Governance for quality management in smallholder-based tropical food chains

Abstract The paper provides a framework that focuses on the linkages between several key dimensions of supply chain organization and performance of perishable tropical food products. The focus is on the relationship between governance regime and quality management. However, two other but related variables are taken into account because they impact on the relationship between governance and quality management. These variables are channel choice and value added distribution in the supply chain. Governance regime is reflecting how to enhance coordination and trust amongst supply chain partners and how to reduce transaction costs. Quality management is dealing with how to manage food technology processes such that required quality levels can be improved and variability in quality of natural products can be exploited. Governance regimes in relation to quality management practices are discussed to the extent that supply chain partners are able, or are enabled, to invest in required quality improve-ments. Reduction of transaction costs, creation of trust-based networks and proper trade-offs between direct and future gains may offer substantial contributions to effective quality management and enforcement. This framework has been applied to nine case studies on smallholder-based food supply chains originating from developing countries (Ruben et al., 2007). Three of these case studies are discussed in this paper to illustrate what challenges can be derived from the case studies. The selected case studies concern fish originating from Kenya, mango originating from Costa Rica and vegetables produced in China

Influence of fat crystals in the oil phase on stability of oil-in-water emulsions

Coalescence at rest and during flow was studied in emulsions of paraffin oil in water with several surfactants and with crystals of solid paraffin or tristearate in the oil phase. Solid fat in the oil phase was estimated by pulsed nuclear magnetic resonance. Without crystals, oil-in-water emulsions were mostly stable and flow hardly influenced coalescence, even of unstable emulsions. Emulsions with crystals in the dispersed oil phase were less stable if crystals appeared at the interface. The contact angle indicated that crystals could be oriented in the interface; if so, instability was promoted by creaming, Couette flow, turbulence or flow with Taylor vortices. Coalescence in such systems could be caused by crystals sticking through the interface and piercing the film between the globule and a second approaching globule. The effect of variables such as type of surfactant, type of crystal, amount of crystalline fat, globule size, volume fraction of fat and ionic strength fitted this view. Natural cream with part of the globular fat crystallised behaved to some degree like the model systems but there were deviations

Bayesian networks to explain the effect of label information on product perception

Interdisciplinary approaches in food research require new methods in data analysis that are able to deal with complexity and facilitate the communication among model users. Four parallel full factorial within-subject designs were performed to examine the relative contribution to consumer product evaluation of intrinsic product properties and information given on packaging. Detailed experimental designs and results obtained from analyses of variance were published [1]. The data was analyzed again with the machine learning modelling technique Bayesian networks. The objective of the current paper is to explain basic features of this technique and its advantages over the standard statistical approach regarding handling of complexity and communication of results. With analysis of variance, visualization and interpretation of main effects and interactions effects becomes difficult in complex systems. The Bayesian network model offers the possibility to formally incorporate (domain) experts knowledge. By combining empirical data with the pre-defined network structure, new relationships can be learned, thus generating an update of current knowledge. Probabilistic inference in Bayesian networks allows instant and global use of the model; its graphical representation makes it easy to visualize and communicate the results. Making use of the most of data from one single experiment, as well as combining data of independent experiments makes Bayesian networks for analysing these and similarly complex and rich data set

Diversity of yeasts involved in the fermentation of tchoukoutou, an opaque sorghum beer from Benin

Opaque sorghum beers are traditional alcoholic beverages in several African countries. Known as tchoukoutou in Benin, the beer is often obtained from an uncontrolled fermentation. It is consumed in an actively fermenting state and has a sour taste. The present study characterized and identified the yeasts involved in the fermentation process of this type of beer using the phenotypical approach. Of 12 beers from 4 different locations, the mean values of the pH, titratable acidity, dry matter content and refractive index were respectively 3.67, 0.70 (% as lactic acid) 18.08% and 7.00. Lactic acid bacteria and yeasts were the predominant microorganisms involved in the fermentation of tchoukoutou. Their counts were respectively 9.1 log cfu/ml and 9.1 logcfu/g. Enterobacteriaceae were not detectable in the beer. Based on the phenotypic characters and the assimilation profiles of 40 isolated yeasts, four genera with seven species of yeasts were identified. The yeast species predominant in the Benin opaque sorghum beer tchoukoutou was Saccharomyces cerevisa

Three Traditional Fermented Baobab Foods from Benin, Mutchayan, Dikouanyouri and Tayohounta: Preparation, Properties and Consumption

Forest food resources contribute significantly to food supply in areas where they grow. Three fermented baobab foods were studied: Dikouanyouri (from seeds, pH = 6.5); Tayohounta (from seed kernels, pH = 7), and Mutchayan (from baobab pulp and sorghum, pH = 4.2). Bacillus spp. (8.5 and 9.5 Log cfu /g) and lactic acid bacteria (8.9 and 8.4 Log cfu /g,) dominate in Dikouanyouri and Tayohounta, respectively. In Mutchayan, lactic acid bacteria (8.1 Log cfu/g) and yeasts (7.2 Log cfu/g) predominated. The arbitrary index of protein cleavage increases from 2.3% (unfermented products) to 13.7% in Dikouanyouri and 21.3% in Tayohounta, indicating significant protein degradation. Mutchayan is the most frequently consumed produc

Evaluation of the Gauss-Eyring model to predict thermal inactivation of micro-organisms at short holding times

Application of mild (non)-thermal processing technologies have received considerable interest as alternative to thermal pasteurisation, because of its shorter holding time and lower temperature aiming for an improved product quality. To understand and develop these alternative technologies, like pulsed electric fields, a proper comparison between the conventional thermal and alternative process is necessary. Up to recent, no suitable models were available to predict the inactivation of micro-organisms by a thermal process at a chosen short holding time, due to non-linearity. The recently developed Gauss-Eyring model with two variables temperature and time has the properties to be a suitable model to apply for short holding times, and was tested for this purpose. Therefore, this study aims to validate if the Gauss-Eyring model can be used to describe non-linear isothermal (a fixed temperature with varying holding time) and isotime (a fixed holding time with varying temperature) thermal inactivation data, and if it is a suitable model to predict the thermal inactivation as a function of temperature for short holding times. Inactivation data of Escherichia coli, Listeria monocytogenes, Lactobacillus plantarum, Salmonella Senftenberg and Saccharomyces cerevisiae in orange juice were collected via isothermal and isotime inactivation kinetics. Survival of the tested micro-organisms was modelled with the Gauss-Eyring model, which contains three parameters σ Tr and Z. The transition of ‘no inactivation’ to ‘inactivation’ (i.e. the ‘shoulder’ in inactivation curves) can be characterised as the temperature-time (T,t) combination where T = Tr − Z · log10(t), with Tr as the reference temperature defined for 1 s treatment, Z as the temperature needed for a 10-fold increase of decrease of the holding time t, and σ as the temperature width of the distribution. The Gauss-Eyring model fitted well to the experimental data, and revealed different sensitivity for the tested micro-organisms. Based on the parameter estimations, survival curves for the desired short holding times were predicted.</p