eChapters

Plant genetic resources are the most valuable and essential basic raw material to meet the current and future need of crop improvement programme. A wider genetic base assume the priority in plant breeding research aimed as developing new varieties for increased crop production. PGR for food and agriculture are a reservoir of genetic adoptability, which act as buffer against environmental changes and economical challenges. The erosion of these resources results in a severe threat to the world long term food securities. The fundamental objective of the genetic resource conservation is the maintenance of genetic diversity within each of the species. In general PGR include all species which contribute to people by providing food, medicine, feed, fiber etc. PGR is our heritage which needs conservation for prosperity.

Genetic resources management is a complex process starting from the identification of a target gene pool for conservation to the use of genetic resources. Many of these activities not only generate but also require geo-referenced data (Spatial data). Analysis of these data can lead to a greater understanding of the eco-geographic representation of existing collections, increasing the efficiency of conserving and managing genetic resources and broadening our understanding of the distribution of genetic diversity. Genetic resource databases could potentially serve as an information resource to a broader scientific community, providing relatively independent global data sets to meet the unique needs of researcher. GIS can merge genetic diversity information with other georeferenced data such as population density, climate, topography and soils, adding value to genetic resources. GIS analysis can help develop conservation strategies, monitor genetic diversity, select potential collecting sites, design in situ reserves and enhance genetic resources use. Currently, GIS use is constrained by limited georeferenced information.

Introduction

Eggplant or aubergine {Solanum melongena L. Solanaceae), is indigenous to a vast area stretching from northeast India and Burma, to Northern Thailand, Laos, Viet nam and Southwest China and wild plants can still be found in these locations. There is a wealth of eggplant common names. Eggplant is a major fruit vegetable with world production exceeding 31 million tonnes (Mt). Leading producers are China (17 Mt) and India (8 Mt), Egypt (1 Mt), Turkey (0.9 Mt), Japan and Italy (0.4 Mt). Eggplant is particularly favoured in Asia where it has been cultivated for millennia, and in India it is considered King of Vegetables. Brinjal, native to India, shares 31 % area (5.1 × 10⁵ ha) and 28% production (82 × 10⁵ t) of the world (Anon., 2003). It contributes 9% of the total vegetable production in the country. Three states/provinces viz. Orissa (1.41 × 10⁵ ha), West Bengal (1.29 × 10⁵ ha) and Bihar (0.72 × 10⁵ ha) cover 67% of total brinjal area in India (Anon., 2006). It is widely adaptive and highly productive (17.78 t/ha) crop of tropical and sub-tropical regions. It is comparable with tomato for nutritional values like vitamin C, iron and fiber, thereby, can play an important role in combating malnutrition in under-nourished regions (Singh et al., 2001). However, genetic improvement, production technology, post harvest handling and plant protection measures are important for increasing the production, productivity and consumption of brinjal.

Introduction

Indian subcontinent is one of the twelve mega diversity centres for cultivated plants and their wild relatives. More than 15000 species of flowering plants are indigenous to this region, which include 160 species of economic importance, 320 species of wild ancestral forms and approximately 800 species of ethnobotanical origin. Global diversity in vegetable crops is estimated around 400, of which 80 originated in India. These indigenous vegetables have immense potential in meeting the vegetable requirement of the country. They are rich in nutritional value especially in proteins, minerals and vitamins. Many of these vegetables have medicinal uses. Most of them are not used to their potential and their popularisation is needed in meeting the vegetable requirement of the country in coming days.

Ash gourd (Benincasa hispida Cong.)

Ash gourd also known as Wax gourd (Benincasa hispida Cong.) belonging to family Cucurbitaceae, is cultivated for its immature and mature fruits used as vegetable and in confectionery and ayurvedic medicines. A small fruited medicinal ash gourd is also grown in Kerala which is good for people suffering from nervousness. In areas where winter is mild, the crop can be grown throughout the year. The released varieties are KAU Local, Indu, Co.1, Co.2, APAU Shakthi, Karikumbala, Boodikumbala and IVAG -502. In addition, MAH-1 and MAH-2 are F1 hybrids released from private sector. The crop is raised by sowing seeds in pits at a spacing of 2.5 × 2.0 m in Tamil Nadu, 3.4 × 1.8-2.5 m in West Bengal and 4.5 × 2.0 m in Kerala. The seeds can also be sown in channels at a spacing of 2 × 0.5m. The crop is trailed on branches and twigs spread on the ground. The fruits are harvested at immature or fully ripe stage depending on demand. Immature fruits can be harvested 21 days after anthesis. Mature fruits for storage, long distance transport and seed extraction are harvested after full development of waxy coating on fruit surface. The average yield are 30-35 t/ha.

Introduction

Peas (Pisum sativum L., 2n = 2x =14) are consumed as fresh vegetables or dry seeds throughout the world. In India, peas are grown as winter vegetable in plains and as summer vegetable in the hills. As field pea, it occupies about 0.45 m ha area in India, accounting for only about 2% of the total pulse area. About 90% of its area and production is limited to Uttar Pradesh alone. The area under vegetable peas is on increase. The acreage of vegetable pea in India is 2.72 lakhs ha with a productivity of 9.9 tons/ha. Pea is a rich source of protein (7.2% in green pea & 20 % in dry pea) and minerals (0.8%).

