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authors |
Moreirinha, C; Nunes, A; Barros, A; Almeida, A; Delgadillo, I |
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nationality |
International |
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journal |
JOURNAL OF FOOD SAFETY |
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keywords |
TRANSFORM INFRARED-SPECTROSCOPY; FT-IR; MULTIVARIATE-ANALYSIS; BACTERIA; IDENTIFICATION; DIFFERENTIATION; ADULTERATION; SPOILAGE; SUGARS; JUICE |
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abstract |
The accurate reliable detection and identification of microorganisms in food is critical to public safety. Consequently, it is extremely important to develop rapid and inexpensive methods for the detection of food microorganisms in order to minimize or even replace the traditional analysis methods that are expensive and time-consuming. In this study, the potential of mid-infrared spectroscopy was evaluated, for the first time, to detect changes in colony forming units of microorganisms in freshly cut ham along the time. A partial least squares regression model was performed and a good linear relationship was obtained between spectra information and microbial load. It was concluded that infrared spectroscopy easily and quickly allows the separation of ham samples according to their microbial content and could be used to predict the microbial concentration from the spectra, using the fingerprint region (1,200-950cm(-1)), without sample preparation or handling. Practical ApplicationsAs it is essential to avoid infections caused by foodborne bacteria, it is important to develop a rapid, low cost and easy to perform technique to face the increasing demands of the food industry. Mid-infrared spectroscopy, coupled to multivariate analysis, has potential to be used as a first-screening approach and to assess the microbial concentration in ham samples, avoiding the traditional plating methods that are time-consuming. |
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publisher |
WILEY-BLACKWELL |
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issn |
0149-6085 |
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year published |
2015 |
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volume |
35 |
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issue |
2 |
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beginning page |
270 |
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ending page |
275 |
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digital object identifier (doi) |
10.1111/jfs.12176 |
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web of science category |
Biotechnology & Applied Microbiology; Food Science & Technology |
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subject category |
Biotechnology & Applied Microbiology; Food Science & Technology |
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unique article identifier |
WOS:000353338300014
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ciceco authors
impact metrics
journal analysis (jcr 2017):
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journal impact factor |
1.275 |
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5 year journal impact factor |
1.246 |
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category normalized journal impact factor percentile |
28.522 |
dimensions (citation analysis):
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altmetrics (social interaction):
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