Rapid Microbial Quality Assessment of Chicken Liver Inoculated or Not With Salmonella Using FTIR Spectroscopy and Machine Learning

Investigating the influence of molybdenum disulfide quantum dots coated with DSPE-PEG-TPP on molecular structures of liver lipids and proteins: An in vivo study

Molybdenum disulfide (MoS2) quantum dots (QDs) based therapeutic approaches hold great promise for biomedical applications, necessitating a thorough evaluation of their potential effects on biological systems. In this study, we systematically investigated the impact of MoS2 QDs coated with 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[amino(polyethyleneglycol)-2000](DPSE-PEG) linked with (3-carboxypropyl)triphenyl-phosphonium-bromide (TPP) on molecular structures of hepatic tissue lipids and proteins through a multifaceted analysis. The DSPE-PEG-TPP-MoS2 QDs were prepared and administered to the mice daily for 7 weeks. Liver tissues were subjected to a comprehensive examination using various techniques, including Fourier-transform infrared (FTIR) spectroscopy, UV-vis spectroscopy, and liver function tests. FTIR revealed subtle changes in the lipid composition of liver tissues, indicating potential modifications in the cell membrane structure. Also, the (CH stretching and amides I and II regions) analysis unveiled tiny alterations in lipid chain length and fluidity without changes in the protein structures, suggesting a minor influence of DSPE-PEG-TPP-MoS2 QDs on the liver's cellular membrane and no effect on the protein structures. Further scrutiny using UV-vis spectroscopy demonstrated that DSPE-PEG-TPP-MoS2 QDs had no discernible impact on the absorbance intensities of aromatic amino acids and the Soret band. This observation implies that the treatment with SPE-PEG-TPP-MoS2 QDs did not induce significant alterations in helical conformation or the microenvironment surrounding prosthetic groups in liver tissues. The liver function tests, including ALP, ALT, AST, and BIL levels, revealed no statistically significant changes in these key biomarkers despite minor fluctuations in their values, indicating a lack of significant liver dysfunction. This study provides a detailed understanding of the effects of DSPE-PEG-TPP-MoS2 QDs on hepatic lipids and proteins, offering valuable insights into the biocompatibility and limited impact on the molecular and functional aspects of the liver tissue. These findings could be essential for the application of MoS2 QDs-based therapies.

AI-based R&D for frozen and thawed meat: Research progress and future prospects

Frozen and thawed meat plays an important role in stabilizing the meat supply chain and extending the shelf life of meat. However, traditional methods of research and development (R&D) struggle to meet rising demands for quality, nutritional value, innovation, safety, production efficiency, and sustainability. Frozen and thawed meat faces specific challenges, including quality degradation during thawing. Artificial intelligence (AI) has emerged as a promising solution to tackle these challenges in R&D of frozen and thawed meat. AI's capabilities in perception, judgment, and execution demonstrate significant potential in problem-solving and task execution. This review outlines the architecture of applying AI technology to the R&D of frozen and thawed meat, aiming to make AI better implement and deliver solutions. In comparison to traditional R&D methods, the current research progress and promising application prospects of AI in this field are comprehensively summarized, focusing on its role in addressing key challenges such as rapid optimization of thawing process. AI has already demonstrated success in areas such as product development, production optimization, risk management, and quality control for frozen and thawed meat. In the future, AI-based R&D for frozen and thawed meat will also play an important role in promoting personalization, intelligent production, and sustainable development. However, challenges remain, including the need for high-quality data, complex implementation, volatile processes, and environmental considerations. To realize the full potential of AI that can be integrated into R&D of frozen and thawed meat, further research is needed to develop more robust and reliable AI solutions, such as general AI, explainable AI, and green AI.

Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models

Predictive microbiology is a rapidly evolving field that has gained significant interest over the years due to its diverse application in food safety. Predictive models are widely used in food microbiology to estimate the growth of microorganisms in food products. These models represent the dynamic interactions between intrinsic and extrinsic food factors as mathematical equations and then apply these data to predict shelf life, spoilage, and microbial risk assessment. Due to their ability to predict the microbial risk, these tools are also integrated into hazard analysis critical control point (HACCP) protocols. However, like most new technologies, several limitations have been linked to their use. Predictive models have been found incapable of modeling the intricate microbial interactions in food colonized by different bacteria populations under dynamic environmental conditions. To address this issue, researchers are integrating several new technologies into predictive models to improve efficiency and accuracy. Increasingly, newer technologies such as whole genome sequencing (WGS), metagenomics, artificial intelligence, and machine learning are being rapidly adopted into newer-generation models. This has facilitated the development of devices based on robotics, the Internet of Things, and time-temperature indicators that are being incorporated into food processing both domestically and industrially globally. This study reviewed current research on predictive models, limitations, challenges, and newer technologies being integrated into developing more efficient models. Machine learning algorithms commonly employed in predictive modeling are discussed with emphasis on their application in research and industry and their advantages over traditional models.

