Coupling hyperspectral imaging with machine learning algorithms for detecting polyethylene (PE) and polyamide (PA) in soils.

Soil, particularly in agricultural regions, has been recognized as one of the significant reservoirs for the emerging contaminant of MPs. Therefore, developing a rapid and efficient method is critical for their identification in soil. Here, we coupled HSI systems [i.e., VNIR (400-1000 nm), InGaAs (800-1600 nm), and MCT (1000-2500 nm)] with machine learning algorithms to distinguish soils spiked with white PE and PA (average size of 50 and 300 µm, respectively). The soil-normalized SWIR spectra unveiled significant spectral differences not only between control soil and pure MPs (i.e., PE 100% and PA 100%) but also among five soil-MPs mixtures (i.e., PE 1.6%, PE 6.9%, PA 5.0%, and PA 11.3%). This was primarily attributable to the 1st-3rd overtones and combination bands of C-H groups in MPs. Feature reductions visually demonstrated the separability of seven sample types by SWIR and the inseparability of five soil-MPs mixtures by VNIR. The detection models achieved higher accuracies using InGaAs (92-100%) and MCT (97-100%) compared to VNIR (44-87%), classifying 7 sample types. Our study indicated the feasibility of InGaAs and MCT HSI systems in detecting PE (as low as 1.6%) and PA (as low as 5.0%) in soil. SYNOPSIS: One of two SWIR HSI systems (i.e., InGaAs and MCT) with a sample imaging surface area of 3.6 mm² per grid cell was sufficient for detecting PE (as low as 1.6%) and PA (as low as 5.0%) in soils without the digestion and separation procedures.

Classification between live and dead foodborne bacteria with hyperspectral microscope imagery and machine learning.

Identifying live foodborne bacteria is essential for ensuring food safety and preventing foodborne illnesses. This study investigated the use of hyperspectral microscope imaging and deep learning methods to accurately distinguish between live and dead foodborne bacteria based on their spectral and morphological features. Three deep learning models, Fusion-Net I, II, and III, were developed and evaluated for their ability to classify live and dead bacterial cells of six pathogenic strains, including Escherichia coli (EC), Listeria innocua (LI), Staphylococcus aureus (SA), Salmonella Enteritidis (SE), Salmonella Heidelberg (SH), and Salmonella Typhimurium (ST). The models utilized both morphological and spectral characteristics of the bacterial cells, with inputs of average spectra and 546 nm band images. Fusion-Net I achieved high accuracy in identifying live bacterial cells, with a classification accuracy of 100% for LI, SE, ST strains and over 92.9% for EC, SA, SH. Fusion-Net II and III models were even more robust, achieving 100% accuracy consistently in classifying dead cells in all six strains. Fusion-Net III also showed the ability to identify bacterial strains with 96.9% accuracy, making it a dual-task model with potential applications in identifying live foodborne bacteria prior to foodborne outbreaks. These findings suggest that the use of hyperspectral microscope imaging and deep learning methods could provide a new tool for quickly and accurately identifying bacterial viability, thereby improving the efficiency and reliability of food safety inspection.

Biopolymer encapsulated silver nitrate nanoparticle substrates with surface-enhanced Raman spectroscopy (SERS) for Salmonella detection from chicken rinse.

Salmonella is commonly found on broiler chickens during processing. This study investigates the Salmonella detection method that reduces the necessary time for confirmation, by collecting surface-enhanced Raman spectroscopy (SERS) spectra from bacteria colonies, applied to a substrate of biopolymer encapsulated AgNO3 nanoparticles. Chicken rinses containing Salmonella Typhimurium (ST) were analyzed by SERS and compared to traditional plating and PCR analyses. SERS spectra from confirmed ST and non-Salmonella colonies appear similar in spectra composition, but with different peak intensities. t-Test on the peak intensities showed that ST and non-Salmonella colonies were significantly different (α = 0.0045) at 5 peaks, 692 cm-1, 718 cm-1, 791 cm-1, 859 cm-1, and 1018 cm-1. A support vector machine (SVM) classification algorithm was able to separate ST and non-Salmonella samples with an overall classification accuracy of 96.7 %.

Resilience of Chemistry: An Introduction to the Agricultural and Food Chemistry Technical Program at the 262nd American Chemical Society National Hybrid Meeting & Exposition, Both Online and in Atlanta, Georgia.

