Intelligent animal fibre identification and classification

To develop an objective and repeatable method of identification and classification of animal fibres, two different integrated systems were developed to mimic the human brain's ability to undertake feature extraction and discrimination of animal fibres. Both integrated systems are basically composed of an image processing system and an artificial neural network system.

A comparative study on the felting propensity of animal fibers

The felting propensity of different animal fibers, particularly alpaca and wool, has been examined. The Aachen felting test method was employed. 1 g of each type of fiber was soaked in 50 ml of wetting solution and agitated in a dyeing machine to make felt balls. The diameter of each ball was measured in nine directions and the ball density was calculated in g/cm3; the higher the density value of the ball, the higher the feltability of the fibers. The effects of fiber diameter and fiber length on the felting propensity of these fibers were investigated. The results show that the alpaca fibers felt to a higher degree than wool fibers, and short and fine cashmere fibers have lower felting propensity than wool fibers at a similar diameter range. There is a higher tendency of felting for bleached and dyed alpaca fibers than for untreated fibers. Fiber length has a remarkable influence on the propensity of fiber felting. Cotton and nylon fibers were also tested for felting propensity to verify the mechanism responsible for the different fiber felting behavior.

Identification and classification of animal fibres using artificial neural networks

It has been an important and challenging task to classify and evaluate the contents in wool blends. Quantitative characterisation of animal fibre scale patterns has attracted considerable attention, since it is the major evidence for identification and subsequent classification purpose. Although techniques such as imaging processing and linear demarcation functions have been used to identify unknown fibre type with some success, a more comprehensive approach is required to perform this task. In this paper, a new approach is presented, which employs non-linear demarcation functions by using an artificial neural network (ANN). Based on scale pattern features extracted by using image processing techniques the artificial neural network (ANN) model is to classify mohair and merino fibres. It is observed that the techniques developed in this work are very effective and have the potential to be applied to other animal fibres.

A Study On The Utilisation Of Plastic Wastes In Stabilised Masonry Blocks

At this era of energy crisis and resource depletion, availability of conventional materials throughout the year in quantity and quality, pose a hectic problem for the builders. Adding fuel to the fire, the demand of these materials increases day by day, since the housing and habitat requirements exponentially increase time to time. There is an international concern over this crisis and researchers are reorienting themselves, so as to evolve appropriate masonry units, using locally available cheap materials and technology. The concept of green material and construction has been well conceived in the research so that marginal materials and unskilled labour can be employed for the mass production of building blocks. In this context, considering earth as a sustainable material, there is a growing interest in the use of it, as a modern construction material. Solid waste management is one of the current major environmental concerns in our country. Our country is left with millions of cubic metre of waste plastics. One of the methods to satisfactorily address this solid waste management and the environmental issues is to suitably accommodate the waste in some form (as fibres). Their employability in block making in the form of fibres (plastic fibre- mud blocks) can be investigated through a fundamental research. Also, the review of the existing literature shows that most studies on natural fibres are focussed on cellulose based/ vegetable fibres obtained from renewable plant resources except in very few cases, where animal fibre, plastic fibre and polystyrene fabric were used. At this context, for the plastic fibre-mud blocks to be more widely applicable, a systematic quantification of the relevant physical and mechanical properties of the fibre masonry units is crucial, to enable an objective evaluation of the composite material’s response to actual field condition. This research highlights the salient observations from the detailed investigation of a systematic study on the effect of embedded fibres, made of plastic wastes on the performance of stabilised mud blocks.

Identification of cashmere and superfine merino wool with wavelet texture analysis

This paper presents the use of the wavelet transform to extract fibre surface texture features for classifying cashmere and superfine merino wool fibres. To extract features from brightness variations caused by the cuticular scale height, shape and interval provides an effective way for characterising different animal fibres and subsequently classifying them. This may enable the development of a completely automated and objective system for animal fibre
identification.

Sources of variation in fibre diameter attributes of Australian alpacas and implications for fleece evaluation and animal selection

Sources of variation in fibre diameter attributes of Australian alpacas and implications for fleece evaluation and animal selection were investigated using data collected in the years 1994–97, from 6 properties in southern Australia. Data were analysed using REML (multiple regression analysis) to determine the effect on mean fibre diameter (MFD) and coefficient of variation of MFD (CV(FD)) of age, origin (property), sex (entire male, female), breed (Huacaya, Suri), liveweight, fibre colour, individual, and interactions of these effects. The mean (n = 100) age (range) was 4.2 years (0.1–11.9), liveweight 72.0 kg (12.0–134 kg), MFD 29.1 μm (17.7–46.6 μm), CV(FD) 24.33% (15.0–36.7%).

