Application of gas-sensor array technology for detection and monitoring of growth of spoilage bacteria in milk: A model study

Paper-based fluorescence sensor array with functionalized carbon quantum dots for bacterial discrimination using a machine learning algorithm

The rapid discrimination of bacteria is currently an emerging trend in the fields of food safety, medical detection, and environmental observation. Traditional methods often require lengthy culturing processes, specialized analytical equipment, and bacterial recognition receptors. In response to this need, we have developed a paper-based fluorescence sensor array platform for identifying different bacteria. The sensor array is based on three unique carbon quantum dots (CQDs) as sensing units, each modified with a different antibiotic (polymyxin B, ampicillin, and gentamicin). These antibiotic-modified CQDs can aggregate on the bacterial surface, triggering aggregation-induced fluorescence quenching. The sensor array exhibits varying fluorescent responses to different bacterial species. To achieve low-cost and portable detection, CQDs were formulated into fluorescent ink and used with an inkjet printer to manufacture paper-based sensor arrays. A smartphone was used to collect the responses generated by the bacteria and platform. Diverse machine learning algorithms were utilized to discriminate bacterial types. Our findings showcase the platform's remarkable capability to differentiate among five bacterial strains, within a detection range spanning from 1.0 × 103 CFU/mL to 1.0 × 107 CFU/mL. Its practicality is further validated through the accurate identification of blind bacterial samples. With its cost-effectiveness, ease of fabrication, and high degree of integration, this platform holds significant promise for on-site detection of diverse bacteria.

Discrimination of Milk Freshness Based on Synchronous Two-Dimensional Visible/Near-Infrared Correlation Spectroscopy Coupled with Chemometrics

Milk is one of the preferred beverages in modern healthy diets, and its freshness is of great significance for product sales and applications. By combining the two-dimensional (2D) correlation spectroscopy technique and chemometrics, a new method based on visible/near-infrared (Vis/NIR) spectroscopy was proposed to discriminate the freshness of milk. To clarify the relationship be-tween the freshness of milk and the spectra, the changes in the physicochemical indicators of milk during storage were analyzed as well as the Vis/NIR spectra and the 2D-Vis/NIR correlation spectra. The threshold-value method, linear discriminant analysis (LDA) method, and support vector machine (SVM) method were used to construct the discriminant models of milk freshness, and the parameters of the SVM-based models were optimized by the grid search method and particle swarm optimization algorithm. The results showed that with the prolongation of storage time, the absorbance of the Vis/NIR spectra of milk gradually increased, and the intensity of autocorrelation peaks and cross peaks in synchronous 2D-Vis/NIR spectra also increased significantly. Compared with the SVM-based models using Vis/NIR spectra, the SVM-based model using 2D-Vis/NIR spectra had a >15% higher prediction accuracy. Under the same conditions, the prediction performances of the SVM-based models were better than those of the threshold-value-based or LDA-based models. In addition, the accuracy rate of the SVM-based model using the synchronous 2D-Vis/NIR autocorrelation spectra was >97%. This work indicates that the 2D-Vis/NIR correlation spectra coupled with chemometrics is a great pattern to rapidly discriminate the freshness of milk, which provides technical support for improving the evaluation system of milk quality and maintaining the safety of milk product quality.

Fabrication of Printable Colorimetric Food Sensor Based on Hydrogel for Low-Concentration Detection of Ammonia

With the increasing market share of ready-to-cook foods, accurate determination of the food freshness and thus food safety has emerged as a concern. To commercialize and popularize food sensing technologies, food sensors with diverse functionalities, low cost, and facile use must be developed. This paper proposes printable sensors based on a hydrogel-containing pH indicator to detect ammonia gas. The sensors were composed of biocompatible polymers such as 2-hydroxyethyl methacrylate (HEMA) and [2-(methacryloyloxy)ethyl] trimethylammonium chloride (MAETC). The p(HEMA-MAETC) hydrogel sensor with bromothymol blue (BTB) demonstrated visible color change as a function of ammonia concentration during food spoilage. Furthermore, polyacrylonitrile (PAN) was added to improve transport speed of ammonium ions as the matrix in the sensors and optimized the viscosity to enable successful printing. The color changed within 3 min at ammonia concentration of 300 ppb and 1 ppm, respectively. The sensor exhibited reproducibility over 10 cycles and selective exposure to various gases generated during the food spoilage process. In an experiment involving pork spoilage, the color change was significant before and after exposure to ammonia gas within 8 h in ambient conditions. The proposed sensor can be integrated in bar codes and QR codes that are easily mass produced.

