Advanced biofuel production: Engineering metabolic pathways for butanol and propane biosynthesis.

Greenhouse gases emitted from energy production and transportation are dramatically changing the climate of Planet Earth. As a consequence, global warming is affecting the living conditions of numerous plant and animal species, including ours. Thus the development of sustainable and renewable liquid fuels is an essential global challenge in order to combat the climate change. In the past decades many technologies have been developed as alternatives to currently used petroleum fuels, such as bioethanol and biodiesel. However, even with gradually increasing production, the market penetration of these first generation biofuels is still relatively small compared to fossil fuels. Researchers have long ago realized that there is a need for advanced biofuels with improved physical and chemical properties compared to bioethanol and with biomass raw materials not competing with food production. Several target molecules have been identified as potential fuel candidates, such as alkanes, fatty acids, long carbon‐chain alcohols and isoprenoids. The current study focuses on the biosynthesis of butanol and propane as possible biofuels. The scope of this research was to investigate novel heterologous metabolic pathways and to identify bottlenecks for alcohol and alkane generation using Escherichia coli as a model host microorganism. The first theme of the work studied the pathways generating butyraldehyde, the common denominator for butanol and propane biosynthesis. Two ways of generating butyraldehyde were described, one via the bacterial fatty acid elongation machinery and the other via partial overexpression of the acetone‐butanol‐ethanol fermentation pathway found in Clostridium acetobutylicum. The second theme of the experimental work studied the reduction of butyraldehyde to butanol catalysed by various bacterial aldehyde‐reductase enzymes, whereas the final part of the work investigated the in vivo kinetics of the cyanobacterial aldehyde deformylating oxygenase (ADO) for the generation of hydrocarbons. The results showed that the novel butanol pathway, based on fatty acid biosynthesis consisting of an acyl‐ACP thioesterase and a carboxylic acid reductase, is tolerant to oxygen, thus being an efficient alternative to the previous Clostridial pathways. It was also shown that butanol can be produced from acetyl‐CoA using acetoacetyl CoA synthase (NphT7) or acetyl‐CoA acetyltransferase (AtoB) enzymes. The study also demonstrated, for the first time, that bacterial biosynthesis of propane is possible. The efficiency of the system is clearly limited by the poor kinetic properties of the ADO enzyme, and for proper function in vivo, the catalytic machinery requires a coupled electron relay system.

Development of thermo-analytical prediction and classification models for food in thermal devices using a multi sensor system and artificial neural networks

Die thermische Verarbeitung von Lebensmitteln beeinflusst deren Qualität und ernährungsphysiologischen Eigenschaften. Im Haushalt ist die Überwachung der Temperatur innerhalb des Lebensmittels sehr schwierig. Zudem ist das Wissen über optimale Temperatur- und Zeitparameter für die verschiedenen Speisen oft unzureichend. Die optimale Steuerung der thermischen Zubereitung ist maßgeblich abhängig von der Art des Lebensmittels und der äußeren und inneren Temperatureinwirkung während des Garvorgangs. Das Ziel der Arbeiten war die Entwicklung eines automatischen Backofens, der in der Lage ist, die Art des Lebensmittels zu erkennen und die Temperatur im Inneren des Lebensmittels während des Backens zu errechnen. Die für die Temperaturberechnung benötigten Daten wurden mit mehreren Sensoren erfasst. Hierzu kam ein Infrarotthermometer, ein Infrarotabstandssensor, eine Kamera, ein Temperatursensor und ein Lambdasonde innerhalb des Ofens zum Einsatz. Ferner wurden eine Wägezelle, ein Strom- sowie Spannungs-Sensor und ein Temperatursensor außerhalb des Ofens genutzt. Die während der Aufheizphase aufgenommen Datensätze ermöglichten das Training mehrerer künstlicher neuronaler Netze, die die verschiedenen Lebensmittel in die entsprechenden Kategorien einordnen konnten, um so das optimale Backprogram auszuwählen. Zur Abschätzung der thermische Diffusivität der Nahrung, die von der Zusammensetzung (Kohlenhydrate, Fett, Protein, Wasser) abhängt, wurden mehrere künstliche neuronale Netze trainiert. Mit Ausnahme des Fettanteils der Lebensmittel konnten alle Komponenten durch verschiedene KNNs mit einem Maximum von 8 versteckten Neuronen ausreichend genau abgeschätzt werden um auf deren Grundlage die Temperatur im inneren des Lebensmittels zu berechnen. Die durchgeführte Arbeit zeigt, dass mit Hilfe verschiedenster Sensoren zur direkten beziehungsweise indirekten Messung der äußeren Eigenschaften der Lebensmittel sowie KNNs für die Kategorisierung und Abschätzung der Lebensmittelzusammensetzung die automatische Erkennung und Berechnung der inneren Temperatur von verschiedensten Lebensmitteln möglich ist.

