Ingrid Måge

Reduction of verotoxigenic Escherichia coli in production of fermented sausages

After a number of foodborne outbreaks of verotoxigenic Escherichia coli involving fermented sausages, some countries have imposed regulations on sausage production. For example, the US Food Safety and Inspection Service requires a 5 log(10) reduction of E. coli in fermented products. Such regulations have led to a number of studies on the inactivation of E. coli in fermented sausages by changing processing and post-processing conditions. Several factors influence the survival of E. coli such as pre-treatment of the meat, amount of NaCl, nitrite and lactic acid, water activity, pH, choice of starter cultures and addition of antimicrobial compounds. Also process variables like fermentation temperature and storage time play important roles. Though a large variety of different production processes of sausages exist, generally the reduction of E. coli caused by production is in the range 1-2 log(10). In many cases this may not be enough to ensure microbial food safety. By optimising ingredients and process parameters it is possible to increase E. coli reduction to some extent, but in some cases still other post process treatments may be required. Such treatments may be storage at ambient temperatures, specific heat treatments, high pressure processing or irradiation. HACCP analyses have identified the quality of the raw materials, low temperature in the batter when preparing the sausages and a rapid pH drop during fermentation as critical control points in sausage production. This review summarises the literature on the reduction verotoxigenic E. coli in production of fermented sausages.

Multi-level binary replacement (MBR) design for computer experiments in high-dimensional nonlinear systems

Computer experiments are useful for studying a complex system, e.g. a high‐dimensional nonlinear mathematical model of a biological or physical system. Based on the simulation results, an empirical “metamodel” may then be developed, emulating the behavior of the model in a way that is faster to compute and easier to understand. In modelometrics, the model phenome of a computer model is recorded, once and for all, by structured simulations according to a factorial design in the model inputs, and with high‐dimensional profiling of its simulation outputs. A multivariate metamodel is then developed, by multivariate analysis of the input–output data, akin to how high‐dimensional data are analyzed in chemometrics. To reveal strongly nonlinear input–output relationships, the factorial design must probe the design space at many different levels for each of the many input factors. A reduced factorial design method may be required if combinatorial explosion is to be avoided. In the multi‐level binary replacement (MBR) design the levels of each input factor are represented as binary numbers, and all the individual binary factor bits are then combined in a fractional factorial (FF) design. The experiment size can thereby be greatly reduced at the price of some binary confounding. The MBR method is here described and then illustrated for the optimization of a nonlinear model of a microbiological growth curve with five design factors, for finding the relevant region in the design space, and subsequently for estimating the optimal design points in that space. Copyright

Screening design for computer experiments: metamodelling of a deterministic mathematical model of the mammalian circadian clock

Computer simulations are faster and cheaper than physical experiments. Still, if the system has many factors to be manipulated, experimental designs may be needed in order to make computer experiments more cost‐effective. Determining the relevant parameter ranges within which to set up a factorial experimental design is a critical and difficult step in the practical use of any formal statistical experimental planning, be it for screening or optimisation purposes. Here we show how a sparse initial range finding design based on a reduced multi‐factor multi‐level design method—the multi‐level binary replacement (MBR) design—can reveal the region of relevant system behaviour. The MBR design is presently optimised by generating a number of different confounding patterns and choosing the one giving the highest score with respect to a space‐spanning criterion. The usefulness of this optimised MBR (OMBR) design is demonstrated in an example from systems biology: A multivariate metamodel, emulating a deterministic, nonlinear dynamic model of the mammalian circadian clock, is developed based on data from a designed computer experiment. In order to allow the statistical metamodel to represent all aspects of the biologically relevant model behaviour, the relevant parameter ranges have to be spanned. The use of an initial OMBR design for finding the widest possible parameter ranges resulting in a stable limit cycle for the mammalian circadian clock model is demonstrated. The same OMBR design is subsequently applied within the selected, relevant sub‐region of the parameter space to develop a functional metamodel based on PLS regression. Copyright

Effect of relevant environmental stresses on survival of enterohemorrhagic Escherichia coli in dry-fermented sausage.