This is the third special issue of the Journal of Agricultural and Food Chemistry (JAFC) based on the Agricultural and Food Chemistry Division (AGFD) technical program, at the 262nd American Chemical Society National Meeting. This was the first national meeting held in a hybrid format, both virtually and in-person in Atlanta, Georgia, U.S.A., on August 22-26, 2021. The AGFD proudly hosted 12 symposia, including three award symposia. There were 34 sessions held in total, with 143 oral presentations and 49 poster presentations. This meeting was highly successful in terms of attendance, and technology issues experienced at the previous virtual meetings were successfully resolved.

Microfluidic Sampling and Biosensing Systems for Foodborne Escherichia coli and Salmonella.

Developments of portable biosensors for field-deployable detections have been increasingly important to control foodborne pathogens in regulatory environment and in early stage of outbreaks. Conventional cultivation and gene amplification methods require sophisticated instruments and highly skilled professionals; while portable biosensing devices provide more freedom for rapid detections not only in research laboratories but also in the field; however, their sensitivity and specificity are limited. Microfluidic methods have the advantage of miniaturizing instrumental size while integrating multiple functions and high-throughput capability into one streamlined system at low cost. Minimal sample consumption is another advantage to detect samples in different sizes and concentrations, which is important for the close monitoring of pathogens at consumer end. They improve measurement or manipulation of bacteria by increasing the ratio of functional interface of the device to the targeted biospecies and in turn reducing background interference. This article introduces the major active and passive microfluidic devices that have been used for bacteria sampling and biosensing. The emphasis is on particle-based sorting/enrichment methods with or without external physical fields applied to the microfluidic devices and on various biosensing applications reported for bacteria sampling. Three major fabrication methods for microfluidics are briefly discussed with their advantages and limitations. The applications of these active and passive microfluidic sampling methods in the past 5 years have been summarized, with the focus on Escherichia coli and Salmonella. The current challenges to microfluidic bacteria sampling are caused by the small size and nonspherical shape of various bacterial cells, which can induce unpredictable deviations in sampling and biosensing processes. Future studies are needed to develop rapid prototyping methods for device manufacturing, which can facilitate rapid response to various foodborne pathogen outbreaks.

Physicochemical indicators coupled with multivariate analysis for comprehensive evaluation of matcha sensory quality.

The sensory quality of matcha is a pivotal factor in determining consumer acceptance. However, the human sensory panel test is difficult to popularize by virtue of professional requirements and inability to evaluate large samples. The analysis showed that physicochemical indicators of matcha were significantly related to sensory quality. Hence, principal component analysis (PCA) based on selected key physicochemical indicators was proposed to evaluate the sensory quality of matcha in this research. The eight key indicators were selected from twenty-four physicochemical indicators based on least absolute shrinkage and selection operator (LASSO) for the establishment of the PCA comprehensive evaluation model. The results demonstrated that the PCA comprehensive evaluation model achieved superior performance, with -0.895 rc (correlation coefficient in calibration set) and -0.883 rp (correlation coefficient in prediction set) for overall sensory quality. This work demonstrated that LASSO-PCA comprehensive evaluation as an objective protocol has great potential in predicting matcha sensory quality.

Simultaneous quantification of chemical constituents in matcha with visible-near infrared hyperspectral imaging technology.

This study aimed to assess the feasibility of identifying multiple chemical constituents in matcha using visible-near infrared hyperspectral imaging (VNIR-HSI) technology. Regions of interest (ROIs) were first defined in order to calculate the representative mean spectrum of each sample. Subsequently, the standard normal variate (SNV) method was applied to correct the characteristic spectra. Competitive adaptive reweighted sampling (CARS) and bootstrapping soft shrinkage (BOSS) were used to optimize the models. They were built based on partial least squares (PLS), creating two models referred to as CARS-PLS and BOSS-PLS. The BOSS-PLS models achieved best predictive accuracy, with coefficients of determination predicted to be 0.8077 for caffeine, 0.7098 for tea polyphenols (TPs), 0.7942 for free amino acids (FAAs), 0.8314 for the ratio of TPs to FAAs, and 0.8473 for chlorophyll. These findings highlight the potential of VNIR-HSI technology as a rapid and nondestructive alternative for simultaneous quantification of chemical constituents in matcha.