Prediction of glucose excursions under uncertain parameters and food intake in intensive insulin therapy for type 1 diabetes mellitus

Considering the difficulty in the insulin dosage selection and the problem of hyper- and hypoglycaemia episodes in type 1 diabetes, dosage-aid systems appear as tremendously helpful for these patients. A model-based approach to this problem must unavoidably consider uncertainty sources such as the large intra-patient variability and food intake. This work addresses the prediction of glycaemia for a given insulin therapy face to parametric and input uncertainty, by means of modal interval analysis. As result, a band containing all possible glucose excursions suffered by the patient for the given uncertainty is obtained. From it, a safer prediction of possible hyper- and hypoglycaemia episodes can be calculated

An engineering model of the human colon

The development and performance of a three-stage tubular model of the large human intestine is outlined. Each stage comprises a membrane fermenter where flow of an aqueous polyethylene glycol solution on the outside of the tubular membrane is used to control the removal of water and metabolites (principally short chain fatty acids) from, and thus the pH of, the flowing contents on the fermenter side. The three stage system gave a fair representation of conditions in the human gut. Numbers of the main bacterial groups were consistently higher than in an existing three-chemostat gut model system, suggesting the advantages of the new design in providing an environment for bacterial growth to represent the actual colonic microflora. Concentrations of short chain fatty acids and Ph levels throughout the system were similar to those associated with corresponding sections of the human colon. The model was able to achieve considerable water transfer across the membrane, although the values were not as high as those in the colon. The model thus goes some way towards a realistic simulation of the colon, although it makes no pretence to simulate the pulsating nature of the real flow. The flow conditions in each section are characterized by low Reynolds numbers: mixing due to Taylor dispersion is significant, and the implications of Taylor mixing and biofilm development for the stability, that is the ability to operate without washout, of the system are briefly analysed and discussed. It is concluded that both phenomena are important for stabilizing the model and the human colon.

Automatic control of food manufacturing processes. 2nd edition

In the ten years since the first edition of this book appeared there have been significant developments in food process engineering, notably in biotechnology and membrane application. Advances have been made in the use of sensors for process control, and the growth of information technology and on-line computer applications continues apace. In addition, plant investment decisions are increasingly determined by quality assurance considerations and have to incorporate a greater emphasis on health and safety issues. The content of this edition has been rearranged to include descriptions of recent developments and to reflect the influence of new technology on the control and operations of automated plant. Original examples have been retained where relevant and these, together with many new illustrations, provide a comprehensive guide to good practice.

Engineering a plant community to deliver multiple ecosystem services

The sustainable delivery of multiple ecosystem services requires the management of functionally diverse biological communities. In an agricultural context, an emphasis on food production has often led to a loss of biodiversity to the detriment of other ecosystem services such as the maintenance of soil health and pest regulation. In scenarios where multiple species can be grown together, it may be possible to better balance environmental and agronomic services through the targeted selection of companion species. We used the case study of legume-based cover crops to engineer a plant community that delivered the optimal balance of six ecosystem services: early productivity, regrowth following mowing, weed suppression, support of invertebrates, soil fertility building (measured as yield of following crop), and conservation of nutrients in the soil. An experimental species pool of 12 cultivated legume species was screened for a range of functional traits and ecosystem services at five sites across a geographical gradient in the United Kingdom. All possible species combinations were then analyzed, using a process-based model of plant competition, to identify the community that delivered the best balance of services at each site. In our system, low to intermediate levels of species richness (one to four species) that exploited functional contrasts in growth habit and phenology were identified as being optimal. The optimal solution was determined largely by the number of species and functional diversity represented by the starting species pool, emphasizing the importance of the initial selection of species for the screening experiments. The approach of using relationships between functional traits and ecosystem services to design multifunctional biological communities has the potential to inform the design of agricultural systems that better balance agronomic and environmental services and meet the current objective of European agricultural policy to maintain viable food production in the context of the sustainable management of natural resources.

