PREFERENCE MAPPING OF TEXTURE OF DULCE DE LECHE

The texture of 12 commercial samples of dulce de leche was characterized by means of texture profile analysis. Two sensory manual texture attributes (hardness and ropiness) were evaluated by a panel of 11 trained assessors. A panel of 50 consumers evaluated the texture acceptability of the samples using a 9‐point structured scale. Consumers showed highly significant different degrees of liking for the texture of the evaluated samples of dulce de leche. Clusters identified with Pearson's correlation coefficient showed opposite preference patterns and provided more useful information regarding market segmentation than clusters identified with Euclidean distances. External preference mapping and partial least squares regression showed that hardness was a driver of liking for one segment of consumers, whereas for the other one it was a driver of disliking. When all the consumers were considered, external preference mapping indicated that the ideal texture of dulce de leche would be intermediate, showing the importance of identifying preference patterns and suggesting that data averaging in consumer preferences might affect information recovery, and may lead to wrong conclusions. Different preference mapping techniques were compared; it was found out that when the relationship between acceptability and texture attributes was not linear, external preference mapping showed the best results.     

Product social impact assessment

Purpose

Whereas the business evolution of environmental sustainability metrics has advanced significantly over the past decade, social sustainability at product level is still relatively immature. Research continues to support the front runners on organisational sustainability, while workable solutions at product level have not yet been addressed sufficiently. Triggered by this imbalance, a group of experts from large companies decided to join forces, initiating the Roundtable for Product Social Metrics.

Methods

Starting in early 2013, this group of companies aimed to (i) consolidate principles for product social sustainability assessment and harmonise approaches, (ii) align with other global initiatives and share with other companies and (iii) develop solutions for cross-cutting implementation issues. In order to be able to produce a comprehensive method for social impact assessment that provides enough flexibility for individual requirements, the Roundtable developed a method based on the approaches of the participant companies and external references such as the UNEP/SETAC Guidelines for Social Life-Cycle Assessment of Products and corporate level standards. Guiding principles were defined for the development of the method.

Results and discussion

The results of the first two phases of the Roundtable for Product Social Metrics are documented in a handbook, which proposes a practical method for organisations to assess the social impacts of a product or a service along its life cycle. The handbook outlines an aligned method for social impact assessment at a product level offering two approaches: quantitative and scale based. The method was developed to allow reasoned assessment of overall performance by including social topics and performance indicators that reflect positive and negative impacts of the product on three stakeholder groups: workers, consumers and local communities. Nineteen social topics are proposed, together with their individual performance indicators, including detailed definitions. Application examples and recommendations for the communication of results are also included in the handbook.

Conclusions

The method can be applied in numerous scenarios, from understanding improvement opportunities and steering product development in different stages, to providing support for decision making and external communications. However, the method still has further potential for improvement, inter alia that the proposed indicators are not fully applicable to small farmers, SMEs and the self-employed, as well as that the indicators are mainly at inventory level. Furthermore, the proposed method is strongly dependent on the availability of data.

     

Application of robust optimization in matrix-based LCI for decision making under uncertainty

Purpose

When product systems are optimized to minimize environmental impacts, uncertainty in the process data may impact optimal decisions. The purpose of this article is to propose a mathematical method for life cycle assessment (LCA) optimization that protects decisions against uncertainty at the life cycle inventory (LCI) stage.

Methods

A robust optimization approach is proposed for decision making under uncertainty in the LCI stage. The proposed approach incorporates data uncertainty into an optimization problem in which the matrix-based LCI model appears as a constraint. The level of protection against data uncertainty in the technology and intervention matrices can be controlled to reflect varying degrees of conservatism.

Results and discussion

A simple numerical example on an electricity generation product system is used to illustrate the main features of this methodology. A comparison is made between a robust optimization approach, and decision making using a Monte Carlo analysis. Challenges to implement the robust optimization approach on common uncertainty distributions found in LCA and on large product systems are discussed. Supporting source code is available for download at https://github.com/renwang/Robust_Optimization_LCI_Uncertainty.

