2016•Journal of Food ScienceRequires access

Application of Electronic Nose for Measuring Total Volatile Basic Nitrogen and Total Viable Counts in Packaged Pork During Refrigerated Storage

Miaoyun Li, Haibiao Wang, Lingxia Sun, Gaiming Zhao, Xianqing Huang

Open publisher page 46 citations

Abstract

The objective of this study was to predict the total viable counts (TVC) and total volatile basic nitrogen (TVB-N) in pork using an electronic nose (E-nose), and to assess the freshness of chilled pork during storage using different packaging methods, including pallet packaging (PP), vacuum packaging (VP), and modified atmosphere packaging (MAP, 40% O2 /40% CO2 /20% N2 ). Principal component analysis (PCA) was used to analyze the E-nose signals, and the results showed that the relationships between the freshness of chilled pork and E-nose signals could be distinguished in the loadings plots, and the freshness of chilled pork could be distributed along 2 first principal components. Multiple linear regression (MLR) was used to correlate TVC and TVB-N to E-nose signals. High F and R2 values were obtained in the MLR output of TVB-N (F = 32.1, 21.6, and 24.2 for PP [R2 = 0.93], VP [R2 = 0.94], and MAP [R2 = 0.95], respectively) and TVC (F = 34.2, 46.4, and 7.8 for PP [R2 = 0.98], VP [R2 = 0.89], and MAP [R2 = 0.85], respectively). The results of this study suggest that it is possible to use the E-nose technology to predict TVB-N and TVC for assessing the freshness of chilled pork during storage.

About this research paper

What this paper is about

The objective of this study was to predict the total viable counts (TVC) and total volatile basic nitrogen (TVB-N) in pork using an electronic nose (E-nose), and to assess the freshness of chilled pork during storage using different packaging methods, including pallet packaging (PP), vacuum packaging (VP), and modified atmosphere packaging (MAP, 40% O2 /40% CO2 /20% N2 ). Principal component analysis (PCA) was used to analyze the E-nose signals, and the results showed that the relationships between the freshness of chilled pork and E-nose signals could be distinguished in the loadings plots, and the freshness of chilled pork could be distributed along 2 first principal components. Multiple linear regression (MLR) was used to correlate TVC and TVB-N to E-nose signals. High F and R2 values were obtained in the MLR output of TVB-N (F = 32.1, 21.6, and 24.2 for PP [R2 = 0.93], VP [R2 = 0.94], and MAP [R2 = 0.95], respectively) and TVC (F = 34.2, 46.4, and 7.8 for PP [R2 = 0.98], VP [R2 = 0.89], and MAP [R2 = 0.85], respectively). The results of this study suggest that it is possible to use the E-nose technology to predict TVB-N and TVC for assessing the freshness of chilled pork during storage.

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Available abstract

The objective of this study was to predict the total viable counts (TVC) and total volatile basic nitrogen (TVB-N) in pork using an electronic nose (E-nose), and to assess the freshness of chilled pork during storage using different packaging methods, including pallet packaging (PP), vacuum packaging (VP), and modified atmosphere packaging (MAP, 40% O2 /40% CO2 /20% N2 ). Principal component analysis (PCA) was used to analyze the E-nose signals, and the results showed that the relationships between the freshness of chilled pork and E-nose signals could be distinguished in the loadings plots, and the freshness of chilled pork could be distributed along 2 first principal components. Multiple linear regression (MLR) was used to correlate TVC and TVB-N to E-nose signals. High F and R2 values were obtained in the MLR output of TVB-N (F = 32.1, 21.6, and 24.2 for PP [R2 = 0.93], VP [R2 = 0.94], and MAP [R2 = 0.95], respectively) and TVC (F = 34.2, 46.4, and 7.8 for PP [R2 = 0.98], VP [R2 = 0.89], and MAP [R2 = 0.85], respectively). The results of this study suggest that it is possible to use the E-nose technology to predict TVB-N and TVC for assessing the freshness of chilled pork during storage.

Key concepts: Electronic nose, Modified atmosphere, Pallet, Principal component analysis, Food science, Nitrogen, Chemistry, Mathematics

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