and Nutrition*, 26(1): 74-78.

Dreyfuss, M.L.; R.J. Stolt; J.B. Shrestha; E.K. Pradhan; E.C. Lellerg; S.K. Khatri; S.R. Shrestha; J. Katz; M. Albonico and K.P. West. 2000. Hookworms, Malaria and Vitamin A deficiency contribute to anaemia and iron deficiency among pregnant women in plains of Nepal. *Journal of Nutrition*, 130: 2527-2536.

http://www.ideasforindia.inhuman development

Kaiser, HF. 1960. The application of electronic computers to factor analysis. *Educational and Psychological Measurement*, 29: 141-151.

Kohler, U. and M. Luniak. 2005. Data Inspection using Biplots. *The Stata Journal: Promoting Communications on Statistics and Stata*, 5(2): 208-223. doi:10.1177/1536867x0500500206

Kulkarni, M.V.; P.M. Durge and N.B. Kasturwar. 2012. Prevalence of anaemia among adolescent girls in an urban slum. *National Journal of Community Medicine*, 3(1): 108-111.

Thomas, D.; J. Chandra; S. Sharma; A. Jain and H.K. Pemde. 2015. Determinants of nutritional anaemia in adolescents. *Indian Paediatrics*, 52(10): 867-869.

Thurber, M.D. and J.W. Fahey. 2009. Adoption of *Moringa oleifera* to combat under-nutrition viewed through the lens of the “Diffusion of Innovations” theory. *Ecology of Food and Nutrition*, 48: 212-225.

Upadhye, J.V. and J.J. Upadhye. 2017. Assessment of anaemia in adolescent girls. *International Journal of Reproduction, Contraception, Obstetrics and Gynecology*, 6: 3113-3117.

Walczyk; R.H. Lena; H. Leif and F.H. Richard. 2003. No enhancing effect of Vitamin A on iron absorption in Humans. *The American Journal of Clinical Nutrition*, 77(1): 144-149.

World Health Organization. *Programming for adolescent health and development*, W H O Tech Rep Ser No. 1996:2