Conclusions

A robust optimization approach for matrix-based LCI is proposed. The approach incorporates data uncertainties into an optimization framework for LCI and provides a mechanism to control the level of protection against uncertainty. The tool computes optimal decisions that protects against worst-case realizations of data uncertainty. The robust optimal solution is conservative and is able to avoid the negative consequences of uncertainty in decision making.     

Robust mutant strain design by pessimistic optimization

Background

Flux Balance Analysis (FBA) based mathematical modeling enables in silico prediction of systems behavior for genome-scale metabolic networks. Computational methods have been derived in the FBA framework to solve bi-level optimization for deriving “optimal” mutant microbial strains with targeted biochemical overproduction. The common inherent assumption of these methods is that the surviving mutants will always cooperate with the engineering objective by overproducing the maximum desired biochemicals. However, it has been shown that this optimistic assumption may not be valid in practice.

Methods

We study the validity and robustness of existing bi-level methods for strain optimization under uncertainty and non-cooperative environment. More importantly, we propose new pessimistic optimization formulations: P-ROOM and P-OptKnock, aiming to derive robust mutants with the desired overproduction under two different mutant cell survival models: (1) ROOM assuming mutants have the minimum changes in reaction fluxes from wild-type flux values, and (2) the one considered by OptKnock maximizing the biomass production yield. When optimizing for desired overproduction, our pessimistic formulations derive more robust mutant strains by considering the uncertainty of the cell survival models at the inner level and the cooperation between the outer- and inner-level decision makers. For both P-ROOM and P-OptKnock, by converting multi-level formulations into single-level Mixed Integer Programming (MIP) problems based on the strong duality theorem, we can derive exact optimal solutions that are highly scalable with large networks.

Results

Our robust formulations P-ROOM and P-OptKnock are tested with a small E. coli core metabolic network and a large-scale E. coli iAF1260 network. We demonstrate that the original bi-level formulations (ROOM and OptKnock) derive mutants that may not achieve the predicted overproduction under uncertainty and non-cooperative environment. The knockouts obtained by the proposed pessimistic formulations yield higher chemical production rates than those by the optimistic formulations. Moreover, with higher uncertainty levels, both cellular models under pessimistic approaches produce the same mutant strains.

Conclusions

In this paper, we propose a new pessimistic optimization framework for mutant strain design. Our pessimistic strain optimization methods produce more robust solutions regardless of the inner-level mutant survival models, which is desired as the models for cell survival are often approximate to real-world systems. Such robust and reliable knockout strategies obtained by the pessimistic formulations would provide confidence for in-vivo experimental design of microbial mutants of interest.

     

SENSORY PROPERTIES AFFECTING ACCEPTABILITY OF "BRESAOLA" FROM PODOLIAN YOUNG BULLS

Three muscles ( Semimembranosus, Quadriceps femoris, Adductor ) from Podolian young bulls (aged 18 months) were processed into bresaolas, which were compared with commercial products, in terms of sensory profile and preference expressed by consumers. Slope analysis was conducted to study the preference drivers. The preference of homogeneous groups of consumers and the relationships with sensory properties of bresaola were investigated using partial least squares (PLS) regression analysis. The products differed for several sensory properties, such as sweetness, flavor intensity ( P <  0.001), saltiness ( P <  0.01), peppered, seasoned and tenderness ( P <  0.05). The slope analysis showed that taste/flavor ( k =  0.86) and appearance ( k =  0.76) play an important role in orienting consumer preference, while texture ( k =  0.54) is less important. No general trend was found for product liking. Conversely, PLS regression clustered the consumers in homogeneous groups: one preferred the commercial products characterized by sensory properties such as peppered, saltiness, seasoned and marbling, another one was more oriented toward properties such as sweetness, odor and chewiness, which characterized Podolian bresaolas.