Modeling of continuous thermal processing of a non-Newtonian liquid food under diffusive laminar flow in a tubular system

The classic conservative approach for thermal process design can lead to over-processing, especially for laminar flow, when a significant distribution of temperature and of residence time occurs. In order to optimize quality retention, a more comprehensive model is required. A model comprising differential equations for mass and heat transfer is proposed for the simulation of the continuous thermal processing of a non-Newtonian food in a tubular system. The model takes into account the contribution from heating and cooling sections, the heat exchange with the ambient air and effective diffusion associated with non-ideal laminar flow. The study case of soursop juice processing was used to test the model. Various simulations were performed to evaluate the effect of the model assumptions. An expressive difference in the predicted lethality was observed between the classic approach and the proposed model. The main advantage of the model is its flexibility to represent different aspects with a small computational time, making it suitable for process evaluation and design. (C) 2012 Elsevier Ltd. All rights reserved.

Physico-chemical and microstructural properties of food dispersions

The macroscopic properties of oily food dispersions, such as rheology, mechanical strength, sensory attributes (e.g. mouth feel, texture and even flavour release) and as well as engineering properties are strongly determined by their microstructure, that is considered a key parameter in the understanding of the foods behaviour . In particular the rheological properties of these matrices are largely influenced by their processing techniques, particle size distribution and composition of ingredients. During chocolate manufacturing, mixtures of sugar, cocoa and fat are heated, cooled, pressurized and refined. These steps not only affect particle size reduction, but also break agglomerates and distribute lipid and lecithin-coated particles through the continuous phase, this considerably modify the microstructure of final chocolate. The interactions between the suspended particles and the continuous phase provide information about the existing network and consequently can be associated to the properties and characteristics of the final dispersions. Moreover since the macroscopic properties of food materials, are strongly determined by their microstructure, the evaluation and study of the microstructural characteristics, can be very important for a through understanding of the food matrices characteristics and to get detailed information on their complexity. The aim of this study was investigate the influence of formulation and each process step on the microstructural properties of: chocolate type model systems, dark milk and white chocolate types, and cocoa creams. At the same time the relationships between microstructural changes and the resulting physico-chemical properties of: chocolate type dispersions model systems dark milk and white chocolate were investigated.

A study of high solids anaerobic digestion of Bucknell University food waste followed by aerobic curing

