Assessment of near infrared spectroscopy for energetic characterization of olive byproducts

Mata-Sánchez, J.; Jiménez, Jose Antonio Pérez; Díaz, María José Moreno; Serrano, Antonio; Núñez, Nieves; Giménez, Jesús López

doi:10.1016/j.renene.2014.08.066

Cited by 13 publications

(5 citation statements)

References 25 publications

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“…Studies have also shown the high potential of NIR spectroscopy to predict other solid biofuels parameters such as cellulose, hemicellulose and lignin [22e24], or parameters such as moisture, ash content, calorific value, chlorine and sulfur in olive stone residues [25,26].…”

Section: Introductionmentioning

confidence: 99%

New techniques developed to quantify the impurities of olive stone as solid biofuel

et al. 2015

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a b s t r a c tSolid biofuels lead towards the replacement of fossil fuels. The olive industry offers potential for biomass production because of the waste generated in olive groves and olive oil industries. Fines and pulp are two substantial components of the olive stone with negative characteristics for combustion processes. Therefore, the main objective of this study is to develop an analytical method to separate olive pulp contained in an olive stone sample and to quantify it at laboratory scale. Thus, fines and pulp have been characterized. Knowing their physicochemical properties, a new separation methodology has been tested to quantify these fractions. Then, a feasibility study of Near-infrared (NIR) spectroscopy in combination with multivariate data analysis has been implemented to check if olive pulp content fraction could be controlled by using this fast technique. On the one hand, the new methodology based on differences in density of the impurities has achieved excellent results. On the other hand, the feasibility study of NIR spectroscopy applied to this analysis has been performed with good results. R 2 of 0.867 for olive stone fraction and 0.908 for olive pulp fraction respectively show the possibility of using this technique as routine analysis.

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Section: Introductionmentioning

confidence: 99%

New techniques developed to quantify the impurities of olive stone as solid biofuel

et al. 2015

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“…Near infrared spectroscopy (NIRS) has been applied as rapid and low-cost technique to determine the quality of various foods and agricultural crops (Chen et al 2015;Mata Sánchez et al 2015;Pan et al 2015;Shao et al 2015). Additionally, it has the advantage of a minimum sample preparation required before measurements (González-Martín et al 2011;Chen et al 2012a, b).…”

Section: Introductionmentioning

confidence: 99%

Rapid Determination of Antioxidant Compounds and Antioxidant Activity of Sudanese Karkade (Hibiscus sabdariffa L.) Using Near Infrared Spectroscopy

et al. 2015

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The extraction conditions applied in destructive analysis of Hibiscus sabdariffa L. (Hs) matrices before the measurement of antioxidant compounds may reduce their activities during isolation and purification due to decomposition. Hence, near infrared spectroscopic system was applied to determine these compounds. The calibration models developed by using partial least squares regression (PLSR) with 48 Hs samples and 24 unknown samples were used to confirm the robustness of the developed model. The results of calibration models effectively allowed the determination of the concentrations of total phenol content (TPC), total flavonoid content (TFC), total antioxidant content (TAC), ferric reducing antioxidant power (FRAP), and 1,1-diphenyl-2-picrylhydrazyl (DPPH) with high correlation coefficients (0.85-0.91) and small standard error of cross-validation (RMSECV) ranged from 0.08 to 0.33. The prediction capacity (RPD) for TPC, TFC, TAC, FRAP, and DPPH ranged from 3.30 to 8.25, thus demonstrating that the near infrared spectroscopic (NIRS) equations developed were applicable to unknown samples. This study demonstrates that NIRS can be considered as a fast tool for the nondestructive determination of antioxidant compounds (phenolics and flavonoids) and antioxidant activity of the Hs.

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“…Data was pre-processed by applying multiplicative scatter correction and second derivative using the mean of the triplicate NIRS scans to normalise the data. Samples were then screened for outliers with samples where the Mahalanobis distance between individual analyte concentration values and the median analyte concentration for the entire sample dataset is > 3.0 with outliers being excluded from the model datasets [3] , [6] . The dataset was then segregated into calibration and validation datasets following the methods described by Jiwen et al [4] and Zhu et al [7] .…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…In the near infra-red range of light, absorptions correspond to overtones and combinations of fundamental bands of molecular vibrations [2] . Data analysis of NIR spectra using multi-linear regression allows for computation of predictive models [3] , [4] . In undertaking regression analysis, more effective and robust correlations are obtained by applying an approach to discriminate within the spectra on which band widths to use in the quantitative modelling [4] .…”

Section: Datamentioning

confidence: 99%

Band width selection data from Near Infra-red Spectral (NIRS) quantitative modelling of energy storage components (protein, lipid, glycogen) for single and multi-bivalve species models

Bartlett¹,

Maher²,

Purss³

2018

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Data presented in this article are related to the research article entitled “Near Infra-red spectroscopy quantitative modelling of bivalve protein, lipid and glycogen composition using single-species versus multi-species calibration and validation sets” [1]. Band width selections were determined using a data-driven approach to modelling Near Infra-red Spectra (NIRS) of protein, lipid and glycogen content in bivalves. Models were produced for single species and combined species of Saccostrea glomerata, Ostrea angasi, Crassostrea gigas, Mytilus galloprovincialis and Anadara trapezia. Band width selection was undertaken using Fourier wavelet transformation coupled with a genetic algorithm (GA) to aggregate adjacent wavelet bands to select the minimum number of IR bands that were consistently identified in the majority of individual spectra.

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Assessment of near infrared spectroscopy for energetic characterization of olive byproducts

Cited by 13 publications

References 25 publications

New techniques developed to quantify the impurities of olive stone as solid biofuel

New techniques developed to quantify the impurities of olive stone as solid biofuel

Rapid Determination of Antioxidant Compounds and Antioxidant Activity of Sudanese Karkade (Hibiscus sabdariffa L.) Using Near Infrared Spectroscopy

Band width selection data from Near Infra-red Spectral (NIRS) quantitative modelling of energy storage components (protein, lipid, glycogen) for single and multi-bivalve species models

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