PRACTICAL APPLICATION

Partial least squares regression analysis clustered the consumers in homogeneous groups according to their liking: those preferring peppered, salty, seasoned and marbling bresaolas versus those oriented toward sweet and odorous products. In addition, the relationship between overall liking and attribute liking (appearance, taste/flavor and texture) was analyzed by linear regression analysis. This may be helpful in order to establish meaningful criteria for grading bresaolas and identify the particular attributes that move its acceptance (i.e., taste/flavor and appearance rather than texture).     

RELIABILITY OF SENSORY ASSESSORS: ISSUES OF COMPLEXITY

The objective of this study was to investigate whether the sensory performance of assessors in a sensory panel maybe explained by complexity of evaluated product. We aimed to investigate whether we could observe a decline in sensory performance when increasing the complexity of the product. The products increased in number of constituents from mixtures of sucrose, sodium chloride, citric acid and caffeine in water, to the foods ice tea and tomato soup constituting different levels of the same substances. Candidates who succeeded evaluating one product were not always successful evaluating others. Few subjects were successful in everything. The conclusion was that there is only minor systematic decline with increasing complexity of products. The authors emphasize that definition of complexity involves more than just counting number of constituents and taste sensations, and suggest that minor differences in the task given to the assessor might explain different performances.

PRACTICAL APPLICATIONS

Practical use of the research presented in the present paper is in a sensory evaluation context. It is important for the users of sensory data to find out how the profiling should be organized to achieve optimum output, and in specific, the need for extensive training when dealing with a more complex product. The present study hypothesized that sensory assessors would have more difficulties evaluating a more complex product. However, the results showed that panel leaders should be more concerned with the task variables in the sensory evaluation. Even a minor shift in task variables had a stronger impact on the performance and reliability of the assessors than increasing number of constituents and/or stimuli sensations of the product. This study did not demonstrate a need for extensive training when dealing with a more complex product as hypothesized.     

Investigating the utility of combining Φ29 whole genome amplification and highly multiplexed single nucleotide polymorphism BeadArray™ genotyping

Background

Sustainable DNA resources and reliable high-throughput genotyping methods are required for large-scale, long-term genetic association studies. In the genetic dissection of common disease it is now recognised that thousands of samples and hundreds of thousands of markers, mostly single nucleotide polymorphisms (SNPs), will have to be analysed. In order to achieve these aims, both an ability to boost quantities of archived DNA and to genotype at low costs are highly desirable. We have investigated Φ29 polymerase Multiple Displacement Amplification (MDA)-generated DNA product (MDA product), in combination with highly multiplexed BeadArray? genotyping technology. As part of a large-scale BeadArray genotyping experiment we made a direct comparison of genotyping data generated from MDA product with that from genomic DNA (gDNA) templates.

Results

Eighty-six MDA product and the corresponding 86 gDNA samples were genotyped at 345 SNPs and a concordance rate of 98.8% was achieved. The BeadArray sample exclusion rate, blind to sample type, was 10.5% for MDA product compared to 5.8% for gDNA.

Conclusions

We conclude that the BeadArray technology successfully produces high quality genotyping data from MDA product. The combination of these technologies improves the feasibility and efficiency of mapping common disease susceptibility genes despite limited stocks of gDNA samples.     

Streamlining scenario analysis and optimization of key choices in value chains using a modular LCA approach

Purpose

The environmental performance of products or services is often a result of a number of key decisions that shape their life cycles (e.g., techology choices). This paper introduces a modular LCA approach that is capable of reducing the effort involved in performing scenario analyses and optimization when several key choices along a product’s value chain lead to many alternative life cycles.

Methods

The main idea is that the value chain of a product can be divided into interconnected but exchangeable modules, which together represent a full life cycle. A module is comprised of unit processes from the practitioner’s LCI database. The inputs, outputs, and system boundaries of each module can be tailored to the context of the studied system. Alternatives arise whenever multiple modules produce substitutable products. Unlike in conventional LCI databases, no copies are necessary to represent the same process with different inputs. A module-product matrix is used to store this information. It can be used as a basis for an automated scenario analysis of all alternatives or as an input to an optimization model.

