Determination of target biogenic amines in fish by GC-MS: investigating seafood quality

Teixeira, Edgar Aleixo; Noronha, João Paulo; Barbosa, Vera; Anacleto, Patrícia; Maulvault, Ana Luísa; Nunes, María Leonor; Marques, António; Diniz, Mário

doi:10.1080/07853890.2018.1561675

Cited by 3 publications

(2 citation statements)

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“…Among them, nine compounds with a boiling point lower than 100°C were categorized as very volatile organic compounds (VVOC) and the other 29 compounds are volatile organic compounds (VOC; EURO Reports & Studies 111, 1989). Volatile amines, a traditional chemical indicator of seafood spoilage, were excluded herein because they are generally known to have a basic character (Chung & Chan, 2009; Monique, Ifremer, & Nantes, 2005; Teixeira et al., 2019) and will be the focus of future studies.…”

Section: Resultsmentioning

confidence: 99%

“…tile organic compounds (VVOC) and the other 29 compounds are volatile organic compounds (VOC;EURO Reports & Studies 111, 1989). Volatile amines, a traditional chemical indicator of seafood spoilage, were excluded herein because they are generally known to have a basic character(Chung & Chan, 2009;Monique, Ifremer, & Nantes, 2005;Teixeira et al, 2019) and will be the focus of future studies.All the 38 target compounds in the current study have been reported in other literature as being correlated with quality change in various types of food(Alasalvar et al, 2005;Bai et al, 2019;Duflos et al, 2005;Joffraud et al, 2001;Jørgensen & Henrik, 2001). The TA B L E 2 Performance of pooled matrix-matched calibration curves…”

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confidence: 99%

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Large volume headspace GC/MS analysis for the identification of volatile compounds relating to seafood decomposition

Wang

Jager

Begley

et al. 2022

Food Science & Nutrition

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Decomposition in seafood products in the United States is monitored by the Food and Drug Administration (FDA) laboratories using sensory testing, which requires highly trained analysts. A large‐volume headspace (LVHS) gas chromatography/mass spectrometry (GC/MS) method was developed to generate analytical results that can be directly compared to sensory evaluation. Headspace vapor was withdrawn from a 1‐L vial containing 50 g seafood sample using a large volume headspace autosampler. Various volatile compounds were collected simultaneously. Analytes were preconcentrated by a capillary column trapping system and then sent through a cryo‐focuser mounted onto the GC injector. A selected ion monitoring (SIM) MS acquisition method was used to selectively monitor 38 compounds of interest. Samples of red snapper, croaker, weakfish, mahi‐mahi, black tiger shrimp, yellowfin tuna, and sockeye salmon that have been assessed and scored by an FDA National Seafood Sensory Expert (NSSE) were used for method performance evaluation. Characteristic compounds potentially associated with seafood quality deterioration for each seafood species were identified by quantitative analysis using pooled matrix‐matched calibrations and two‐sample t‐test statistical analysis. Classification of fresh and decomposed samples was visualized on the analysis of variance (ANOVA)–principal component analysis (PCA) score plots. The results determined that the LVHS‐GC/MS technique appeared promising as a screening tool to identify compounds representative of sensory analysis.

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Section: Resultsmentioning

confidence: 99%

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Large volume headspace GC/MS analysis for the identification of volatile compounds relating to seafood decomposition

Wang

Jager

Begley

et al. 2022

Food Science & Nutrition

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Monitoring of Biogenic Amines in Tilapia Flesh (Oreochromis niloticus) by a Simple and Rapid High-Performance Thin-Layer Chromatography Method

Dergal

Douny

Gustin

et al. 2022

Journal of Aquatic Food Product Technology

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Intelligent Evaluation and Dynamic Prediction of Oyster Freshness with Electronic Nose Based on the Distribution of Volatile Compounds Using GC–MS Analysis

Wang,

Dou,

Liu

et al. 2024

Foods

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The quality of oysters is reflected by volatile organic components. To rapidly assess the freshness level of oysters and elucidate the changes in flavor substances during storage, the volatile compounds of oysters stored at 4, 12, 20, and 28 °C over varying durations were analyzed using GC-MS and an electronic nose. Data from both GC-MS and electronic nose analyses revealed that alcohols, acids, and aldehydes are the primary contributors to the rancidity of oysters. Notably, the relative and absolute contents of Cis-2-(2-Pentenyl) furan and other heterocyclic compounds exhibited an upward trend. This observation suggests the potential for developing a simpler test for oyster freshness based on these compounds. Linear Discriminant Analysis (LDA) demonstrated superior performance compared to Principal Component Analysis (PCA) in differentiating oyster samples at various storage times. At 4 °C, the classification accuracy of the optimal support vector machine (SVM) and random forest (RF) models exceeded 90%. At 12 °C, 20 °C, and 28 °C, the classification accuracy of the best SVM and RF models surpassed 95%. Pearson correlation analysis of the concentrations of various volatile compounds and characteristic markers with the sensor response values indicated that the selected sensors were more aligned with the volatiles emitted by oysters. Consequently, the volatile compounds in oysters during storage can be predicted based on the response information from the sensors in the detection system. This study also demonstrates that the detection system serves as a viable alternative to GC-MS for evaluating oysters of varying freshness grades.

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Determination of target biogenic amines in fish by GC-MS: investigating seafood quality

Cited by 3 publications

References 0 publications

Large volume headspace GC/MS analysis for the identification of volatile compounds relating to seafood decomposition

Large volume headspace GC/MS analysis for the identification of volatile compounds relating to seafood decomposition

Monitoring of Biogenic Amines in Tilapia Flesh (Oreochromis niloticus) by a Simple and Rapid High-Performance Thin-Layer Chromatography Method

Intelligent Evaluation and Dynamic Prediction of Oyster Freshness with Electronic Nose Based on the Distribution of Volatile Compounds Using GC–MS Analysis

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