Rapid methods and sensors for milk quality monitoring and spoilage detection

High water resistance starch based intelligent label for the freshness monitoring of beverages

The traditional starch-based intelligent freshness labels struggle to maintain long-term structural stability when exposed to moisture. To solve this problem, we prepared composite crosslinked labels using phytic acid for double crosslinking of corn starch and soybean isolate proteins, with anthocyanin serving as the chromogenic dye. The mechanical properties, hydrophobic characteristics, and pH responsivity of these crosslinked labels were assessed in this study. The prepared double-crosslinked labels showed reduced moisture content (15.96%), diminished swelling (147.21%), decreased solubility (28.55%), and minimized water permeability, which suggested that they have enhanced hydrophobicity and densification. The crosslinked labels demonstrated the ability to maintain morphological stability when immersed in water for 12 h. Additionally, the mechanical properties of the crosslinked labels were enhanced without compromising their pH-sensing capabilities, demonstrated a color response visible to the naked eye for milk and coconut water freshness monitoring, suggesting great potential for application in beverages freshness monitoring.

Recent Development in Detection and Control of Psychrotrophic Bacteria in Dairy Production: Ensuring Milk Quality

Milk is an ideal environment for the growth of microorganisms, especially psychrotrophic bacteria, which can survive under cold conditions and produce heat-resistant enzymes. Psychrotrophic bacteria create the great problem of spoiling milk quality and safety. Several ways that milk might get contaminated by psychrotrophic bacteria include animal health, cowshed hygiene, water quality, feeding strategy, as well as milk collection, processing, etc. Maintaining the quality of raw milk is critically essential in dairy processing, and the dairy sector is still affected by the premature milk deterioration of market-processed products. This review focused on the recent detection and control strategies of psychrotrophic bacteria and emphasizes the significance of advanced sensing methods for early detection. It highlights the ongoing challenges in the dairy industry caused by these microorganisms and discusses future perspectives in enhancing milk quality through innovative rapid detection methods and stringent processing controls. This review advocates for a shift towards more sophisticated on-farm detection technologies and improved control practices to prevent spoilage and economic losses in the dairy sector.

Machine learning supported single-stranded DNA sensor array for multiple foodborne pathogenic and spoilage bacteria identification in milk

Ensuring food safety through rapid and accurate detection of pathogenic bacteria in food products is a critical challenge in the food supply chain. In this study, a non-specific optical sensor array was proposed for the identification of multiple pathogenic bacteria in contaminated milk samples. Fluorescence-labeled single-stranded DNA was efficiently quenched by two-dimensional nanoparticles and subsequently recovered by foreign biomolecules. The recovered fluorescence generated a unique fingerprint for each bacterial species, enabling the sensor array to identify eight bacteria (pathogenic and spoilage) within a few hours. Four traditional machine learning models and two artificial neural networks were applied for classification. The neural network showed a 93.8 % accuracy with a 30-min incubation. Extending the incubation to 120 min increased the accuracy of the multiplayer perceptron to 98.4 %. This sensor array is a novel, low-cost, and high-accuracy approach for the identification of multiple bacteria, providing an alternative to plate counting and ELISA methods.

Principles and challenges of whole cell microbial biosensors in the food industry

Whole cell microbial biosensors (WCMB) are mostly genetically modified microorganisms used to detect target molecules as indicators of biological and chemical contaminants as well as in the identification of compounds of interest in the food industry. The specificity and sensitivity of these biosensors are achieved through the design of genetic circuits that make use of genetic sequences such as promoters, terminators, genes encoding regulatory proteins or reporter proteins, among others. Despite the advances of WCMBs for their application, significant challenges are faced, such as cell stability, regulatory restrictions, and the need to optimize response times so that they can be a competitive detection tool in the market. This review explores the technological progress, potential and limitations of WCMBs in the food industry, starting by reviewing the operating principles of biosensors. The importance of selecting appropriate chassis cells and the integration of recognition elements and transducers to maximize their effectiveness in the detection of contaminants and compounds of interest in the food industry is highlighted.

Phosphorylation-amplified synchronized droplet microfluidics sensitizes bacterial growth kinetic real-time monitoring

The necessity for rapid and accurate bacterial growth monitoring is imperative across various domains, including healthcare and environmental safety. We introduce the self-synchronized droplet-amplified electrical screening cytometry (SYNC) system, a novel meld of droplet microfluidics and electrochemical amplification tailored for precise bacterial growth kinetic monitoring. SYNC encapsulates single bacteria in picolitre droplets, enabling real-time, fluorescence-free electrochemical monitoring. A specially devised phosphorylation-amplified culture medium translates bacterial metabolic activity into discernible electrical impedance changes. The dual-channel design and a rail-based structure in SYNC facilitate parallel screening and self-synchronization of droplets, addressing the limitations of conventional impedance cytometry. SYNC showcases a 5-fold enhancement in detection sensitivity and reduces 50% of the detection time compared to traditional approaches. Notably, SYNC is pioneering in providing exact initial bacterial concentrations, achieve to 104 bacteria/ml, a capability unmatched by existing real-time techniques measuring electrochemical variations. Along with its robust performance, this earmarks SYNC as a powerful tool for applications such as antibiotic susceptibility testing, food quality monitoring, and real-time water bacteria monitoring, paving the way for enhanced microbial process management and infection control.