Results and discussion

Our approach is illustrated in two case studies: (1) Passenger car fuel choices are modeled by 15 modules representing 33 alternative value chains for diesel, petrol, natural gas and electric cars. The automated comparison of LCA results indicates that electric mobility is often the preferable option from a climate perspective, but impacts depend strongly on the electricity source. (2) A dynamic optimization model including stocks is built from eight modules to analyze the optimal use of wood for material and energy applications. Results indicate that although direct substitution benefits are higher for energy applications, cascading use of wood can maximize environmental performance over the entire life cycle.

Conclusions

The modular LCA approach permits an efficient modeling and comparison of alternative product life cycles, enabling practitioners to focus on key decisions. It can be applied to exploit a potential that is hidden in LCI databases, which is that they contain many specific inventories but not all useful combinations in the context of scenario analyses. The user-defined level of abstraction that is introduced through modules can be helpful in the communication of LCA results. The modular approach also facilitates the integration of LCA and optimization as well as other industrial ecology methods. An open source software is provided to enable others to apply and further develop our implementation of a modular LCA approach.

     

DFBAlab: a fast and reliable MATLAB code for dynamic flux balance analysis

Background

Dynamic Flux Balance Analysis (DFBA) is a dynamic simulation framework for biochemical processes. DFBA can be performed using different approaches such as static optimization (SOA), dynamic optimization (DOA), and direct approaches (DA). Few existing simulators address the theoretical and practical challenges of nonunique exchange fluxes or infeasible linear programs (LPs). Both are common sources of failure and inefficiencies for these simulators.

Results

DFBAlab, a MATLAB-based simulator that uses the LP feasibility problem to obtain an extended system and lexicographic optimization to yield unique exchange fluxes, is presented. DFBAlab is able to simulate complex dynamic cultures with multiple species rapidly and reliably, including differential-algebraic equation (DAE) systems. In addition, DFBAlab’s running time scales linearly with the number of species models. Three examples are presented where the performance of COBRA, DyMMM and DFBAlab are compared.

Conclusions

Lexicographic optimization is used to determine unique exchange fluxes which are necessary for a well-defined dynamic system. DFBAlab does not fail during numerical integration due to infeasible LPs. The extended system obtained through the LP feasibility problem in DFBAlab provides a penalty function that can be used in optimization algorithms.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0409-8) contains supplementary material, which is available to authorized users.     

Upregulation of barrel GABAergic neurons is associated with cross-modal plasticity in olfactory deficit

Background

Loss of a sensory function is often followed by the hypersensitivity of other modalities in mammals, which secures them well-awareness to environmental changes. Cellular and molecular mechanisms underlying cross-modal sensory plasticity remain to be documented.

Methodology/Principal Findings

Multidisciplinary approaches, such as electrophysiology, behavioral task and immunohistochemistry, were used to examine the involvement of specific types of neurons in cross-modal plasticity. We have established a mouse model that olfactory deficit leads to a whisking upregulation, and studied how GABAergic neurons are involved in this cross-modal plasticity. In the meantime of inducing whisker tactile hypersensitivity, the olfactory injury recruits more GABAergic neurons and their fine processes in the barrel cortex, as well as upregulates their capacity of encoding action potentials. The hyperpolarization driven by inhibitory inputs strengthens the encoding ability of their target cells.

Conclusion/Significance

The upregulation of GABAergic neurons and the functional enhancement of neuronal networks may play an important role in cross-modal sensory plasticity. This finding provides the clues for developing therapeutic approaches to help sensory recovery and substitution.     

