Impact of sheep and goats ectoparasites on the tanning industry in Tigray Region, Ethiopia

A study was carried out to assess the effect of ectoparasites on the quality of processed skin and defects that cause downgrading and rejection of skins in pickled sheep and wet blue goat-skins at Wukro Sheba tannery in Tigray region. A total of 120 sheep and 120 goat pelts with known infestation by ectoparasites and ectoparasite free control pelts were purchased and processed in Sheba tannery and the corresponding skin defects were analyzed. Accordingly, three groups of 40 sheep pelts each (one group infested with M. ovinus, one group infested with Damalina ovis and a control group of ectoparasite free sheep pelts) and three groups of 40 goat pelts each (one group infested with sarcoptic mange, one group infested with Linognathus spp. and a control group of ectoparasite free goat pelts) were used. Defects observed at pickled stage include cockle, scratch, scar, technical defects due to flaying and old age. A strong association (P<0.001) was observed between cockle lesion and infestation of sheep pelts with D. ovis and Mellophagus ovinus and infestation of goat pelts with sarcoptic mange. Further more, the severity of infestation of sheep pelts with M. ovinus and D. ovis and infestation of goat pelts with sarcoptic mange were found to correlate significantly (P<0.001) with severity of cockle defect. Further investigation on 1000 pickled sheep and 1000 wet blue goat skins revealed that scratch defect was the dominant (43.4 % and 53 %), followed by cockle (35 % and 21.5 %), scar (7 % and 6.8 %) and knife cut (3.4 % and 6.2 %) in pickled sheep and wet blue goat skins, respectively. There was a significant difference (P<0.001) in proportion of cockle between pickled sheep and wet blue goat skins. In addition, a strong association (P<0.001) was observed between cockle and scratch, and cockle and scars on both pickled skins. The economic loss due to quality deterioration of exported skin in the study tannery was estimated to be 778,199.41 USD for pickled sheep and 247,677.61 USD for wet blue goat skins per annum. The growing threat of ectoparasites to small ruminant production and to the tanning industry needs well coordinated and urgent control intervention.Keywords: Ectoparasites, Goat, Sheep, Skin defects, Tigray Region, Ethiopia

The Role of Leather Microbes in Human Health

Leather tanned from raw hides and skins have been used to cover and protect the human body since early man. The skin of an animal carries thousands of microbes. Some are beneficial and protect the animal while others are pathogenic and cause diseases. Some microbes have no defined roles in animals. These microbes end up in the human body through contact with the animal skin. In recent years, the human body has been studied as an ecosystem where trillions of microorganisms live as a community called microbiome. Humans need beneficial microbes like Bacillus subtilis on the skin surface to stay healthy. Many microbes need the human body to survive. Not many studies have looked into the close link between animal leather and the human microbiome. The assumption is that conventional leather processes inhibit the pathogens on skins from carrying any risk of microbial hazard to the human body. This chapter identifies endemic microbes of “animal skin microbiome” that withstand extreme acidity and alkalinity of leather manufacture and their transmission to humans. Some cause allergic reactions, skin lesion, infections or death to tannery employees with weakened immune systems. This promotes the need to look at leather product microbiome impact on human health

Optimized Fuzzy C-means Clustering Methods for Defect Detection on Leather Surface

In this paper, captured images are segmented for the defective part, that is used for the further process of grading the quality of the products using automated inspection systems employed in industries such as leather, fabrics, textiles, tiles... etc.. These industries are the greatest conventional industries that need automatic detection systems as a basic part in diminishing investigation time and expanding production rate. Initially in this work, the input image is wet blue leather fed into a contrast enhancement process that improves the visibility of the image features. This contrast-enhanced image is employed with segmentation process that utilizes Fuzzy C-means algorithm (FCM) technique. This paper proposes two different optimization techniques, Grey Wolf Optimization (GWO) & Monarch Butterfly Optimization (MBO) for executing centroid optimization in FCM and results are compared with Modified Region Growing with GWO of leather segmentation method. The results exemplify that incorporation of optimization technique with FCM has a quite evident impact on segmentation accuracy of 96.90% over context techniques

Optimized Fuzzy C-means Clustering Methods for Defect Detection on Leather Surface