The geographical region comprising of Central Asia, the Near East, Abyssinia and the Mediterranean is considered as centre of origin based on genetic diversity. According to Blixt (1970), the Mediterranean is the primary center of diversity with secondary centers in Ethiopia and the Near East.

Garden pea, seeds have been observed in archeological excavations of Mohan Jodaro and Harappa, there are distinct hot weather tolerant and round-seeded varieties which carry high resistance to fusarium wilt and powdery mildew (Erysiphe polygoni). These varieties have a different use as a pulse crop for meeting protein requirements of common people. These are considered as intermediate stages of domestication of the present day vegetable (garden) pea, which is sweet and wrinkled-seeded. In fact, the quick spread of Arkel variety, an introduction from England in seventies, has displaced some of the native round-seeded varieties like Asauji, Hara Bonia, Hoshiarpuri, Kaparkheda, Kalanagni, Lucknow Bonia etc. there is need to conserve these valuable germplasm, before they are swept away by genetic erosion.

Introduction

Radish (Raphanus sativus L., 2n=2x=18) is a root cum leafy vegetable suitable for sub tropical and temperate climate. Radish probably originated in Europe and Asia. It has been under extensive cultivation in Egypt since long. It was introduced to England and France in the beginning of 16^th century. In 1806, it was introduced to America. Radish does not exist in wild stack. It is believed to have originated from R. raphanistrum which is widely distributed as weed in Europe. Radish is primarily a winter crop but with the development and release of new varieties, it can be grown all the year round. The new varieties can withstand heat and does not bolt in spring. Radish is a favourite crop because of its quick growth and availability in 4-7 weeks.

Its fleshy roots are modified roots, developed from both the primary root and hypocotyl. The leaves and roots are consumed both as salad and cooked. The radish root is a good appetizer; its different preparations are useful in curing liver and gall bladder problems. Roots are also used in treating urinary complaints and piles. The juice of fresh leaves is useful as diuretic and laxative. Pungency in radish is due to volatile isothiocyanates while pink colour is due to pigment anthocyanin.

Radish is a good source of vitamin C containing 15-40 mg per 100g of edible portion. Roots also contain proteins, fat, minerals, fibre and carbohydrates, besides trace elements like aluminium, barium, lithium, manganese, silicon, titanium, fluorine and iodine. Pink skinned radish is generally richer in vitamin C than the white skinned type. Radish contains glucose as the major sugar and also contains fructose and sucrose in smaller quantities. Pectin and pentosans are also reported to be present. The leaves are good source of extraction of protein on a commercial scale and seeds are potential source for non-drying fatty oil suitable for soap making, illuminating and for the edible purposes.

Introduction

Turnip is an important root crop which is very popular particularly in northern India. It is a cool season crop mainly grown as a fall or winter crop in the hills and winter crop in the plains. Turnip (Brassica rapa L. syn. Brassica compestris var. rapa) belong to family Brassicaceae. It has a chromosome number of 2n=2x=20. Two main centers of origin have been indicated. The Mediterranean area is considered to be the primary centre of origin of European type/temperate types. While Eastern Afghanistan with adjoining areas of Pakistan is considered to be another primary centre with Asia Minor, Transcaucasus and Iran as secondary centre. The parents of cultivated turnip are found in the wild form in Russia, Siberia and Scandnavia. It was cultivated by the ancients and ranked next to grapes and cereals in Italy. It was introduced in England probably in 1590 and United States in 1606 (Yawalkar, 1980). Turnips are now cultivated throughout the world.

Turnips are botanically biennial herbs, but are cultivated as annual root crops for both animal and human consumption. The turnip root contains 41.6% moisture, 6.2 g carbohydrates, 0.5 g protein, 0.2 g fat, 0.04 mg thiamin, 0.04 mg riboflavin, 43 mg ascorbic acid, 30 mg Ca, 40 mg P and 0.4 mg Fe per 100 g of edible portion. The turnip green contains 15,669 IU vitamin A per 100 g (Aykroyd, 1963). Turnip is grown for its enlarged root as well as for its foliage. Extra seedlings from thinning are often used as greens. The fresh roots are consumed in salads or cooked as vegetable or used in pickles. The young leaves contain high amount of ascorbic acid and iron, rank second in vitamin A content and are cooked as greens.

The fleshy thickened underground portion of turnip is actually the hypocotyl. The colour and shape of this underground portion vary with the cultivars and the roots are flat globular to top-shaped and long. The colour of under ground portion may be white or yellow, while that of above ground portion may be red, purple, white, yellow or green. A distinct tap root and secondary roots arise from the lower part of the swollen hypocotyl. Thickening begins in the central part of the hypocotyle, followed by the upper and lower parts. Normally, the roots attain edible maturity in 40-80 days depending on cultivar and climatic conditions.