Artificial olfactory sensor technology that mimics the olfactory mechanism: a comprehensive review

Artificial olfactory sensors that recognize patterns transmitted by olfactory receptors are emerging as a technology for monitoring volatile organic compounds. Advances in statistical processing methods and data processing technology have made it possible to classify patterns in sensor arrays. Moreover, biomimetic olfactory recognition sensors in the form of pattern recognition have been developed. Deep learning and artificial intelligence technologies have enabled the classification of pattern data from more sensor arrays, and improved artificial olfactory sensor technology is being developed with the introduction of artificial neural networks. An example of an artificial olfactory sensor is the electronic nose. It is an array of various types of sensors, such as metal oxides, electrochemical sensors, surface acoustic waves, quartz crystal microbalances, organic dyes, colorimetric sensors, conductive polymers, and mass spectrometers. It can be tailored depending on the operating environment and the performance requirements of the artificial olfactory sensor. This review compiles artificial olfactory sensor technology based on olfactory mechanisms. We introduce the mechanisms of artificial olfactory sensors and examples used in food quality and stability assessment, environmental monitoring, and diagnostics. Although current artificial olfactory sensor technology has several limitations and there is limited commercialization owing to reliability and standardization issues, there is considerable potential for developing this technology. Artificial olfactory sensors are expected to be widely used in advanced pattern recognition and learning technologies, along with advanced sensor technology in the future.

Metabolomic Markers of Storage Temperature and Time in Pasteurized Milk

The current date labeling system for pasteurized milk is based on the predicted growth of spoilage microorganisms, but inherent inaccuracies and the inability to account for environmental factors (e.g., temperature fluctuations) contribute to household and retail food waste. Improved shelf-life estimation can be achieved by monitoring milk quality in real-time. In this study, we identify and quantify metabolites changing over storage temperature and time, the main factors affecting milk stability. Pasteurized 2% fat milk was stored at 4, 10, 15, and 20 °C. Metabolite change was analyzed using untargeted and targeted nuclear magnetic resonance (NMR) metabolomics approaches. Several metabolites correlated significantly to storage time and temperature. Citric acid decreased linearly over time at a temperature-dependent rate. Ethanol, formic acid, acetic acid, lactic acid, and succinic acid increased non-linearly after an initial period of minimal increase. Butyric acid exhibited strong inverse temperature dependencies. This study provides the first analysis of the effect of time and temperature on the concentration of key metabolites during milk storage. Candidate molecules for shelf-life monitoring have been identified, and the results improve our understanding of molecular changes during milk storage. These results will inform the development of real-time shelf-life indicators for milk, helping to reduce milk waste.

Enzymatic Time-Temperature Indicator Prototype Developed by Immobilizing Laccase on Electrospun Fibers to Predict Lactic Acid Bacterial Growth in Milk during Storage

Laccase was immobilized on a chitosan/polyvinyl alcohol/tetraethylorthosilicate electrospun film (ceCPTL) and colored with guaiacol to obtain a laccase time-temperature indicator (TTI) prototype. The activation energy (Ea) of coloration of the prototype was 50.89-33.62 kJ/mol when 8-25 μg/cm² laccase was immobilized on ceCPTL, and that of lactic acid bacteria (LAB) growth in milk was 73.32 kJ/mol. The Ea of coloration of the TTI prototype onto which 8-10 μg/cm² laccase was immobilized was in the required range for predicting LAB growth in milk. The coloration endpoint of the TTI prototype onto which 10 μg/cm² (0.01 U) laccase was immobilized could respond to the LAB count reaching 10⁶ colony-forming units (CFU)/mL in milk during a static temperature response test, and the prediction error was discovered to be low. In dynamic temperature response experiments with intermittent temperature changes between 4 and 25 °C, the coloration rate of the laccase TTI prototype was consistent with LAB growth. The results of this study indicate that the laccase TTI prototype can be applied as a visual monitoring indicator to assist in evaluating milk quality in cold chains.