Label-Free Immunoassay for Multiplex Detections of Foodborne Bacteria in Chicken Carcass Rinse with Surface Plasmon Resonance Imaging.

The frequent outbreaks of foodborne pathogens have stimulated the demand of biosensors capable of rapid and multiplex detection of contaminated food. In this study, surface plasmon resonance imaging (SPRi) was used in simultaneous label-free detection of multiple foodborne pathogens, mainly Salmonella spp. and Shiga-toxin producing Escherichia coli (STEC), in commercial chicken carcass rinse. The antibodies were immobilized on the same SPRi sensor chip as a label-free immunoassay. Their immobilization concentrations were optimized to be ranging from 0.25 to 1.0 mg/mL, and independent of pH values. This label-free immunoassay achieved 106 colony-forming unit (CFU)/mL limit of detection for Salmonella, which was further improved to 1.0 CFU/mL with overnight bacteria enrichment. The injected samples with different bacteria, Salmonella Enteritidis, STEC, and Listeria monocytogenes, have been identified by the same biochip. Moreover, the SPRi signals revealed complex interference effects among coexisting bacteria species in heterogeneous bacteria solutions. This SPRi-based immunoassay demonstrates the great potential in high-throughput screening of multiple pathogenic bacteria coexisting in chicken carcass rinse. The reliability of antibody immobilization and cross-reactions of different antibodies on the same biochip are the major challenges of practical application of SPRi.

Earthworms increase the potential for enzymatic bio-activation of biochars made from co-pyrolyzing animal manures and plastic wastes.

We assessed the enzymatic activation of four different biochars produced from pyrolyzing swine manure and poultry litter, and by co-pyrolyzing these livestock residues with agricultural spent mulch plastic film wastes (plastichars). Enzymatic activation consisted of incubating biochars in soil inoculated with earthworms (Lumbricus terrestris), which acted as biological vectors to facilitate retention of extracellular enzymes onto biochar surface. The activity of carboxylesterase ‒a pesticide-detoxifying enzyme‒ was measured in non-bioturbed soils (reference), linings of the burrows created by earthworms, casts (feces) and biochar particles recovered from the soil. Our results revealed that: 1) biochar increased soil carboxylesterase activity respect to biochar-free (control) soils, which was more prominent in the presence of earthworms. 2) The maximum enzyme activity was found in soils amended with plastichars. 3) The plastichars showed higher enzyme binding capacities than that of the biochars produced from animal manure alone, corroborating the pattern of enzyme distribution found in soil. 4) The presence of earthworms in soil significantly increased the potential of the plastichars for enzymatic activation. These findings suggest that the plastichars are suitable for increasing and stabilizing soil enzyme activities with no toxicity on earthworms.

Immunoassay Biosensing of Foodborne Pathogens with Surface Plasmon Resonance Imaging: A Review.

Surface plasmon resonance imaging (SPRi) has been increasingly used in the label-free detections of various biospecies, such as organic toxins, proteins, and bacteria. In combination with the well-developed microarray immunoassay, SPRi has the advantages of rapid detection in tens of minutes and multiplex detection of different targets with the same biochip. Both prism-based and prism-free configurations of SPRi have been developed for highly integrated portable immunosensors, which have shown great potential on pathogen detection and living cell imaging. This review summarizes the recent advances in immunoassay biosensing with SPRi, with special emphasis on the multiplex detections of foodborne pathogens. Additionally, various spotting techniques, surface modification protocols, and signal amplification methods have been developed to improve the specificity and sensitivity of the SPRi biochip. The challenges in multiplex detections of foodborne pathogens in real-world samples are addressed, and future perspectives of miniaturizing SPRi immunosensors with nanotechnologies are discussed.

Changing the Landscape: An Introduction to the Agricultural and Food Chemistry Technical Program at the 258th American Chemical Society National Meeting in San Diego.

This special issue of the Journal of Agricultural and Food Chemistry (JAFC) is a highlight of the Agricultural and Food Chemistry Division (AGFD) technical program at the 258th National Meeting of the American Chemical Society (ACS) in San Diego, CA, U.S.A., on August 25-29, 2019. At the conference, AGFD had 44 oral sessions at 19 symposia and 100 poster presentations with more than 400 abstract submissions. The technical program covered a broad range of current research and development topics in agricultural and food chemistry, including bioactive food components, diet and human nutrition, utilization of agricultural materials in food systems, food packaging, nanotechnology, and food safety, as well as several special award symposia. This is the first JAFC special issue that highlights an ACS national meeting program with joint efforts from AGFD.

