Three new Multi x Bi combinations M.Con.4 x (SK6 x SK7), M.Con.4 x NB4D2 and Nistari x (SK6 x SK7) were studied in five seasons i.e. during the period of 18 th June-12 th July, 30 th August-23 rd September, 3 rd November-2 nd December, 29 th January-2 nd March and 30 th March-25 th April with one control Nistari x NB4D2 considering nine reeling characteristics along with shell percent and cocoon yield per 100 disease free layings (DFLs) to establish the seasonal effect on all the parameters. Results reveal that all the characters performed well in November-December. Highest yield per 100 DFLs was recorded during November-December in all combinations showing >60 kg yield. Higher correlation of yield/100 DFLs with filament length, non-breakable filament length, denier, raw silk percent, reelability percent and recovery percent was recorded in all combinations of Multi x Bi considered in this study. Variability observed among four combinations indicates the effect of different temperature and humidity of different seasons on expression of different traits. The results indicate that there is significant relationship of raw silk percent with reelability percent, recovery percent and evenness. Reelability percent showed higher correlation with recovery percent. It was observed that neatness was positively correlated with evenness. The overall performance of the newly evolved combinations with regard to productivity and reeling characteristics is discussed emphasizing their utilization at commercial level.
Purpose
This study aims to propose a fuzzy linear regression (FLR) model to deal with the vagueness or fuzziness of the underlying relationship between silk cocoon and yarn quality.
Design/methodology/approach
Shell ratio percentage, defective cocoon percentage and cocoon volume are considered as significant independent variables to predict the quality of silk cocoons. Input and output parameters of the FLR model are considered as non-fuzzy, but the underlying relationship between the variables is assumed to be fuzzy.
Findings
The fuzzy regression model shows its superiority against conventional multiple linear regression model for estimation of silk cocoon characteristics. It is inferred that the fuzziness in underlying relationship between the parameters can be handled efficiently by FLR model.
Originality/value
A rigorous experimental work has been carried out on 40 lots of mulberry silk cocoons to generate real-world data set to characterize silk cocoons’ quality in a fuzzy environment.
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