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Washington D.C., USA September 8-12, 2009

The potential of end-product metabolites on predicting the shelf life of minced beef stored under aerobic and modified atmospheres with or without the effect of essential oil. A.A. Argyri 1,2 , E.Z. Panagou 1 , R. Jarvis 3 , R. Goodacre 3 , G.-J.E. Nychas 1.

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Washington D.C., USA September 8-12, 2009

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  1. The potential of end-product metabolites on predicting the shelf life of minced beef stored under aerobic and modified atmospheres with or without the effect of essential oil A.A. Argyri1,2, E.Z. Panagou1, R. Jarvis3, R. Goodacre3, G.-J.E. Nychas1 1Laboratory of Microbiology and Biotechnology of Foods, Dept of Food Science and Technology, Agricultural University of Athens, Greece 2Laboratory of Applied Microbiology, Cranfield Health, Cranfield University, UK 3 Laboratory of Bioanalytical Spectroscopy, School of Chemistry, University of Manchester, UK Washington D.C., USA September 8-12, 2009

  2. Background and Rationale • The relationship between microbial growth and chemical changes occurring during meat storage has been continuously recognized as a potential means to reveal indicators that may be useful for quantifying beef quality or freshness (Nychas et al., 2008). • The imposed different storage conditions and preservatives could influence the production of these potential indicators, through the establishment of a transient microbial association defined as the ‘Ephemeral spoilage micro-organisms’ - ESO (Nychas and Skandamis, 2005). Nychas, G.-J.E., Skandamis, P., Tassou, C.C., Koutsoumanis, K. (2008) Meat spoilage during distribution. Meast Science, 78: 77-89. Nychas, G.-J.E., Skandamis, P. (2005) Fresh meat spoilage and modified atmosphere packaging. In J.N. Sofos (Ed.), Improving the Safety of Fresh Meat, CRC/Woodhead Publishing Ltd, Cambridge, UK.

  3. Background and Rationale There is need for a holistic approach in introducing shelf-life indicators that could be applied irrespective of storage temperature or packaging system and be eligible to the income of new technologies. This approach is based on the mining of qualitative and quantitative data of metabolomics e.g. indigenous or metabolic compounds associated with meat spoilage, due to interaction of ESO with nutrients existing in meat (Ellis at al., 2001). The use of HPLC to monitor changes in the organic acid profile from food models systems, poultry, fish stored under different storage conditions, has been considered as a relatively simple and promising method. Ellis, D.I., Goodacre, R. (2001) Rapid and quantitative detection of the microbial spoilage of muscle foods: Current status and future applications. Trends in Food Science and Technology, 12: 414-424.

  4. Objectives of the work The aim of the present work was to investigate the potential of HPLC spectral data of organic acids, as a quick analytical method, in combination with an appropriate data analysis strategy to: Discriminate among different quality classes of minced beef samples during storage at different temperatures (0, 5, 10, 15°C) and packaging conditions (aerobic, MAP, MAP+EO). Correlate the microbial load of different microbial groups at different temperatures, packaging conditions and storage times with spectral data, in an effort to predict microbial population directly from HPLC measurements.

  5. Materials & Methods Product: Minced beef Packaging: Aerobic, MAP (40% CO2, 30% O2, 30% N2), MAP + oregano essential oil volatile compounds Storagetemperature: 0, 5, 10, 15C Microbiological analysis:Total viable counts, Pseudomonads, Enterobacteriaceae, lactic acid bacteria, Brochotrix thermosphacta, and yeasts and moulds Organoleptic assessment: Spoilage detection based on changes in colour, odour and taste based on a five member taste panel (Score range 1-3; 1=Fresh, 1.5 Semi-Fresh, 2-3 Spoiled). HPLC analysis of organic acids: Collection of spectral data from the HPLC (areas under peaks)to monitor biochemical changes in meat during storage.

  6. Materials & Methods HPLC analysis of organic acids Sample preparation: 2g meat + 4mL dH2O + 1%TFA Organic Acid Standards: oxalic, citric, malic, lactic, acetic, formic, tartaric, succinic and propionic Apparatus: Jasco, Model PU-980 Inteligent pump, Model LG-980-02 ternary gradient unit, MD-910 multiwavelength detector at 210 nm

  7. Support vector machines regression (SVR) Partial least squares regression (PLS-R) Data analysis Collection of the HPLC spectral data (areas under peaks) Data mean centered and standardized 1st Principal components analysis (PCA) (Investigation of the peaks that significantly fluctuate during storage) 2nd PCA Regression Models predict the counts of the different microbial groups Factorial Discriminant Analysis (FDA) predict the spoilage status of a sample; fresh, semi-fresh, and spoiled

  8. Multivariate calibration

  9. Building calibration models We start off with a data matrix, and a corresponding output vector which indicates the value associated with each sample. (Bacterial counts) (Meat) (HPLC areas under peak) We build a calibration model that relates the matrix to the vector.

