A holistic decision-making approach for identifying influential parameters affecting sustainable production process of canola bast fibres and predicting end-use textile choice using principal component analysis (PCA).

Recent research has discovered and validated that canola fibre polymer has a lower density than major industrial fibres like cotton, jute, hemp, or flax. A few studies have identified key backgrounds that relate to canola fibre polymer production parameters; however, none have modelled an analytical hierarchy process to identify the influential parameters while producing the canola fibre polymers. The current study used Plackett-Burman design analysis to optimize the fibre polymer yield (%) during retting Statistical tools including Fisher's LSD, ANOVA, Pearson's correlation coefficient, and principal component analysis (PCA) were applied for a comparative analysis among four different canola cultivars (HYHEAR 1, Topas, 5440, 45H29). Physical testing and non-parametric statistical analysis tools like Chi-square (X2) test were used to investigate the effect of cultivar on the physique of the stems--the source of biomass. This holistic approach was taken to correlate key factors for the sustainable manufacturing of canola fibre polymers. Such knowledge will lay an effective foundation for future material-science research works, consumer wearable manufacturing industries, and engineering design for composite or nonwoven fabrication using this lightweight natural fibre polymer.