Rapid and Label-Free Immunosensing of Shiga Toxin Subtypes with Surface Plasmon Resonance Imaging.

Shiga toxin-producing Escherichia coli (STEC) are responsible for gastrointestinal diseases reported in numerous outbreaks around the world as well as in the United States. Current detection methods have limitation to implement for rapid field-deployable detection with high volume of samples that are needed for regulatory purposes. Surface plasmon resonance imaging (SPRi) has proved to achieve rapid and label-free screening of multiple pathogens simultaneously, so it was evaluated in this work for the detection of Shiga toxins (Stx1a and Stx2a toxoids were used as the less toxic alternatives to Stx1 and Stx2, respectively). Multiple antibodies (Stx1pAb, Stx1-1mAb, Stx1-2mAb, Stx1d-3mAb, Stx1e-4mAb, Stx2pAb, Stx2-1mAb, Stx2-2mAb, and Stx2-10mAb) were spotted one by one by programed microarrayer, on the same high-throughput biochip with 50-nm gold film through multiple crosslinking and blocking steps to improve the orientation of antibodies on the biochip surface. Shiga toxins were detected based on the SPRi signal difference (ΔR) between immobilized testing antibodies and immunoglobulin G (IgG) control. Among the antibodies tested, Stx1pAb showed the highest sensitivity for Stx1 toxoid, with the limit of detection (LOD) of 50 ng/mL and detection time of 20 min. Both Stx2-1mAb and Stx2-2mAb exhibited high sensitivity for Stx2 toxoid. Furthermore, gold nanoparticles (GNPs) were used to amplify the SPRi signals of monoclonal antibodies in a sandwich platform. The LOD reached the level of picogram (pg)/mL with the help of GNP-antibody conjugate. This result proved that SPRi biochip with selected antibodies has the potential for rapid, high-throughput and multiplex detection of Shiga toxins.

Classification of foodborne bacteria using hyperspectral microscope imaging technology coupled with convolutional neural networks‡.

Foodborne pathogens have become ongoing threats in the food industry, whereas their rapid detection and classification at an early stage are still challenging. To address early and rapid detection, hyperspectral microscope imaging (HMI) technology combined with convolutional neural networks (CNN) was proposed to classify foodborne bacterial species at the cellular level. HMI technology can simultaneously obtain both spatial and spectral information of different live bacterial cells, while two CNN frameworks, U-Net and one-dimensional CNN (1D-CNN), were employed to accelerate the data analysis process. U-Net was used for automating cellular regions of interest (ROI) segmentation, which generated accurate cell-ROI masks in a shorter timeframe than the conventional Otsu or Watershed methods. The 1D-CNN was employed for classifying the spectral profiles extracted from cell-ROI and resulted in a higher accuracy (90%) than k-nearest neighbor (81%) and support vector machine (81%). Overall, the CNN-assisted HMI technology showed potential for foodborne bacteria detection.

Rapid Identification of Campylobacter Strains Cultured Under Aerobic Incubation Using Hyperspectral Microscope Imaging.

ABSTRACT: Campylobacter is an organism of concern for food safety and is one of the leading causes of foodborne bacterial gastroenteritis. This pathogen can be found in broiler chickens, and the level of allowable contamination of processed poultry is regulated by federal agency guidelines. Traditional methods for detecting and isolating this pathogen from broiler chicken carcasses require time, expensive reagents, and artificially generated microaerophilic atmospheres. An aerobic medium that simplifies the procedure and reduces the expense of culturing Campylobacter has been recently described, and Campylobacter can be grown in this medium in containers that are incubated aerobically. Hyperspectral microscopic imaging (HMI) has been proposed for early and rapid detection of pathogens at the cellular level. The objective of the present study was to utilize HMI to compare differences between Campylobacter cultures grown under artificially produced microaerobic atmospheres and cultures grown in aerobic medium. Hyperspectral microscopic images of three Campylobacter strains were collected cultures grown for 48 h microaerophilically and for 24 and 48 h aerobically, and a quadratic discriminant analysis was used to characterize the bacterial variability. Microaerobically cultured bacteria were detected with 98.7% accuracy, whereas detection accuracy of cultures grown in the novel medium was slightly reduced (-4.8 and -3.2% for 24 and 48 h, respectfully). The Mahalanobis distance multivariate metric was applied to quantify strain variability under all three treatment conditions. Across all strains and treatments, little cluster variation was present (4.22 to 4.42), indicating the consistency of the images collected from the three strains. The classification and spectral consistency was similar for cultures incubated in the aerobic medium for 24 h and cultures grown for 48 h under microaerobic conditions.

