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Spices are those plants, the products of which imparts strong flavour and aroma to the food. This is attributed to the presence of compounds such as volatile oils present in the plant parts. India is renowned for wide variety of spices and is designated as land of spices. Spices are indispensable ingredient in almost all Indian dishes and cuisines foreign invaders invaded India for spices in ancient times. India is figured as the largest producer, consumer and exporter of spices and spice products in the world. Turmeric and Ginger are the major rhizomatous spices of vegetable origin widely cultivating and using in India. They are known for its antimicrobial and antioxidant properties, hence using in medicines for the treatment of various ailments. Apart from these rhizomatous spices, India is also contributing a major share in the production of seed spices. Around twenty spices among sixty-three spices growing in our country are classified as seed spices. Major seed spices of India are coriander, cumin, fennel, fenugreek, dill, ajowain, celery, anise, nigella, caraway etc. These seed spices are extensively cultivated in the arid to semi-arid region of India as Rabi crop.

It is estimated that, spice production in our country increased from 67.64 lakh tonnes in 2014-15 to 106.79 lakh tonnes in 2020-21 with an annual growth rate 7.9%. There is a corresponding increase in the area of cultivation also. An increase in area from 32.24 lakh hectare to 45.28 lakh hectare was observed. Among the major spices, cumin (14.8%), garlic (14.7%), ginger (7.5%), fennel (6.8%), coriander (6.2%), fenugreek (5.8%), red chilli (4.21%) and turmeric (1.3 %), showed significant growth rate in production. The export of spices contributes around 41% of the total export earnings from all horticulture crops in the country and it ranks fourth among agricultural commodities, after marine products, non-basmati rice and basmati rice.

Introduction to Statistics

The word statistics has been derived from Latin word “status”, the German word “statistik”, the Italian word “statistica” or the French word “statistique”. During early periods, it was used by emperors and kings only for collecting the information about state and other information which was needed about their people, their number, land, status of people, revenue of the state etc. Gradually, it meaning and uses extended and there onwards its nature changed.

Some scientist/ researchers were given definition of statistics as per time and place.

• An ancient date, A.L. Bowley gives some definitions according their use and place. He seems to be inadequate because these do not include all aspects of statistics. As per time, place or requirement, he gave three definitions of statistics:

• Statistics may be called the science of counting

• Statistics may rightly be called the science of average

• Statistics as the science of counting and average

Statistical Series: When observations of any characteristic present in the individual of a group are recorded and arranged one after the other in a systematic order, they form a series, known as statistical series. For example: milk yield of 20 cows, height of the students in a class in a systematic order, etc. These are some important types of series:

• Discrete series: In this series, observations can take up only exact values and not any fractional values, is called discrete series. That means, this type of series has whole number of counting. For example: number of the students in a class, members of a family, number of births in a certain year, etc

• Continuous series: A series which can take up any numerical value (integral/ fractional) within a certain range is called continuous series. For example: milk yield of 20 cows, height, weight, temperature, etc.

• Array series: When we arranged the observations of a series either in ascending order of their magnitude or descending order of their magnitude, then it becomes an array series.

• Spatial series: When items of a series are given according to space or place or time, then such type of series is known as spatial series. For example: month wise milk yield of a herd of cows, month wise income or expenditure of an industry, population of a country according to states, etc.

Population: The word population or universe in statistics means a set of observations relating to a phenomenon under investigation.

Sample: It is a fraction or a subset of population drawn through a valid statistical procedure so that it can be regarded as representative of the entire population.

Sampling: The statistical procedure of drawing a sample from the population is called Sampling.

Census enquiry or complete enumeration: When each and every unit of the population is investigated for the characteristic under study, known as census enquiry or complete enquiry.

Sample enquiry or sample enumeration: It is a part of the population is investigated for the characteristic under study, known as sample enquiry or sample enumeration.