833-836In this paper, captured images are segmented for the defective part, that is used for the further process of grading the quality of the products using automated inspection systems employed in industries such as leather, fabrics, textiles, tiles... etc.. These industries are the greatest conventional industries that need automatic detection systems as a basic part in diminishing investigation time and expanding production rate. Initially in this work, the input image is wet blue leather fed into a contrast enhancement process that improves the visibility of the image features. This contrast-enhanced image is employed with segmentation process that utilizes Fuzzy C-means algorithm (FCM) technique. This paper proposes two different optimization techniques, Grey Wolf Optimization (GWO) & Monarch Butterfly Optimization (MBO) for executing centroid optimization in FCM and results are compared with Modified Region Growing with GWO of leather segmentation method. The results exemplify that incorporation of optimization technique with FCM has a quite evident impact on segmentation accuracy of 96.90% over context techniques

Assessment of Intermittent Leather based on Image Score Pattern

The process of intermittent leather inspection is being predominantly carried out with the support of human intervention based on homogenous distribution of colors. However, results of the observations between one experts to another expert may be different in opinion. Therefore, to emphasis some sort of supporting hand to the experts while taking decision, the authors have introduced to align intermitted leather images based on the Image Score Pattern algorithm. Which separates defect versus non-defect intermittent leather images from feature image datasets namely DGF, DLO, DGFLO and DLOGF consisting of 32 features generated from Gray Level Co-occurrence Matrix, Simple Linear Iterative Clustering and Minimum Spanning Tree Clustering from the training and testing datasets of about 1132 and 404 generated respectively. Gradient Boosting has implemented in finding the key feature among the Contrast, Dissimilarity, Homogeneity, Energy, Correlation and Angular Second Moment. The results of the classifier Support Vector Machine for these datasets confirms the accuracy of 84% for the proposed Image Score Pattern algorithm. The other performance measures such as Error Rate, Recall, False Positive Rate, Specificity, Precision and Prevalence  are also confirming that proposed method is performing in aligning of intermittent leathe

Deteção automática de defeitos em couro

Dissertação de mestrado em Informatics EngineeringEsta dissertação desenvolve-se em torno do problema da deteção de defeitos em couro. A deteção de defeitos em couro é um problema tradicionalmente resolvido manualmente, usando avaliadores ex perientes na inspeção do couro. No entanto, como esta tarefa é lenta e suscetível ao erro humano, ao longo dos últimos 20 anos tem-se procurado soluções que automatizem a tarefa. Assim, surgiram várias soluções capazes de resolver o problema eficazmente utilizando técnicas de Machine Learning e Visão por Computador. No entanto, todas elas requerem um conjunto de dados de grande dimensão anotado e balanceado entre as várias categorias. Assim, esta dissertação pretende automatizar o processo tradicio nal, usando técnicas de Machine Learning, mas sem recorrer a datasets anotados de grandes dimensões. Para tal, são exploradas técnicas de Novelty Detection, as quais permitem resolver a tarefa de inspeção de defeitos utilizando um conjunto de dados não supervsionado, pequeno e não balanceado. Nesta dis sertação foram analisadas e testadas as seguintes técnicas de novelty detection: MSE Autoencoder, SSIM Autoencoder, CFLOW, STFPM, Reverse, and DRAEM. Estas técnicas foram treinadas e testadas com dois conjuntos de dados diferentes: MVTEC e Neadvance. As técnicas analisadas detectam e localizam a mai oria dos defeitos das imagens do MVTEC. Contudo, têm dificuldades em detetar os defeitos das imagens do dataset da Neadvance. Com base nos resultados obtidos, é proposta a melhor metodologia a usar para três diferentes cenários. No caso do poder computacional ser baixo, SSIM Autoencoder deve ser a técnica usada. No caso onde há poder computational suficiente e os exemplos a analisar são de uma só cor, DRAEM deve ser a técnica escolhida. Em qualquer outro caso, o STFPM deve ser a opção escolhida.This dissertation develops around the leather defects detection problem. The leather defects detec tion problem is traditionally manually solved, using experient assorters in the leather inspection. However, as this task is slow and prone to human error, over the last 20 years the searching for solutions that automatize this task has continued. In this way, several solutions capable to solve the problem effi ciently emerged using Machine Learning and Computer Vision techniques. Nonetheless, they all require a high-dimension dataset labeled and balanced between all categories. Thus, this dissertation pretends to automatize the traditional process, using the Machine Learning techniques without requiring a large dimensions labelled dataset. To this end, there will be explored Novelty Detection techniques, that in tend to solve the leather inspection task using an unsupervised small and non-balanced dataset. This dissertation analyzed and tested the following Novelty Detection techniques: MSE Autoencoder, SSIM Autoencoder, CFLOW, STFPM, Reverse, and DRAEM. These techniques are trained and tested in two distinct datasets: MVTEC and Neadvance. The analyzed techniques detect and localize most MVTEC defects. However, they have difficulties in defect detection on Neadvance samples. Based on the ob tained results, it is proposed the best methodology to use for three distinct scenarios. In the case where the computational power available is low, SSIM Autoencoder should be the technique to use. In the case where there is enough computational power and the samples to inspect have the same color, DRAEM should be the chosen technique. In any other case, the STFPM should be the chosen option