Identifying non-O157 Shiga toxin-producing Escherichia coli (STEC) using deep learning methods with hyperspectral microscope images.

Non-O157 Shiga toxin-producing Escherichia coli (STEC) serogroups such as O26, O45, O103, O111, O121 and O145 often cause illness to people in the United States and the conventional identification of these "Big-Six" are complex. The label-free hyperspectral microscope imaging (HMI) method, which provides spectral "fingerprints" information of bacterial cells, was employed to classify serogroups at the cellular level. In spectral analysis, principal component analysis (PCA) method and stacked auto-encoder (SAE) method were conducted to extract principal spectral features for classification task. Based on these features, multiple classifiers including linear discriminant analysis (LDA), support vector machine (SVM) and soft-max regression (SR) methods were evaluated. Different sizes of datasets were also tested in search for the suitable classification models. Among the results, SAE-based classification models performed better than PCA-based models, achieving classification accuracy of SAE-LDA (93.5%), SAE-SVM (94.9%) and SAE-SR (94.6%), respectively. In contrast, classification results of PCA-based methods such as PCA-LDA, PCA-SVM and PCA-SR were only 75.5%, 85.7% and 77.1%, respectively. The results also suggested the increasing number of training samples have positive effects on classification models. Taking advantage of increasing dataset, the SAE-SR classification model finally performed better than others with average accuracy of 94.9% in classifying STEC serogroups. Specifically, O103 serogroup was classified with the highest accuracy of 97.4%, followed by O111 (96.5%), O26 (95.3%), O121 (95%), O145 (92.9%) and O45 (92.4%), respectively. Thus, the HMI technology coupled with SAE-SR classification model has the potential for "Big-Six" identification.

Detection of Salmonella from chicken rinsate with visible/near-infrared hyperspectral microscope imaging compared against RT-PCR.

Salmonella is an organism of importance to the poultry industry with increasingly stringent government regulatory standards. Real-time polymerase chain reaction (RT-PCR) and plating procedures on nutrient enriched growth media have been the standard detection methods of Salmonella from broiler chicken carcasses for years. These methods are proven, but offer disadvantages in the amount of time or reoccurring sample cost. Here, we propose the use of a hyperspectral microscope imaging system (HMI) for comparison to standard detection methods. Broiler chicken carcasses were rinsed and plated on Salmonella selective agar. Colonies from plates were picked and RT-PCR was used as a confirmation test to verify plating results, while HMI was collected from the same colonies. Spectral signatures of cells were extracted between 450 and 800 nm from HMI collected with 100x objective. A quadratic discriminant analysis (QDA) was used to classify cells as either Salmonella positive or negative (n = 341). Spectra preprocessing minimized the influence of cellular shape on the spectra, increasing the initial classification accuracy of 81.8-98.5%, yielding a sensitivity of 1.0, and a specificity of 0.963. Results showed the potential as an initial investigation of HMI as a microbial confirmation tool, compared to RT-PCR.

Water-based binary polyol process for the controllable synthesis of silver nanoparticles inhibiting human and foodborne pathogenic bacteria.

The polyol process is a widely used strategy for producing nanoparticles from various reducible metallic precursors; however, it requires a bulk polyol liquid reaction with additional protective agents at high temperatures. Here, we report a water-based binary polyol process using low concentrations of high-molecular-weight polyethylene glycol (100 000 g mol-1, 2 wt%) and ethylene glycol (5 wt%). The entangled conformation of the polyethylene glycol in water and the increased number of reducing sites by the ethylene glycol cooperatively contributed to the stability and effectiveness of reduction reaction and particle growth, producing uniformly sized silver nanoparticles (15.8 ± 2.2 nm) with no additional protective agents at a mild temperature of 80 °C. The measurement of particle size throughout the reaction and the dependence of the optical density of a silver colloidal solution on the concentration of ethylene glycol revealed three stages of particle growth. The minimum inhibitory concentrations of the purified silver nanoparticles against four representative human and foodborne pathogenic bacteria-S. aureus, P. aeruginosa, S. enterica, and E. coli-were 4.7, 2.3, 2.3, and 1.2 µg mL-1, respectively.