Necessity of sampling: Since, the process of collecting information from all the elements of a large population may be, in general, expensive, time consuming and difficult. We help to sampling and generalise the properties of the sample to the entire population. The statistical procedures which are used for drawing inferences or conclusions about the population from sample data are covered under Inferential Statistics or Statistical inference. Thus, sampling theory is the basis of statistical inference in which we wish to obtain maximum information about the population with minimum effort and maximum precision.

Diagrammatic Presentation of Data

The quantitative data when classify are converted into averages and if the number of average is large then it is not possible to draw a conclusion from their large numbers of averages. Moreover quantitative data are dull, confusing, boring for many of us. Therefore, a statistical device to make such confusing data more attractive was developed i.e. if such complex data are presented by geometric f igures, pictures, graphs or curves then it will become more attractive and more impressive, is called as diagrammatic representation of data. These diagrams are nothing but the use of geometrical figures to improve the overall presentation and offer visual assistance for the reader.

Types of Diagrams

Generally, diagrams are made for a special series or a time series and they are divided into five different categories:

1) One dimensional diagrams

2) Two dimensional diagrams

3) Three dimensional diagrams

4) Pictograms

5) Cartograms

Need of an average

Through frequency distribution are the condensed form of raw data, but these are not sufficient for making comparison between two or more data. Therefore further condensation of frequency distribution into a single figure is made. This single f igure is always representative of the frequency distribution and such a figure is known as average.

Measure of Central Tendency

According to Croxten and Cowden “An average is a single value within the range of the data that is used to represent all the values in the series”. A statistical measure used for representing the centre of central value of a set of observations is known as “Measure of Central Tendency”. It is also called as “Measure of f irst order”.

Characteristics of an ideal Measure of Central Tendency

An ideal measure of central tendency should possess the following properties:

1) It should be rigidly defined i.e. it has a fixed and finite value

2) It should be easy to understand 3) It should be easy to calculate

4) Its calculation should be based on all the observations

5) It should be capable for further algebraic treatment

6) It should not be affected by extreme values of the observations

7) It should be least affected by fluctuations of sampling

Dispersion

Measure of central tendency tells about the central position of the series. They do not throw like on the formation of series because in some cases, two series may have a same measure of central tendency but differ in their composition. In order to study the formation, composition, structure and scatteredness of the items of the series, measure of second order are necessary. The measure of second order will give idea about the formation of the series. As the representation of a series on the basis of its measure of central tendency is not sufficient but when a series is represented on the basis of its measure of central tendency as well as its measure of dispersion, then it will give the complete adequate representation of the series. Therefore, it is defined as the extent or degree of which data tend to spread around an average is called the dispersion. It is also known as variability or measure of second order.

Characteristics of an ideal Measure of Dispersion

An ideal measure of dispersion should possess the following properties:

1) It should be rigidly defined i.e. it has a fixed and finite value

2) It should be easy to understand

3) It should be easy to calculate

4) Its calculation should be based on all the observations

5) It should be capable for further algebraic treatment

6) It should not be affected by extreme values of the observations

7) It should be least affected by fluctuations of sampling

Correlation

In a statistical analysis, when two variables are studied simultaneously, then it is called “Bivariate study’. Now, in a bivariate study, if it is found that change in the values of one variable then in sympathy with the change in the values of other variable, then these two variables are said to be associated and the association is termed as correlation. In other words, the term correlation indicates the relationship between two such variables in which with changes in the values of one variable, the values of the other variable also change. In short, the tendency of simultaneous variation between two variables is called correlation.

Types of Correlation

Correlation can be classified by-

• Positive and Negative correlation: The correlation may be classified according to the direction of change in the two variables. In this regard, correlation may be either positive or negative. Positive correlation refers to the movement of variables in the same direction. Negative correlation efers to the movement of variables in the opposite direction.

• Simple, Partial and Multiple correlations: When only two variables are studied it is a problem of simple correlation. When three or more variables are studied simultaneously, it is a problem of multiple correlations. On the other hand, in partial correlation we recognize more than two variables, but consider only two variables to be influencing each other the effect of other influencing variables being kept constant.