Sustainable production of cellulosic biopolymers for enhanced smart food packaging: An up-to-date review

Biodegradable and sustainable food packaging (FP) materials have gained immense global importance to reduce plastic pollution and environmental impact. Therefore, this review focused on the recent advances in biopolymers based on cellulose derivatives for FP applications. Cellulose, an abundant and renewable biopolymer, and its various derivatives, namely cellulose acetate, cellulose sulphate, nanocellulose, carboxymethyl cellulose, and methylcellulose, are explored as promising substitutes for conventional plastic in FP. These reviews focused on the production, modification processes, and properties of cellulose derivatives and highlighted their potential for their application in FP. Finally, we reviewed the effects of incorporating cellulose derivatives into film in various aspects of packaging properties, including barrier, mechanical, thermal, preservation aspects, antimicrobial, and antioxidant properties. Overall, the findings suggest that cellulose derivatives have the potential to replace conventional plastics in food packaging applications. This can contribute to reducing plastic pollution and lessening the environmental impact of food packaging materials. The review likely provides insights into the current state of research and development in this field and underscores the significance of sustainable food packaging solutions.

Critical review and recent advances of emerging real-time and non-destructive strategies for meat spoilage monitoring

Monitoring and evaluating food quality, especially meat quality, has received a growing interest to ensure human health and decrease waste of raw materials. Standard analytical approaches used for meat spoilage assessment suffer from time consumption, being labor-intensive, operation complexity, and destructiveness. To overcome shortfalls of these traditional methods and monitor spoilage microorganisms or related metabolites of meat products across the supply chain, emerging analysis devices/systems with higher sensitivity, better portability, on-line/in-line, non-destructive and cost-effective property are urgently needed. Herein, we first overview the basic concepts, causes, and critical monitoring indicators associated with meat spoilage. Then, the conventional detection methods for meat spoilage are outlined objectively in their strengths and weaknesses. In addition, we place the focus on the recent research advances of emerging non-destructive devices and systems for assessing meat spoilage. These novel strategies demonstrate their powerful potential in the real-time evaluation of meat spoilage.

Microfluidics in smart food safety

The evolution of food safety practices is crucial in addressing the challenges posed by a growing global population and increasingly complex food supply chains. Traditional methods are often labor-intensive, time-consuming, and susceptible to human error. This chapter explores the transformative potential of integrating microfluidics into smart food safety protocols. Microfluidics, involving the manipulation of small fluid volumes within microscale channels, offers a sophisticated platform for developing miniaturized devices capable of complex tasks. Combined with sensors, actuators, big data analytics, artificial intelligence, and the Internet of Things, smart microfluidic systems enable real-time data acquisition, analysis, and decision-making. These systems enhance control, automation, and adaptability, making them ideal for detecting contaminants, pathogens, and chemical residues in food products. The chapter covers the fundamentals of microfluidics, its integration with smart technologies, and its applications in food safety, addressing the challenges and future directions in this field.

Possibilities of an Electronic Nose on Piezoelectric Sensors with Polycomposite Coatings to Investigate the Microbiological Indicators of Milk

Milk and dairy products are included in the list of the Food Security Doctrine and are of paramount importance in the diet of the human population. At the same time, the presence of many macro- and microcomponents in milk, as available sources of carbon and energy, as well as the high activity of water, cause the rapid development of native and pathogen microorganisms in it. The goal of the work was to assess the possibility of using an array of gas chemical sensors based on piezoquartz microbalances with polycomposite coatings to assess the microbiological indicators of milk quality and to compare the microflora of milk samples. Piezosensors with polycomposite coatings with high sensitivity to volatile compounds were obtained. The gas phase of raw milk was analyzed using the sensors; in parallel, the physicochemical and microbiological parameters were determined for these samples, and species identification of the microorganisms was carried out for the isolated microorganisms in milk. The most informative output data of the sensor array for the assessment of microbiological indicators were established. Regression models were constructed to predict the quantity of microorganisms in milk samples based on the informative sensors' data with an error of no more than 17%. The limit of determination of QMAFAnM in milk was 243 ± 174 CFU/cm3. Ways to improve the accuracy and specificity of the determination of microorganisms in milk samples were proposed.