Dry-fermented sausages (DFSs) have been linked to several serious foodborne outbreaks of enterohemorrhagic Escherichia coli (EHEC). The ability of pathogens to utilize adaptive responses to different stressful conditions intended to control their growth in foods, food preparation and production processes may enhance their survival. In certain cases, induced tolerance to one type of stress may lead to enhanced resistance to the applied stress as well as to other stresses. We exposed two EHEC strains, MF3582 of serotype O157:H- and MF5554 of serogroup O145, to different stresses commonly encountered during a production process. The two EHEC strains, previously shown to have different abilities to survive DFS production process conditions, were subjected to low temperatures (4°C and 12°C), 5% NaCl or 1% lactic acid for 6days prior to being added to sausage batters. Survival of EHEC was recorded in salami of two recipes, fermented at two temperatures (20°C and 30°C). The results showed that recipe type had the largest impact on EHEC reductions where Moderate recipe (MR) salami batters containing increased levels of NaCl, glucose and NaNO2 provided enhanced EHEC reductions in salami (2.6 log10) compared to Standard recipe (SR) salami (1.7 log10). Effects of pre-exposure stresses were dependent both on strain and recipe. While acid adaptation of MF5554 provided enhanced log10 reductions from 2.0 to 3.0 in MR sausages, adaptation to a combination of acid and salt stress showed the opposite effect in SR sausages with reductions of only 1.1 log10 as compared to the average of 1.8 log10 for the other SR sausages. Otherwise, the salt and acid adaptation single stresses had relatively small effects on EHEC survival through the DFS production process and subsequent storage and freeze/thaw treatments. Growing cells and cells frozen in batter survived poorly in MR sausages with an average reduction of 3.4 and 3.2 log10, respectively. The reductions of EHEC after storage of DFS increased with higher temperature and storage time. Up to 3.7 log10 additional reduction was obtained when MF3582 was stored for 2months at 20°C. In conclusion, adaptation of EHEC to acid, salt and low temperatures prior to being introduced in a DFS production process has limited, but strain dependent effects on EHEC reductions. Producers should avoid conditions leading to acid and salt adapted cells that can contaminate the sausage batter. Recipe parameters had the largest impact on EHEC reductions while storage at 20°C is effective for enhanced reductions in finished products.

Effects of post-processing treatments on sensory quality and Shiga toxigenic Escherichia coli reductions in dry-fermented sausages

The effects of post-processing treatments on sensory quality and reduction of Shiga toxigenic Escherichia coli (STEC) in three formulations of two types of dry-fermented sausage (DFS; salami and morr) were evaluated. Tested interventions provided only marginal changes in sensory preference and characteristics. Total STEC reductions in heat treated DFS (32°C, 6days or 43°C, 24h) were from 3.5 to >5.5 log from production start. Storing of sausages (20°C, 1month) gave >1 log additional STEC reduction. Freezing and thawing of sausages in combination with storage (4°C, 1month) gave an additional 0.7 to 3.0 log reduction in STEC. Overall >5.5 log STEC reductions were obtained after storage and freezing/thawing of DFS with increased levels of glucose and salt. This study suggests that combined formulation optimisation and post-process strategies should be applicable for implementation in DFS production to obtain DFS with enhanced microbial safety and high sensory acceptance and quality.

Near-Infrared Hyperspectral Imaging of Fusarium-Damaged Oats (Avena sativa L.)

ABSTRACT The feasibility of hyperspectral imaging (HSI) to detect deoxynivalenol (DON) content and Fusarium damage in single oat kernels was investigated. Hyperspectral images of oat kernels from a Fusarium-inoculated nursery were used after visual classification as asymptomatic, mildly damaged, and severely damaged. Uninoculated kernels were included as controls. The average spectrum from each kernel was paired with the reference DON value for the same kernel, and a calibration model was fitted by partial least squares regression (PLSR). To correct for the skewed distribution of DON values and avoid nonlinearities in the model, the DON values were transformed as DON* = [log(DON)]3. The model was optimized by cross-validation, and its prediction performance was validated by predicting DON* values for a separate set of validation kernels. The PLSR model and linear discriminant analysis classification were further used on single-pixel spectra to investigate the spatial distribution of infection in the kerne...