Growth potential and biofilm development of nonstarter bacteria on surfaces exposed to a continuous whey stream

Commercial Cheddar cheese production uses an automated, continuous production system that provides favorable conditions for specific undesirable bacterial subpopulations in certain sections of the processing system. The draining and matting conveyor (DMC) is a large, fully enclosed series of conveyor belts that separates curd and whey on the first drain belt and supports the cheddaring process in subsequent sections. In a previous study, we demonstrated that coliforms increase in the draining section of the DMC (pH 6.0-6.3, 36°C) over a typical 18-h production shift and can lead to detectable coliforms in finished cheese. Sampling at the commercial plant indicated 2 sources of very low levels of coliforms: (1) subpasteurized whey and curd entering the DMC and (2) surfaces in the DMC after sanitation. Mitigation of these sources would require different approaches. The aim of this study was to investigate whether naturally low levels of coliforms in whey could increase in the bulk liquid and attach to different surface materials within 18 h. A laboratory-scale system was created to mimic the conditions of the initial draining section of the DMC and consisted of single-pass, naturally contaminated whey (pH 6.3, 35°C) flowing through a bioreactor (1.11 L/h) containing coupons of surface types found in the DMC (stainless steel and polypropylene). Whey inside the bioreactor chamber and surface coupons were enumerated for bacterial subpopulations on selective media for planktonic and attached bacteria, respectively, at 0, 12, 15, and 18 h. Bacterial isolates were identified by 16S rDNA sequencing. Nonstarter bacteria present in the whey at 0 h included coliforms (Enterobacter), Pseudomonas, and Acinetobacter (0.80, 2.55, and 2.32 log cfu/mL, respectively), with each increasing significantly in whey (6.18, 7.00, and 5.89 log cfu/mL) and on coupons (5.20, 6.85, and 5.29 log cfu/cm², respectively) after 18 h in the continuous flowing system. Scanning electron microscopy confirmed bacterial attachment on both surfaces, with early biofilm development evident on polypropylene coupons by 18 h. Results from this laboratory-scale study demonstrated that naturally low levels of coliforms entering the DMC in the whey could replicate within the conditions of the draining section of the DMC to the levels found in the commercial production environment.

Machine learning-enabled non-destructive paper chromogenic array detection of multiplexed viable pathogens on food

Fast and simultaneous identification of multiple viable pathogens on food is critical to public health. Here we report a pathogen identification system using a paper chromogenic array (PCA) enabled by machine learning. The PCA consists of a paper substrate impregnated with 23 chromogenic dyes and dye combinations, which undergo colour changes on exposure to volatile organic compounds emitted by pathogens of interest. These colour changes are digitized and used to train a multi-layer neural network (NN), endowing it with high-accuracy (91-95%) strain-specific pathogen identification and quantification capabilities. The trained PCA-NN system can distinguish between viable Escherichia coli, E. coli O157:H7 and other viable pathogens, and can simultaneously identify both E. coli O157:H7 and Listeria monocytogenes on fresh-cut romaine lettuce, which represents a realistic and complex environment. This approach has the potential to advance non-destructive pathogen detection and identification on food, without enrichment, culturing, incubation or other sample preparation steps.

Betacyanin as Bioindicator Using Time-Temperature Integrator for Smart Packaging of Fresh Goat Milk

