Detecting Bacterial Biofilms Using Fluorescence Hyperspectral Imaging and Various Discriminant Analyses

Development of super nanoantimicrobials combining AgCl, tetracycline and benzalkonium chloride

In this work, we demonstrate that a simple argentometric titration is a scalable, fast, green and robust approach for producing AgCl/antibiotic hybrid antimicrobial materials. We titrated AgNO3 into tetracycline hydrochloride (TCH) aqueous solution, thus forming AgCl/TCH in a one-step procedure. Furthermore, we investigated the one-pot synthesis of triply synergistic super-nanoantimicrobials, combining an inorganic source of Ag+ ions (AgCl), a disinfecting agent (benzyl-dimethyl-hexadecyl-ammonium chloride, BAC) and a molecular antibiotic (tetracycline hydrochloride, TCH). Conventional antimicrobial tests, industrial biofilm detection protocols, and in situ IR-ATR microbial biofilm monitoring, have been adapted to understand the performance of the synthesized super-nanoantimicrobial. The resulting hybrid AgCl/BAC/TCH nanoantimicrobials are found to be synergistically active in eradicating Salmonella enterica and Lentilactobacillus parabuchneri bacteria and biofilms. This study paves the way for the development of a new class of super-efficient nanoantimicrobials that combine relatively low amounts of multiple active species into a single (nano)formulation, thus preventing the development of antimicrobial resistance towards a single active principle.

Characterization of Salmonella phage of the genus Kayfunavirus isolated from sewage infecting clinical strains of Salmonella enterica

The emergence of multi-drug resistance in Salmonella, causing food-borne infections, is a significant issue. With over 2,600 serovars in in Salmonella sp., it is crucial to identify specific solutions for each serovar. Phage therapy serves as an alternate treatment option. In this study, vB_SalP_792 phage was obtained from sewage, forming plaques in eight out of 13 tested clinical S. enterica isolates. Transmission electron microscopy (TEM) examination revealed a T7-like morphotype. The phage was characterized by its stability, life cycle, antibiofilm, and lytic ability in food sources. The phage remains stable throughout a range of temperatures (-20 to 70°C), pH levels (3-11), and in chloroform and ether. It also exhibited lytic activity within a range of MOIs from 0.0001 to 100. The life cycle revealed that 95% of the phages attached to their host within 3 min, followed by a 5-min latent period, resulting in a 50 PFU/cell burst size. The vB_SalP_792 phage genome has a dsDNA with a length of 37,281 bp and a GC content of 51%. There are 42 coding sequences (CDS), with 24 having putative functions and no resistance or virulence-related genes. The vB_SalP_792 phage significantly reduced the bacterial load in the established biofilms and also in egg whites. Thus, vB_SalP_792 phage can serve as an effective biocontrol agent for preventing Salmonella infections in food, and its potent lytic activity against the clinical isolates of S. enterica, sets out vB_SalP_792 phage as a successful candidate for future in vivo studies and therapeutical application against drug-resistant Salmonella infections.

Machine learning assisted biosensing technology: An emerging powerful tool for improving the intelligence of food safety detection

Recently, the application of biosensors in food safety assessment has gained considerable research attention. Nevertheless, the evaluation of biosensors' sensitivity, accuracy, and efficiency is still ongoing. The advent of machine learning has enhanced the application of biosensors in food security assessment, yielding improved results. Machine learning has been preliminarily applied in combination with different biosensors in food safety assessment, with positive results. This review offers a comprehensive summary of the diverse machine learning methods employed in biosensors for food safety. Initially, the primary machine learning methods were outlined, and the integrated application of biosensors and machine learning in food safety was thoroughly examined. Lastly, the challenges and limitations of machine learning and biosensors in the realm of food safety were underscored, and potential solutions were explored. The review's findings demonstrated that algorithms grounded in machine learning can aid in the early detection of food safety issues. Furthermore, preliminary research suggests that biosensors could be optimized through machine learning for real-time, multifaceted analyses of food safety variables and their interactions. The potential of machine learning and biosensors in real-time monitoring of food quality has been discussed.

