Fast determination of the resin and rubber content in Parthenium argentatum biomass using near infrared spectroscopy

• Published in: Industrial crops and products Vol. 45; pp. 44 - 51
• Main Authors: Suchat, Sunisa, Pioch, Daniel, Palu, Serge, Tardan, Eric, van Loo, Eibertus Nicolaas, Davrieux, Fabrice
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2013
• Subjects: accelerated solvent-extraction; Biomass; Calibration; Guayule; high-throughput; latex; Mathematical analysis; Mathematical models; Multivariate data analysis; natural-rubber; Near infrared spectroscopy; plants; Polymers; quantification; reflectance spectroscopy; Resin; Resins; Rubber; soxhlet; Near infrared spectroscopy; Guayule; Rubber; Resin; Multivariate data analysis
• ISSN: 0926-6690; 1872-633X
• DOI: 10.1016/j.indcrop.2012.09.025

► ASE was selected as reference method owing to lower experimental error. ► Acetone and hexane were used for quantifying resin and rubber in guayule. ► Developed NIRS predictive models are efficient and accurate. ► The method allow high-throughput solvent-free analysis of powdered biomass. Guayule (Parthenium argentatum), a plant native of semi-arid regions of northern Mexico and southern Texas, United States, is an under-used source of hypoallergenic latex, a solution to the serious latex allergy IgE problem worldwide. This study aimed to develop near infrared spectroscopy (NIRS) calibrations to assess resin and rubber contents in guayule plants. For achieving this goal a reference method (ASE; accelerated solvent extraction) was selected and optimized among three alternatives also including Soxhlet and Polytron. First resin (lipids, terpenes) was extracted with acetone from ground biomass at 40°C, and then rubber was extracted with hexane from left solid at 120°C. A set of 215 samples of guayule biomass (stems and branches) was collected from plants in two experimental fields located in France and in Spain and was analyzed for moisture, rubber and resin contents using the two solvent selected ASE methods. Near infrared spectra were recorded for all samples. Two thirds of the samples were randomly selected for calibration, the remaining being used for validation. For each constituent, calibration equations were developed using modified partial least squares regression. The equation performances were evaluated using the performance to deviation ratio (RPDp) and Rp2 parameters, obtained by comparison of the validation set NIR predictions and corresponding laboratory values. Moisture content (RPDp=6.91; Rp2=0.98) calibration enabled accurate determination of these traits. NIR models for hexane extract (rubber content) (RPDp=4.59; Rp2=0.96) and acetone extract (resin content) (RPDp=4.87; Rp2=0.96) were highly efficient and enabled accurate characterization of guayule biomass. On the other hand, this analysis showed that both laboratory tools, coupled with multivariate analytical techniques, could be used to differentiate the samples and accurately predict the chemical composition of this disparate set of agricultural biomass samples. This study demonstrated the ability of NIRS analysis for high throughput determination of resin and rubber contents in guayule biomass.