Introduction

Carrot (Daucus carota L., 2n=2x=18) is a cool-season root vegetable grown all over the world in temperate as well as sub-tropical climates. In India, it is cultivated over an area of 24,000 hectares with an annual production of 3,50,000 metric tones with a productivity much lower (14.58 t/ha) than the world average (22.17 t/ha) as per FAO, 2004. Carrots are used for human consumption as well as for animal feed. Carrot can thus be used for augmenting fresh fodder supply to milch animals during the lean winter in Kashmir (Hussan and Ahmed, 1999). Carrot has a significant place as an ingredient in soups and sauces and in dietary composition and also as a salad. A sweet preparation called Gajar Halwa is very famous dish in North India. Besides canning, it is also used in preparation of pickles. It is also exported in the form of fresh roots to the countries like Kuwait and Sharjah. Carrot is a rich source of á and â- carotene. The total carotenoid content in the edible portion of carrot roots ranges from 6.00 to 54.80 mg/100g. The yellow and orange coloured roots contain relatively more carotenoid. Carrot roots contain protein, fat, carbohydrate, minerals (especially Ca, Mg, Na, K, Cu, S etc.) besides vitamin A, B₁, B₂, nicotinic acid and vitamin C. In carrot roots, sucrose is the most abundant with endogenous sugar contents 10 times than those of glucose and fructose.

Asia minor, Afghanistan, North West India, Iran and Turkey are the centers of origin of carrot. As per Encyclopedia Britannica, carrots have originated from Afghanistan, Punjab and Kashmir with secondary centers in Ethiopia and North America. The European carotene carrot (syn. Western carotene carrot, temperate carrot) has been derived from the Asiatic anthocyanin containing forms of carrot (syn. Eastern anthocyanin carrot, tropical carrot). Afghanistan is the centre of diversity of the purple coloured carrot (anthocyanin carrot) and as such this country is suggested as the primary centre for origin. Integration between the Asiatic and European carrots has given the present day forms which are fleshy, smoother, less forked and better coloured.

Punjab and Kashmir are also considered as primary centre of origin with secondary centers in Ethopia and North America. It is still found in its wild form in Himachal Pradesh and Kashmir valley where wild animals especially brown bear feed on its roots. People still eat the wild carrots in some parts of Kashmir (Yawalkar, 1980). In north India highly coloured types of carrots are found which are not available in Europe. The colour of carrot ranges from absolute colourless to light lemon, light orange, orange and deep orange, light purple, deep purple and almost black. It is also an indication of this region being a primary centre of origin of carrot.

Introduction

The traditional approach to transferring genes from wild to cultivated species is based on interspecific hybridization followed by selection of hybrids that combine the trait with the cultivated genetic background. This breeding strategy is achieved by various backcross generations in which the selected hybrids at each generation are crossed back to the cultivated genotype with the aim of reducing the wild genome and its undesirable traits. The use of molecular markers has allowed this breeding approach to be greatly improved, since these markers directly reveal genetic variability through DNA analysis (Staub et al. 1996), and therefore their detection is not influenced by environmental effects. Since the development of numerous molecular markers for plant genometatic analysis, the possibility to select the genotype instead of the phenotype has been closely examined, leading to the concept of molecular marker-assisted selection (MAS; Paterson et al. 1991). The basic concept of MAS for crop improvement has been well discussed by Collard et al. (2005) which is most appropriate for this chapter. The most widely used markers suitable for MAS are RFLP, RAPD, AFLP, and SSR. Their common origin is point mutation or chromosome rearrangements that were accumulated during the evolution of the species without negatively influencing their survival and reproduction. The choice of the most suitable markers for MAS can differ and depends on the labor required for their detection, possibility of revealing single or multiple loci, dominant or co dominant nature and costs.

Introduction

Chilli is one of the most important spice and condiment of daily use in all most all the Indian houses. The fresh and dry fruits are the rich source of Vitamin A (292 IU) and Vitamin C (111 mg/100g). Its pungency not only improves the taste of the different preparations but also improve their storability. The colour of the fruits is due to the presence of the pigment capsanthin and pungency and acridity is due to the presence of alkaloid oleoresins knows as capsaicin (C₁₈ H₂₇ O₃ N). The capsaicin has a very good medicinal importance and also having importance for the pharmaceuticals industries for preparation of the different types of the drugs, because of the presence of the antioxidants like Vitamin A, C and P (Rutin). The green and red sweet pepper, bell pepper, Pimiento (heart shape) and cherry pepper are used in the fresh market processing industries for stuffing pickling and dehydrated processed meat. It is used as green as well as dry powder, pickles, salad and for industry to prepare the medicines for external and internal uses.

Introduction

Potato is a wonder crop that can be grown under diverse range of agro-climatic conditions. Short duration; high yield per unit area and time and wide flexibility in planting and harvesting time are important virtues of potato that enable its inclusion in intensive cropping systems.

Potato is not an Indian crop but is a native of high Andean region of South America. It was introduced in India perhaps in 16^th or early 17^th century by either the Portuguese traders or the British missionaries. The present day cultivated potatoes (tetraploid, 2n=4x=48) in most of the world represent Solanum tuberosum spp. tuberosum and S. tuberosum spp. andigena, although it’s numerous diploid (2n=2x=24) relatives are still under cultivation in and around its primary and secondary centers of origin in South America (Pandey and Sarkar, 2005). The great Irish famine in the 19^th century, caused by late blight disease, nearly wiped out all the earliest potato introductions in the Europe and dramatically changed the potato cultivation and production scenario in rest of the world.

