In today's global food market, especially considering the rising world population, it is more important, now more than ever, to ensure and maintain high quality standards of our foods. And food experts certainly do not have to treat these as “just one of those regulatory requirements”, but as a critical factor in industry sustainability and in maintaining consumer trust. When these standards are not met, it can lead to significant financial losses and health risks that are avoidable. As a response to these challenges, the food industry has seen a remarkable surge in the development and adoption of high-performing quality control systems throughout the production chain.
Three innovative technologies in particular, have emerged as frontrunners in the field of food quality assessment: the electronic tongue (E-tongue), electronic nose (E-nose), and near-infrared (NIR) spectroscopy. These devices are in the limelight now majorly because of their versatility, speed, and ability to process data in real-time, offering an interesting paradigm shift in how we approach food quality control from farm to fork.
Historical Perspective and Technological Advancements
The concept of mimicking human sensory perception through electronic means goes as far back as the 1950s. It was not until the late 1980s and early 1990s that considerable progress was made in developing practical E-tongue and E-nose systems. NIR spectroscopy on the other hand is older in its origins but has seen a renaissance in food applications over the past few decades.
E-tongues are specifically designed to mimic human taste perceptions and typically employ an array of non-specific chemical sensors coupled with pattern recognition software. E-noses, on the other hand, aim to replicate the olfactory system, using gas sensors to detect volatile organic compounds. NIR spectroscopy is a rapid non-invasive analytical technique that uses the near-infrared region [700 nm (14,000 cm−1) to 2500 nm (4,000 cm−1)] of the electromagnetic spectrum to provide information about the chemical composition of foods.
The rapid evolution of these technologies over the decades can be attributed to the advancements in sensor materials, data processing capabilities, and the integration of artificial intelligence and machine learning algorithms. This progression has significantly enhanced their sensitivity, selectivity, and overall performance, even in complex food matrices.
How They Have Been Applied in Food Quality Control
The versatility of E-tongues, E-noses, and NIR spectroscopy has led to their widespread application across various food categories, as defined by the Codex General Standards. These instruments have proven invaluable in:
1. Dairy Products: Detection of milk adulteration, assessment of cheese ripening, and prediction of yogurt quality parameters
2. Sweeteners: Identification and quantification of different sugars and artificial sweeteners in food products.
3. Beverages: Authentication of wine and spirits, adulteration detection in roselle juice (sobolo), quality control of fruit juices, and monitoring of fermentation processes in beer production
4. Fruits and Vegetables: Assessment of ripeness, prediction of post-harvest quality, and detection of pesticide residues
5. Meat and Fish: Evaluation of freshness, detection of spoilage, and authentication of species
6. Processed Foods: Monitoring of cooking processes, assessment of shelf-life, and detection of off-flavours
In each of these applications, these instruments have demonstrated their ability to not only detect and classify adulterants but also to predict multiple physicochemical and sensory parameters. This elaborate approach to quality assessment provides a thorough understanding of food properties than traditional single-parameter methods.
Chemometric Tools: Enhancing Data Interpretation
The true power of E-tongues, E-noses, and NIR spectroscopy lies in their synergy with advanced chemometric tools. These mathematical and statistical techniques transform complex spectral or sensor data into meaningful information about food quality. Common chemometric methods include:
1. Principal Component Analysis (PCA): For data visualization and exploration
2. Partial Least Squares Regression (PLSR): For quantitative prediction of food properties
3. Linear Discriminant Analysis (LDA): For classification of food samples
4. Artificial Neural Networks (ANN): For modelling complex non-linear relationships in food systems
The integration of these chemometric tools has significantly enhanced the predictive power and reliability of E-tongue, E-nose, and NIR spectroscopy measurements, allowing for more accurate and robust quality assessments.
Pros and Cons
The adoption of these technologies in food quality control offers several compelling advantages:
1. Speed: Rapid analysis times, often in minutes or seconds, enable real-time monitoring and quick decision-making
2. Cost-effectiveness: Reduced need for expensive reagents and specialized laboratory personnel
3. Non-destructive analysis: Samples can often be analysed without preparation or destruction, preserving product integrity
4. Versatility: Ability to assess multiple quality parameters simultaneously
5. Potential for in-line monitoring: Facilitates continuous quality control in production settings
However, these technologies are not without challenges. The high sensitivity of E-tongues, E-noses, and NIR spectroscopy can sometimes lead to difficulties in controlling environmental influences. Factors such as temperature, humidity, and sample presentation can affect measurements. To address these issues, researchers have developed various mathematical correction techniques, including orthogonal signal correction and multiplicative scatter correction.
Future Perspectives
As we look to the future, the role of E-tongues, E-noses, and NIR spectroscopy in food quality control is certainly set to expand further. Ongoing research focuses on:
1. Miniaturization: Development of portable, handheld devices for field-testing applications
2. Sensor improvements: Enhancing selectivity and stability of sensor arrays
3. Data fusion: Combining data from multiple instruments for more comprehensive quality assessments
4. Artificial Intelligence integration: Leveraging machine learning for improved pattern recognition and predictive modelling
E-tongues, E-noses, and NIR spectroscopy represent a significant leap forward in food quality assessment technology. Their ability to provide rapid, affordable, and reliable measurements makes them invaluable tools in ensuring food safety, authenticity, and quality. As these technologies continue to evolve, they show promise in being in a leading role in maintaining the integrity of our global food supply chain.
Reference
Aouadi B, Zaukuu J-LZ, Vitális F, Bodor Z, Fehér O, Gillay Z, Bazar G, Kovacs Z. Historical Evolution and Food Control Achievements of Near Infrared Spectroscopy, Electronic Nose, and Electronic Tongue—Critical Overview. Sensors. 2020; 20(19):5479. https://doi.org/10.3390/s20195479
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