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Artificial Intelligence (AI) technologies have been powerful solutions used to improve food yield, quality, and nutrition, increase safety and traceability while decreasing resource consumption, and eliminate food waste. Compared with several qualitative reviews on AI in food safety, we conducted an in-depth quantitative and systematic review based on the Core Collection database of WoS (Web of Science). To discover the historical trajectory and identify future trends, we analysed the literature concerning AI technologies in food safety from 2012 to 2022 by CiteSpace. In this review, we used bibliometric methods to describe the development of AI in food safety, including performance analysis, science mapping, and network analysis by CiteSpace. Among the 1855 selected articles, China and the United States contributed the most literature, and the Chinese Academy of Sciences released the largest number of relevant articles. Among all the journals in this field, PLoS ONE and Computers and Electronics in Agriculture ranked first and second in terms of annual publications and co-citation frequency. The present character, hot spots, and future research trends of AI technologies in food safety research were determined. Furthermore, based on our analyses, we provide researchers, practitioners, and policymakers with the big picture of research on AI in food safety across the whole process, from precision agriculture to precision nutrition, through 28 enlightening articles.

Dry surface biofilms in the food processing industry: An overview on surface characteristics, adhesion and biofilm formation, detection of biofilms, and dry sanitization methods

Bacterial biofilm formation in low moisture food processing (LMF) plants is related to matters of food safety, production efficiency, economic loss, and reduced consumer trust. Dry surfaces may appear dry to the naked eye, however, it is common to find a coverage of thin liquid films and microdroplets, known as microscopic surface wetness (MSW). The MSW may favor dry surface biofilm (DSB) formation. DSB formation is similar in other industries, it occurs through the processes of adhesion, production of extracellular polymeric substances, development of microcolonies and maturation, it is mediated by a quorum sensing (QS) system and is followed by dispersal, leading to disaggregation. Species that survive on dry surfaces develop tolerance to different stresses. DSB are recalcitrant and contribute to higher resistance to sanitation, becoming potential sources of contamination, related to the spoilage of processed products and foodborne disease outbreaks. In LMF industries, sanitization is performed using physical methods without the presence of water. Although alternative dry sanitizing methods can be efficiently used, additional studies are still required to develop and assess the effect of emerging technologies, and to propose possible combinations with traditional methods to enhance their effects on the sanitization process. Overall, more information about the different technologies can help to find the most appropriate method/s, contributing to the development of new sanitization protocols. Thus, this review aimed to identify the main characteristics and challenges of biofilm management in low moisture food industries, and summarizes the mechanisms of action of different dry sanitizing methods (alcohol, hot air, UV-C light, pulsed light, gaseous ozone, and cold plasma) and their effects on microbial metabolism.

Intelligent detection for fresh-cut fruit and vegetable processing: Imaging technology

Fresh-cut fruits and vegetables are healthy and convenient ready-to-eat foods, and the final quality is related to the raw materials and each step of the cutting unit. It is necessary to integrate suitable intelligent detection technologies into the production chain so as to inspect each operation to ensure high product quality. In this paper, several imaging technologies that can be applied online to the processing of fresh-cut products are reviewed, including: multispectral/hyperspectral imaging (M/HSI), fluorescence imaging (FI), X-ray imaging (XRI), ultrasonic imaging, thermal imaging (TI), magnetic resonance imaging (MRI), terahertz imaging, and microwave imaging (MWI). The principles, advantages, and limitations of these imaging technologies are critically summarized. The potential applications of these technologies in online quality control and detection during the fresh-cut processing are comprehensively discussed, including quality of raw materials, contamination of cutting equipment, foreign bodies mixed in the processing, browning and microorganisms of the cutting surface, quality/shelf-life evaluation, and so on. Finally, the challenges and future application prospects of imaging technology in industrialization are presented.

