Revolutionizing food processing with infrared heating: New approaches to quality and efficiency
Bibliographic record
Abstract
Infrared (IR) heating technology represents a cutting-edge thermal technology in food processing, offering energy-efficient and effective solutions for various unit operations such as blanching, microbial decontamination, peeling, roasting, and thawing. Recent advancements in IR heating have highlighted its advantages over conventional methods, including short processing time, uniform heating and product temperature, superior quality of products, low energy consumption, high heat transfer rate, high energy efficiency, ease of control, compact equipment and eco-friendliness over the conventional heating systems. Considering these merits, IR has found several applications in food processing with increased interest and research in the last decade. Thus, this review discusses recent studies and technological advancements in IR heating, addressing the benefits, limitations, and challenges of its implementation across different food processing applications. In addition, it explores the synergistic utilization of IR radiation when combined with other novel technologies by assessing their combined impact on process efficiency, energy savings, and food quality. The findings of this study showed that in blanching, IR heating has shown potential to reduce nutrient loss and preserve sensory quality, enhancing the retention of vitamins and minerals. For microbial decontamination, it provides a non-chemical approach to pathogen reduction, minimizing foodborne risks while preserving product quality. In peeling applications, IR heating can achieve efficient removal of skin with minimal waste and damage to the edible portion, making it an eco-friendly option. When used for roasting, it contributes to the development of desirable colour, flavor, and texture attributes. Finally, in thawing applications, IR technology provides faster and more uniform heating, reducing drip loss and improving product quality. From the foregoing, it was established that the adoption of IR heating technology in food processing represents a significant advancement in enhancing product quality and process efficiency across the various applications. Moreover, combining IR heating with other novel food processing technologies further improved process efficiency, enhanced energy savings, and preserved or even boosted food quality. Further research on optimization, process modelling, and the integration of IR heating with other technologies could expand its applicability and effectiveness, paving the way for more sustainable and high-quality food processing solutions. • IR heating improves processing efficiency and food quality, enhancing consumer appeal • IR integration with other technologies creates synergy for improved overall food processing • Optimizing the heating process and IR equipment in food processing offers numerous benefits • The applicability of IR is anticipated to grow in the coming years due to its merits
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".