• Linear and non-linear correlations: In linear correlation for every unit change in the values of one variable, there is constant change in the value of other variable. The perfect positive and negative correlations are also linear correlations. In non-linear correlation the change in the values of one variable does not have a constant ratio to the change in the other variable.

The theory of probability has its origin in the games of chance related to gambling such as throwing a die, tossing a coin, drawing cards from a pack of cards etc. Jerame Cardon (1501-1576), an Italian mathematician was the first man to write a book on the subject entitled “Book on Games and Chance”, which was published after his death in 1663. The systematic and scientific foundation of the mathematical theory of probability was laid in mid-seventeenth century by two French mathematicians B. Pascal (1623-1662) and Pierre de Fermat (1601 1665).

Important terms and concepts

To understand probability, let us first get acquainted with some important terms and concepts associated with probability.

• Trial: An experiment is any natural or planned activity for which it is not possible to predict the particular result called a trial.

• Events: The possible outcomes of experiments of a trial are called events. For example: Tossing of a coin in an experiment and one toss of the coin is called trial. In tossing of a coin there are only two outcomes or events, i.e. head or tail.

Types of events

Events can be of following different types:

• Exhaustive events

• Equally likely events

• Mutually exclusive events

• Dependent events

• Independent events

• Null or impossible events

• Exhaustive events: It is the total number of all the possible outcomes of an experiment.

Theoretical Probability Distribution

Many random variables associated with statistical experiments have similar properties, and as such can be described by the same probability functions. The distribution obtained from a theoretical listing of outcomes and probabilities, which can be described by a mathematical formula representing same phenomenon of interest is called a theoretical probability distribution. The distributions of discrete and continuous random variables are accordingly called discrete or continuous probability distributions.

Discrete Probability Distribution: It can be defined as a probability distribution of discrete random variables. Such a distribution will represent data that has a f inite countable number of outcomes. If the random variable X is a discrete random variable, the probability function P(X) is called probability mass function and its distribution is called discrete probability distribution. There are two conditions that a discrete probability distribution must satisfy. These are given as follows:

Types of Discrete Probability Distributions

There are several important discrete probability distributions-

• Uniform distribution

• Binomial distribution

• Negative Binomial distribution

• Geometric distribution

• Poisson distribution

• Hyper-Geometric distribution

Time Series

A series of observations recorded over time is known as a time series. In other words, a time series is a set of observations taken at specified times, usually at equal intervals. For example: Annual retail sales, rainfall measurements, heartbeats per minute, etc. Such studies are to be based on the analysis of time series data collected over time. Thus, the analysis of time series plays an important role in an investigation of economic, social, commercial and even biological phenomenon.

Components of time series

In a long time series, generally, there are following four components:

• Secular trend or long term movements

• Seasonal variations

• Cyclic variations • Random or irregular movements

• Secular trend or long term movements: It is that characteristic of a time series which extends consistently throughout the entire period of time under consideration. It shows a long term tendency of an activity to grow or to decline. The term long period of time in a relative phenomenon and cannot be defined exactly. For some cases a period as small as a weak may be fairly long while in other cases, a period as long as two years may not be assumed long.

An index number is a statistical measure designed to show changes in a variable or group of related variables with respect to time, geographical location or other characteristics like: income, profession, etc. A collection of index numbers for different years, months, locations, etc. is called an index series. Since index number are usually used to measure the level of business and economic activities over a period of time, so they are rightly called barometers of economic activities or simple called economic barometers. The various problems faced in the construction of an index number are to known the specific purpose of index number, selection of items, data for index numbers, choice of base periods, choice of an average, selection of weights and choice of a suitable formula.

Methods of Constructing Index Numbers

A number of formulae have been developed for constructing index numbers, which may be grouped into the following categories:

• Unweighted index numbers

• Weighted index numbers

• Unweighted index numbers: In this case, weights are not expressly assigned. 

