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Irradiation is a safe and effective method to improve food safety and quality. Ionizing irradiation employs gamma rays (cobalt-60 and cesium-137 as radioactive sources), x-rays (machine-generated), and e beam (high-energy electrons, machine-generated) as treatments to successfully kill microbes in foods. Irradiation damages microbial DNA, resulting in cell death. According to Aymerich et al. (2008), viruses are most resistant to irradiation, followed by bacterial spores, yeasts, molds, Gram-positive bacteria, and Gram-negative bacteria. This technology has excellent penetration power. For example, x-rays and gamma rays can penetrate 80 to 100 cm while e beams have less penetrating power, ranging from 8 to 10 cm. None of these ionizing treatments make food radioactive, making questionable negative consumer fears about the technology. Irradiated foods should bear the internationally recognized radura symbol (Fig. 14.1) together with a treated with irradiation statement on the label to inform consumers. Two levels of irradiation processes are recognized based on absorbed dose: radurization (1 to 10 kGy) and radapperdization (20 kGy and up) (Murano 2003). Reviews of irradiation treatment of meats and meat products are available elsewhere (Thayer et al. 1986; Newsome 1987; Monk et al. 1995; Farkas 1998). A summary of most recent meat gamma irradiation studies is presented in Table 14.2.

Abstract: Fuzzy neural networks are hybrid systems that combine the theories of fuzzy logic and neural networks. By incorporating in these hybrid systems the ability to selforganize their network structure, self-organizing fuzzy neural networks (SOFNNs) are created. The SOFNNs have enhanced ability to identify adaptive models, mainly for representing nonlinear and time-varying complex systems, where little or insuf cient expert knowledge is available to describe the underlying behavior. Problems that arise in these systems are large dimensions, time-varying characteristics, large amounts of data, and noisy measurements, as well as the need for an interpretation of the resulting model. This chapter presents an algorithm for online identi cation of a self-organizing fuzzy neural network. The SOFNN provides a singleton or Takagi-Sugeno (TS)-type fuzzy model. It therefore facilitates extracting fuzzy rules from the training data. The algorithm is formulated to guarantee the convergence of both the estimation error and the linear network parameters. It generates a fuzzy neural model with high accuracy and compact structure. Superior performance of the algorithm is demonstrated through its applications for function approximation, system identi cation, and time-series prediction in both industrial and biological systems.

Proceedings of the Electrical Overstress/Electrostatic Discharge (EOS/ESD) Symposium, 1979 p 140 6 12 Whalen JJ, Calcatera MC, Thorn M Microwave nanosecond pulse burnout properties of GaAs MESFETs Proceedings of the IEEE MTT-S International Microwave Symposium, May 1979 p 443 5 13 Whalen JJ, Calcatera MC, Thorn M Microwave nanosecond pulse burnout properties of GaAs MESFETs IEEE Transactions on Microwave Theory and Techniques 1979;MTT-27;1026 31 14 Thorn ML Failure diagnostics of gallium arsenide metal semiconductor eld effect transistors overstressed with high power microwave pulses MS Thesis, Department of Electrical Engineering, State University of New York at Buffalo; September 1979 15 Whalen JJ, Thorn ML, Rastefano E, Calcatera MC Microwave nanosecond pulse burnout properties of one micron MESFETs Proceedings of the Electrical Overstress/Electrostatic Discharge (EOS/ESD) Symposium, 1979 p 147 57 16 Rubalcava AL, Stunkard D, Roesch WJ Electrostatic discharge effects on gallium arsenide integrated circuits.

Alomar, D., C. Gallo, M. Casta eda, and R. Fuschslocher. 2003. Chemical and discriminant analysis in bovine meat by near infrared re ectance spectroscopy (NIRS). Meat Science 63:441 450. Andr s, S., I. Murray, E. A. Navajas, A. V. Fisher, N. R. Lambe, and L. B nger. 2007. Prediction of sensory characteristics of lamb meat samples by near infrared re ectance spectroscopy. Meat Science 76(3): 509 516. Andr s, S., A. Silva, A. L. Soares-Pereira, C. Martins, A. M. Bruno-Soares, and I. Murray. 2008. The use of visible and near infrared re ectance spectroscopy to predict beef M. longissimus thoracis et lumborum quality attributes. Meat Science 78(3):217 224. Barlocco, N., A. Vadell, F. Ballesteros, G. Galietta, and D. Cozzolino. 2006. Predicting intramuscular fat, moisture and Warner-Bratzler shear force in pork muscle using near infrared re ectance spectroscopy. Animal Science 82:111 116. Belton, P. S. 1997. Spectroscopic approaches to the measurement of food quality. Pure and Applied Chemistry 69(1):47 50. Boggaard, C., L. B. Christensen, and Bo L. Jespersen. 2003. Re ection mode microwave spectroscopy for on-line measurement of fat in trimmings. Presented at

MP-inverse of a matrix: Short for Moore-Penrose inverse matrix. Multiplication by a scalar: The product of an (Tn x n) matrix A = [a,j] and a number c (a scalar) is defined as