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Rapid and quantitative detection of the microbial spoilage of beef by Fourier transform infrared spectroscopy and machine learning [An article from: Analytica Chimica Acta]
Book Details
Author(s)Ellis, D.I.
PublisherElsevier
ISBN / ASINB000RR006K
ISBN-13978B000RR0064
AvailabilityAvailable for download now.
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from Analytica Chimica Acta, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
Beef is a commercially important and widely consumed muscle food and central to the protein intake of many societies. In the food industry no technology exists for the rapid and accurate detection of microbiologically spoiled or contaminated beef. Fourier transform infrared (FT-IR) spectroscopy is a rapid, reagentless and non-destructive analytical technique whose continued development is resulting in manifold applications across a wide range of biosciences. FT-IR was exploited to measure biochemical changes within the fresh beef substrate, enhancing and accelerating the detection of microbial spoilage. Separately packaged fresh beef rump steaks were purchased from a national retailer, comminuted for 15s and left to spoil at ambient room temperature for 24h. Every hour, FT-IR measurements were collected directly from the sample surface using attenuated total reflectance, in parallel the total viable counts of bacteria were obtained by classical microbiological plating methods. Quantitative interpretation of FT-IR spectra was undertaken using partial least squares regression and allowed for accurate estimates of bacterial loads to be calculated directly from the meat surface in 60s. Machine learning methods in the form of genetic algorithms and genetic programming were used to elucidate the wavenumbers of interest related to the spoilage process. The results obtained demonstrated that using FT-IR and machine learning it was possible to detect bacterial spoilage rapidly in beef and that the most significant functional groups selected could be directly correlated to the spoilage process which arose from proteolysis, resulting in changes in the levels of amides and amines.
Description:
Beef is a commercially important and widely consumed muscle food and central to the protein intake of many societies. In the food industry no technology exists for the rapid and accurate detection of microbiologically spoiled or contaminated beef. Fourier transform infrared (FT-IR) spectroscopy is a rapid, reagentless and non-destructive analytical technique whose continued development is resulting in manifold applications across a wide range of biosciences. FT-IR was exploited to measure biochemical changes within the fresh beef substrate, enhancing and accelerating the detection of microbial spoilage. Separately packaged fresh beef rump steaks were purchased from a national retailer, comminuted for 15s and left to spoil at ambient room temperature for 24h. Every hour, FT-IR measurements were collected directly from the sample surface using attenuated total reflectance, in parallel the total viable counts of bacteria were obtained by classical microbiological plating methods. Quantitative interpretation of FT-IR spectra was undertaken using partial least squares regression and allowed for accurate estimates of bacterial loads to be calculated directly from the meat surface in 60s. Machine learning methods in the form of genetic algorithms and genetic programming were used to elucidate the wavenumbers of interest related to the spoilage process. The results obtained demonstrated that using FT-IR and machine learning it was possible to detect bacterial spoilage rapidly in beef and that the most significant functional groups selected could be directly correlated to the spoilage process which arose from proteolysis, resulting in changes in the levels of amides and amines.