Statistical Inference

It is the process of drawing valid inference or conclusion about the population on the basis of a sample drawn from the population. In other words, Statistical inference is the process of using a sample to infer the properties of a population. It requires studies pertaining to three aspects:

• Point Estimation • Interval Estimation

• Testing of Hypothesis • Point Estimation: It is to find a single value, which is used as an estimator of unknown parameter. For example, sample mean may be used as an estimate of the population mean µ.

• Interval Estimation: It is to find confidence limits based on sample values, within which the unknown parameter lies with confidence coefficient described in probability term. In other words, we estimate an unknown parameter using an interval of values that is likely to contain the true value of that parameter and state how confident we are that this interval indeed captures the true value of the parameter.

• Testing of Hypothesis: The tests used to ascertain whether the differences between estimator and the parameter or between two estimators are real or due to chance are called tests of hypothesis or tests of significance. In other words, the procedures, which enable us to decide whether to accept or reject hypothesis, are called tests of hypothesis or tests of significance. Two types of problems mainly arise in tests of significance:

• Testing the significance of the difference between estimator and the pa rameter.

In 1935, Prof. Ronald A. Fisher laid the foundation for the subject in his monumental work entitled “The Designs of Experiments”. Experimental designs concern the arranging of treatments in such a manner that the inferences and conclusions regarding the effects of these treatments can be easily done and their reliability measured. Experiments are made with a view to find the validity of a particular hypothesis and to have an idea about the extent of the reliability that can be placed on a particular conclusion arrived. The selection of the design will have a very great bearing on the accuracy of the ultimate results. By a random selection of experimental units it is possible to remove the ambiguity about the casual interpretation of the observed associations. Random sampling is the most essential ingredient of all experimental designs. Besides, there are many devices for increasing the precision of the inference and the calculations.

To understand design of experiments, let us first get acquainted with some important terms and concepts associated with design of experiments:

• Experiment: Experiment is a scientifically planned method. The experiment is conducted draw a valid conclusion about a particular problem. The conclusion is based on statistically observation.

• Treatment: The object or the procedures under comparison in an experiment are known as treatment. For Example: Fertilizers, varieties, cultivation practices, irrigation level in case of agricultural experiments and drugs, breeds, farms, etc. in case of animal experiments.

• Yield: The response of the treatment is measured by some indicator such as crop production, milk production, body temperature, etc. Such an indicator is called yield. The treatments are applied to some units such as field plots, sample of cows, sample of patients, etc. and the effect on the yield is observed.

• Experimental material: The experimental material is nothing but a set of experimental units. In other words, the material which is used in experiment is known as experimental material. For Example: A piece of land i.e. Agricultural field in agricultural experiments and a group of cattle i.e. animals in animal experiments.

A non parametric test (sometimes called a distribution free test) is a statistical analysis method that does not assume the population data belongs to some prescribed distribution which is determined by some parameters. That means, a non-parametric test can be defined as a test that is used in statistical analysis when the data under consideration does not belong to a parameterized family of distributions. When the data does not meet the requirements to perform a parametric test, a non-parametric test is used to analyze it. The non-parametric test compared to parametric test, which makes assumptions about a population’s parameters (for example, the mean or standard deviation); When the word “non parametric” is used in stats, it means that population data does not have a normal distribution.

If at all possible, we should us parametric tests, as they tend to be more accurate. Parametric tests have greater statistical power, which means they are likely to find a true significant effect. Nonparametric tests can perform well with non-normal continuous data if the data of the sample are sufficiently large. Non parametric do not assume that the data is normally distributed. The only non parametric test we are likely to come across in elementary stats is the Chi-square test. In nonparametric tests have several advantages as compared to parametric tests. It includes:

• More statistical power when assumptions for the parametric tests have been violated.

• Fewer assumptions, i.e. the assumption of normality don’t apply.

• Small sample sizes are acceptable.

• It is used for all data types, including nominal data, rank data or ordinal data

• Knowledge of the population is not required to conduct this test.

• Nonparametric tests are valid when our sample size is small and our data are potentially non-normal.