  10. Using calibration models The developed model on known data, can be then applied to unknown samples (Predicted bacterial count) (New meat sample) (HPLC area under peak)

  11. Multivariate calibration approaches • There are two mainpattern recognition approaches based on: • Multivariate statistics • Multiple Linear Regression (MLR) • Principal Components Regression (PCR) • Partial Least Squares Regression (PLS-R) • Machine learning • Artificial neural networks • Support Vector Machines (SVM)

  12. SVM underlying principle* • The idea behind SVMs is to project the original data from a low dimensional input space to a higher dimensional feature space. • This operation is called feature mapping and it is a key element in SVM building. • Dimension superiority plays a vital role in SVMs. • The data contain more information as the dimension increases. Li, H., Liang, Y., Xu, Q. (2009) Support vector machines and its applications in chemistry. Chemometrics and Intelligent Laboratory Systems, 95: 188-198.

  13. SVM underlying principle* Li, H., Liang, Y., Xu, Q. (2009) Support vector machines and its applications in chemistry. Chemometrics and Intelligent Laboratory Systems, 95: 188-198.

  14. SVM underlying principle • Data projection into a higher dimensional space is carried out by a kernel function that serves as a dimension increasing technique and further transforms the linearly inseparable data into linearly separable one. • There are number of kernels that can be used in Support Vector Machines models. These include linear, polynomial, radial basis function (RBF) and sigmoid. Li, H., Liang, Y., Xu, Q. (2009) Support vector machines and its applications in chemistry. Chemometrics and Intelligent Laboratory Systems, 95: 188-198.

  15. Results 0 h 48 h Pre-spoilage Post-spoilage 15°C Air 60 h 54 h Post-spoilage Post-spoilage 15°C MAP 15°C MAP+OEO 17 pure peaks were selected for analysis ; RT of 6.2, 6.9 (citric acid), 7.0, 7.9, 8.3, 9.7, 10.9 (lactic acid), 11.9 (formic acid), 12.9 (acetic acid), 14.9, 15.1 (propionic acid), 16.1, 17.8, 18.6, 20.5, 24.6 and 28.1.

  16. Results Lactic acid  Aerobic storage,  Storage under MAP, Storage under MAP + OEO

  17. Before the end of shelf life After the end of shelf life Results Qualitative classification of the samples Discriminant analysis similarity map determined by discriminant factors 1 (F1) and 2 (F2) for HPLC spectral data of the 3 different beef fillets freshness groups: Fresh (F),Semi-fresh (SF),and Spoiled (S).

  18. Results Confusion matrix for the cross-validation results of DFA Overall correct classification (accuracy): 89.33%

  19. Results Prediction of the microbial population – Performance of regression models PLS-R Linear SVR Radial SVR Sigmoid SVR

  20. PLS - R Linear SV R Radial basis SV R Sigmoid al SV R Microbial group B A B A B A B A f f f f f f f f TVC 0.99 1.10 0.99 1.10 1.00 1.10 1.00 1.10 Pseudomonas spp 1.02 1.17 1.35 1.39 1.00 1.15 1.02 1.19 Br. thermosphacta 1.00 1.18 0.99 1.18 1.01 1.17 1.00 1.18 LAB 1.00 1.09 1.01 1.09 1.01 1.08 1.02 1.08 Enterobacteriaceae 0.99 1.16 1.00 1.14 1.00 1.14 0.98 1.14 Yeasts & Molds 0.99 1.15 1.00 1.15 0.99 1.11 1.02 1.14 Results Calculation of performance indices (Bias and Accuracy factors)

  21. Results Prediction of the microbial loads - Regression models’ Performance

  22. Concluding remarks • Good correlation of the sensorial evaluation of spoilage with the dynamic changes of the chromatographic areas of organic acids at different time intervals. • In general the PLS-R, radial basis SVR and sigmoid SVR exhibited slightly better performance than the Linear SVR whereas the models that described the estimates of the TVC, as well as the LAB, had better performance, regardless of the type of the model built. • HPLC analysis of organic acids can be proved as a potential technique for meat analysis in predicting the spoilage status and the microbial load of a meat sample regardless of the storage conditions.

  23. Acknowledgements This work was supported by the EU projects Symbiosis [7th Framework Programme (Con. No 21638)] and ProSafeBeef [6th Framework Programme (ref. Food-CT-2006-36241)].

  24. Thank you for your attention

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