Anaerobic digestion of food scraps has the potential to accomplish waste minimization, energy production, and compost or humus production. At Bucknell University, removal of food scraps from the waste stream could reduce municipal solid waste transportation costs and landfill tipping fees, and provide methane and humus for use on campus. To determine the suitability of food waste produced at Bucknell for high-solids anaerobic digestion (HSAD), a year-long characterization study was conducted. Physical and chemical properties, waste biodegradability, and annual production of biodegradable waste were assessed. Bucknell University food and landscape waste was digested at pilot-scale for over a year to test performance at low and high loading rates, ease of operation at 20% solids, benefits of codigestion of food and landscape waste, and toprovide digestate for studies to assess the curing needs of HSAD digestate. A laboratory-scale curing study was conducted to assess the curing duration required to reduce microbial activity, phytotoxicity, and odors to acceptable levels for subsequent use ofhumus. The characteristics of Bucknell University food and landscape waste were tested approximately weekly for one year, to determine chemical oxygen demand (COD), total solids (TS), volatile solids (VS), and biodegradability (from batch digestion studies). Fats, oil, and grease and total Kjeldahl nitrogen were also tested for some food waste samples. Based on the characterization and biodegradability studies, Bucknell University dining hall food waste is a good candidate for HSAD. During batch digestion studies Bucknell University food waste produced a mean of 288 mL CH4/g COD with a 95%confidence interval of 0.06 mL CH4/g COD. The addition of landscape waste for digestion increased methane production from both food and landscape waste; however, because the landscape waste biodegradability was extremely low the increase was small.Based on an informal waste audit, Bucknell could collect up to 100 tons of food waste from dining facilities each year. The pilot-scale high-solids anaerobic digestion study confirmed that digestion ofBucknell University food waste combined with landscape waste at a low organic loading rate (OLR) of 2 g COD/L reactor volume-day is feasible. During low OLR operation, stable reactor performance was demonstrated through monitoring of biogas production and composition, reactor total and volatile solids, total and soluble chemical oxygendemand, volatile fatty acid content, pH, and bicarbonate alkalinity. Low OLR HSAD of Bucknell University food waste and landscape waste combined produced 232 L CH4/kg COD and 229 L CH4/kg VS. When OLR was increased to high loading (15 g COD/L reactor volume-day) to assess maximum loading conditions, reactor performance became unstable due to ammonia accumulation and subsequent inhibition. The methaneproduction per unit COD also decreased (to 211 L CH4/kg COD fed), although methane production per unit VS increased (to 272 L CH4/kg VS fed). The degree of ammonia inhibition was investigated through respirometry in which reactor digestate was diluted and exposed to varying concentrations of ammonia. Treatments with low ammoniaconcentrations recovered quickly from ammonia inhibition within the reactor. The post-digestion curing process was studied at laboratory-scale, to provide a preliminary assessment of curing duration. Digestate was mixed with woodchips and incubated in an insulated container at 35 °C to simulate full-scale curing self-heatingconditions. Degree of digestate stabilization was determined through oxygen uptake rates, percent O2, temperature, volatile solids, and Solvita Maturity Index. Phytotoxicity was determined through observation of volatile fatty acid and ammonia concentrations.Stabilization of organics and elimination of phytotoxic compounds (after 10–15 days of curing) preceded significant reductions of volatile sulfur compounds (hydrogen sulfide, methanethiol, and dimethyl sulfide) after 15–20 days of curing. Bucknell University food waste has high biodegradability and is suitable for high-solids anaerobic digestion; however, it has a low C:N ratio which can result in ammonia accumulation under some operating conditions. The low biodegradability of Bucknell University landscape waste limits the amount of bioavailable carbon that it can contribute, making it unsuitable for use as a cosubstrate to increase the C:N ratio of food waste. Additional research is indicated to determine other cosubstrates with higher biodegradabilities that may allow successful HSAD of Bucknell University food waste at high OLRs. Some cosubstrates to investigate are office paper, field residues, or grease trap waste. A brief curing period of less than 3 weeks was sufficient to produce viable humus from digestate produced by low OLR HSAD of food and landscape waste.

High Solids Co-Digestion of Food and Landscape Waste and the Potential for Ammonia Toxicity

A pilot-scale study was completed to determine the feasibility of high-solids anaerobic digestion (HSAD) of a mixture of food and landscape wastes at a university in central Pennsylvania (USA). HSAD was stable at low loadings (2g COD/L-day), but developed inhibitory ammonia concentrations at high loadings (15g COD/L-day). At low loadings, methane yields were 232L CH4/kg COD fed and 229L CH4/kg VS fed, and at high loadings yields were 211L CH4/kg COD fed and 272L CH4/kg VS fed. Based on characterization and biodegradability studies, food waste appears to be a good candidate for HSAD at low organic loading rates; however, the development of ammonia inhibition at high loading rates suggests that the C:N ratio is too low for use as a single substrate. The relatively low biodegradability of landscape waste as reported herein made it an unsuitable substrate to increase the C:N ratio. Codigestion of food waste with a substrate high in bioavailable carbon is recommended to increase the C:N ratio sufficiently to allow HSAD at loading rates of 15g COD/L-day. Copyright 2014 Elsevier Ltd. All rights reserved.