Coconut husk-lignin derived carbon dots incorporated carrageenan based functional film for intelligent food packaging

Carbon dots (CDs) derived from sustainable natural feed-stocks like lignin have gained wide acceptance by virtue of their renewability and promising potential in intelligent sensing applications. The precursor lignin is isolated from agro-biomass waste, coconut husk through sodium hydroxide based extraction process. CDs are synthesised from amine functionalized lignin through solvothermal process and integrated into carrageenan biopolymer matrix (1, 2 and 3 wt%). The composite film with 2 wt% CDs (CARR2CD) showed optimum fluorescent emission intensity, excellent pH dependent fluorescent color change in the food pH range, reasonable tensile strength (46.50 ± 1.32 MPa) and 27 % increase in elongation at break. CDs imparted UV-light blocking properties (70 % UV-light) and enhanced hydrophobicity of the carrageenan matrix. CARR2CD film showed 84 % visible light transparency, 79 % reduction in oxygen transmittance rate (OTR), 81 % reduction in CO2 gas permeability and excellent antioxidant and antibacterial properties (against E. coli and S. aureus). As a practical application, the developed responsive packaging material is used to track pH change associated with milk spoilage via noticeable color change in fluorescent emission of the composite film. Thus, the developed responsive composite film paves a way for use as green and sustainable transparent intelligent food packaging material.

μMET: A Novel Reusable Microfluidic Chip for Precision Microbial Enumeration Tests

This work describes μMET, a novel microfluidic device for precise microbial enumeration tests (MET), essential in pharmaceutical, cosmetic, and food industries for ensuring microbiological safety standards. The μMET chip, comprising two hydrophobic glass plates, features a 15-μm deep μMET chamber enhanced by nanopillars and air supply units, facilitating both immediate and growth-dependent MET. Experimental results, with E. coli as a model bacterium, demonstrate that μMET provides counting linearity that outperforms traditional hemocytometers. The chip's design mitigates challenges like evaporation and ensures high-resolution imaging, making it a cost-effective and reusable alternative to conventional methods. Notably, bright-field μMET eliminates the need for fluorescent staining, streamlining operations with deep-learning algorithms for bacterial counts. Furthermore, we have developed a high-parallel μMET chip featuring 16 counting chambers, enhancing throughput and accommodating immediate and growth-dependent MET approaches. Its innovative design and adaptability render the μMET chip as a valuable tool for microbiology, medicine, and industry applications.

Multi-Sensing Platform Based on 2D Monoelement Germanane

Covalently functionalized germanane is a novel type of fluorescent probe that can be employed in material science and analytical sensing. Here, a fluorometric sensing platform based on methyl-functionalized germanane (CH3 Ge) is developed for gas (humidity and ammonia) sensing, pH (1-9) sensing, and anti-counterfeiting. Luminescence (red-orange) is seen when a gas molecule intercalates into the interlayer space of CH3 Ge and the luminescence disappears upon deintercalation. This allows for direct detection of gas absorption via fluorometric measurements of the CH3 Ge. Structural and optical properties of CH3 Ge with intercalated gas molecules are investigated by density functional theory (DFT). To demonstrate real-time and on-the-spot testing, absorbed gas molecules are first precisely quantified by CH3 Ge using a smartphone camera with an installed color intensity processing application (APP). Further, CH3 Ge-paper-based sensor is integrated into real food packets (e.g., fish and milk) to monitor the shelf life of perishable foods. Finally, CH3 Ge-based rewritable paper is applied in water jet printing to illustrate the potential for secret communication with quick coloration and good reversibility by water evaporation.

A handheld contactless conductivity detector for monitoring the desalting of low-volume virus and cell samples

Desalting of biosamples is crucial for analytical techniques intolerant to abundant salts. However, there is no simple tool to monitor the desalting of low-volume biosamples so far. Here we developed a handheld capacitively coupled contactless conductivity detector (hC4D) as a miniaturized device to measure the conductivity of 75 μL biosamples. Polyether-ether-ketone (PEEK) tubing was selected as the sample reservoir for sample loading via a pipette. Another pipetting of air pushed the sample solution out of the tubing to recollect the sample. Owing to the low sample consumption and easy sample recollection, hC4D is advantageous for testing expensive biosamples, such as viruses and cells. In addition, the whole process of sample injection, conductivity measurement, recollection, and calibration of conductivity can be completed within 1 min. To verify the feasibility of hC4D, we monitored the desalting progress of gel filtration (GF) of 200 μL blood samples, ultrafiltration (UF) of 300 μL virus samples, and dialysis of 7 mL cell samples. Three rounds of GF and UF completely removed the salts but led to poor sample recovery. In contrast, low concentrations of residual salts remained and better recovery was achieved after two rounds of GF and UF. We further utilized the hC4D to monitor the dialysis and tuned the salt concentration in the cell sample, such that we maintained the viability of cells in a low conductivity environment. These results indicated that hC4D is a promising tool for optimizing the desalting procedure of low-volume biosamples.

