Governance for quality management in smallholder-based tropical food chains

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.Agribusiness, Agricultural and Food Policy,

Modeling food matrix effects on chemical reactivity : Challenges and perspectives

The same chemical reaction may be different in terms of its position of the equilibrium (i.e., thermodynamics) and its kinetics when studied in different foods. The diversity in the chemical composition of food and in its structural organization at macro-, meso-, and microscopic levels, that is, the food matrix, is responsible for this difference. In this viewpoint paper, the multiple, and interconnected ways the food matrix can affect chemical reactivity are summarized. Moreover, mechanistic and empirical approaches to explain and predict the effect of food matrix on chemical reactivity are described. Mechanistic models aim to quantify the effect of food matrix based on a detailed understanding of the chemical and physical phenomena occurring in food. Their applicability is limited at the moment to very simple food systems. Empirical modeling based on machine learning combined with data-mining techniques may represent an alternative, useful option to predict the effect of the food matrix on chemical reactivity and to identify chemical and physical properties to be further tested. In such a way the mechanistic understanding of the effect of the food matrix on chemical reactions can be improved.</p

Modeling food matrix effects on chemical reactivity : Challenges and perspectives

The same chemical reaction may be different in terms of its position of the equilibrium (i.e., thermodynamics) and its kinetics when studied in different foods. The diversity in the chemical composition of food and in its structural organization at macro-, meso-, and microscopic levels, that is, the food matrix, is responsible for this difference. In this viewpoint paper, the multiple, and interconnected ways the food matrix can affect chemical reactivity are summarized. Moreover, mechanistic and empirical approaches to explain and predict the effect of food matrix on chemical reactivity are described. Mechanistic models aim to quantify the effect of food matrix based on a detailed understanding of the chemical and physical phenomena occurring in food. Their applicability is limited at the moment to very simple food systems. Empirical modeling based on machine learning combined with data-mining techniques may represent an alternative, useful option to predict the effect of the food matrix on chemical reactivity and to identify chemical and physical properties to be further tested. In such a way the mechanistic understanding of the effect of the food matrix on chemical reactions can be improved.</p

The Most Probable Curve method - A robust approach to estimate kinetic models from low plate count data resulting in reduced uncertainty

A novel method is proposed for fitting microbial inactivation models to data on liquid media: the Most Probable Curve (MPC) method. It is a multilevel model that makes a separation between the “true” microbial concentration according to the model, the “actual” concentration in the media considering chance, and the actual counts on the plate. It is based on the assumptions that stress resistance is homogeneous within a microbial population, and that there is no aggregation of microbial cells. Under these assumptions, the number of colonies in/on a plate follows a Poisson distribution with expected value depending on the proposed kinetic model, the number of dilutions and the plated volume. The novel method is compared against (non)linear regression based on a normal likelihood distribution (traditional method), Poisson regression and gamma-Poisson regression using data on the inactivation of Listeria monocytogenes. The conclusion is that the traditional method has limitations when the data includes plates with low (or zero) cell counts, which can be mitigated using more complex (discrete) likelihoods. However, Poisson regression uses an unrealistic likelihood function, making it unsuitable for survivor curves with several log-reductions. Gamma-Poisson regression uses a more realistic likelihood function, even though it is based mostly on empirical hypotheses. We conclude that the MPC method can be used reliably, especially when the data includes plates with low or zero counts. Furthermore, it generates a more realistic description of uncertainty, integrating the contribution of the plating error and reducing the uncertainty of the primary model parameters. Consequently, although it increases modelling complexity, the MPC method can be of great interest in predictive microbiology, especially in studies focused on variability analysis

Governance for quality management in smallholder-based tropical food chains

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

Fluidized bed roasting of cocoa nibs speeds up processing and favors the formation of pyrazines

Roasting is an important step in cocoa processing causing water loss and generating volatile compounds responsible for chocolate aroma like nitrogen-heterocycles. In this study, the comparison of two techniques, oven roasting, and fluidized bed roasting, in terms of effective water diffusivity (De) and activation energies of formation (Ea) of nitrogen-heterocycles was achieved with cocoa nibs. Fluidized bed roasting, recognized for its energy efficiency and low-footprint synthesis, was 16 times faster than oven roasting. The order of magnitude of De in fluidized-bed-roasted nibs was −8, while it was −9 in the oven-roasted nibs. Moreover, the aw was 50% higher in fluidized-bed-roasted nibs than in the oven-roasted ones. The Ea of nitrogen-heterocycles ranged roughly between 40 and 80 kJ/mol. Those values were lower under fluidized bed roasting than under oven roasting. The more effortless water mobility within fluidized-bed-roasted cocoa demanded lower Ea, and favored the formation of nitrogen-heterocyclics. Industrial relevance: This study can inspire cocoa manufacturers and equipment designers to pursue the formation of nitrogen-heterocycles during the roasting process of cocoa. It can be done either by adapting and scaling the current fluidized bed coffee roasters to cocoa beans or nibs; or by exploring other alternatives capable of leading enough water diffusivity and water activity in the cocoa nibs, as reported here. These physicochemical conditions undoubtedly boosted the formation of volatile compounds responsible for chocolate aroma, e.g., the pyrazines, without carrying the formation of typical-burn volatile compounds. This natural way of favoring the generation of pyrazines in cocoa nibs could contribute to clean labels by reducing or avoiding the subsequent use of flavorings. The implementation of efficient heat-transfer techniques during roasting, e.g., fluidized bed roasting, could reduce the processing cost and improve sustainability. Studies in the matter of sensory profile, and energy consumption/conversion are called for future research