Multivariate Calibration to Determine Phorbol Esters in Seeds of Jatropha curcas L. Using Near Infrared and Ultraviolet Spectroscopies

Abstract

The building of partial least squares (PLS) regression models using near infrared (NIR) and ultraviolet (UV) spectroscopies to estimate the concentrations of phorbol esters (PEs) in Jatropha curcas L. is presented. The models were built using two algorithms for variable selection, ordered predictors selection (OPS) and genetic algorithm (GA). Chromatographic analyses were performed to determine the concentrations of PEs. Spectral data were obtained from seeds and oil extract. The results of PLS models were performed by analyzing statistical parameters of quality such as root mean square error of prediction (RMSEP) and correlation coefficient of external predictions (R p ). The parameters obtained for NIR-PLS and UV-PLS models with OPS were respectively: RMSEP 0.48 and 0.22 mg g -1 and R p 0.49 and 0.96. For GA were obtained, respectively: RMSEP 0.52 and 0.28 mg g -1 and R p 0.12 and 0.95. The models built from seeds and oil extracts can be used respectively for screening and to accurately predict the PEs content. The OPS method provided simpler and more predictive models compared to those obtained by the selection of variables using the GA. Thus, the UV-PLS-OPS model can be used as an alternative method to quantification of PEs.Keywords: phorbol esters, Jatropha curcas L., multivariate calibration, near infrared, ultraviolet spectroscopy IntroductionCurrently, fossil fuels are the major energy source used by mankind. However, this has resulted in an increase in anthropogenic greenhouse gas emissions in the atmosphere that contributes to the increase in average temperature of the earth and complex changes in climate.1 Fossil fuels, such as gasoline and natural gas, are limited and will be insufficient for the world's energy demands in the near future. Biofuels, such as biodiesel, are a strategy that combines energy security and sustainability. 2,3 Biodiesel derived from vegetable seed oils is an alternative to diesel fuel as a renewable and environmentally clean source of energy. This fuel has been produced from a variety of vegetable sources, such as soybean, 4 sunflower, 5 palm oil 6 and physic nut. 7,8 In this context, Jatropha curcas L., also known as physic nut, stands out as one of the most promising non-edible oil seeds for the production of biofuel. 9,10 J. curcas is a small tree or large shrub, which belongs to the Euphorbiaceae family and has a life expectancy of up to 50 years. 11 Mexico is the center of origin and domestication of J. curcas, 8,12 but this plant spontaneously grows in different regions of Brazil and around the world, including Africa, India, Southeast Asia and China. 13 The J. curcas seed contains approximately 31% oil (ranging from 16 to 45%), 14 presenting a composition of fatty acids that provide biodiesel with excellent properties. 15 The press cake remaining after oil extraction is rich in proteins and has potential for use in animal feed, adding value to the productive chain of biodiesel. 16,17 However, the press cake contains toxic compounds, such as phorbol esters (PEs), 18 whi...

Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits

“…In PLS regression, the algorithm identifies the principal components (latent variables) that best describe the data in terms of variance, and it does so by constructing linear combinations of all predictors. Furthermore, unlike other dimension reduction models such as principal component regression, the fitting procedure of PLS involves finding the latent variables that maximize the covariance between the predictors and phenotypes while minimizing the error [34,35].…”

Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits

“…A wide range of references regarding the utilization of NIR spectroscopy in agriculture-related topics is available. For instance, studies employing NIR as an analytical tool include post-harvest quality monitoring [ 84 ], toxic compounds detection in seeds [ 35 ], and grain composition determination [ 85 , 86 ]. Further, Hayes et al (2017) [ 87 ] performed NIR predictions of 19 wheat end-use quality traits using multi-trait analysis and obtained improved accuracies of genomic predictions.…”

“…In PLS regression, the algorithm identifies the principal components (latent variables) that best describe the data in terms of variance, and it does so by constructing linear combinations of all predictors. Furthermore, unlike other dimension reduction models such as principal component regression, the fitting procedure of PLS involves finding the latent variables that maximize the covariance between the predictors and phenotypes while minimizing the error [ 34 , 35 ].…”

June 2020

“…NIR has an excellent potential application (Montes. et al, 2013;Roque. et al, 2017) due to its ease of use, speed, accuracy and the absence of waste generation (Pasquini, 2003;Valderrama et al, 2007).…”