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Quantitative TLC-SERS detection of histamine in seafood with support vector machine analysis

Tan, Ailing, Zhao, Yong, Sivashanmugan, Kundan, Squire, Kenneth, Wang, Alan X.
Food control 2019 v.103 pp. 111-118
Raman spectroscopy, allergens, ambient temperature, anaphylaxis, cost effectiveness, detection limit, diatomaceous earth, food safety, gold, histamine, meat, models, nanogold, nanoparticles, poisoning, principal component analysis, pruritus, scombroid poisoning, seafood consumption, seafoods, spectral analysis, support vector machines, thin layer chromatography, tuna, United States
Scombroid fish poisoning caused by histamine intoxication is one of the most prevalent allergies associated with seafood consumption in the United States. Typical symptoms range from mild itching up to fatal cardiovascular collapse seen in anaphylaxis. In this paper, we demonstrate rapid, sensitive, and quantitative detection of histamine in both artificially spoiled tuna solution and real spoiled tuna samples using thin layer chromatography in tandem with surface-enhanced Raman scattering (TLC-SERS) sensing methods, enabled by machine learning analysis based on support vector regression (SVR) after feature extraction with principal component analysis (PCA). The TLC plates used herein, which were made from commercial food-grade diatomaceous earth, served simultaneously as the stationary phase to separate histamine from the blended tuna meat and as ultra-sensitive SERS substrates to enhance the detection limit. Using a simple drop cast method to dispense gold colloidal nanoparticles onto the diatomaceous earth plate, we were able to directly detect histamine concentration in artificially spoiled tuna solution down to 10 ppm. Based on the TLC-SERS spectral data of real tuna samples spoiled at room temperature for 0–48 h, we used the PCA-SVR quantitative model to achieve superior predictive performance exceling traditional partial least squares regression (PLSR) method. This work proves that diatomaceous earth based TLC-SERS technique combined with machine-learning analysis is a cost-effective, reliable, and accurate approach for on-site detection and quantification of seafood allergen to enhance food safety.