In recent years, the high prevalence of Salmonella has emerged as a serious threat to public safety, prompting attempts to utilize accurate, rapid, and direct methods to ensure food safety. In this study, a multifunctional platform featuring dual-mode detection channels (colorimetric-fluorescence) combined with polymer chain reaction (PCR) was proposed for the sensitive and rapid detection of Salmonella. Additionally, the colorimetric measurements were achieved by color changes induced by methylene blue (MB) insertion into the double-stranded DNA, and the fluorescence measurements were performed by internal filter effect (IFE)-induced fluorescence quenching of upconversion nanoparticles (UCNPs) by MB. The results showed that the IFE and PCR amplification processes improved the sensitivity of the sensor towards Salmonella detection, with a limit of detection (LOD) of 21.8 CFU/mL. Moreover, this colorimetric-fluorescence dual-mode PCR biosensor was applied to determine Salmonella in food samples, such as chicken, egg, and fish, which produced satisfactory results. Overall, the present study results demonstrate the potential for combining PCR amplification with IFE to develop an efficient and reliable dual-mode analysis platform to safeguard food security.

Shigella as a typical foodborne pathogen has strong survivability in the environment or food, leading to infectious diseases, yet its rapid detection technology with high selectivity and sensitivity remains challenging. In this study, complementary strand modified upconversion nanoparticles (UCNPs) can offer stable yellow-green fluorescence at 500–700 nm excited by a 980 nm laser. Importantly, Shigella aptamer modified gold nanoparticles (GNPs) formed by “Au−S” bond act as a fluorescence resonance energy transfer (FRET) donor and recognition element that can bind specifically to Shigella and significantly quench the fluorescence of complementary strand modified UCNPs. As a result, the fluorescence of our developed nanoprobe increased linearly with the increase in Shigella in a wide range from 1.2 × 10² to 1.2 × 10⁸ CFU/mL and the detection limit was as low as 30 CFU/mL. Moreover, the fabricated upconversion fluorescence nanoprobe can achieve Shigella detection in contaminated chicken without enrichment in 1 h.

Shigella, a typical and fatal foodborne pathogen with strong infectivity and survivability in foodstuff, demands a simple and sensitive detecting method. In this study, we reported a novel nanoplatform based on biofunctionalized magnetic nanoparticles (MNPs) modified upconversion nanoparticles (UCNPs) for rapid and specific determination of Shigella. Due to base pairing, Shigella aptamer-functionalized horseradish peroxidase (HRP) combined with complementary strand-modified MNPs@UCNPs. In the absence of Shigella, HRP associated with MNPs@UCNPs were magnetically separated, and colorless 3,3′,5,5′-tetramethylbenzidine (TMB) was oxidized into blue oxTMB. The overlap between oxTMB’s absorption peak and MNPs@UCNPs’ emission peak caused the fluorescence quenching at 545 nm. The MNPs@UCNPs fluorescence biosensor was achieved to detect Shigella in 1 h, with a limit of detection of 32 CFU/mL. This work showed a rapid and specific sensing platform and produced satisfactory chicken sample results.

Eugenol is hepatotoxic and potentially hazardous to human health. This paper reports on a rapid non-destructive quantitative method for the determination of eugenol concentration in curdlan (CD) biofilms by electronic nose (E-nose) combined with gas chromatography-mass spectrometry (GC-MS). Different concentrations of eugenol were added to the film-forming solution to form a series of biofilms by casting method, and the actual eugenol concentration in the biofilm was determined. Analysis of the odor collected on the biofilms was carried out by GC-MS and an E-nose. The E-nose data was subjected to principal component analysis (PCA) and linear discriminant analysis (LDA) in order to establish a discriminant model for determining eugenol concentrations in the biofilms. Further analyses involving the application of all sensors and featured sensors, the prediction model-based partial least squares (PLS) and support vector machines (SVM) were carried out to determine eugenol concentration in the CD biofilms. The results showed that the optimal prediction model for eugenol concentration was obtained by PLS at R²_p of 0.952 using 10 sensors. The study described a rapid, non-destructive detection and quantitative method for determining eugenol concentration in bio-based packaging materials.

In the original publication, there is a mistake in Figure 1: A duplication error between 1B and 1C, which occurred due to the similarity of the images of the three nanoparticles