IoT-Enabled Electronic Nose System for Beef Quality Monitoring and Spoilage Detection

Food spoilage is a major concern in the food industry, especially for highly perishable foods such as beef. In this paper, we present a versatile Internet of Things (IoT)-enabled electronic nose system to monitor food quality by evaluating the concentrations of volatile organic compounds (VOCs). The IoT system consists mainly of an electronic nose, temperature/humidity sensors, and an ESP32-S3 microcontroller to send the sensors' data to the server. The electronic nose consists of a carbon dioxide gas sensor, an ammonia gas sensor, and an ethylene gas sensor. This paper's primary focus is to use the system for identifying beef spoilage. Hence, the system performance was examined on four beef samples stored at different temperatures: two at 4 °C and two at 21 °C. Microbial population quantifications of aerobic bacteria, Lactic Acid Bacteria (LAB), and Pseudomonas spp., in addition to pH measurements, were conducted to evaluate the beef quality during a period of 7 days to identify the VOCs concentrations that are associated with raw beef spoilage. The spoilage concentrations that were identified using the carbon dioxide, ammonia, and ethylene sensors were 552 ppm-4751 ppm, 6 ppm-8 ppm, and 18.4 ppm-21.1 ppm, respectively, as determined using a 500 mL gas sensing chamber. Statistical analysis was conducted to correlate the bacterial growth with the VOCs production, where it was found that aerobic bacteria and Pseudomonas spp. are responsible for most of the VOCs production in raw beef.

Application of Electrochemical Biosensors for Determination of Food Spoilage

Food security is significantly affected by the mass production of agricultural produce and goods, the growing number of imported foods, and new eating and consumption habits. These changed circumstances bring food safety issues arising from food spoilage to the fore, making food safety control essential. Simple and fast screening methods have been developed to detect pathogens and biomarkers indicating the freshness of food for safety. In addition to the traditional, sequential, chemical analytical and microbiological methods, fast, highly sensitive, automated methods suitable for serial tests have appeared. At the same time, biosensor research is also developing dynamically worldwide, both in terms of the analytes to be determined and the technical toolkit. Consequently, the rapid development of biosensors, including electrochemical-based biosensors, has led to significant advantages in the quantitative detection and screening of food contaminants. These techniques show great specificity for the biomarkers tested and provide adequate analytical accuracy even in complex food matrices. In our review article, we summarize, in separate chapters, the electrochemical biosensors developed for the most important food groups and the food safety issues they can ensure, with particular respect to meat and fish products, milk and dairy products, as well as alcoholic and non-alcoholic beverages.

Using a random laser to measure the content of protein in skim milk

The luminescence of skim milk samples with distinct protein content doped with rhodamine B was investigated. The samples were excited by a nanosecond laser tuned at 532 nm, and the emission was characterized as a random laser. Its features were analyzed as a function of the protein aggregate content. The results showed a linear correlation between the random laser peak intensity and the protein content. This paper proposes a rapid detection photonic method to evaluate the protein content in skim milk based on the intensity of the random laser emission.

SERS-based immunosensor for E. coli contaminants detection in milk using silver-coated nanoporous silicon substrates

The dairy sector is frequently affected by contagious and environmental factors that spread between animals by numerous means and induce the inflammatory disease of bovine mastitis (BM). Herein, silver decorated porous silicon (Ag-pSi) SERS platform was designed for rapid and reliable Escherichia coli (predominant BM pathogen) detection in various milk origins. The inherent surface void and pore morphology were physically optimized to augment the SERS effect using 4-aminothiphenol (4ATP) while achieving an enhancement factor >4.6 × 10⁷. An indirect immunoassay evaluated the residual unreacted antibodies using an optimized 4ATP/Ag-pSi SERS platform modified with secondary antibodies. Under optimized conditions, the porous substrate offered high sensitivity toward target bacteria detection of 3 CFU mL^-1 and linear response of 10¹-10⁵ CFU mL^-1. Moreover, the selectivity and specificity of the designed sensing platform were cross-validated against other interfering bacteria without compromising its performance efficiencies. Finally, the applicability of the developed system for real-life conditions was elucidated in different milk samples (bovine, goat, sheep) with recovery values of 78-115% compared to the conventional culture technique. Considering the complex media analysis, the miniaturized SERS platform is highly reliable, rapid and accurate that could be applicable for routine on-site analysis of various emerging pathogens relevant to BM management.

Innovative processes in smart packaging. A systematic review

Smart packaging provides one possible solution that could reduce greenhouse gas emissions. In comparison with traditional packaging, which aims to extend the product's useful life and to facilitate transport and marketing, smart packaging allows increased efficiency, for example by ensuring authenticity and traceability from the product's origin, preventing fraud and theft, and improving security. Consequently, it may help to reduce pollution, food losses, and waste associated with the food supply chain. However, some questions must be answered to fully understand the advantages and limitations of its use. What are the most suitable smart packaging technologies for use in agro-industrial subsectors such as meat, dairy, fruits, and vegetables, bakery, and pastry? What are the opportunities from a perspective of life extension, process optimization, traceability, product quality, and safety? What are the future challenges? An up-to-date, systematic review was conducted of literature relevant to the application of indicator technologies, sensors, and data carriers in smart packaging, to answer these questions. © 2022 Society of Chemical Industry.