A number of variables affected MFD and CV(FD). MFD increased to 7.5 years of age, and correlations between MFD at 1.5 and 2 years of age with the MFD at older ages were much higher than correlations at younger ages. Fibre diameter 'blowout' (increase with age) was positively correlated with the actual MFD at ages 2 years and older. There were important effects of farm, and these effects differed with year and shearing age. Suris were coarser than Huacayas with the effect reducing with increased liveweight; there was no effect of sex. Fleeces of light shade were 1 μm finer than dark fleeces. CV(FD) declined rapidly between birth and 2 years of age, reaching a minimum at about 4 years of age and then increasing; however, CV(FD) measurements on young animals were very poor predictors of CV(FD) at older ages, and the response of CV(FD) to age differed with farm and year. Suris had a higher CV(FD) than Huacayas on most properties, and MFD, liveweight, and sex did not affect CV(FD). Fleeces of dark shade had higher CV(FD) than fleeces of light shade in 2 of the years. It is concluded that there are large opportunities to improve the MFD and CV(FD) of alpaca fibre through selection and breeding. The potential benefit is greatest from reducing the MFD and CV(FD) of fibre from older alpacas, through reducing the between-animal variation in MFD and CV(FD). Sampling alpacas at ages <2 years is likely to substantially decrease selection efficiency for lifetime fibre diameter attributes.

Variation of fibre diameter coefficient of variation and fibre curvature across mohair fleeces: Implications for animal selection, genetic selection and fleece evaluation

The presentwork aimed to determine howthe average fibre diameter coefficient of variation (CVD) and fibre curvature (FC) differences between nine sampling sites vary between sex and flock, to identify differences in variability between sampling sites as a result of between animal and between sire variability and to determine correlations between sampling sites in between animal and between sire variability. Australian Angoras (n = 313) from two farms in southern Australia were sampled at 12 and 18 months of age at nine sites (mid side, belly, brisket, hind flank, hip, hock, mid back, neck, shoulder). Staples were taken prior to shearing at skin level and CVD and FC determined. For each shearing, differences in CVD and FC between sampling sites, how these differences were affected by farm, sex, and sire, and the covariance between sites for sire and individual animal effects were investigated by restricted maximum likelihood (REML) analyses. The median mid side CVD at 12 and 18 months of age ranged from 23.6 to 25.1% but the actual range was 16.8–34.2%. The median mid side FC at 12 and 18 months of age ranged from 14.4 to 18.6◦/mm but the actual range was 10.5–26.3◦/mm. The general pattern for CVDwas for the mid back, hip and neck sites to have similar CVD, the brisket, hind flank and hock sites to have larger CVD and the belly to have smaller CVD than the mid side site. The between animal variation for CVD was lowest at the mid back site. This implies that the mid back would be the most effective site for between animal selection for CVD. Heritabilities for CVD (range at 18 months 0.18–0.30) were only about half the heritabilities for mean fibre diameter in the same study. There was a marked anterior–posterior increase in FC at both farms and with both ages. The results give no clear indication of the best site for between animal selection for FC, other than that the hock should be avoided. Heritabilities for FC are moderate to high (range at 18 months 0.44–0.77) and the genetic correlations are high except for the hock. Thus genetic selection for FC at any site, other than the hock, should be effective for changing FC over the entire fleece. There was more variability between animals than between sites and sires. These results are put into context with associated research on variation in mean fibre diameter and staple length.

Variation in the softness and fibre curvature of cashmere, alpaca, mohair and other rare animal fibres

Softness of apparel textiles is a major attribute sought by consumers. There is surprisingly little objective information on the softness properties of rare animal fibres, particularly cashmere, alpaca and mohair. Samples of these and other rare animal fibres from different origins of production and processors were objectively measured for fibre diameter, fibre curvature (FC, crimp) and resistance to compression (softness). While there were curvilinear responses of resistance to compression to FC and to mean fibre diameter, FC accounted for much more of the variance in resistance to compression. Fibre type was an important determinant of resistance to compression. The softest fibres were alpaca, mohair and cashgora and all of the fibres measured were softer than most Merino wool. Quivet, llama, camel, guanaco, vicuña, yak wool, bison wool, dehaired cow down and Angora rabbit were also differentiated from alpaca, mohair and cashmere. There were important differences in the softness and FC of cashmere from different origins with cashmere from newer origins of production (Australia, New Zealand and USA) having lower resistance to compression than cashmere from traditional sources of China and Iran. Cashmere from different origins was differentiated on the basis of resistance to compression, FC and fibre diameter. Cashgora was differentiated from cashmere by having a lower FC and lower resistance to compression. There were minority effects of colour and fibre diameter variation on resistance to compression of cashmere. The implications of these findings for the identification and use of softer raw materials are discussed.

Mohair fibre quality and production as related to animal size

Mohair is a luxury fibre produced by Angora goats. Mohair has special textile properties and is famed for its lustre. There are many reports of the relationships between mohair attributes and processing. While mohair production can be profitable to farmers there are severe price discounts for faults and poor quality. While genetics is known to affect mohair quality, fundamental relationships between body size and mohair quality have not been determined. We conducted a series of investigations to quantify the relationships between important mohair quality attributes and live weight and other lifetime factors associated with Angora goats.