review · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy
Foodborne disease outbreaks cause severe social and economic damage, making the microbial safety of widely consumed meat a global priority. Conventional microbial testing methods are time-consuming and cannot provide the real-time feedback required during food production. Hyperspectral imaging, specifically visible-near infrared systems, offers a rapid and non-destructive technique capable of identifying foodborne pathogens directly in samples. Recent advancements in artificial intelligence and machine learning offer opportunities to improve turnaround times for microbial evaluation in the meat sector. Although research investigating hyperspectral imaging for food applications has expanded significantly, its practical implementation for microbial assessment of meat is not yet optimal. A review of the field details the operational basics of visible-near infrared systems, recent uses in meat testing, ongoing operational challenges, and prospective future applications.
Foodborne illnesses present major public health risks and economic costs worldwide. Because meat is a staple protein source, ensuring it is safe without altering its quality or sensory traits is critical. Developing rapid, non-destructive testing tools allows producers to identify dangerous pathogens quickly, avoiding contaminated shipments and reducing reliance on slow laboratory testing procedures.
The technology could enable inline, automated pathogen monitoring tools for meat processors, quality assurance teams, and food production facilities. Coupling optical hardware with machine learning algorithms offers potential integration into active production lines. However, the abstract indicates the technology is at an early research and review stage, noting that commercial utilisation in meat microbial assessment is not yet optimal and faces unresolved operational challenges.
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Food safety is always of paramount importance globally due to the devasting social and economic effects of foodborne disease outbreaks. There is a high consumption rate of meat worldwide, making it an essential protein source in the human diet, hence its microbial safety is of great importance. The food industry stakeholders are always in search of methods that ensure safe food whilst maintaining food quality and excellent sensory attributes. Currently, there are several methods used in microbial food analysis, however, these methods are often time-consuming and do not allow real-time analysis. Considering the recent technological breakthroughs in artificial intelligence and machine learning, it raises the question of whether these advancements could be leveraged within the meat industry to improve turnaround time for microbial assessments. Hyperspectral imaging (HSI) is a highly prospective technology worth exploring for microbial analysis. The rapid, non-destructive method has the potential to be integrated into food production systems and allows foodborne pathogen detection in food samples, thus saving time. Although there has been a substantial increase in research on the utilisation of HSI in food applications over the past years, its use in the microbial assessment of meat is not yet optimal. This review aims to provide a basic understanding of the visible-near infrared HSI system, recent applications in the microbial assessment of meat products, challenges, and possible future applications.
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DOI: 10.1016/j.saa.2024.124261
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