Unlike in European Countries 80% of the potato is grown in India under short day conditions as a short duration crop i.e. 70-90 days. The tubers are exposed to high temperatures during planting (September-October) and harvesting (February-May). The crop is irrigated and suffers mid day water stress. The potato varieties introduced from Europe were unsuitable for growing under the above conditions. Systematic potato research in India spanned over the last half-a-century of the 20^th century in the past millennium. The Central Potato Research Institute (CPRI), which was established in 1949, has bean mainly responsible for tremendous growth of potato production in the country. The indigenous technological advances made it possible to extend potato cultivation in almost all part of the country. Now in India, compared to the production, area and yield of potato in 1949-50, the increase over the same period was 1513%, 559% and 270% respectively (Naik and Thakur, 2007). According to the FAO, potato production worldwide stands at 329 million tones and covers more then 19 million ha.

Introduction

Spices have played a very important role in shaping the history of human culture and civilization. Spices condiments and aromatic plants were the first articles traded by ancient people. These prompted the foreigners to the land of spices. Among spices, seed spices is a group well distributed over different agro-climatic regions in India covering major part in semi-arid to arid region largely in Rajasthan and Gujarat. The other states where one or more of seed spices grown are Haryana, Punjab, M.P., Bihar, U.P., West Bengal, Orissa, Tamil Nadu and Karnataka. The seed spices are grown in different parts of the world covering mainly Mediterranean region, South Europe and Asia. These crops were introduced to India from Mediterranean and Central Asian region, perhaps a long time ago. Today India enjoys the position of largest producer and exporter in the world for these crops. The country is bestowed with immensely rich land races diversity in seed spices crops. Much of the country’s agro-biodiversity is in the custody of farming community who followed age old farming systems. However the land races are being lost due to expansion of agriculture production in the frontier areas or replacement with the improved cultivars. Hence, scientific management of these valuable resources on different dimensions of activities in India viz. survey and collection, characterization, evaluation, documentation and conservation has assumed prime importance today. Out of the total 20 seed spices grown in India the ten are prominent. These important seed spices are divided into major and minor group of seed spices. The crops covered as major seed spices are coriander, cumin, fennel and fenugreek, whereas, ajowan, dill, nigella, celery and anise constitute minor group. The semi-arid to arid region is considerably rich in plant biodiversity of seed spices and it is required to conserve the natural wealth of these crops. The increasing loss of plant diversity owing to numerous natural and manmade causes calls for immediate efforts for its collection and safeguard.

Introduction

In 1990, the magnitude of fungal diversity, that is, the actual number of species worldwide, was estimated conservatively to be at least 1.5 million. Of the 1.5 million estimated fungi, it has been estimated that 14,000 species produce fruiting bodies of sufficient size and suitable structure to be considered macro-fungi which can be called mushrooms. Of these, about 50 % or 7,000 species are considered to possess varying degrees of edibility and more than 3,000 species from 31 genera are regarded as prime edible mushrooms. To date, only 200 of them are experimentally grown, 100 economically cultivated, approximately 60 commercially cultivated and about 10 have reached an industrial scale of production in many countries (Hawksworth, 2001). Of the 14,000 species of mushrooms in the world, around 700 have known for medicinal properties. Thus, mushrooms have vast prospects as sources of medicines. The early herbalists were more interested in the medicinal properties of mushrooms than in their basic value as a source of food. Humankind has constantly searched for new substances that can improve biological functions and thereby make people fitter and healthier. About 3.5 billion people worldwide, all over half of the world populations rely on plant-based medicines and dietary supplements for their primary health care. Of the plant materials involved in medicines or health tonics, quite a few are fungi. The practice of using fungi as herbal medicines can be traced in different early records of the ‘materia medica’. The earliest book on medicinal materials the “Shen Nongs Herbal” recorded the medicinal effects of several fungi such as Ganoderma lucidum, Poria cocos, Tremella fuciformis, Polyporus umbillatus and other unidentified fungi. The most famous of all work on the traditional medicines “Pen Ts’ao Kang Mu” which was compiled by Li Shi-Zhen of the Ming Dynasty recorded the medicinal fungi totaling more than twenty species, including Ganoderma lucidum, Poria cocos, Polyporus umbellatus, Lentinula edodes, Termitomyces albuminosus, Auricularia auricula, Pleurotus ostreatus, Armillaria mellea etc. A very unique insect-infecting fungus, Cordyceps sinensis, was for the first time taken as a medicinal fungus in the book “Essentials of Materia Medica”. There have been new discoveries and developments in the field of medicinal fungi, which have greatly enriched the treasure house of our traditional medical and medicinal sciences. As a result of large number of scientific studies on medicinal mushrooms in the past three decades, the traditional uses of many mushrooms have been confirmed and new wider uses found. Some traditionally important and leading medicinal fungi in the Oriental medicines are presented below

Milk is a product of biological origin and is a suitable vehicle to accommodate any additive without apparent changes in its look. The quality of milk and its products depends upon quality of raw milk used in their manufacture, processing and handling conditions. It is therefore, necessary to check quality of raw milk to assess its suitability for processing, through various quick tests called “Platform Tests”

Raw milk quality assessment

(A) At collection center: - Milk from various suppliers is generally collected at dairy factory or privately owned collection centers located in marked area of milk sheds. The quantum of supply may vary from 2 – 100 Liters. As each supplier has to be paid in relation to quantity and quality of his produce, it is important to draw samples and ascertain its quality by performing quick tests for the purpose of acceptance or rejection.