CONSUMER PREFERENCE OF DULCE DE LECHE AMONG STUDENTS IN THE UNITED KINGDOM

Dulce de Leche is a dairy-based confectionary product virtually unknown outside Latin America. A 2⁴ factorial design was implemented to study the influence of sugar composition, type of neutralizer, cooking time and presence of vanilla essence on the preference of Dulce de Leche by UK university students. A fractional factorial pattern was used in sen'ing samples to assessors. Analysis of variance showed that the preferred levels were: 40% replacement of sucrose by glucose syrup, 100% sodium bicarbonate as neutralizer, and no vanilla essence. Cooking time did not significantly affect preference. Partial least squares regression was used to relate preference to sensory profile scores. The correlation was good for appearance and texture, not so for flavor and aftertaste. A mapping technique showed consumers to be uniformly grouped for appearance and texture, not so for flavor. Results from preference and general acceptability were encouraging in promoting this new dairy product in Great Britain.     

Answering biological questions by analysis of the strawberry metabolome

Background

The qualitative and quantitative analysis of all low molecular weight metabolites within a biological sample, known as the metabolome, provides powerful insights into their roles in biological systems and processes. The study of all the chemical structures, concentrations, and interactions of the thousands of metabolites is called metabolomics. However present state of the art methods and equipment can only analyse a small portion of the numerous, structurally diverse groups of chemical substances found in biological samples, especially with respect to samples of plant origin with their huge diversity of secondary metabolites. Nevertheless, metabolite profiling and fingerprinting techniques have been applied to the analysis of the strawberry metabolome since their early beginnings.

Aim

The application of metabolomics and metabolite profiling approaches within strawberry research was last reviewed in 2011. Here, we aim to summarize the latest results from research of the strawberry metabolome since its last review with a special emphasis on studies that address specific biological questions.

Key scientific concepts

Analysis of strawberry, and other fruits, requires a plethora of analytical methods and approaches encompassing the analysis of primary and secondary metabolites, as well as capturing and quantifying volatile compounds that are related to aroma as well as fruit development, function and plant-to-plant communication. The success and longevity of metabolite and volatile profiling approaches in fruit breeding relies upon the ability of the approach to uncover biologically meaningful insights. The key concepts that must be addressed and are reviewed include: gene function analysis and genotype comparison, analysis of environmental effects and plant protection, screening for bioactive compounds for food and non-food uses, fruit development and physiology as well as fruit sensorial quality. In future, the results will facilitate fruit breeding due to the identification of metabolic QTLs and candidate genes for fruit quality and consumer preference.

     

Consequential and attributional environmental assessment of biofuels: implications of modelling choices on climate change mitigation strategies

Purpose

The study aims to assess climate change mitigation potentials when using biomass-based fuels to replace fossil energy under consequential and attributional modelling approaches. The objectives are also to determine policy implications and to recommend the specific contexts suitable for each modelling choice by using specific illustrative cases on biofuels.

Methods

Consequential and attributional modelling approaches are chosen for life cycle greenhouse gas emission assessment of several bioenergy options. The assessed functional unit is 1 MJ of energy from molasses-based ethanol, palm-based biodiesel and electricity production from rice straw. The fossil fuel comparators are gasoline (for molasses-based ethanol), diesel (for palm-based biodiesel) and coal and gas (for rice straw). The substituted and substituting product systems are modelled under the global and national markets depending on the market delimitation of each product.

Results and discussion

The climate change mitigation potentials when using different approaches are dissimilar, because the affected product systems being included in the analysis are not the same. The palm biodiesel could reduce greenhouse gas emissions. The molasses-based ethanol and rice straw-based electricity may or may not mitigate the climate change, since it depends on the methodological choices as well as the baseline situations of the product systems being investigated. The main characteristics of consequential modelling as additionality and the inclusion of only actually affected processes under market-based mechanisms while those of attributional modelling as specification and attribution/allocation have limitations. The limitations lead to potential risks on unintended and undesirable consequences (for the attributional model), unfairness and sub-optimisation (for the consequential model) in policy recommendations.