Preservative effect of pomegranate-based marination with β-resorcylic acid and cinnamaldehyde on the microbial quality of chicken liver

Chicken liver is considered a delicacy in the Middle East where pomegranate molass is commonly used as a salad dressing and in marinade recipes. Marinated chicken liver is a common entrée and represents a value-added product compared to the otherwise unmarinated liver which commands a lower price. The aim of this study was to investigate the inhibitory effects of a pomegranate-based marinade alone or following the addition of cinnamaldehyde or β-resorcylic acid on the spoilage microorganisms present in chicken liver during storage for 14 d at 4°C or under mild temperature abuse conditions (10°C). The pH and microbial populations of total plate count (TPC), lactic acid bacteria (LAB), Pseudomonas spp. (PS), yeast and mold (YM), and Enterobacteriaceae (EN) were tested during the storage period and the shelf life was determined (defined as 107 log cfu/g). Sensory analysis was also conducted. The pH increased by a greater extent in unmarinated samples as compared to marinated samples (with or without antimicrobials) upon storage. The initial TPC, LAB, PS, YM, and EN microbial populations in the chicken liver were 3.85 ± 0.79, 3.73 ± 0.85, 3.85 ± 0.79, 3.73 ± 0.87, and 3.69 ± 0.23 log cfu/g, respectively. The marinade decreased the microbial populations by 2 to 4 log cfu/g. The marinade and antimicrobial mixture decreased the microbial populations by 3 to 4 log cfu/g. Except for 1 sample, none of the marinated chicken liver samples with or without antimicrobials reached the end of shelf life even up to 14 d of storage at both 4°C and 10°C. The overall sensory score was rated around 6/9 for the treated samples.

Sublethally injured microorganisms in food processing and preservation: Quantification, formation, detection, resuscitation and adaption

Sublethally injured state has been recognized as a survival strategy for microorganisms suffering from stressful environments. Injured cells fail to grow on selective media but can normally grow on nonselective media. Numerous microorganism species can form sublethal injury in various food matrices during processing and preservation with different techniques. Injury rate was commonly used to evaluate sublethal injury, but mathematical models for the quantification and interpretation of sublethally injured microbial cells still require further study. Injured cells can repair themselves and regain viability on selective media under favorable conditions when stress is removed. Conventional culture methods might underestimate microbial counts or present a false negative result due to the presence of injured cells. Although the structural and functional components may be affected, the injured cells pose a great threat to food safety. This work comprehensively reviewed the quantification, formation, detection, resuscitation and adaption of sublethally injured microbial cells. Food processing techniques, microbial species, strains and food matrix all significantly affect the formation of sublethally injured cells. Culture-based methods, molecular biological methods, fluorescent staining and infrared spectroscopy have been developed to detect the injured cells. Cell membrane is often repaired first during resuscitation of injured cells, meanwhile, temperature, pH, media and additives remarkably influence the resuscitation. The adaption of injured cells negatively affects the microbial inactivation during food processing.

Small Contaminations on Broiler Carcasses Are More a Quality Matter than a Food Safety Issue

Depending on the interpretation of the European Union (EU) regulations, even marginally visibly contaminated poultry carcasses could be rejected for human consumption due to food safety concerns. However, it is not clear if small contaminations actually increase the already present bacterial load of carcasses to such an extent that the risk for the consumers is seriously elevated. Therefore, the additional contribution to the total microbial load on carcasses by a small but still visible contamination with feces, grains from the crop, and drops of bile and grease from the slaughter line was determined using a Monte Carlo simulation. The bacterial counts (total aerobic plate count, Enterobacteriaceae, Escherichia coli, and Campylobacter spp.) were obtained from the literature and used as input for the Monte Carlo model with 50,000 iterations for each simulation. The Monte Carlo simulation revealed that the presence of minute spots of feces, bile, crop content, and slaughter line grease do not lead to a substantial increase of the already existing biological hazards present on the carcasses and should thus be considered a matter of quality rather than food safety.

Artificial Intelligence Models for Zoonotic Pathogens: A Survey

Zoonotic diseases or zoonoses are infections due to the natural transmission of pathogens between species (animals and humans). More than 70% of emerging infectious diseases are attributed to animal origin. Artificial Intelligence (AI) models have been used for studying zoonotic pathogens and the factors that contribute to their spread. The aim of this literature survey is to synthesize and analyze machine learning, and deep learning approaches applied to study zoonotic diseases to understand predictive models to help researchers identify the risk factors, and develop mitigation strategies. Based on our survey findings, machine learning and deep learning are commonly used for the prediction of both foodborne and zoonotic pathogens as well as the factors associated with the presence of the pathogens.