Effect of Aerobic Bacteria on Hides and Skins and Leather Quality

The present work was carried out to isolate and identify aerobic bacteria associated with raw cattle hides and sheep skins and to examine their effect on tissue of hides and skins, in Gezira region. A total of 160 samples were collected. They were collected from slaughterhouse, warehouse and tannery. Samples collected were hides and skins treated with salt, washed and air dried. Samples taken 2 hours after slaughter and samples delivered without treatment. The bacterial damage was clear in raw hides and skins delivered without treatment. They showed signs of putrefaction, offensive odour and hair slipping. A number of bacteria were isolated. A number of bacteria were isolated also from traditional salted hides and skins and this was probably due to delay in curing and absence of bacteriocides. A number of bacteria were isolated from samples taken from washed and air dried hides and skins and samples 2 hours taken after slaughter, this probably due to poor hygiene, large number of labors and bad condition of collection room of raw hides and skins. A total of 414 organisms were isolated and consist of 379 Grampositive bacteria and 35 were Gram- negative bacteria. One hundred and thirty four bacterial strains were isolated from fresh and washed cattle hides and sheep skins in slaughterhouse these include: Staphylococcus spp, Micrococcus spp, Corynebacterium spp, Aerococcus homorri, Enterococcus casselifarus, Aerococcus viridans, Enterococcus faecalis, Gamella haemolysan, Stomococcus spp, Pseudomonas spp and Eschericha coli. Also one hundred and sixty three strains were isolated from salted and dried cattle hides and sheep skins in warehouse these include: Staphylococcus spp, Micrococcus spp, Corynebacterium spp, Enterococcus spp, Streptococcus faecalis, Stomatococcus mucilaginosus, Bacillus spp, Morexell bovis, Proteus vulgaris bigroup II, Pseudomonas spp and Eschericha coli. One hundred and seventeen bacterial strains were isolated from raw hides and skins delivered without treatment to tannery these include: Staphylococcus spp, Micrococcus spp, Corynebacterium spp, Lactobacillus jensenii, Streptococcus spp, Enterococcus spp, Stomatococcus mucilaginous, Bacillus spp, Aerococcus viridans , Proteus vulgaris biogroupII, Escherichia coli and Pseudomonas spp. Staphylococcus spp, Micrococcus spp, Corynebacterium spp, Bacillus spp, Escherichia coli and Pseudomonas spp were the predominant microorganisms isolated in this study. Staphylococcus sacchrolyticus, Staphylococcus capitis, Staphylococcus hyicus, Micrococcus lylate, Corynebacterium bovis, Corynebacterium xerosis, Lactobacillus jensenii, Bacillus cereus, Staphylococcus intermedius, Bacillus amylogliguesta, Staphylococcus saprophyticus, Staphylococcus auricularis, Staphylococcus hominis, Staphylococcus epidermidis, Staphylococcus xylosus, Micrococcus varinas, Micrococcus lentus, Corynebacterium bovis , Proteus vulgaris bigroup II and Morexella bovis were isolated from putrefied hides and skins. In this study the histological examination of putrefied area showed the most affected structures of skin layer were epidermis and dermis. The epidermis became thin with no cellular structure and appearing ribbon like and detached from dermis. The dermis became loose structures. This indicated the most affected tissue is epidermis and dermis which are valuable tissue in leather industry