Meat's freshness is closely related to food safety and human health and has received increasing attention nowadays. To on-site visually screen meat freshness in a fast and non-destructive manner, we rationally constructed a series of fluorescent probes (, , and ) with distinct electron-withdrawing substitution groups based on julolidine-fused coumarin. These probes underwent an aza-Michael addition followed by an elimination reaction with cadaverine to generate a colorimetric and ratiometric fluorescence response, and their sensing performance was rationally enhanced by improving the electron-withdrawing strength of substitution groups. Particularly, with a dicyanovinyl group as the reaction site exhibited outstanding sensing performance including rapid response (∼60 s), high selectivity, and low detection limit (14 nM). Furthermore, was fabricated into test kits to detect cadaverine vapor with a high-contrast fluorescence change from red to green. Based on two-color visualization of cadaverine vapor, on-site non-contact and non-destructive monitoring of meat freshness was successfully achieved. The good sensing performance rendered test kits a promising real-time fluorescence screening platform for rapid, non-destructive, and accurate evaluation of meat freshness.

Download full-text PDF

Source
http://dx.doi.org/10.1021/acs.analchem.2c03326DOI Listing

Publication Analysis

Top Keywords

meat freshness
16
sensing performance
12
real-time fluorescence
8
fluorescence screening
8
screening platform
8
substitution groups
8
test kits
8
cadaverine vapor
8
freshness
5
meat
4

Similar Publications

Poultry represents a rich source of multiple nutrients. Refrigeration is commonly employed for poultry preservation, although extended storage duration can adversely affect the meat quality. Current research on this topic has focused on the analysis of biochemical indices in chilled goose meat, with limited information on changes in metabolites that influence the quality of the meat during storage.

View Article and Find Full Text PDF
Article Synopsis
  • Hyperspectral imaging (HSI) combined with machine learning (ML) offers a promising method for evaluating the freshness of meat by measuring fluorescence, which is closely linked to bacterial density.
  • The study introduces a freshness index (FI) as a quantifiable metric for meat freshness, enabling the processing of hyperspectral data to estimate freshness even in unknown states.
  • This technology could revolutionize consumer electronics, enhancing devices like refrigerators and smartphones with advanced sensing capabilities for more personalized user experiences.
View Article and Find Full Text PDF

In this study, smart films of EFS, EFS-SiO and EFS-SiO-CRE were successfully developed by using Euryale ferox starch (EFS), nano-SiO and Chinese rose extract (CRE). The Chinese rose flower had a high content of anthocyanins (1.73 mg/g) and CRE exhibited different colors in varying pH buffers (2-13).

View Article and Find Full Text PDF

Porkolor: A deep learning framework for pork color classification.

Meat Sci

December 2024

Sun Yat-sen University, No. 132 Waihuandong Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, China. Electronic address:

Pork color is crucial for assessing its safety and freshness, and traditional methods of observing through human eyes are inefficient and subjective. In recent years, several methods have been proposed based on computer vision and deep learning have been proposed, which can provide objective and stable evaluations. However, these methods suffer from a lack of standardized data collection methods and large-scale datasets for training, leading to poor model performance and limited generalization capabilities.

View Article and Find Full Text PDF

A combinatorial approach to chicken meat spoilage detection using color-shifting silver nanoparticles, smartphone imaging, and artificial neural network (ANN).

Food Chem

December 2024

Department of Biochemistry, Faculty of Science, Ege University, 35100, Bornova, Izmir, Türkiye.. Electronic address:

Article Synopsis
  • A new colorimetric sensor using green-colored silver nanoparticles (AgNPs) detects biogenic amines like histamine, which indicate spoilage in protein-rich foods.
  • After optimizing various parameters, the sensor achieved a detection limit of 0.21 μg/mL for histamine, which was further improved to 0.09 μg/mL using smartphone imaging and artificial neural networks.
  • The sensor effectively monitored histamine levels in chicken meat over three days, demonstrating its potential for real-time food freshness monitoring.
View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!