Validation of parameters in HACCP verification using univariate and multivariate statistics. Application to the final phases of poultry meat production

González-Miret, M. Lourdes; Coello, María‐Teresa; Alonso, S. González; Heredia, Francisco J.

doi:10.1016/s0956-7135(01)00007-x

Cited by 15 publications

(6 citation statements)

References 2 publications

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“…This could be achieved by means of the application of uni-and multivariant statistical studies considering different factors, points and processes, with regard to the production chain, obtaining results from which 0956 one can take decisions about the process (González-Miret, Alonso, & Heredia, 1998, 2000González-Miret, Coello, Alonso, & Heredia, 2001;González-Miret, Escudero, Alonso, & Heredia, 2001).…”

Section: Introductionmentioning

confidence: 99%

The establishment of critical control points at the washing and air chilling stages in poultry meat production using multivariate statistics

2006

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Section: Introductionmentioning

confidence: 99%

The establishment of critical control points at the washing and air chilling stages in poultry meat production using multivariate statistics

2006

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“…(23,49) and Campylobacter spp. (11,39), in conjunction with consumer demand for a product of consistently high quality, have focused attention on a particular area of the food production industry, namely, the requirement for a rapid (less than a few minutes) and accurate detection system for microbiologically spoiled or contaminated meat (3).At present, no such technology exists in the food industry within the Hazard Analysis Critical Control Point system for the microbiological safety and quality of meat and poultry products (20,26,48).Muscle foods, such as meat and poultry, are described as spoiled if organoleptic changes make them unacceptable to the consumer. These organoleptic characteristics may include changes in appearance (discoloration), the development of off odors, slime formation, changes in taste, or any other characteristic which makes the food undesirable for consumption (25,26).…”

mentioning

confidence: 99%

“…At present, no such technology exists in the food industry within the Hazard Analysis Critical Control Point system for the microbiological safety and quality of meat and poultry products (20,26,48).…”

mentioning

confidence: 99%

Rapid and Quantitative Detection of the Microbial Spoilage of Meat by Fourier Transform Infrared Spectroscopy and Machine Learning

Ellis

Broadhurst

Kell

et al. 2002

Appl Environ Microbiol

284

197

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Fourier transform infrared (FT-IR) spectroscopy is a rapid, noninvasive technique with considerable potential for application in the food and related industries. We show here that this technique can be used directly on the surface of food to produce biochemically interpretable "fingerprints." Spoilage in meat is the result of decomposition and the formation of metabolites caused by the growth and enzymatic activity of microorganisms. FT-IR was exploited to measure biochemical changes within the meat substrate, enhancing and accelerating the detection of microbial spoilage. Chicken breasts were purchased from a national retailer, comminuted for 10 s, and left to spoil at room temperature for 24 h. Every hour, FT-IR measurements were taken directly from the meat surface using attenuated total reflectance, and the total viable counts were obtained by classical plating methods. Quantitative interpretation of FT-IR spectra was possible using partial leastsquares regression and allowed accurate estimates of bacterial loads to be calculated directly from the meat surface in 60 s. Genetic programming was used to derive rules showing that at levels of 10 7 bacteria·g ؊1 the main biochemical indicator of spoilage was the onset of proteolysis. Thus, using FT-IR we were able to acquire a metabolic snapshot and quantify, noninvasively, the microbial loads of food samples accurately and rapidly in 60 s, directly from the sample surface. We believe this approach will aid in the Hazard Analysis Critical Control Point process for the assessment of the microbiological safety of food at the production, processing, manufacturing, packaging, and storage levels.The last decade has seen an exponential increase in the consumer demand for poultry and poultry products, fueled in part by dietary health considerations. Fears over microbiological food safety issues, especially the incidence of Salmonella spp. (23,49) and Campylobacter spp. (11,39), in conjunction with consumer demand for a product of consistently high quality, have focused attention on a particular area of the food production industry, namely, the requirement for a rapid (less than a few minutes) and accurate detection system for microbiologically spoiled or contaminated meat (3).At present, no such technology exists in the food industry within the Hazard Analysis Critical Control Point system for the microbiological safety and quality of meat and poultry products (20,26,48).Muscle foods, such as meat and poultry, are described as spoiled if organoleptic changes make them unacceptable to the consumer. These organoleptic characteristics may include changes in appearance (discoloration), the development of off odors, slime formation, changes in taste, or any other characteristic which makes the food undesirable for consumption (25,26). While endogenous enzymatic activity within muscle tissue postmortem can contribute to changes during storage (1,25,32,44), it is generally accepted that detectable organoleptic spoilage is a result of decomposition and the formation of metaboli...

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“…Statistical patterns can be effectively applied in each stage of the HACCP system (Hayes, Scallan & Wong, 1997;González-Miret, Alonso & Heredia, 1998;2000;González-Miret, Escudero, Alonso & Heredia, 2001;Tsola, Drosinos & Zoiopoulos, 2008). Multivariate statistical techniques such as Multiple Analysis of Variance (MANOVA), Cluster Analysis or Stepwise Discriminant Analysis (SDA) have been used to select parameters of validation (González-Miret, Coello, Alonso & Heredia, 2001), by identifying the most useful variables among all the variables involved, avoiding parameters giving redundant information.…”

Section: Introductionmentioning

confidence: 99%

Application of multivariate statistical analysis to quality control systems. Relevance of the stages in poultry meat production

2014

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Validation of parameters in HACCP verification using univariate and multivariate statistics. Application to the final phases of poultry meat production

Cited by 15 publications

References 2 publications

The establishment of critical control points at the washing and air chilling stages in poultry meat production using multivariate statistics

The establishment of critical control points at the washing and air chilling stages in poultry meat production using multivariate statistics

Rapid and Quantitative Detection of the Microbial Spoilage of Meat by Fourier Transform Infrared Spectroscopy and Machine Learning

Application of multivariate statistical analysis to quality control systems. Relevance of the stages in poultry meat production

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