Incorporating interactions in multi-block sequential and orthogonalised partial least squares regression

This paper is about how to incorporate interaction effects in multi‐block methodologies. The method proposed is inspired by polynomial regression modelling in the case with only a few independent variables but extends/generalises the idea to situations where the blocks are potentially very large with respect to the number of variables. The method follows a so‐called type I sums of squares strategy where the linear effects (main effects) are incorporated sequentially and before the interactions. The sequential and orthogonalised partial least squares (SO‐PLS) technique is used as a basis for the proposal. The SO‐PLS method is based on sequential estimation of each new block by the PLS regression method after orthogonalisation with respect to blocks already fitted. The new method preserves the invariance already established for SO‐PLS and can be used for blocks with different dimensionality. The method is tested on one real data set with two independent blocks with different complexity and on a simulated data set with a large number of variables in each block. Copyright

Common and distinct components in data fusion

In many areas of science, multiple sets of data are collected pertaining to the same system. Examples are food products that are characterized by different sets of variables, bioprocesses that are online sampled with different instruments, or biological systems of which different genomic measurements are obtained. Data fusion is concerned with analyzing such sets of data simultaneously to arrive at a global view of the system under study. One of the upcoming areas of data fusion is exploring whether the data sets have something in common or not. This gives insight into common and distinct variation in each data set, thereby facilitating understanding of the relationships between the data sets. Unfortunately, research on methods to distinguish common and distinct components is fragmented, both in terminology and in methods: There is no common ground that hampers comparing methods and understanding their relative merits. This paper provides a unifying framework for this subfield of data fusion by using rigorous arguments from linear algebra. The most frequently used methods for distinguishing common and distinct components are explained in this framework, and some practical examples are given of these methods in the areas of medical biology and food science.

FTIR as a rapid tool for monitoring molecular weight distribution during enzymatic protein hydrolysis of food processing by-products

Enzymatic protein hydrolysis of food processing by-products is a well-recognized strategy for producing peptide-rich formulations of added value. The biochemical complexity of this process poses a significant challenge in the use of classical analytical methods as process monitoring tools in both laboratory and industrial setups. In the present study, we are reporting an FTIR-based multivariate approach for monitoring the change in the molecular size distribution of proteins during enzymatic hydrolysis of chicken fillets and processing by-products. For 129 protein hydrolysates, weight-average molecular weight derived from size-exclusion chromatographic analysis was established as a pragmatic measure of the extent of hydrolysis. FTIR spectra acquired from dry films of the hydrolysates were used to build multivariate calibration models using partial least squares (PLS) regression. The best predictions were obtained when the data from two different raw materials, i.e., chicken fillets and mechanical chicken deboning residues, were modeled individually. A good prediction model was also achieved for the combined data from the two raw materials using canonical PLS (CPLS) regression or variable selection. The results from the current study underline the potential of FTIR spectroscopy as a rapid analytical tool for monitoring enzymatic protein hydrolysis of complex raw materials.

Feed-Forward Prediction of Product Qualities in Enzymatic Protein Hydrolysis of Poultry By-products: a Spectroscopic Approach

Enzymatic protein hydrolysis (EPH) is one of the industrial bioprocesses used to recover valuable constituents from food processing by-products. Extensive heterogeneity of by-products from, for example, meat processing is a major challenge in production of protein hydrolysates with stable and desirable quality attributes. Therefore, there is a need for process control tools for production of hydrolysates with defined qualities from such heterogeneous raw materials. In the present study, we are reporting a new feed-forward process control strategy for enzymatic protein hydrolysis of poultry by-products. Four different spectroscopic techniques, i.e., NIR imaging scanner, a miniature NIR (microNIR) instrument, fluorescence and Raman, were evaluated as tools for characterization of the raw material composition. Partial least squares (PLS) models for ash, protein, and fat content were developed based on Raman, fluorescence, and microNIR measurements, respectively. In an effort to establish feed-forward process control tools, we developed statistical models that enabled prediction of end-product characteristics, i.e., protein yield and average molecular weight of peptides (Mw), as a function of raw material quality and hydrolysis time. A multiblock sequential orthogonalised-PLS (SO-PLS) model, where spectra from one or more techniques and hydrolysis time were used as predictor variables, was fitted for the feed-forward prediction of product qualities. The best model was obtained for protein yield based on combined use of microNIR and fluorescence (R2 = 0.88 and RMSE = 4.8). A Raman-based model gave a relatively moderate prediction model for Mw (R2 = 0.56 and RMSE = 150). Such statistical models based on spectroscopic measurements of the raw material can be vital process control tools for EPH. To our knowledge, the present work is the first example of a spectroscopic feed-forward process control for an industrially relevant bioprocess.