Immobilization of Firefly Bioluminescent System: Development and Application of Reagents

The present study describes the method of preparing reagents containing firefly luciferase (FLuc) and its substrate, D-luciferin, immobilized into gelatin gel separately or together. The addition of stabilizers dithiothreitol (DTT) and bovine serum albumin (BSA) to the reagent is a factor in achieving higher activity of reagents and their stability during storage. The use of immobilized reagents substantially simplifies the procedure of assay for microbial contamination. The mechanism of action of the reagents is based on the relationship between the intensity of the bioluminescent signal and the level of ATP contained in the solution of the lysed bacterial cells. The highest sensitivity to ATP is achieved by using immobilized FLuc or reagents containing separately immobilized FLuc and D-luciferase. The limit of detection of ATP by the developed reagents is 0.3 pM, which corresponds to 20,000 cells·mL-1. The linear response range is between 0.3 pM and 3 nM ATP. The multicomponent reagent, containing co-immobilized FLuc and D-luciferin, shows insignificantly lower sensitivity to ATP-0.6 pM. Moreover, the proposed method of producing an immobilized firefly luciferin-luciferase system holds considerable promise for the development of bioluminescent biosensors intended for the analysis of microbial contamination.

Metabolomics insights into the potential of encapsulated essential oils as multifunctional food additives

Growing consumer concern about foodborne disease outbreaks and health risks associated with chemical additives has propelled the usage of essential oils (EOs) as novel food additives, but are limited by instability. In this regard, a series of EOs nano/micro-capsules have been widely used to enhance their stability and improve food quality. However, classical food quality assessment methods are insufficient to fully characterize the effects of encapsulated EOs on food properties, including physical, biochemical, organoleptic, and microbial changes. Recently, the rapid development of high-throughput sequencing is accelerating the application of metabolomics in food safety and quality analysis. This review seeks to present the most recent achievements in the application of non-targeted metabolomics to identify and quantify the overall metabolite profile associated with food quality, which can guide the development of emerging food preservation technologies. The scientific findings confirm that metabolomics opens up exciting prospects for biomarker screening in food preservation and contributes to an in-depth understanding of the mechanisms of action (MoA) of EOs. Future research should focus on constructing food quality assessment criteria based on multi-omics technologies, which will drive the standardization and commercialization of EOs for food industry applications.

Halochromic Inks Applied on Cardboard for Food Spoilage Monitorization

Control of food spoilage is a critical concern in the current world scenario, not only to ensure the quality and safety of food but also to avoid the generation of food waste. This paper evaluates a dual-sensor strategy using six different pH indicators stamped on cardboard for the detection of spoilage in three different foods: beef, salmon, and strawberries. After function validation and formulation optimizations in the laboratory, the halochromic sensors methyl orange and bromocresol purple 2% (w/v) were stamped on cardboard and, in contact with the previously mentioned foods, were able to produce an easily perceptible signal for spoilage by changing color. Additionally, when it comes to mechanical characterization the inks showed high abrasion (>100 cycles) and adhesion resistance (>91%).

Stimuli-Responsive Dendritic Macromolecules for Optical Detection of Metal Ions and Acidic Vapors by the Photoinduced Electron Transfer Mechanism: Paper-Based Indicator for Food Spoilage Sensing

Visual detection of analytes has been a significant challenge in the design and development of optical chemosensors. Sensing of analytes in aqueous solution by organic molecules has encountered some issues, such as poor water solubility and quenching of optical properties. In this study, a new category of smart dendritic macromolecules was designed and synthesized by functionalization of the poly(amidoamine) (PAMAM) dendrimer with spiropyran molecules to afford a photoluminescent dendritic structure (SP-PAMAM). Smart optical sensors were prepared by physical incorporation of four different oxazolidine derivatives containing hydroxyl and nitro substituted groups into the SP-PAMAM structure. Investigation of optical properties demonstrated photoinduced electron transfer (PET) between the spiropyran end group of SP-PAMAM and oxazolidine derivatives (in a concentration of about 0.0002 M), which can result in quenching of fluorescence emission of spiropyran photoswitch in the form of merocyanine (MC). Treatment of the oxazolidine-doped SP-PAMAM samples with metal ions resulted in changes in the PET mechanism (switching on or off), as observed in the case of Fe³⁺, Pb²⁺, Cu²⁺, Zn²⁺, Cd²⁺, Co²⁺, and Ni²⁺ by different oxazolidine derivatives through various mechanisms (increase or decrease of fluorescence emission). These smart photoluminescent dendritic macromolecules have potential applications for photodetection of metal ions in aqueous media as optical chemosensors. In addition, the smart macromolecules displayed disconnection of PET between MC and oxazolidine and also showed red fluorescence emission under acidic conditions (pH 1-5). It is due to the protonation of the MC to MCH form and demonstrates a remarkable red shift in fluorescence spectra. The pH-responsivity of smart macromolecules was used for designing a paper-based pH indicator for visual detection of spoilage in the food industry, especially in the case of milk. The prepared papers applied on cap of the milk bottles did not show any fluorescence emission in the case of fresh milk; however, a red fluorescence emission was observed after milk spoilage as a result of adsorption of acidic volatile components generated by bacterial degradation and oxidation process on the paper surface. The reported smart papers can serve as optical portable pH indicators for timely detection of spoilage in food materials, which are usable in food packaging as smart indicator tags.