Smart packaging is a packaging system with embedded sensor or indicator technology, which provides information on the quality of the product, especially perishable foods such as goat milk. One application of smart packaging is to use a time-temperature bioindicator. The purpose of this study was to determine the quality of fresh goat milk during storage at freezing temperatures (-20 ± 2°C) for 31 days and room temperature (25 ± 3°C) for 24 hours using a time-temperature indicator by utilizing a natural dye betacyanin. The method used was descriptive analysis, and the data obtained were processed using the correlation regression test. The samples were observed at freezing temperature every 24 hours and room temperature at 0, 1, 2, 3, 4, 5, 6, 8, 10, and 24 hours. The observation criteria consisted of changes in bioindicator color, milk pH, and total microbes. The results showed that color changes of the bioindicator film at room temperature were more noticeable than at freezing temperature. Based on changes in color of the bioindicator at room temperature, the sample was safe for consumption until the 5th hour with pH 6.51, and the biofilm color characteristics were L∗ = 82.49, a∗ = 21.46, and b∗ = -7.33, but the total number of microbes did not fulfil Indonesian National Standard at the 24th hour, i.e., 1.36 × 10⁶ CFU/ml. At freezing temperatures, fresh goat milk was still safe for consumption until the 31st day with pH 6.51 and total microbe of 1.89 × 10⁵ CFU/ml, and the biofilm color characteristics were L∗ = 80.52, a∗ = 24.15, and b∗ = -7.91. The results of this study concluded that the milk expiration limit based on the betacyanin indicator was 5 hours at room temperature and 31 days at freezing temperature.

Cellulose/chitosan pH-responsive indicator incorporated with carrot anthocyanins for intelligent food packaging

In this study, the possible use of anthocyanins of black carrot (ABC) as a chemo-responsive dye to fabricate a colorimetric pH indicator in a cellulose-chitosan matrix was investigated in order to the monitoring of spoilage in pasteurized milk. Cellulose paper was impregnated with a chitosan solution prepared by a sol-gel method containing ABC (total anthocyanins content of 10 mg/100 mL) and characterized. The swelling and water solubility increased by incorporation of ABC into the chitosan-cellulose film. The colorimetric pH indicator showed an obvious color variation from pink to khaki at different pH values (pH 2-11). Stability tests revealed that the indicator had acceptable color stability during one-month storage at 20 °C. The results also confirmed the immobilization of ABC into the matrix of the polymeric indicator with no significant effect on the chemical and super-molecular structure of the samples. In food trial, fresh pasteurized milk was entirely discerned through a perceptible color change from blue to violet rose color after 48 h storage at 20 °C, which was comfortably observable by the naked eye. The results proved that the fabricated indicator could be used as food grade biomaterials to monitor freshness/spoilage of milk.

Development of Organic-Inorganic Hybrid Optical Gas Sensors for the Non-Invasive Monitoring of Pathogenic Bacteria

Hybrid optical gas sensors, based on different organic and inorganic materials, are proposed in this paper, with the aim of using them as optical artificial nose systems. Three types of organic and inorganic dyes, namely zinc-porphyrin, manganese-porphyrin, and zinc-phthalocyanine, were used as gas sensing materials to fabricate a thin-film coating on glass substrates. The performance of the gas sensor was enhanced by a thermal treatment process. The optical absorption spectra and morphological structure of the sensing films were confirmed by UV-Vis spectrophotometer and atomic force microscope, respectively. The optical gas sensors were tested with various volatile compounds, such as acetic acid, acetone, ammonia, ethanol, ethyl acetate, and formaldehyde, which are commonly found to be released during the growth of bacteria. These sensors were used to detect and discriminate between the bacterial odors of three pathogenic species (Staphylococcus aureus, Escherichia coli and Pseudomonas aeruginosa) grown in Luria-Bertani medium. Based on a pattern recognition (PARC) technique, we showed that the proposed hybrid optical gas sensors can discriminate among the three pathogenic bacterial odors and that the volatile organic compound (VOC) odor pattern of each bacterium was dependent on the phase of bacterial growth.

Automated classification of food products using 2D low-field NMR

In this work, low-field proton (¹H) and sodium (²³Na) relaxation and diffusion measurements are used to detect and classify different types of food products. A compact and low-cost system based on a small 0.5 T permanent magnet has been developed to autonomously authenticate such products. The system uses a simple but efficient double-tuned matching network suitable for ¹H/²³Na NMR. Various machine learning algorithms are used to classify food samples based on T₁-T₂ and D-T₂ data generated by the system, and the accuracy and prediction speed of these algorithms are studied in detail. The influence of temperature drift upon prediction accuracy is also studied. Experimental results demonstrate reliable classification of cooking oils, milk, and soy sauces.