Sampling procedures - The sampling procedure differs according to the nature of material and the purpose for which the sample is required. The sample collected for chemical analysis should be representative of the entire batch. Thorough mixing with proper instrument is essential to ensure representative sample. Samples for chemical analysis are to be delivered the same day for testing. If un-avoided, add small amount of preservative (Formaldehyde) to prevent deterioration and bear label to the effect.

Sample from single container – The procedure involved is thorough mixing of contents. If it is small container the contents should be poured from one vessel to another with swirling. In case of can the contents should be mixed with the help of plunger which should be moved up and down and sideways for about 10-15 times. The sample is then drawn with the help of dipper from different levels and collected in clean and dry sample bottles.

Laboratory Tests

Sampling procedures

Samples from number of containers- As soon as the cans are received at the reception dock, remove the lid, mix contents of each can with a plunger and draw equal quantities from each container and mix together. Draw approximately 80 – 100 ml of sample for testing. If the cans are of same diameter, Alternatively, milk from different containers are first poured into a vat and then the sample is drawn by thorough mixing as above..

Samples from storage tank – Before drawing sample from storage tank, switch on the mechanical agitator and allow the contents to mix for 15-20 minutes. Discard first 1.0 L of milk. Transfer 250-500 ml of the mixed milk from the outlet valve into a sample bottle.

Samples from rail / road tankers- Open the lid of the tanker and insert a big plunger through the main hole and move it forward, downward and backward bring the plunger to the surface at each stroke. Continue mixing for 15-20 minutes. Draw proportionate quantities of sample with the help of suitable dipper from different levels. Draw 250-500 ml of well-mixed sample into the sample bottle. The total volume of individual composite sample should not be less than 150 ml.

In the last decade or so, the problem of determining the microbial content and their activities in dairy products has been under close scrutiny. The dairy industries still adopt the dye reduction test because it is cheap, simple and satisfactory. However, other new techniques have been introduced along with some modifications in traditional tests. The microbiological quality must be maintained. To assess the bacterial content and their activities in dairy products, various methods are available for the microbiological testing of milk. The specific tests should be chosen as per the application and other factors like cost etc. On the basis of public health and economic consideration it is necessary to test microbiological quality of milk at a number of points along the chain from producer to consumer which includes - raw milk supply, bulk milk arriving at processors premises, monitoring bulk milk during storage prior to processing and monitoring the procession & the quality control of finished products. The techniques for estimating bacterial numbers in raw material are divided into two categories.

A. Estimation of Calcium

Requirements : Pottasium permanganate solution (0.1N) – Dissolve by heating 33g of potassium permanganate in 1000ml distilled water and allow to stand in coloured bottle for several days. Filter contents through 6mm thick film of asbestos mat on Gooch crucible.

Asbestos mat – Digest asbestos with dilute hydrochloric acid(1:3) for 2-3 days, wash free from acid and digest with 10% sodium hydroxide for few days, wash free from alkali. Digest with dilute Nitric acid(1:3) for several hours and wash. Shake with water into fine pulp and digest with 0.1N potassium permanganate solution that has been acidified with dilute sulphuric acid (1:3), for 1 hour and wash. Use this pulp for layering 6mm film on Gooch crucible.

Dilute hydrochloric acid (1:4), Ammonium hydroxide solution (1:1), Saturated ammonium oxalate solution, Diluted sulphuric acid (1:4)

India has emerged as the largest producer of milk in the World. This has boosted the confidence of producers, planners, dairy entrepreneurs and scientist alike. India has poised to play a major role in global dairy market in coming decade. The rapid growth of science and technology has positive impact on our life style and with strong trend of urbanization several demographic changes are likely to occur. There going to be ever increasing demand on the role of milk and milk products for human health. Accordingly, the demand for higher standards of milk products in terms of hygiene and safety of dairy products is to be followed. The competitiveness for the export of milk products among different dairy industry has increased in recent times due to GATT agreement. The biggest challenge before Indian dairy industry is the quality and safety of milk products indented for human consumption. Legal standards are made to have control over quality of food offered for sale and to safe guard the consumer form health hazards. It is therefore, imperative to determine different parameters that define product quality and safety. The purpose is to ensure production and distribution of products within well defined and accepted standards. This chapter deals with various parameters of product testing in order to maintain requisite prescribed standards.