Plant-Derived Antimicrobial Peptides: Novel Preservatives for the Food Industry

Food spoilage is a widespread issue brought on by the undesired growth of microbes in food products. Thousands of tons of usable food or food products are wasted every day due to rotting in different parts of the world. Several food preservation techniques are employed to prevent food from rotting, including the use of natural or manufactured chemicals or substances; however, the issue persists. One strategy for halting food deterioration is the use of plant-derived antimicrobial peptides (AMPs), which have been investigated for possible bioactivities against a range of human, plant, and food pathogens. The food industry may be able to benefit from the development of synthetic AMPs, produced from plants that have higher bioactivity, better stability, and decreased cytotoxicity as a means of food preservation. In order to exploit plant-derived AMPs in various food preservation techniques, in this review, we also outline the difficulties in developing AMPs for use as commercial food preservatives. Nevertheless, as technology advances, it will soon be possible to fully explore the promise of plant-derived AMPs as food preservatives.

Biosensors for rapid detection of bacterial pathogens in water, food and environment

Conventional techniques (e.g., culture-based method) for bacterial detection typically require a central laboratory and well-trained technicians, which may take several hours or days. However, recent developments within various disciplines of science and engineering have led to a major paradigm shift in how microorganisms can be detected. The analytical sensors which are widely used for medical applications in the literature are being extended for rapid and on-site monitoring of the bacterial pathogens in food, water and the environment. Especially, within the low-resource settings such as low and middle-income countries, due to the advantages of low cost, rapidness and potential for field-testing, their use is indispensable for sustainable development of the regions. Within this context, this paper discusses analytical methods and biosensors which can be used to ensure food safety, water quality and environmental monitoring. In brief, most of our discussion is focused on various rapid sensors including biosensors and microfluidic chips. The analytical performances such as the sensitivity, specificity and usability of these sensors, as well as a brief comparison with the conventional techniques for bacteria detection, form the core part of the discussion. Furthermore, we provide a holistic viewpoint on how future research should focus on exploring the synergy of different sensing technologies by developing an integrated multiplexed, sensitive and accurate sensors that will enable rapid detection for food safety, water and environmental monitoring.

Dairy 4.0: Intelligent Communication Ecosystem for the Cattle Animal Welfare with Blockchain and IoT Enabled Technologies

An intelligent ecosystem with real-time wireless technology is now playing a key role in meeting the sustainability requirements set by the United Nations. Dairy cattle are a major source of milk production all over the world. To meet the food demand of the growing population with maximum productivity, it is necessary for dairy farmers to adopt real-time monitoring technologies. In this study, we will be exploring and assimilating the limitless possibilities for technological interventions in dairy cattle to drastically improve their ecosystem. Intelligent systems for sensing, monitoring, and methods for analysis to be used in applications such as animal health monitoring, animal location tracking, milk quality, and supply chain, feed monitoring and safety, etc., have been discussed briefly. Furthermore, generalized architecture has been proposed that can be directly applied in the future for breakthroughs in research and development linked to data gathering and the processing of applications through edge devices, robots, drones, and blockchain for building intelligent ecosystems. In addition, the article discusses the possibilities and challenges of implementing previous techniques for different activities in dairy cattle. High computing power-based wearable devices, renewable energy harvesting, drone-based furious animal attack detection, and blockchain with IoT assisted systems for the milk supply chain are the vital recommendations addressed in this study for the effective implementation of the intelligent ecosystem in dairy cattle.

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.

A review on colorimetric indicators for monitoring product freshness in intelligent food packaging: Indicator dyes, preparation methods, and applications

Intelligent food packaging system exhibits enhanced communication function by providing dynamic product information to various stakeholders (e.g., consumers, retailers, distributors) in the supply chain. One example of intelligent packaging involves the use of colorimetric indicators, which when subjected to external stimuli (e.g., moisture, gas/vapor, electromagnetic radiation, temperature), display discernable color changes that can be correlated with real-time changes in product quality. This type of interactive packaging system allows continuous monitoring of product freshness during transportation, distribution, storage, and marketing phases. This review summarizes the colorimetric indicator technologies for intelligent packaging systems, emphasizing on the types of indicator dyes, preparation methods, applications in different food products, and future considerations. Both food and nonfood indicator materials integrated into various carriers (e.g., paper-based substrates, polymer films, electrospun fibers, and nanoparticles) with material properties optimized for specific applications are discussed, targeting perishable products, such as fresh meat and fishery products. Colorimetric indicators can supplement the traditional "Best Before" date label by providing real-time product quality information to the consumers and retailers, thereby not only ensuring product safety, but also promising in reducing food waste. Successful scale-up of these intelligent packaging technologies to the industrial level must consider issues related to regulatory approval, consumer acceptance, cost-effectiveness, and product compatibility.