Vital Statistics

It is defined as that branch of biometry which deals with data and the laws of human mortality, morbidity and demography. According to N.B. Ryder, they “provide cumulative summaries for successive time periods of population movements like birth, death, migration, marriage and marital dissolution as well as demographic and other relevant characteristics of the individuals involved in these events.”

Measures of Vital Statistics of Population

The total number of vital events such as births and deaths are converted into rates and ratios for comparison of vital statistics from place to place or year to year. In order to determine the population at any time ‘t’ after the census or between two censuses, a number of methods have been devised. Therefore, a suitable method which makes use of births, deaths and migration statistics, if we assume that:

(i) The census data gives us the total size of the population of a region or community together with age and sex distribution

(ii) The birth, death and migration statistics during different periods are obtained from registers.

Anatomy

The study of gross internal structure of plant organs by the technique of section cutting is called anatomy .The term anatomy was derived from two words ‘ana’ means as under and ‘temnein’ meaning to cut. Anatomy is the branch of science which deals with internal structures and organization of the organism. It is also known as internal morphology. Plant anatomy also describes tissue and tissue systems of the plant body.

Arrangement of plants and animals into groups according to their close resemblance, naming and describing them is called classification. Classifying the plants into groups is called systematic botany. The main aim of classification is to name the plant to describe them and to bring out their resemblances in order to throw light on their relationship and common ancestor. It is to indicate primitive and advanced plants systematically.

Need for Classification

It is not possible for anyone to study all the organisms. But if they are grouped in some convenient way the study would become easier as the characters of a particular group or a family would apply to all the individuals of that group. Classification allows us to understand diversity better.

Classification of plants

Entire plant kingdom is divided into four main groups.

1. Thallophyta 2. Bryophyta 3. Pteridophyta 4. Spermatophyta or Phenerogams

Development of Plant Body

A vascular plant begins in existence as a morphologically simple unicellular zygote (2n). The zygote develops into the embryo which in the long run develops into the mature sporophyte. The development of the sporophyte (2n) involves division and differentiation of cells and organization of cells into the tissue systems. The embryo of the seed plant possesses relatively simple structure as compared to the mature sporophyte. The embryo bears a limited number of parts containing generally only axis bearing one or more cotyledons. The cells and the tissues of this structure are less differentiated. However, the embryo grows further, because of the presence of the meristems, at two opposite ends of the axis, of future shoot and root. After germination of the seed during development of the shoot and root new apical meristems appear which cause a vegetative growth and reproductive stage of the plant attained.

Fundamental Parts of the Plant Body

The plant body consists of a number of organ i.e., root, stem, leaf and flower. Each part has different functions for plant growth.Flower consists of sepal, petals, stamens, carpels and sometimes also sterile members. Each organ is made up of number of tissue systems; each tissue system consists of number of tissues. Tissue consists of group of cells that are alike in origin, structure and function.

Meristematic Tissues

These are composed of cells that are in state of division or retain the power of dividing. These cells are essentially alike and isodiametric. They may be spherical, oval or polygonal and their walls thin and homogeneous; the protoplasm in them abundant and active with large nuclei, and the vacuoles small or absent.

Classification of Meristems

Meristems are classified in different ways on the basis of certain factors.

A) According to their origin and development

1. Promeristem or primordial meristem

2. Primary meristem and

3. Secondary meristem.

Primary Dicot Stem

A stem with only primary permanent tissues is called primary stem. It represents a young stem. The different tissues present in dicot stem are arranged in concentric layers. e.g. Sunflower

I) Epidermis

Epidermis is the outermost layer of the stem. It is made up of compactly arranged parenchymatous cell, which look rectangular in transverse section. The cells are transparent and devoid of chloroplasts. The outer walls are convex, thickened and cutinized. On the outer side they possess a layer of cuticle. The internal walls of the epidermal cells are thin. The radial walls are thick towards the outer side and gradually become thin towards the inner side. Pits occur in the radial walls. The epidermis of sunflower bears several unbranched multicellular hair. At place the epidermis contains minute pores called stomata or stoma. Each stomata or stoma has a pair of specialized kidney shaped cells called guard cells. The guard cells have a few chloroplasts. By their swelling, the two guard cells can form a pore between them.