The growing chemicalization of milk presents a great challenge to the dairy industry. A number of chemical compounds get entry into milk, intentionally or un-intentionally, through their direct or indirect usage for crop, animal; or dairy husbandry practices, as environmental contamination. Several of these contaminants may be naturally present on grazing grass or use as pesticides for green fodder or they may get entry through use of compounded animal feeds. These eventually get excreted into milk as residues and pose problems to food safety. The risk of health hazards arises from veterinary drugs, residual pesticides, heavy metals, detergents and micotoxins etc. It is therefore, become necessary to detect these contaminants in order to make food safe for human consumption.

Detection of Aflatoxin M 1 in Milk

Material requirements : Glass column(30 cm x 1 cm), Silica gel (63-200 µg particle size), volumetric flasks, beakers, micro pipette, HPLC equipment fitted with C 18 column(25 cm x 4.6 mm) and fluorescence detector.

Reagents: Sodium chloride solution (400g in 1000ml water), acetonitrile/water mixture (25:75), sodium sulphsate, chloroform, toluene/acetic acid mixture (9:1), acetonitrile/diethyle ether/hexane mixture (1:3:6), chloroform/acetone mixture (8:2), n-hexane and aflatoxin standard ampule.

Preparation of stock solution : Transfer contents from 2.5ml or 0.5ml ampoule, quantitatively, to 25ml or 50ml volumetric flask respectively. Rinse ampoule with small quantities of chloroform and collect rinsing into flask. Makeup the volume with chloroform, mix well and store at 5º C or below. These solutions will contain 1 µg per ml aflatoxin M1.

Working solution : Transfer 0.5ml of stock solution to a 10ml conical flask. Evaporate contents to dryness using inert gas. Dissolve residue in 10ml of acetonitrile/water mixture. Prepare series of dilutions containing 0.0025, 0.005, 0.01 and 0.025 µg/ml aflatoxin. Similarly treat 5ml of stock solution to give a concentration of 0.5 µg/ml.

Over the past several years, there has been significant change in consumer perception on quality and food safety. Consumers are demanding healthy, nutritious and cost-effective products processed under controlled hygienic conditions. Food safety concern, in recent years, has become dominant and important. The requirement of food safety and quality assurance has never been so demanding as it is now. Globally food safety is being addressed independent of food quality. In anticipation of consumer demands on food safety, food sanitary standards at national and international levels are under constant review. Several inter governmental agencies / organizations are engaged in work related to quality assurance and food safety. Amongst these World Health Organization (WHO) and Codex Alimentarius Commission (CAC) play the major role in protecting public health and defining standards of quality and safety aspects of foods for regional and or world wide usage.

Regulatory Institutions

The quality environment in the Indian food processing sector revolves around compulsory legislation which specify minimum standard and certification system. Legal standards are generally made to have control over the quality of foods offered for sale and to safeguard the consumer from health hazards posed by practice of adulteration. There are basically three types of Indian standards namely

Frequency Distribution: A tabular presentation of the data in which the fre- quencies of values of a variable are given along with them is called a frequen- cy distribution. Two types of frequency distribution are available

1. Discrete Frequency Distribution or Ungrouped Frequency Distribution
2. Continuous Frequency Distribution or Grouped Frequency Distribution

Objective (a): Prepare a discrete frequency distribution from the following sentence in English: “Today there is hardly a phase of Endeavour which does not find statistical devices at least occasionally useful.”

Theory: This problem relates to discrete frequency distribution i. e. a frequency distribution which is formed by distinct values of a discrete or continuous variable.

The important convincing appealing and easily understood method of presenting the statistical data is the use of diagrams and graphs. Diagram makes the comparison of the data but graphs makes the relationship among the data.

Objective: Aggregated figures for merchandise export in India for eight years are as follows

Pie charts are useful to compare different parts of a whole amount. They are often used to present financial information. E.g. A farmer’s expenditure on farm can be shown to be the sum of its parts including different expense cate- gories such as cultivation, irrigation, sowing, harvesting, and general running costs (i.e. rent, electricity, fuel etc).

A pie chart is a circular chart in which the circle is divided into sectors. Each sector visually represents an item in a data set to match the amount of the item as a percentage or fraction of the total data set.

Measures of Central Tendency

The term ‘Mean” or ‘average’ is something, we have been familiar with from a very early age when we start analyzing our marks on report card. We add together all of our test results and then divide it by the sum of the total number of marks there are. We often call it the average. However, statistically it’s the Mean!

The Median is the ‘middle value’. When the totals of the list are odd, the me- dian is the middle entry in the list after sorting the list into increasing order. When the totals of the list are even, the median is equal to the sum of the two middle (after sorting the list into increasing order) numbers divided by two. Thus, remember to line up values, the middle number is the median!

The Mode in a list of numbers refers to the list of numbers that occur most frequently. A trick to remember this one is to remember that mode starts with the same first two letters that most does. Most frequently - Mode.

Objective: The distribution of age of males at the time of marriage was as follows

Paratition Value

Median is calculated for dividing the series into two equal parts. If the series is divided into four, eight, ten or hundred parts then the measures are called Quartile, Octile, Decile and Percentile respectively denoted by Q_(1,2,3), O_(1,2..7), D_(1,2…9) and P_(1,2…99).

