ArticleFoods (Basel, Switzerland)2020
Detection of Beef Adulterated with Pork Using a Low-Cost Electronic Nose Based on Colorimetric Sensors.
Article in Foods (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- From Biomass Waste to Multifunctional Biochar: Tailored Preparation and Emerging Applications in Energy, Environment, and Sensing.Molecules (Basel, Switzerland) · 2026Review
- Leakage-Free Benchmarking of Electronic Noses for Beef Freshness: A Signal-Richness Criterion for Model Selection.Foods (Basel, Switzerland) · 2026Article
- Quality Assessment in Frozen Seafood: Advances in Sensing Technologies and Artificial Intelligence.Foods (Basel, Switzerland) · 2026Review
- AI-Driven Nondestructive Measurement Technologies for Meat Quality and Safety: A Review.Foods (Basel, Switzerland) · 2026Review
- Colorimetric Sensor Arrays Technology in Food Quality and Safety Analysis.Micromachines · 2026Review
- Intelligent Gas Sensors for Food Safety and Quality Monitoring: Advances, Applications, and Future Directions.Foods (Basel, Switzerland) · 2025Review
- Recent Advances in Functionalized Carbon Quantum Dots Integrated with Metal-Organic Frameworks: Emerging Platforms for Sensing and Food Safety Applications.Foods (Basel, Switzerland) · 2025Review
- Review of Recent Advances in Intelligent and Antibacterial Packaging for Meat Quality and Safety.Foods (Basel, Switzerland) · 2025Review
- Application of machine learning approach on halal meat authentication principle, challenges, and prospects: A review.Heliyon · 2024Review
- Assessing food availability and healthier options in an urban Chinese university: a case study using the Chinese Nutrition Environment Measurement Survey for Stores (C-NEMS-S).BMC public health · 2024Article
- Bacteriological evaluation and advanced SYBR-green multiplex real-time PCR assay for detection of minced meat adulteration.Open veterinary journal · 2024Article
- A Highly Sensitive Indirect Enzyme-Linked Immunosorbent Assay (ELISA) Based on a Monoclonal Antibody Specific to Thermal Stable-Soluble Protein in Pork Fat for the Rapid Detection of Pork Fat Adulterated in Heat-Processed Beef Meatballs.Food science of animal resources · 2023Article
- Differentiating True and False Cinnamon: Exploring Multiple Approaches for Discrimination.Micromachines · 2023Review
- Coupled Gold Nanoparticles with Aptamers Colorimetry for Detection of Amoxicillin in Human Breast Milk Based on Image Preprocessing and BP-ANN.Foods (Basel, Switzerland) · 2022Article
- A Droplet Digital PCR Based Approach for Identification and Quantification of Porcine and Chicken Derivatives in Beef.Foods (Basel, Switzerland) · 2022Article
- Qualitative and Quantitative Detection of Food Adulteration Using a Smart E-Nose.Sensors (Basel, Switzerland) · 2022Article
- Determination of Pork Meat Storage Time Using Near-Infrared Spectroscopy Combined with Fuzzy Clustering Algorithms.Foods (Basel, Switzerland) · 2022Article
- Machine-learning assisted modelling of multiple elements for authenticating edible animal blood food.Food chemistry: X · 2022Article
- LC-QTOF-MS evaluation of rabbit-specific peptide markers for meat quantitation.Journal of food and drug analysis · 2022Article
- Preparation of Mesoporous Silica Nanosphere-Doped Color-Sensitive Materials and Application in Monitoring the TVB-N of Oysters.Foods (Basel, Switzerland) · 2022Article
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Authors and funding
5 authors.
Funding
Abstract
The present study was aimed at developing a low-cost but rapid technique for qualitative and quantitative detection of beef adulterated with pork. An electronic nose based on colorimetric sensors was proposed. The fresh beef rib steaks and streaky pork were purchased and used from the local agricultural market in Suzhou, China. The minced beef was mixed with pork ranging at levels from 0%~100% by weight at increments of 20%. Protein, fat, and ash content were measured for validation of the differences between the pure beef and pork used in basic chemical compositions. Fisher linear discriminant analysis (Fisher LDA) and extreme learning machine (ELM) were utilized comparatively for identification of the ground pure beef, beef-pork mixtures, and pure pork. Back propagation-artificial neural network (BP-ANN) models were built for prediction of the adulteration levels. Results revealed that the ELM model built was superior to the Fisher LDA model with higher identification rates of 91.27% and 87.5% in the training and prediction sets respectively. Regarding the adulteration level prediction, the correlation coefficient and the root mean square error were 0.85 and 0.147 respectively in the prediction set of the BP-ANN model built. This suggests, from all the results, that the low-cost electronic nose based on colorimetric sensors coupled with chemometrics has a great potential in rapid detection of beef adulterated with pork.
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