Assessment of Various Process Parameters for Optimized Sterilization Conditions Using a Multi-Sensing Platform

In this study, an online multi-sensing platform was engineered to simultaneously evaluate various process parameters of food package sterilization using gaseous hydrogen peroxide (H₂O₂). The platform enabled the validation of critical aseptic parameters. In parallel, one series of microbiological count reduction tests was performed using highly resistant spores of B. atrophaeus DSM 675 to act as the reference method for sterility validation. By means of the multi-sensing platform together with microbiological tests, we examined sterilization process parameters to define the most effective conditions with regards to the highest spore kill rate necessary for aseptic packaging. As these parameters are mutually associated, a correlation between different factors was elaborated. The resulting correlation indicated the need for specific conditions regarding the applied H₂O₂ gas temperature, the gas flow and concentration, the relative humidity and the exposure time. Finally, the novel multi-sensing platform together with the mobile electronic readout setup allowed for the online and on-site monitoring of the sterilization process, selecting the best conditions for sterility and, at the same time, reducing the use of the time-consuming and costly microbiological tests that are currently used in the food package industry.

Electronic Nose and Tongue for Assessing Human Microbiota

The technological developments of recent times have allowed the use of innovative approaches to support the diagnosis of various diseases. Many of such clinical conditions are often associated with metabolic unbalance, in turn producing an alteration of the gut microbiota even during asymptomatic stages. As such, studies regarding the microbiota composition in biological fluids obtained by humans are continuously growing, and the methodologies for their investigation are rapidly changing, making it less invasive and more affordable. To this extent, Electronic Nose and Electronic Tongue tools are gaining importance in the relevant field, making them a useful alternative—or support—to traditional analytical methods. In light of this, the present manuscript seeks to investigate the development and use of such tools in the gut microbiota assessment according to the current literature. Significant gaps are still present, particularly concerning the Electronic Tongue systems, however the current evidence highlights the strong potential such tools own to enter the daily clinical practice, with significant advancement concerning the patients’ acceptability and cost saving for healthcare providers.

Application of E-nose technology combined with artificial neural network to predict total bacterial count in milk

Total bacterial count (TBC) is a widely accepted index for assessing microbial quality of milk, and cultivation-based methods are commonly used as standard methods for its measurement. However, these methods are laborious and time-consuming. This study proposes a method combining E-nose technology and artificial neural network for rapid prediction of TBC in milk. The qualitative model generated an accuracy rate of 100% when identifying milk samples with high, medium, or low levels of TBC, on both the testing and validating subsets. Predicted TBC values generated by the quantitative model demonstrated strong coefficient of multiple determination (R² > 0.99) with reference values. Mean relative difference between predicted and reference values (mean ± standard deviation) of TBC were 1.1 ± 1.7% and 0.4 ± 0.8% on the testing and validating subsets involving 24 and 28 tested samples, respectively. Paired t-test implied that the difference between predicted and reference values of TBC was insignificant for both the testing and validating subsets. As low as ∼1 log cfu/mL of TBC present in tested samples were precisely predicted. Results of this study indicated that combination of E-nose technology and artificial neural network generated reliable predictions of TBC in milk. The method proposed in this study was reliable, rapid, and cost efficient for assessing microbial quality milk, and thus would potentially have realistic application in dairy section.

Analysis of Milk Using a Portable Potentiometric Electronic Tongue Based on Five Polymeric Membrane Sensors

A portable potentiometric electronic tongue (PE-tongue) was developed and applied to evaluate the quality of milk with different fat content (skimmed, semi-skimmed, and whole) and with different nutritional content (classic, calcium-enriched, lactose-free, folic acid–enriched, and enriched in sterols of vegetal origin). The system consisted of a simplified array of five sensors based on PVC membranes, coupled to a data logger. The five sensors were selected from a larger set of 20 sensors by applying the genetic algorithm (GA) to the responses to compounds usually found in milk including salts (KCl, CaCl₂, and NaCl), sugars (lactose, glucose, and galactose), and organic acids (citric acid and lactic acid). Principal component analysis (PCA) and support vector machine (SVM) results indicated that the PE-tongue consisting of a five-electrode array could successfully discriminate and classify milk samples according to their nutritional content. The PE-tongue provided similar discrimination capability to that of a more complex system formed by a 20-sensor array. SVM regression models were used to predict the physicochemical parameters classically used in milk quality control (acidity, density, %proteins, %lactose, and %fat). The prediction results were excellent and similar to those obtained with a much more complex array consisting of 20 sensors. Moreover, the SVM method confirmed that spoilage of unsealed milk could be correctly identified with the simplified system and the increase in acidity could be accurately predicted. The results obtained demonstrate the possibility of using the simplified PE-tongue to predict milk quality and provide information on the chemical composition of milk using a simple and portable system.