A relative measure of dispersion based on the quartile deviation is called the coefficient of quartile deviation. It is defined as

MEAN DEVIATION

The mean deviation is the first measure of dispersion that we will use that ac- tually uses each data value in its computation. It is the mean of the distances between each value and the mean. It gives us an idea of how spread out from the center the set of values is.

The mean deviation is based on all the observations, a property which is not possessed by the range and the quartile deviation. The formula of the mean de- viation gives a mathematical impression that is a better way of measuring the variation in the data. Any suitable average among the mean, median or mode can be used in its calculation but the value of the mean deviation is minimum if the deviations are taken from the median.

COEFFICIENT OF THE MEAN DEVIATION

A relative measure of dispersion based on the mean deviation is called the coefficient of the mean deviation or the coefficient of dispersion. It is defined as the ratio of the mean deviation to the average used in the calculation of the mean deviation.

STANDARD DEVIATION AND THEIR COEFFICIENT

The standard deviation is a measure of the spread of scores within a set of data. Usually, we are interested in the standard deviation of a population. However, as we are often presented with data from a sample only, we can estimate the population standard deviation from a sample standard deviation. These two standard deviations - sample and population standard deviations - are calculat- ed differently. In statistics, we are usually presented with having to calculate sample standard deviations.

FOR GROUPED DATA

Objective: The following data shows the marks of students and their frequencies find the standard deviation and its coefficient.

COEFFICIENT OF VARIATION:

The most important of all the relative measure of dispersion is the coefficient of variation. This word is variation not variance. There is no such thing as coefficient of variance. The coefficient of variation( C.V.) is defined

FOR GROUPED DATA

Objective: Data regarding lives of two new models of refrigerators collected in a recent survey are

MOMENTS

The term moment in mechanics refers to the measure of a force with reference to its tendency to produce rotation. The strength of this tendency is depen- dent upon the amount of the force and the distance from the origin at which the force is applied. If on both the sides of the origin the force are equal than there will be a balance, at a balanced position the positive product equals the negative product.

Moment is used in Statistics in a quite analogous sense. The class frequencies are looked upon as the forces and the deviations of the different values from the mean are taken as the distances. In other words, moment is the mean of the first, second, third, fourth, etc. powers of deviations of the different values from the arithmetic mean. The first moment about the mean is always zero and second moment about the mean is variance.

If the moment calculated from arithmetic mean are called central moments and denoted by μ with a subscript which indicate the order of the moment. Moments are the mean values of powers of the deviations in any frequency distribution taken about three points

SKEWNESS

Skewness means lack of symmetry. Skewness denotes the tendency of a distri- bution to depart from symmetry. A frequency distribution is said to be skewed if the frequencies decrease with markedly greater rapidity on one side of the central maximum than on the other side. This characteristic of a frequency distribution is known as skewness and the measures of asymmetry are usually called measures of skewness.

The main object of measuring skewness is to know the direction of the varia- tion from an average and to compare the frequency distribution and the shape of their curves.

There are two types of skewness

(i) Positive Skewness (ii) Negative Skewness

KURTOSIS

Kurtosis refers to the extent to which unimodal frequency curve is peaked. Kurtosis is a measure that refers to the peakedness of the top of the curve. Kurtosis gives the degree of flatness in the region about the mode of a frequency distribution.

Types of Kurtosis

There are three categories of kurtosis that can be displayed by a set of data. All measures of kurtosis are compared against a standard normal distribution, or bell curve.

The first category of kurtosis is a mesokurtic distribution. This type of kurtosis is the most similar to a standard normal distribution in that it also resembles a bell curve. However, a graph that is mesokurtic has fatter tails than a standard normal distribution and has a slightly lower peak. This type of kurtosis is considered normally distributed but is not a standard normal distribution.

The second category is a leptokurtic distribution. Any distribution that is leptokurtic displays greater kurtosis than a mesokurtic distribution. Characteristic of this type of distribution is one with extremely thick tails and a very thin and tall peak. The prefix of “lepto-” means “skinny,” making the shape of a leptokurtic distribution easier to remember. T-distributions are leptokurtic.

The final type of distribution is a platykurtic distribution. These types of distributions have slender tails and a peak that’s smaller than a mesokurtic distribution. The prefix of “platy-” means “broad,” and it is meant to describe a short and broad-looking peak. Uniform distributions are platykurtic.

CORRELATION

In case of bivariate or multivariate normal distributions, we may be interested in discovering and measuring the magnitude and direction of the relationship between two or more variables. For this purpose we use correlation. The sys- tematic interrelationship between the variables is termed as correlation. When only two variables are involved the correlation is known as simple correlation. If more than two variables are involved the correlation is known as multiple correlation. An increase in one variable may cause an increase in the other variable, or a decrease in one variable may cause a decrease in the other vari- able, then they are said to be positively correlated. If a decrease in one variable causes an increase in the other variable or vise versa, the variables are said to be negatively correlated.