Comparing PTR-MS profile of milk inoculated with pure or mixed cultures of spoilage bacteria

The volatile organic compounds (VOCs) associated with UHT milk (n=8) inoculated with either pure inoculums of Pseudomonas fluorescens (two strains tested) or Chryseobacterium sp., or with mixed cultures of 2 or all 3 of the bacterial strains, and held at 4.5 °C for up to 26 days was measured using proton transfer reaction - mass spectrometry (PTR-MS). The VOCs evolved included a range of carbonyl compounds, alcohols, esters, and acids and had significant qualitative and quantitative differences between the inoculums. Milks inoculated with paired (mixed) bacterial cultures attained patterns similar to the VOC composition of one of the pure inoculums, which could be attributed to the domination of these bacteria within the mixed inoculum. This study will help to characterize the spoilage of milk and provide important insights into understanding the factors that limit the shelf life of milk.

Polydiacetylene/triblock copolymer nanoblend applied as a sensor for micellar casein: A thermodynamic approach

Polydiacetylene (PDA) and triblock copolymer nanoblends were synthesized to detect micellar casein (MC), the main milk protein and an indicator of milk quality. UV-Vis spectrum showed that MC induced blue-to-red transition in nanoblends. When nanoblends and MC were separated by dialysis membrane colorimetric response (CR) was similar, whereas a remarkable CR reduction was noticed after addition of dialyzed-MC, suggesting that small molecules present in MC (salts) caused PDA color change. Interaction enthalpy variation between nanoblends and MC showed an abrupt increase that coincided with MC concentration when colorimetric transition occurred. Copolymer hydrophobic/hydrophilic balance and presence of other molecules in the system affected nanoblends CR. MC salts were found to interact with nanoblends leading to color changes. MC concentration, MC salt release, copolymer hydrophobic/hydrophilic balance, and presence of other molecules in the system affected responses of the sensors. These results contribute to future applications of PDA/copolymer nanosensors to dairy models.

Sensing the quality parameters of Chinese traditional Yao-meat by using a colorimetric sensor combined with genetic algorithm partial least squares regression

Yao-meat is a traditional Chinese salted meat. Total volatile basic nitrogen content (TVB-N), total viable bacterial count (TVC), and residual nitrite (RN) level are important indexes of freshness for Yao-meat. This paper attempted the feasibility to determine TVB-N content, TVC and RN level in Yao-meat by a colorimetric sensor array chip. A color change profile for each sample was obtained by differentiating the image of sensor array before and after exposure to Yao-meat's volatile organic compounds. Genetic algorithm partial least squares regression (GA-PLS) was proposed to build the relationship between the TVB-N content, TVC, RN and the color change profiles of sensor array, and to select informative chemically responsive dyes for the three quality parameters. The GA-PLS models were achieved with RTVB-N=0.812, RTVC=0.856, RRN=0.855, in prediction set. This study demonstrated that colorimetric sensory array with GA-PLS algorithm could be used successfully to analyze the quality of Chinese traditional Yao-meat.

Gold nanoparticle chemiresistors operating in biological fluids

Functionalised gold nanoparticle (Au(NP)) chemiresistors are investigated for direct sensing of small organic molecules in biological fluids. The principle reason that Au(NP) chemiresistors, and many other sensing devices, have limited operation in biological fluids is due to protein and lipid fouling deactivating the sensing mechanism. In order to extend the capability of such chemiresistor sensors to operate directly in biofluids, it is essential to minimise undesirable matrix effects due to protein and lipidic components. Ultrafiltration membranes were investigated as semi-permeable size-selective barriers to prevent large biomolecule interactions with Au(NP) chemiresistors operating in protein-loaded biofluids. All of the ultrafiltration membranes protected the Au(NP) chemiresistors from fouling by the globular biomolecules, with the 10 kDa molecular weight cut-off size being optimum for operation in biofluids. Titrations of toluene in different protein-loaded fluids indicated that small molecule detection was possible. A sensor array consisting of six different thiolate-functionalised Au(NP) chemiresistors protected with a size-selective ultrafiltration membrane successfully identified, and discriminated the spoilage of pasteurised bovine milk. This proof-of-principle study demonstrates the on-chip protein separation and small metabolite detection capability, illustrating the potential for this technology in the field of microbial metabolomics. Overall, these results demonstrate that a sensor array can be protected from protein fouling with the use of a membrane, significantly increasing the possible application areas of Au(NP) chemiresistors ranging from the food industry to health services.

