**1. Introduction**

One of the most important aspects to consider as part of quality in food processing is food safety. The ability to produce food free from pathogens is critical in the food processing industry [1, 2]. Different food technologies have been studied whit the aim to inhibit or inactivate deteriorative microorganisms and pathogens. Thermal processing is highly effective and commonly used to eliminate or reduce pathogens; however it is not always suitable to use since it can induce chemical and physical changes leading to the degradation of nutritional content and organoleptic properties [2–4]. This has led researchers to investigate nonthermal technologies (NTT), or emerging technologies in food processing, such as High Intensity Light Pulses (HILP).

HILP treatments apply short light pulses of broad spectrum (200–1100 nm) that are rich in UVC light to decontaminate food and surfaces in short periods of time [5, 6]. Its main inactivation mechanism is DNA denaturation and dimer formation, preventing bacterial cells to replicate and eventually causing cell death. There is also a photothermal effect due to the mild increase of temperature and a photophysical effect due to microorganisms' cell membrane damage and consequent elution of proteins [3, 5].

The effects of HILP on inactivating different microorganisms have been studied on solid foods such as vegetables [7, 8], fruits [9], seeds [3, 10], meat, and fish [11]. The wide range of the log reduction on microbial population reported in different studies show that it depends on: processing parameters (number of pulses, discharge voltage and distance from the light source), food chemical composition, food type (liquid, semisolid or solid), and resistance of the microorganisms being inactivated [5].

Thermal treatments in food processing require detailed information on target microbial reduction parameters such as inactivation rate constant (*k*; min<sup>1</sup> ) for first order kinetics, decimal reduction time (*D*; min), and temperature resistance coefficient (*z*; °C) [1, 12]. In thermal processing, *D* is the time required to inactivate 90% of the initial population at a given temperature, while *z* is the increase in temperature required to achieve a logarithmic reduction. Both values, in thermal processing, can be found in the literature for different food-microorganism systems. Nevertheless, the practical application of any NTT including HILP, requires detailed basic studies on various microbial reduction effects [1]. Particularly, for HILP technology such parameters have not yet been widely reported for different types of food, in part because in NTT, inactivation curves do not follow first order kinetics.

Most inactivation curves in NTT display non-linear concave shapes and therefore have low values of correlation coefficient (R2 ) when modeling with first order kinetic equations. The reason for this is that in complex biochemical systems like foods, the microbial population and processing conditions lack uniformity which is required for a better fit of a linear kinetic model [12].

A simple reliable alternative to linear models is the cumulative form of the Weibull distribution, simply known as the Weibull model in food microbiology [1, 12]. This model has been extensively applied to model lifetime data in medical, biological, and engineering sciences [13]. The Weibull survival function predicts the survival ratio of a microbial population as the result of the cumulative exposure of individual microorganisms with different resistances to a given lethal agent [12, 14]. On this model, parameter *b* is equivalent to the inactivation rate constant, *k* (min<sup>1</sup> ), while the exponent *n* indicates the shape of the curve.

Forcing the fit of NTT inactivation curves, including those of HILP, to linear models corresponding to first order kinetics produces a lack of accuracy and might result in either consumers' health risk due to under-processing or in undesired losses in nutritional and sensorial quality of foods due to over-processing [14]. Therefore, the use of process design parameters that are equivalent to *D* and *z-*value, derived from non-linear kinetics, such as the Weibull model, might contribute substantially to NTT development and guidelines for their industrial implementation [12].

But, although there have been several studies offering information on the microbial reduction on foods by HILP, further mathematical modeling is missing in most cases to describe the inactivation effects of this technology and the factors affecting it.

For that reason, the aim of this work is to present the Weibull modeling of different food-microorganism systems previously investigated by other authors; and to analyze the obtained information to further understand HILP inactivation and give useful information in the form of t *D*-value thermal processing like parameters for the implementation of this technology at industrial scale.