Primary Dicot Root

A young dicot root which does not possess secondary tissues but only the primary permanent tissues (tissues derived from the apical growing point) is called primary dicot root. It is generally cylindrical in outline and possesses the following tissues.

I) Epiblema or Piliferous Layer

It is the outermost layer of the root. It is made of compactly arranged thin walled and slightly elongated parenchymatous cells. Epiblema of a root differs from the epidermis of the stem in being devoid of distinct cuticle and stomata. Some cells of the epiblema give rise to thin – walled tubular outgrowths called root hairs. In some plants the root hair mother cells are shorter in comparison to other epiblema cells. The root hairs and piliferous layer (pilus – hair, ferre to carry). The root hair and thin walled epiblema cells absorb water and mineral salts from the soil. Root hairs commonly do not live for more than one week. With their death the epiblema cells become suberised and cutinised.

II) Cortex

It lies below the epiblema. The cortex is made up of many layers of thin walled parenchyma cells. The parenchyma cells may be rounded or angular. They enclose inter-cellular spaces for diffusion of gases.The cells of the cortex store food. They also conduct water from the epiblema to the inner tissues.

Secondary Growth in Dicot Stem

The formation of secondary tissue from the lateral meristem is called secondary growth. It increase in diameter of the stem. A stem with secondary tissues is called secondary stem. Secondary tissues are formed by two types of lateral meristem, vascular cambium and cork cambium or phellogen. Vascular cambium produces secondary vascular tissues while phellogen form periderm. Secondary growth occurs in perennial dicots such as trees and shrubs. It is also found in the woody stems of herbs. In such cases, the secondary growth is equivalent to one annual ring, e.g. sunflower.

Formation of secondary vascular tissues

They are formed by the vascular cambium. Vascular cambium is produces by two types of meristems: intrafascicular and interfascicular cambium.

1. Intrafascicular cambium is a primary meristem which occurs as strips in vascular bundles.

2. Interfascicular cambium arises secondarily from the cells of medullary rays which occur at the level of intrafascicular strips.

3. These two types of meristematic tissues get connected to form a ring of vascular cambium

The tissue system

A group of tissues performing a similar function irrespective of its position in the plant body is called a tissue system. In 1875, Sachs recognized three tissue systems in the plants.

a) Epidermal tissue system

b) Vascular tissue system

c) Fundamental tissue system.

a) Epidermal tissue system

Epidermal tissue system is the outermost covering of plants. It consists of epidermis, stomata and epidermal outgrowths. Epidermis is generally composed of single layer of parenchymatous cells compactly arranged without intercellular spaces. But it is interrupted by stomata. In leaves, some specialized cells which surround the stomata are called the guard cells. Chloroplasts are present only in the guard cells of the epidermis. Other epidermal cells usually do not have chloroplasts. The outer wall of epidermis is usually covered by cuticle. Stoma is a minute pore surrounded by two guard cells. The stomata occur mainly in the epidermis of leaves. In some plants such as sugarcane, the guard cells are bounded by some special cells. They are distinct from other epidermal cells. These cells are called subsidiary or accessory cells. Trichomes and root hairs are some epidermal outgrowths. The unicellular or multicellular appendages that originate from the epidermal cells are called trichomes. Trichomes may be branched or unbranched. Rhizodermis has two types of epidermal cells - long cells and short cells. The short cells are called trichoblasts. Root hairs are produced from these trichoblasts.

The macroscopic characteristics of wood give the clues to the conditions under which the wood is grown, provide an indication of physical properties and serve as an aid in wood identification.

