Use this URL to cite or link to this record in EThOS:
Title: Understanding and predicting alcohol yield from wheat
Author: Misailidis, Nikiforos
ISNI:       0000 0004 2688 211X
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2010
Availability of Full Text:
Access from EThOS:
Access from Institution:
Bioethanol is a promising renewable biofuel and wheat is currently the main candidate asthe feedstock for its production in the UK context. The quality of the numerous varieties ofwheat developed in the past by plant breeders has been well examined in terms of bread, biscuitand pasta producing industries. In general, the end-use quality determination of wheat in termsof alcohol yield is less investigated. This work focused on understanding and predicting thealcohol yield from wheat according to its physical, physicochemical and chemicalcharacteristics. The research ran alongside the GREEN Grain project and utilised its wheatsamples, which consist of a range of wheat varieties, agronomic regimes and growing sitesfrom four harvests years 2005-2008. The combined dataset consists of a diverse range ofchemical, physicochemical and physical characteristics of the GREEN Grain wheats. An initial multivariate analysis (PCA) indicated that the first principal component, whichexplains most of the variability of the wheat characteristics, is related with the classification ofwheat as hard or soft. High alcohol yielding wheats typically have high starch, mealiness andalbumin+globulin fraction, and also low protein, gliadin fraction and hardness. They also havelarger and more spherical kernels. Analysis of Variance (ANOVA) was applied in order to identify differences between thevarieties, the sites and the application or not of N fertiliser. The ANOVA showed that theapplication of N fertiliser increases all the protein components, although it increases the Gliadinand the LMW glutenins more. N fertiliser also yields smaller (TGW, width, depth) and moreelongated kernels. High alcohol yielding varieties tend to be softer with lower protein andlarger and more spherical kernels. This consistent variability allowed prediction of the alcoholyield based on easily measured parameters. The following model, based on the SKCS reportedvalues plus protein, could predict the alcohol yield with an R2 of about 78%:Alcohol yield = 466.62 - 5.07 × Protein - 0.21 × hardness + 11.6 × diameter ±6.94 l/dry tonIt is frequently hypothesised that larger and more rounded kernels produce more alcoholbecause they have a smaller relative amount of the unfermentable outer layers. In an effort totest this hypothesis, the pericarp thicknesses and the crease characteristics of the wheat sampleswere measured. It was found that pericarp thickness and crease dimensions vary with kernelsize, with significant differences between varieties. A physical model was developed thatconsiders these differences and calculates the endosperm to non-endosperm ratio. None of thevariables obtained by the physical model could be related to alcohol yield. The SKCS fundamental data were further analysed in an effort to improve the alcoholyield predictability. It was found that the averaged Crush Response Profiles are morereproducible than the hardness index itself. It was shown that the initial peak does not occurbecause of the "shell" (i.e. the bran layers) as suggested in the literature, but because of thecrease. Examination of the effects of moisture content on the aCRPs showed that their 1stquarter is equivalent to the stress-strain plots of dedicated rheological tests. The remaining partsof the curve relate to the post-failure behaviour of the kernels and with hardness as used incereal science. The aCRP parameters could improve the alcohol yield predictability of theGREEN Grain wheats to an R2 of about 82.3% and a standard error of the regression of6.3 l/dry ton. Further standardisation and calibration with respect to the moisture content and tothe size of the kernels could improve the predictions even further. Textural testing of cereals is constrained by the complexity of the wheat kernel structureand exacerbated by the between-kernel variation. The current work has demonstrated howSKCS data can be interpreted more insightfully in order to improve end-use quality predictions. The aCRP parameters clearly contain rheological information about wheats. Further research toestablish their examination by more standardised methodologies will allow effectiveinvestigation of connections between the rheological properties, chemical characteristics,processing behaviour and end-use quality prediction of wheat.
Supervisor: Campbell, Grant Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Bioethanol ; alcohol yield ; wheat ; SKCS ; pericarp ; bran ; rheology ; crush response profile ; hardness