Conclusions

This research clearly illustrates how certain modelling choices affect the climate change mitigation potentials of biomass-based fuels in comparison with fossil energy. Specific questions and conditions which could be more suitable for each modelling choice are addressed. The attributional modelling is more suitable for national environmental taxation and emission labelling/accounting for import-export, while the consequential modelling is more appropriate for new production development and eco-design. Due to the potential environmental risks arising from the modelling limitations, the consideration of both the widely applied approaches could support decisions more comprehensively.

     

Uninformative polymorphisms bias genome scans for signatures of selection

Background

A female preference for intense sexual visual signals is widespread in animals. Although the preferences for a signal per se and for the intensity of the signal were often regarded to have the identical origin, no study has demonstrated if this is true. It was suggested that the female fiddler crabs prefer males with courtship structures because of direct benefit to escape predation. Here we tested if female preference for both components (i.e. presence and size) of the courtship structure in Uca lactea is from the sensory bias to escape predation. If both components have the identical origin, females should show the same response to different-sized courtship structures regardless of predation risk.

Results

First, we observed responses of mate-searching female U. lactea to courting males with full-sized, half-sized and no semidomes which were experimentally manipulated. Females had a directional preference for males with bigger semidomes within normal variation. Thereafter, we tested the effect of predation risk on the female bias in the non-courtship context. When threatened by an avian mock predator, females preferentially approached burrows with full-sized semidomes regardless of reproductive cycles (i.e. reproductive periods and non-reproductive periods). When the predator cue was absent, however, females preferred burrows with semidomes without discriminating structure size during reproductive periods but did not show any bias during non-reproductive periods.

Conclusions

Results indicate that selection for the size of courtship structures in U. lactea may have an origin in the function to reduce predation risk, but that the preference for males with structures may have evolved by female choice, independent of predation pressure.     

FORMULATING A NEW PASSION FRUIT JUICE BEVERAGE WITH DIFFERENT SWEETENER SYSTEMS

The aim of this work was to optimize the acceptability of a natural passion fruit beverage using different levels of passion fruit pulp and sucrose, and to determine the equi-sweet concentrations of aspartame, sucralose, and a blend of 80% aspartame  +  20% acesulfame-K for the optimized formula. A 2² central composite design was used to optimize the acceptability of the sucrose-sweetened beverage, which was accessed using a 9-point structured hedonic scale. Acceptability data were fitted to a second-order model equation provided in the design. The selected pulp content and sucrose concentration were, respectively, 2.5 ° Brix and 10%. Measurements of sweetness equivalence were accomplished using two types of sensory methods: magnitude estimation and difference-from-control tests. The concentrations of aspartame, sucralose and a blend of 80% aspartame  +  20% acesulfame-K found as equi-sweet to 10% sucrose in the studied passion fruit beverage were, respectively, 0.043, 0.016 and 0.026%.

PRACTICAL APPLICATIONS

This article deals with formulating a new passion fruit juice beverage with different sweetener systems. The research makes a very important contribution to the literature on sensory science used on product development by demonstrating the necessity to study the substitution of sucrose by high-intensity sweeteners every time a formulation is changed or a new product is developed.     

THOUGHTS ON SUBJECTIVE MEASUREMENT, SENSORY METRICS AND USEFULNESS OF OUTCOMES

This note presents a discussion of direct scaling versus paired comparison from the viewpoint of historical, design, field, analytical and interpretation issues. A lot of sensory and consumer research use procedures such as direct measurement, which in turn can lead to major development‐relevant insights. At the same time, other research with the same objectives uses choice methods, which in the author's mind do not, and cannot, lead to these desired development‐relevant insights. It is important to differentiate between the two types of approaches and understand their pros and cons, as well as their intellectual histories. Simply choosing a measurement procedure by the diktat of management or of current fads may actually be counterproductive to the specific task in particular, and to the field of sensory research in general.