Growth rings do not always appear as distinct alternating bands of earlywood and latewood. Some hardwoods, for example, form large-diameter pores early in a growing season and much smaller pores later in the year; such woods are called ring-porous. Discontinuous rings are occasionally found in trees having one-sided crowns and in heavily defoliated, suppressed, and over mature trees. Examination of a stem cross-section often reveals a dark-colored center portion surrounded by a lighter-colored outer zone. The dark center area is known as heartwood and the lighter tissue as sapwood. Trees in which fibers are spirally arranged about the stem axis are said to have spiral grain.

Wood can often be identified by macroscopic characteristics, particularly by colour, gloss, odour, weight and structure. Wood have two types of features:

A) Macroscopic or Gross feature

B) Minute structure feature

The microscopic features reveals that wood is composed of minute units called cells. The fundamental differences between different kinds of woods are founded on the types, sizes, proportions, pits and arrangements of different cells that compose the wood. Softwoods have a simpler basic structure than do hardwoods due to the presence of only two cell types, and relatively little variation in structure within these cell types. Hardwoods have greater structural complexity because they have both a greater number of basic cell types and a far greater degree of variability within the cell types. In each case, however, there are fine details of structure that could affect the use of a wood.

The structure of a typical softwood is relatively simple. The axial or vertical system is composed mostly of axial tracheids, the radial or horizontal system and the rays, are composed mostly of ray parenchyma cells. According to estimates, 1 cubic metre (about 35 cubic feet) of Spruce wood contains 350 billion to 500 billion cells. The basic cell types are called tracheids, vessel members, fibres, and parenchyma

The structure of a typical hardwood is much more complicated than that of a softwood. The axial or vertical system is composed of fibrous elements of various kinds of vessel elements in various sizes and arrangements, and axial parenchyma cells in various patterns and abundance. Like softwoods, the radial or horizontal system are the rays, which are composed of ray parenchyma cells, but unlike softwoods, hardwood rays are much more diverse in size and shape.

Physical properties of wood

The versatility of wood is demonstrated by a wide variety of products. This variety is a result of a spectrum of desirable physical characteristics or proper ties among the many species of wood. In many cases, more than one property of wood is important to the end product. For example, to select a wood species for a product, the value of appearance-type properties, such as texture, grain pattern and color, may be evaluated against the influence of characteristics such as machinability, dimensional stability and decay resistance.

Cell walls mainly consist of cellulose, hemicellulose, and lignin in a 4:3:3 ratio. This ratio differs from sources such as hardwood, softwood, and herbs. Besides these three components, natural lignocellulosic materials contain a small amount of pectin, nitrogenous compounds, and the secret ash.

1. Cellulose

2. Hemicellulose

3. Lignin

Cellulose

Cellulose is a glucan polymer consisting of linear chains of 1,4-ß -bonded anhydroglucose units. Cellulose is the structural component of the primary cell wall of the green plants.About 33% of all plant matter is cellulose (the cellulose content of cotton fiber is 90%, that of wood is 40-50% and that of dried hemp is approximately 45%. Cellulose is a straight chain polymer.The number of sugar units in one molecular chain is referred to as the degree of polymerization (DP).Cellulose is insoluble in most solvents including strong alkali. It is difficult to isolate from wood in pure form because it is intimately associated with the lignin and hemicelluloses.

Ring Porous

• having vessels more numerous and usually larger in cross section in the springwood with a resulting more or less distinct line between the springwood and the last season’s wood— compare diffuse-porous.

• In some species (e.g. Oak (Red Oak, White Oak) Teak and ash elm, hickory), the largest pores are in the earlywood while those in the latewood are more evenly distributed and uniform in size.

• These woods typically have distinct figures and patterns, and the uneven uptake of stain (the large pores soak up more colour) make the figure more pronounced.

• These are also known as open-grain woods

Semi-ring porous or Semi-diffuse porous

• In some species (e.g. Butternut, Black Cherry, Black Walnut, Pecan, Tanoak), pores are large in the earlywood and smaller toward the latewood, but without the distinct zoning seen in ring-porous woods.

• Also, some species that are usually ring-porous (e.g. Cottonwood) occasionally tend toward semi-ring porous.