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Biofuels and Biomass
Prediction of Carboxylic and Polyphenolic Chemical Feedstock Quantities in Sweet Sorghum Minori Uchimiya, and Joseph Edward Knoll Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.8b00491 • Publication Date (Web): 05 Mar 2018 Downloaded from http://pubs.acs.org on March 10, 2018
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Prediction of Carboxylic and Polyphenolic Chemical
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Feedstock Quantities in Sweet Sorghum
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Minori Uchimiya*,a and Joseph E. Knollb
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a
USDA-ARS Southern Regional Research Center, 1100 Robert E. Lee Boulevard, New Orleans, LA 70124
b
USDA-ARS Crop Genetics and Breeding Research Unit, 115 Coastal Way, Tifton, GA 31793
*Corresponding
author
fax:
(504)
286-4367,
phone:
[email protected] (M. Uchimiya)
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(504)
286-4356,
email:
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Abstract
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Quantitative chemical phenotyping is in an increasing demand to develop sweet sorghum genotypes
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targeted to accumulate carboxylate and polyphenolic secondary products as the plant-derived feedstocks
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for renewable biobased products including plastics. Of 24 sweet sorghum genotypes investigated, No.5
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Gambela (i) accumulated as much as 6-fold higher (p Brix > sucrose), but shifted towards bagasse parameters in 2016 (bagasse 286 nm > bagasse 320 nm > bagasse 13 Environment ACS Paragon Plus
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extract PARAFAC %2). For the anodic peak areas (an estimate for the amount of electrons stored by polyphenols) by trapezoidal and Gaussian fits,26 juice absorbance at 320 nm dominated the positive correlation in both years. For Epa, correlation with TOC became dominant in 2016 (r=0.75 as opposed to 0.44 in 2015). Above-described shifts in the highest linearity of Pearson’s correlations collectively suggest greater dominance of non-sugar secondary products in 2016, relative to the previous planting year. For growth parameters (bold in Table 6), positive (r>0.6) correlations were observed for days to harvest/flower against CV areas and Epa in 2016, and aromatic fluorescence juice fingerprint in 2015. In conclusion, this study systematically developed PLS based on inexpensive UV/visible spectrophotometry to calibrate and predict renewable chemical feedstocks/defense phytochemicals, accounting for diverse environmental and genotypic variations. To our knowledge, this is the first report utilizing solid-phase in situ fluorescence technique to evaluate the molecular structures responsible for the redox reactivity of biomass. The present study specifically focused on sweet sorghum, which is receiving increasing industrial interests for producing bioenergy and renewable products such as plasticizers, composites, and antioxidant food additives.50-52 Sorghum has small diploid genome and phenotypic diversity,53 and represents C4 type of photosynthesis considered more efficient by fixing CO2 at high temperature climate, compared to the C3 route of rice or wheat.54 High gene flow is expected between cultivated sorghum, wild types, and their hybrids as well as weedy relatives, constraining transgenic approach.55 Chemical phenotyping, particularly PLS calibration and prediction of secondary product concentrations (Table 5) will expedite sorghum breeding efforts and enable precision agriculture targeting the accumulation of plant-derived chemical feedstocks52 or defense phytochemicals.7 New PLS methods will also aid accurate, rapid, inexpensive, and quantitative QA/QC developments at biorefineries and chemical plants, in place of traditionally employed colorimetric methods that are sensitive to experimental artifacts, including overlapping spectra near the detection wavelength, reactivity of non-target structures, and interferences from reaction media and kinetics. Our subsequent report in this series will explore the relationships between sugarcane aphid population/damage and chemical signatures determined in this study. 14 Environment ACS Paragon Plus
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Supporting Information. Sections I-VI: Inbred and hybrid cultivars, UV/visible spectra of sweet sorghum juice and bagasse, bagasse EEM/PARAFAC with and without dilution, PLS cross-validation and prediction scatter plots for bagasse, cultivar effects, and mean values and ratios against No. Gambela. Disclaimer Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. USDA is an equal opportunity provider and employer. References (1) Colares, F.; Michaud, J. P.; Bain, C. L.; Torres, J. B., Relative toxicity of two aphicides to Hippodamia convergens (Coleoptera: Coccinellidae): Implications for integrated management of sugarcane aphid, Melanaphis sacchari (Hemiptera: Aphididae). J. Econ. Entomol. 2017, 110, 52-58. (2) Elliott, N.; Brewer, M.; Seiter, N.; Royer, T.; Bowling, R.; Backoulou, G.; Gordy, J.; Giles, K.; Lindenmayer, J.; McCornack, B.; Kerns, D., Sugarcane aphid spatial distribution in grain sorghum fields. Southwest. Entomol. 2017, 42, 27-35. (3) Mbulwe, L.; Peterson, G. C.; Scott-Armstrong, J.; Rooney, W. L., Registration of sorghum germplasm Tx3408 and Tx3409 with tolerance to sugarcane aphid [Melanaphis sacchari (Zehntner)]. J. Plant Regist. 2016, 10, 51-56. (4) Powell, G.; Tosh, C. R.; Hardie, J., Host plant selection by aphids: Behavioral, evolutionary, and applied perspectives. In Annual Review of Entomology, 2006; Vol. 51, pp 309-330. (5) Stanton, C.; Starek, M. J.; Elliott, N.; Brewer, M.; Maeda, M. M.; Chu, T., Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment. J. Appl. Remote Sens. 2017, 11. 15 Environment ACS Paragon Plus
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Peterson, D. G.; Mehboob Ur, R.; Ware, D.; Westhoff, P.; Mayer, K. F. X.; Messing, J.; Rokhsar, D. S., The Sorghum bicolor genome and the diversification of grasses. Nature 2009, 457, 551-556. Figure Captions Figure 1. Three EEM/PARAFAC fingerprints obtained for juice (a-c, 20-fold dilution) and methanol extract of bagasse (d-f, 10-fold dilution of 20 g L-1 extract) from 23 (24 in 2016) sweet sorghum cultivars in 2015-2016. Figure 2. Solid-phase EEM/PARAFAC fingerprints obtained from bagasse powder. Figure 3. Scatter plots of cross-validated (a) and predicted (b) versus measured (x-axis) trans-aconitic aid concentration. Lines are the PLS model fit (red) and 1:1 (green).
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Table 1. Concentrations of primary (sugars) and secondary (carboxylates) organic carbon products in juice of sweet sorghum planted in May of 2015 and 2016. Total number of samples (n for 23 cultivars (24 in 2016)×2 years×triplicate field plots), mean, standard deviation (s.d.), minimum, maximum, and number of non-zero values for each variable. Significant (p
