Identification and Quantification of Volatile Chemical Spoilage Indexes Associated with Bacterial Growth Dynamics in Aerobically Stored Chicken

Abstract

Volatile organic compounds (VOCs) as chemical spoilage indexes (CSIs) of raw chicken breast stored aerobically at 4, 10, and 21 °C were identified and quantified using solid phase microextraction (SPME) combined with gas chromatography-mass spectrometry (GC-MS). The growth dynamics of total viable count (TVC), psychrotrophs, Pseudomonas spp., lactic acid bacteria (LAB), Brochothrix thermosphacta and H2 S producing bacteria were characterized based on maximum growth rates (μmax ), maximal microbial concentration (Nmax ) and at the moment of microbial shelf life (Svalues ), calculated from Gompertz-fitted growth curves. Pseudomonas spp. was predominant species, while B. thermosphacta was characterized by the highest μmax . The microbiological and sensory shelf lives were estimated based on TVC, Pseudomonas spp., and B. thermosphacta counts and sensory evaluation, respectively. Among 27 VOCs identified by GC-MS in spoiled chicken samples, ethanol (EtOH), 1-butanol-3-methyl (1But-3M), and acetic acid (C2 ) achieved the highest Pearson's correlation coefficients of 0.66, 0.61, and 0.59, respectively, with TVC, regardless of storage temperature. Partial least squares (PLS) regression revealed that the synthesis of 1But-3M and C2 was most likely induced by the metabolic activity of B. thermosphacta and LAB, while EtOH was attributed to Pseudomonas spp. The increase in concentration of selected volatile spoilage markers (EtOH, 1But-3M, and C2 ) in the headspace over spoiled chicken breast was found to be statistically significant (P < 0.05) with TVC growth. These findings highlight the possibility of analyzing the combination of 3 selected spoilage markers: EtOH, 1But-3M, and C2 as rapid evaluation for poultry quality testing using SPME-GC-MS.

The use of photography to perform an online consumer test on the freshness of chicken breast and the extension of shelf life

“…and the facultative anaerobes or microaerophilic LAB, which can grow faster in the presence of oxygen (Balamatsia, Paleologos, Kontominas, & Savvaidis, ; Jimenez et al, ). The presence of higher counts of Pseudomonas in the MAP samples could have been associated with the higher rejection rate of these samples, since these microorganisms are very prone to form a biofilm on the surface of cuts of meat and are highly proteolytic, contributing to the occurrence of rotten and ammoniac odors (Mikš‐Krajnik et al, ; Rouger et al, ). Nonetheless, given the theoretical expectancy of having a worse odor in the MAP samples, the results of the present study strongly suggest that the aspect was the major criterion used in freshness evaluation since both the evaluation of freshness on a 5‐point scale and the purchasing intention were similar when the samples were observed online and when they were observed and smelled live.…”

“…Several studies have been made to evaluate the shelf life of chicken, based on the spoilage microflora or in chemical indicators of spoilage. When microbial counts were used, the limit of 7 Log cfu/g of total viable microorganisms is the cut‐off point usually considered to define the separation between fresh and spoiled chicken meat (Mikš‐Krajnik, Yoon, Ukuku, & Yuk, ; Rouger, Moriceau, Prévost, Remenant, & Zagorec, ; Soncu et al, ). Chemical indicators, such as total basic volatile nitrogen, biogenic amines, thiobarbituric acid reactive substances, and volatile compounds have also been used or proposed to define the end of shelf life (Balamatsia, Patsias, Kontominas, & Savvaidis, ; Mikš‐Krajnik et al, ; Rukchon, Nopwinyuwong, Trevanich, Jinkarn, & Suppakul, ).…”

“…When microbial counts were used, the limit of 7 Log cfu/g of total viable microorganisms is the cut‐off point usually considered to define the separation between fresh and spoiled chicken meat (Mikš‐Krajnik, Yoon, Ukuku, & Yuk, ; Rouger, Moriceau, Prévost, Remenant, & Zagorec, ; Soncu et al, ). Chemical indicators, such as total basic volatile nitrogen, biogenic amines, thiobarbituric acid reactive substances, and volatile compounds have also been used or proposed to define the end of shelf life (Balamatsia, Patsias, Kontominas, & Savvaidis, ; Mikš‐Krajnik et al, ; Rukchon, Nopwinyuwong, Trevanich, Jinkarn, & Suppakul, ). Despite their pertinence and usefulness, as demonstrated by the articles previously reported indirect methods have some weaknesses, namely, the costs of some approaches and their indirect nature, so that they need to be correlated with the perception of freshness by the final user of the product—the consumer (Hough & Garitta, ).…”

The role of smart packaging system in food supply chain

“…Molecules, like dimethylamine, trimethylamine, ammonia, histamine, carbon, sulfuric compounds, and ethanol, are among the byproducts of metabolism of bacteria which can be utilized as different kinds of indicators for food spoilage (Bibi, Guillaume, Gontard, & Sorli, ). Solid‐phase microextraction combined with the gas chromatography‐mass spectrometry (Mikṧ‐Krajnik, Yoon, Ukuku, & Yuk, ), UV‐VIS spectroscopy (Aliakbarian, Bagnasco, Perego, Leardi, & Casale, ), and near‐infrared spectroscopy (Aliakbarian et al., ) have been used to identify and quantify the volatile compounds in food. However, most of these techniques are expensive, complicated, and labor intensive when compared with smart sensor‐enabled RFIDs which have been considered as cost effective, nonobtrusive, and user friendly technique for food packaging (Badia‐Melis, Mc Carthy, Ruiz‐Garcia, Garcia‐Hierro, & Villalba, ; Fidders & Yan, ).…”

Modeling the Growth and Interaction Between Brochothrix thermosphacta, Pseudomonas spp., and Leuconostoc gelidum in Minced Pork Samples

“…Using R software (R Core Team, 2019), correlations between the minimal shelf life (MSL) and the growth parameters (µ max , LPD, N 0 , N max ) were obtained by the Pearson's correlation coefficient (r) in mono-culture and co-culture experiments (Liu et al, 2006;Miks-Krajnik et al, 2016). High correlations were considered when |r| > 0.7000 (Miks-Krajnik et al, 2016). The best influencing growth parameter on the microbial shelf life was chosen according to the Pearson's correlations coefficient.…”