Electronic Nose for Microbiological Quality Control of Food Products

Electronic noses (ENs) have recently emerged as valuable candidates in various areas of food quality control and traceability, including microbial contamination diagnosis. In this paper, the EN technology for microbiological screening of food products is reviewed. Four paradigmatic and diverse case studies are presented: (a)Alicyclobacillusspp. spoilage of fruit juices, (b) early detection of microbial contamination in processed tomatoes, (c) screening of fungal and fumonisin contamination of maize grains, and (d) fungal contamination on green coffee beans. Despite many successful results, the high intrinsic variability of food samples together with persisting limits of the sensor technology still impairs ENs trustful applications at the industrial scale. Both advantages and drawbacks of sensor technology in food quality control are discussed. Finally, recent trends and future directions are illustrated.

Invited review article: an odor-sensing system--powerful technique for foodstuff studies

This work examines gas sensor array technology combined with multivariate data processing methods and demonstrates a promising potential for rapid, non-destructive analysis of food. Main attention is focused on detailed description of sensor used in e-nose instruments, construction, and principle of operation of these systems. Moreover, this paper briefly reviews the progress in the field of artificial olfaction and future trends in electronic nose technology, namely, e-nose based on mass spectrometry. Further discussion concerns a comparison of artificial nose with gas chromatography-olfactometry and the application of e-nose instruments in different areas of food industry.

Electronic nose as an innovative tool for the diagnosis of grapevine crown gall

For the first time, a portable electronic nose was used to discriminate between healthy and galled grapevines, experimentally inoculated with two tumourigenic strains of Agrobacterium vitis. The volatile profile of target cutting samples was analysed by headspace solid phase microextraction coupled with gas chromatography-mass spectrometry. Spectra from tumoured samples revealed the presence of styrene which is compatible with decarboxylation of cinnamic acid involved in secondary metabolism of plants. Principal Component Analysis confirmed the difference in volatile profiles of infected vines and their healthy controls. Linear Discriminant Analysis allowed the correct discrimination between healthy and galled grapevines (83.3%, cross-validation). Although a larger number of samples should be analysed to create a more robust model, our results give novel interesting clues to go further with research on the diagnostic potential of this innovative system associated with multi-dimensional chemometric techniques.

Demonstrating Pathogenicity of Enterobacter cloacae on Macadamia and Identifying Associated Volatiles of Gray Kernel of Macadamia in Hawaii

Gray kernel is an important disease of macadamia (Macadamia integrifolia) that affects the quality of kernels, causing gray discoloration and a permeating, foul odor. Gray kernel symptoms were produced in raw, in-shell kernels of three cultivars of macadamia that were inoculated with strains of Enterobacter cloacae. Koch's postulates were fulfilled for three strains, demonstrating that E. cloacae is a causal agent of gray kernel. An inoculation protocol was developed to consistently reproduce gray kernel symptoms. Among the E. cloacae strains studied, macadamia strain LK 0802-3 and ginger strain B193-3 produced the highest incidences of disease (65 and 40%, respectively). The other macadamia strain, KN 04-2, produced gray kernel in 21.7% of inoculated nuts. Control treatments had 1.7% gray kernel symptoms. Some abiotic and biotic factors that affected incidence of gray kernel in inoculated kernels were identified. Volatiles of gray and nongray kernel samples also were analyzed. Ethanol and acetic acid were present in nongray and gray kernel samples, whereas volatiles from gray kernel samples included the additional compounds, 3-hydroxy-2-butanone (acetoin), 2,3-butanediol, phenol, and 2-methoxyphenol (guaiacol). This is believed to be the first report of the identification of volatile compounds associated with gray kernel.