Nondestructive multiplex detection of foodborne pathogens with background microflora and symbiosis using a paper chromogenic array and advanced neural network

We have developed an inexpensive, standardized paper chromogenic array (PCA) integrated with a machine learning approach to accurately identify single pathogens (Listeria monocytogenes, Salmonella Enteritidis, or Escherichia coli O157:H7) or multiple pathogens (either in multiple monocultures, or in a single cocktail culture), in the presence of background microflora on food. Cantaloupe, a commodity with significant volatile organic compound (VOC) emission and large diverse populations of background microflora, was used as the model food. The PCA was fabricated from a paper microarray via photolithography and paper microfluidics, into which 22 chromogenic dye spots were infused and to which three red/green/blue color-standard dots were taped. When exposed to VOCs emitted by pathogens of interest, dye spots exhibited distinguishable color changes and pattern shifts, which were automatically segmented and digitized into a ΔR/ΔG/ΔB database. We developed an advanced deep feedforward neural network with a learning rate scheduler, L₂ regularization, and shortcut connections. After training on the ΔR/ΔG/ΔB database, the network demonstrated excellent performance in identifying pathogens in single monocultures, multiple monocultures, and in cocktail culture, and in distinguishing them from the background signal on cantaloupe, providing accuracy of up to 93% and 91% under ambient and refrigerated conditions, respectively. With its combination of speed, reliability, portability, and low cost, this nondestructive approach holds great potential to significantly advance culture-free pathogen detection and identification on food, and is readily extendable to other food commodities with complex microflora.

Assessment of Physicochemical Characteristics and Microbiological Quality in Broiler Chicken Breast Muscle (Pectoralis major) Subjected to Different Temperatures and Lengths of Cold Transportation

Cold truck transportation is considered one of the most integral parts in a food processing chain. However, countless cases of product spoilage and food poisoning incidents have proven that temperature control during transport has been neglected. Literature on the impact of temperature during distribution is scarce. The objective of this study was to investigate the impact of various transportation temperatures and travel duration on the meat quality and microbial population of broiler chicken breast muscle. Sixty broiler chickens (42 days old) were slaughtered and eviscerated; they then had their breast muscles removed (each bird provided two breast muscle samples: left breast and right breast), which were wrapped in plastic film. All 120 packed boneless breasts (PBBs) were then placed at −18 °C for 24 h. After 24 h, the 60 PBB samples were subjected to transportation for 1 h at 4 °C (20 PBBs), 10 °C (20 PBBs), and 15 °C (20 PBBs) while the remaining 60 samples were transported for 5 h at 4 °C (20 PBBs), 10 °C (20 PBBs), and 15 °C (20 PBBs) before analyses. The samples transported at higher temperatures exhibited higher populations of coliform and Salmonella than those transported at lower temperatures. A significant impact of the transportation duration on the Salmonella population was only observed in samples transported at 4 °C for 5 h. However, a significant impact of transportation temperature on color was only recorded for the redness (a*) values, where the samples transported at higher temperatures exhibited higher redness (a*) values. Significant increases in lightness (L*) and yellowness (b*) values as well as decreases in redness (a*) and pH values were recorded in samples subjected to longer durations of transportation across all the temperatures observed in this study.

Magnetron Sputtering Thin Films as Tool to Detect Triclosan in Infant Formula Powder: Electronic Tongue Approach

Triclosan (TCS) is being detected in breast milk and in infants of puerperal women. The harmful effects caused by this compound on living beings are now critical and thus it is pivotal find new tools to TCS monitoring. In the present study, an electronic tongue (e-tongue) device comprising an array of sputtered thin films based on Multi-Walled Carbon Nanotubes and titanium dioxide was developed to identify TCS concentrations, from 10−15 to 10−5 M, in both water and milk-based solutions. Impedance spectroscopy was used for device signal transducing and data was analyzed by principal component analysis (PCA). The e-tongue revealed to be able to distinguish water from milk-based matrices through the two Principal Components (PC1 and PC2), which represented 67.3% of the total variance. The PC1 values of infant formula milk powder prepared with tap water (MT) or mineral water (MMW) follows a similar exponential decay curve when plotted with the logarithm of concentration. Therefore, considering the TCS concentration range between 10−15 and 10−9 M, the PC1 values are fitted by a straight line and values of −1.9 ± 0.2 and of 7.6 × 10−16 M were calculated for the sensor sensitivity and sensor resolution, respectively. Additionally, a strong correlation (R = 0.96) between MT and MMW PC1 data was found. These results have shown that the proposed device corresponds to a promisor method for the detection of TCS in milk-based solutions.