Automatic Food Intake Assessment Using Camera Phones

Obesity is becoming an epidemic phenomenon in most developed countries. The fundamental cause of obesity and overweight is an energy imbalance between calories consumed and calories expended. It is essential to monitor everyday food intake for obesity prevention and management. Existing dietary assessment methods usually require manually recording and recall of food types and portions. Accuracy of the results largely relies on many uncertain factors such as user's memory, food knowledge, and portion estimations. As a result, the accuracy is often compromised. Accurate and convenient dietary assessment methods are still blank and needed in both population and research societies. In this thesis, an automatic food intake assessment method using cameras, inertial measurement units (IMUs) on smart phones was developed to help people foster a healthy life style. With this method, users use their smart phones before and after a meal to capture images or videos around the meal. The smart phone will recognize food items and calculate the volume of the food consumed and provide the results to users. The technical objective is to explore the feasibility of image based food recognition and image based volume estimation. This thesis comprises five publications that address four specific goals of this work: (1) to develop a prototype system with existing methods to review the literature methods, find their drawbacks and explore the feasibility to develop novel methods; (2) based on the prototype system, to investigate new food classification methods to improve the recognition accuracy to a field application level; (3) to design indexing methods for large-scale image database to facilitate the development of new food image recognition and retrieval algorithms; (4) to develop novel convenient and accurate food volume estimation methods using only smart phones with cameras and IMUs. A prototype system was implemented to review existing methods. Image feature detector and descriptor were developed and a nearest neighbor classifier were implemented to classify food items. A reedit card marker method was introduced for metric scale 3D reconstruction and volume calculation. To increase recognition accuracy, novel multi-view food recognition algorithms were developed to recognize regular shape food items. To further increase the accuracy and make the algorithm applicable to arbitrary food items, new food features, new classifiers were designed. The efficiency of the algorithm was increased by means of developing novel image indexing method in large-scale image database. Finally, the volume calculation was enhanced through reducing the marker and introducing IMUs. Sensor fusion technique to combine measurements from cameras and IMUs were explored to infer the metric scale of the 3D model as well as reduce noises from these sensors.

Genetic engineering and the world trade system

While the WTO agreements do not regulate the use of biotechnology per se, their rules can have a profound impact on the use of the technology for both commercial and non-commercial purposes. This book seeks to identify the challenges to international trade regulation that arise from biotechnology. The contributions examine whether existing international obligations of WTO Members are appropriate to deal with the issues arising for the use of biotechnology and whether there is a need for new international legal instruments, including a potential WTO Agreement on Biotechnology. They combine various perspectives on and topics relating to genetic engineering and trade, including human rights and gender; intellectual property rights; traditional knowledge and access and benefit sharing; food security, trade and agricultural production and food safety; and medical research, cloning and international trade.

A Food Recognition System for Diabetic Patients based on an Optimized Bag of Features Model

Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the Bag of Features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters. For the design and evaluation of the prototype system, a visual dataset with nearly 5,000 food images was created and organized into 11 classes. The optimized system computes dense local features, using the scale-invariant feature transform on the HSV color space, builds a visual dictionary of 10,000 visual words by using the hierarchical k-means clustering and finally classifies the food images with a linear support vector machine classifier. The system achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset.

Segmentation and Recognition of Multi-Food Meal Images for Carbohydrate Counting

In this paper, we propose novel methodologies for the automatic segmentation and recognition of multi-food images. The proposed methods implement the first modules of a carbohydrate counting and insulin advisory system for type 1 diabetic patients. Initially the plate is segmented using pyramidal mean-shift filtering and a region growing algorithm. Then each of the resulted segments is described by both color and texture features and classified by a support vector machine into one of six different major food classes. Finally, a modified version of the Huang and Dom evaluation index was proposed, addressing the particular needs of the food segmentation problem. The experimental results prove the effectiveness of the proposed method achieving a segmentation accuracy of 88.5% and recognition rate equal to 87%

Food Volume Computation for Self Dietary Assessment Applications

There is great demand for easily-accessible, user-friendly dietary self-management applications. Yet accurate, fully-automatic estimation of nutritional intake using computer vision methods remains an open research problem. One key element of this problem is the volume estimation, which can be computed from 3D models obtained using multi-view geometry. The paper presents a computational system for volume estimation based on the processing of two meal images. A 3D model of the served meal is reconstructed using the acquired images and the volume is computed from the shape. The algorithm was tested on food models (dummy foods) with known volume and on real served food. Volume accuracy was in the order of 90 %, while the total execution time was below 15 seconds per image pair. The proposed system combines simple and computational affordable methods for 3D reconstruction, remained stable throughout the experiments, operates in near real time, and places minimum constraints on users.