Computer-assisted colour prediction and quality control have become increasingly important to the dyeing process in many consumer goods manufacturing industries, including textile and leather. The most challenging aspect concerns dye recipe prediction for the production of the required shade on a given substrate. Computer recipe prediction based on the conventional and widely used Kubelka-Munk model often fails under a variety of conditions. In the present investigation, an attempt has been made to develop an artificial neural network model to predict colour in terms of tristimulus values (X, Y, Z) given the concentration of dyes. An artificial neural network model was trained with 300 pairs of known input vectors, i.e. dye concentrations, and output vectors, i.e. colour parameters, using a backpropagation algorithm. The artificial neural network topology consists of three neurons in the input layer to represent the concentration of dyes, three neurons in the output layer to represent the tristimulus values X, Y, and Z, and five neurons in the hidden layer with a log-sigmoid transfer function. The artificial neural network results showed a good level of colour prediction during the training and testing phase. The results also indicate that the artificial neural network has the potential to give better predictive performance than the conventional Kubelka-Munk model.
This study is aimed at identification of biocide tolerant/resistant fungal strains afflicting the leather industry. Fungal infestation occurs sometimes despite biocide treatment during leather processing. This persistent growth can be due to the development of biocide resistance which can lead to health hazards and economic loss. As no study has so far been reported to either confirm this or to identify such fungal strains, a systematic approach has been made in this study to address these aspects. Fungal strains were collected from infested leathers from tanneries to identify biocide resistant fungal strains afflicting leather industry. Phenotypic characterization revealed Aspergillus as the most dominant with 58% occurrence. Ten isolates were subjected to 18s rRNA sequencing and four strains were identified as Aspergillus niger. An in-vitro susceptibility to four leather fungicides was assessed to identify the biocide tolerant strains. S-6 A. niger strain was found to be the most tolerant as evidenced by high MIC (7.81µg ml-1) against the most effective biocide, 2-(thiocyanomethylthio) benzothiazole. In-vivo studies on chrome-tanned leathers also confirmed this finding. SEM studies revealed considerable morphological changes in S-6 compared to wild strain providing further evidence that it may have developed biocide resistance.
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