Recent advances in muscle food safety evaluation: Hyperspectral imaging analyses and applications

As there is growing interest in process control for quality and safety in the meat industry, by integrating spectroscopy and imaging technologies into one system, hyperspectral imaging, or chemical or spectroscopic imaging has become an alternative analytical technique that can provide the spatial distribution of spectrum for fast and nondestructive detection of meat safety. This review addresses the configuration of the hyperspectral imaging system and safety indicators of muscle foods involving biological, chemical, and physical attributes and other associated hazards or poisons, which could cause safety problems. The emphasis focuses on applications of hyperspectral imaging techniques in the safety evaluation of muscle foods, including pork, beef, lamb, chicken, fish and other meat products. Although HSI can provide the spatial distribution of spectrum, characterized by overtones and combinations of the C-H, N-H, and O-H groups using different combinations of a light source, imaging spectrograph and camera, there still needs improvement to overcome the disadvantages of HSI technology for further applications at the industrial level.

Detection of Biofilm Formation on Material Surfaces by Ag+ Coating

The evaluation of biofilm formation is important, given the ubiquity and problematic nature of biofilms in industrial and medical settings, as well as in everyday life. Basically, biofilms are formed on substrates. Therefore, it is essential to consider the properties of the substrates during biofilm evaluation. The common dye staining method to evaluate biofilm formation requires a short evaluation time and enables the evaluation of a large area of the sample. Furthermore, it can be easily determined visually, and quantitative evaluation is possible by quantifying color adsorption. Meanwhile, the dye staining method has the problem of adsorption even on substrate surfaces where no biofilm has formed. Therefore, in this study, we focused on Ag+ reduction reaction to devise a novel biofilm evaluation method. Ag+ is highly reductive and selectively reacts with organic substances, such as saccharides, aldehydes, and proteins contained in biofilms, depositing as metallic Ag. First, to simply evaluate biofilm formation, we used a glass substrate as a smooth, transparent, and versatile oxide material. We observed that the amount of Ag deposited on the substrate was increased proportionally to the amount of biofilm formed under light irradiation. Upon comparing the Ag deposition behavior and adsorption behavior of crystal violet, we discovered that for short immersion times in AgNO3 solution, Ag deposition was insufficient to evaluate the amount of biofilm formation. This result suggests that the Ag reduction reaction is more insensitive than the crystal violet adsorption behavior. The results of the Ag deposition reaction for 24 h showed a similar trend to the crystal violet dye adsorption behavior. However, quantitative biofilm evaluation using the proposed method was difficult because of the Ag+ exchange with the alkali metal ions contained in the glass substrate. We addressed this issue by using the basic solution obtained by adding an ammonia solution to aqueous AgNO3. This can cause Ag+ to selectively react with the biofilm, thus enabling a more accurate quantitative evaluation. The optimum was determined at a ratio of distilled water to aqueous ammonia solution of 97:3 by weight. This biofilm was also evaluated for materials other than ceramics (glass substrate): organic material (polyethylene) and metal material (pure iron). In the case of polyethylene, a suitable response and evaluation of biofilm formation was successfully achieved using this method. Meanwhile, in the case of pure iron, a significantly large lumpy deposit of Ag was observed. The likely reason is that Ag precipitation occurred along with the elution of iron ions because of the difference in ionization tendency. It could be concluded that the detection of biofilm formation using this method was effective to evaluate biofilm formation on materials, in which the reduction reaction of [Ag(NH3)2]+ does not occur. Thus, a simple and relatively quantitative evaluation of biofilms formed on substrates is possible using this method.

Using Vis-NIR Spectroscopy for Predicting Quality Compounds in Foods

Over the past four decades, near-infrared reflectance spectroscopy (NIRS) has become one of the most attractive and used technique for analysis as it allows for fast and simultaneous qualitative and quantitative characterization of a wide variety of food samples [...].