Spectroscopic Data for the Rapid Assessment of Microbiological Quality of Chicken Burgers

The rapid assessment of the microbiological quality of highly perishable food commodities is of great importance. Spectroscopic data coupled with machine learning methods have been investigated intensively in recent years, because of their rapid, non-destructive, eco-friendly qualities and their potential to be used on-, in- or at-line. In the present study, the microbiological quality of chicken burgers was evaluated using Fourier transform infrared (FTIR) spectroscopy and multispectral imaging (MSI) in tandem with machine learning algorithms. Six independent batches were purchased from a food industry and stored at 0, 4, and 8 °C. At regular time intervals (specifically every 24 h), duplicate samples were subjected to microbiological analysis, FTIR measurements, and MSI sampling. The samples (n = 274) acquired during the data collection were classified into three microbiological quality groups: “satisfactory”: 4−7 log CFU/g, “acceptable”: 7−8 log CFU/g, and “unacceptable”: >8 logCFU/g. Subsequently, classification models were trained and tested (external validation) with several machine learning approaches, namely partial least squares discriminant analysis (PLSDA), support vector machine (SVM), random forest (RF), logistic regression (LR), and ordinal logistic regression (OLR). Accuracy scores were attained for the external validation, exhibiting FTIR data values in the range of 79.41−89.71%, and, for the MSI data, in the range of 74.63−85.07%. The performance of the models showed merit in terms of the microbiological quality assessment of chicken burgers.

Recent Advances and Applications of Rapid Microbial Assessment from a Food Safety Perspective

Unsafe food is estimated to cause 600 million cases of foodborne disease, annually. Thus, the development of methods that could assist in the prevention of foodborne diseases is of high interest. This review summarizes the recent progress toward rapid microbial assessment through (i) spectroscopic techniques, (ii) spectral imaging techniques, (iii) biosensors and (iv) sensors designed to mimic human senses. These methods often produce complex and high-dimensional data that cannot be analyzed with conventional statistical methods. Multivariate statistics and machine learning approaches seemed to be valuable for these methods so as to "translate" measurements to microbial estimations. However, a great proportion of the models reported in the literature misuse these approaches, which may lead to models with low predictive power under generic conditions. Overall, all the methods showed great potential for rapid microbial assessment. Biosensors are closer to wide-scale implementation followed by spectroscopic techniques and then by spectral imaging techniques and sensors designed to mimic human senses.

The Clash of Microbiomes: From the Food Matrix to the Host Gut

Food fermentation has led to the improvement of the safety characteristics of raw materials and the production of new foodstuffs with elevated organoleptic characteristics. The empirical observation that these products could have a potential health benefit has garnered the attention of the scientific community. Therefore, several studies have been conducted in animal and human hosts to decipher which of these products may have a beneficial outcome against specific ailments. However, despite the accumulating literature, a relatively small number of products have been authorized as ‘functional foods’ by regulatory bodies. Data inconsistency and lack of in-depth preclinical characterization of functional products could heavily contribute to this issue. Today, the increased availability of omics platforms and bioinformatic algorithms for comprehensive data analysis can aid in the systematic characterization of microbe–microbe, microbe–matrix, and microbe–host interactions, providing useful insights about the maximization of their beneficial effects. The incorporation of these platforms in food science remains a challenge; however, coordinated efforts and interdisciplinary collaboration could push the field toward the dawn of a new era.

Diagnosis of Lung Cancer by ATR-FTIR Spectroscopy and Chemometrics

Lung cancer is the leading cause of cancer-related death in the world. Early diagnosis has great significance for the survival of patients with lung cancer. In this paper, attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy combined with chemometrics was used to study the serum samples from patients with lung cancer and healthy people. The results of spectral band area comparison showed that the concentrations of protein, lipid and nucleic acids molecules in serum of patients with lung cancer were increased compared with those in healthy people. The original spectra were preprocessed to improve the accuracy of principal component regression (PCR) and partial least squares-discriminant analysis (PLS-DA) models. PLS-DA results for first derivative spectral data in nucleic acids (1250-1000cm-1) band showed 80% sensitivity, 91.89% specificity and 87.10% accuracy with highR c 2 of 0.8949 andR v 2 of 0.8153, low RMSEC of 0.3136 and RMSEV of 0.4180. It is shown that ATR-FTIR spectroscopy combined with chemometrics might be developed as a simple method for clinical screening and diagnosis of lung cancer.