首页|Guayulin content, resin and rubber fraction by near infrared spectroscopy in guayule stems (Parthenium argentatum, A. Gray)
Guayulin content, resin and rubber fraction by near infrared spectroscopy in guayule stems (Parthenium argentatum, A. Gray)
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NSTL
Elsevier
? 2022 The AuthorsGuayule (Parthenium argentatum, A. Gray) is a promising alternative source to natural rubber from Hevea brasiliensis L. Recently, another important fraction of its composition, the resin, has been the focus of many studies due to its promising commercial applicability. In the resin extract there are guayulins (A, B, C and D), belonging to the sesquiterpene family, which are usually analyzed by HPLC-DAD and, lately, with other labor-intensive analytical techniques. Near infrared spectroscopy (NIR) has been successfully used in fresh guayule and dry biomass to assess moisture, and total rubber and resin content. The purpose of the present study is to estimate, for the first time ever, the detailed guayulin content in guayule dry stems using NIR spectroscopy. So, a set of 144 samples were analyzed by a Perkin Elmer Spectrum One FT-NIR equipment coupled with a near infrared reflectance accessory (NIRA). In addition, guayulins A and B standards, isolated in the lab, were scanned to generate the best partial least squares regression (PLSR) model. The best correlative PLSR models for resin, rubber and guayulins were developed within the range of 1100–2500 nm, showing an excellent calibration correlation (r2c = 0.92–1.00) and cross validation (r2cv = 0.87–0.92). The residual predicative deviation (RPD) was above 3 in the case of resin, rubber and guayulins A, B and D; while in guayulin C was 2.8. These high RPD values demonstrated a good prediction power of the model. In conclusion, the use of NIR spectroscopy for the estimation of resin and guayulin content in ground guayule stems is an excellent option for the routine analysis instead of time-consuming and labor-intensive traditional techniques.
GuayuleGuayulinsNear infrared spectroscopyPartial least squares regression modelResinRubber
Garcia-Martinez M.M.、Latorre G.、Rozalen J.、Zalacain A.、Jara F.M.、Carrion M.E.、Carmona M.
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Universidad de Castilla-La Mancha Cátedra de Química Agrícola Escuela Técnica Superior de Ingenieros Agrónomos y de Montes
Instituto Técnico Agronómico Provincial (ITAP) S.A. Polígono Industrial Campollano
Universidad de Castilla-La Mancha Institute for Regional Development (IDR) Food Quality Research Group
NSTL
Elsevier