TEST OF SIGNIFICANCE FOR CORRELATION COEFFICIENT

Coefficient of correlation is a numeric characteristic of a population and is estimated from a sample of the bi-variate population. If ρ be the coefficient of correlation in the population and r be its sample estimate based on n pairs of observations, then standard error of r is given

REGRESSION

Regression means to revert or return back. The term was first introduced by Sir Francis Galton in1877. Regression technique is applicable in all those fields where two or more relative variables have the tendency to go back to the mean. Regression analysis refers to the methods by which estimates are made of the values of a variable from the knowledge of the values of one or more other variables and to the measurement of the errors involved in this estimation pro- cess.

In statistical modeling, regression analysis is a statistical process for estimat- ing the relationships among variables. It includes many techniques for mod- eling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. Regres- sion analysis helps one understand how the typical value of the dependent variable (or ‘criterion variable’) changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables – that is, the average value of the dependent variable when the independent variables are fixed.

t TEST FOR SINGLE MEAN

A t-test is any statistical hypothesis test in which the test statistic follows a Student’s t distribution if the null hypothesis is supported. It can be used to determine if two sets of data are significantly different from each other, and is most commonly applied when the test statistic would follow a normal distribution if the value of a scaling term in the test statistic were known. When the scaling term is unknown and is replaced by an estimate based on the data, the test statistic (under certain conditions) follows a Student’s t distribution.

t TEST FOR TWO SAMPLE MEAN:

Comparison of two sample means  and assumed to have been obtained on the basis of random samples of sizes n₁ and n₂ from the same population which is assumed to be normal.

t TEST FOR PAIRED OBSERVATION

This test is used for testing whether two series of paired observations are gen- erated from the same population on the basis of the difference in their sample means.

F TEST (VARIANCE RATIO TEST)

F distribution is applied in several tests of significance relating to the equality of two sampling variances drawn on the basis of independent samples from a normal population.

STANDARD NORMAL DISTRIBUTION

If μ = 0 and σ = 1, the distribution is called the standard normal distribution or the unit normal distribution, and a random variable with that distribution is a standard normal deviate. The approximate test is given by

CHI- SQUARE TEST (χ₂ TEST)

The chi square test is used to determine if the two attributes are independent of each other. Chi square is a measure to evaluate the difference between observed frequencies and expected frequencies to examine whether the difference so obtained is due to a chance factor or due to sampling error.

In a contingency table if each attribute is divided into two classes it is known as 2×2 contingency table. When one attribute is divided into two classes and another into r or c resultant contingency table is known as r×2 or 2×c contingency table. Here, r denotes the number of rows and c the number of columns. For such data, the statistical hypothesis under test is that the two attribute are independent of one another. To test this hypothesis we use the test statistic

The chi square is a continuous distribution based on the assumption that the ul- timate class frequency is not less than 5 otherwise the value of χ₂ will be over- estimated and may cause too many rejections of null hypothesis. Therefore, to maintain the continuity of χ₂ and to draw the correct inference in 2×2 we apply a correction which is known as Yates’s correction for continuity.

ANALYSIS OF VARIANCE

Analysis of Variance (ANOVA) is a hypothesis-testing technique used to test the equality of two or more population (or treatment) means by examining the variances of samples that are taken. ANOVA allows one to determine whether the differences between the samples are simply due to random error (sampling errors) or whether there are systematic treatment effects that cause the mean in one group to differ from the mean in another.

Most of the time ANOVA is used to compare the equality of three or more means, however when the means from two samples are compared using ANOVA it is equivalent to using a t-test to compare the means of independent samples.

ANOVA is based on comparing the variance (or variation) between the data samples to variation within each particular sample. If the between variation is much larger than the within variation, the means of different samples will not be equal. If the between and within variations are approximately the same size, then there will be no significant difference between sample means.

The two-way ANOVA compares the mean differences between groups that have been split on two independent variables (called factors). The primary purpose of a two-way ANOVA is to understand if there is an interaction be- tween the two independent variables on the dependent variable. For example, you could use a two-way ANOVA to understand whether there is an interac- tion between gender and educational level on test anxiety amongst university students, where gender (males/females) and education level (undergraduate/ postgraduate) are your independent variables, and test anxiety is your depen- dent variable. Alternately, you may want to determine whether there is an in- teraction between physical activity level and gender on blood cholesterol con- centration in children, where physical activity (low/moderate/high) and gender (male/female) are your independent variables, and cholesterol concentration is your dependent variable.

The interaction term in a two-way ANOVA informs you whether the effect of one of your independent variables on the dependent variable is the same for all values of your other independent variable (and vice versa). For example, is the effect of gender (male/female) on test anxiety influenced by educational level (undergraduate/postgraduate)? Additionally, if a statistically significant interaction is found, you need to determine whether there are any “simple main effects”, and if there are, what these effects are (we discuss this later in our guide).

A simple random sample (SRS) of size n consists of n individuals from the population chosen in such a way that every set of n individuals has an equal chance to be the sample actually selected.

A simple random sample is a subset of a statistical population in which each member of the subset has an equal probability of being chosen. An example of a simple random sample would be the names of 25 employees being chosen out of a hat from a company of 250 employees. In this case, the population is all 250 employees, and the sample is random because each employee has an equal chance of being chosen.