Effect of immunomagnetic bead size on recovery of foodborne pathogenic bacteria.

Immunomagnetic separation (IMS) as a culture-free enrichment sample preparation technique has gained increasing popularity in the development of rapid detection methods for foodborne pathogens. While the use of magnetic nanoparticles in IMS is on the rise due to substantially larger surface area compared to conventional magnetic microparticles, the effects of immunomagnetic bead (IMB) size on pathogen cell recovery are not fully understood. In this study we used IMBs of different sizes (100, 500, and 1000nm diameters) to capture Salmonella Enteritidis, a common foodborne pathogen, from buffer solutions as well as food matrices (chicken carcass rinse and liquid egg white). The IMS recovery and non-specific binding rate were compared. The recoveries of Salmonella cells in buffers was highest using the 100nm IMBs (88-96%), followed by the 500nm (31-89%) and 1000nm (4.1-61%) IMBs, demonstrating a significant size effect. The non-specific binding rates of E. coli also increased as IMB size decreased. A 2-72% reduction in Salmonella recovery was observed in chicken carcass rinse and liquid egg white samples compared to in buffers, and this reduction was more significant using 500 and 1000nm IMBs. However, lower IMS recoveries (10-56%) was found in 100nm IMBs two months after preparation. Overall, magnetic nanoparticles yielded superior IMS efficiency to micrometer size IMBs and were less subjective to interference from food matrices. Nevertheless, their long term stability remains an obstacle towards successful use in IMS.

Label-free screening of foodborne Salmonella using surface plasmon resonance imaging.

It is estimated that 95% of the foodborne infections are caused by 15 major pathogens. Therefore, rapid and effective multiplex screening techniques for these pathogens with improved efficiencies could benefit public health at lower costs. Surface plasmon resonance imaging (SPRi) provides a label-free, multiplex analytical platform for pathogen screening. In this study, we have developed a singleplex immunoassay for Salmonella to evaluate the potential of SPRi in pathogen detection. Anti-Salmonella and control ligands were arrayed onto the SPRi sensor chip in a microarray format. The influences of ligand immobilization pH and concentration were optimized, and a pause flow protocol was adopted to improve assay rapidity and sensitivity. The method shows good specificity against 6 non-Salmonella species and was able to detect 5 of 6 Salmonella serotypes, including 3 serotypes most frequently associated with outbreaks. Limits of detection were found to be 2.1 × 106 CFU/mL in phosphate-buffered saline and 7.6 × 106 CFU/mL in the presence of chicken rinse matrix with 8.9 × 107 CFU/mL of indigenous microflora. The condition of antibody array regeneration was optimized for sequential sample injections. Finally, the SPRi immunoassay was used to detect Salmonella directly from artificially spiked chicken carcass rinse samples. As low as 6.8 CFU/mL of Salmonella could be detected after overnight enrichment in buffered peptone water, demonstrating the potential in streamlined pathogen screening with minimal sample preparation and without detection labels. Graphical abstract ᅟ.

Label-free biosensing of Salmonella enterica serovars at single-cell level.

BACKGROUND: The emerging nanotechnologies have greatly facilitated the development of label-free biosensors. The atomic force microscopy (AFM) has been used to study the molecular mechanism of the reactions for protein and aptamers. The surface plasmon resonance (SPR) have been used in fast detections of various pathogens such as bacteria. This study used both AFM and SPR to investigate the complex reactions between aptamers and outer membrane proteins (OMPs) on the surface of S. typhimurium. RESULTS: Two DNA aptamers were used for the label-free detections of S. typhimurium by AFM and SPR. The aptamers have specific binding affinities to the OMPs of S. typhimurium. At single-molecule level, the high resolution AFM topography and recognition images distinguished the OMPs on the bacteria surface, which is the first time the location of individual outer membrane protein have been determined on Salmonella surface. E. coli in the control experiments didn't generate recognition signals, which proved the specificity of these two aptamers to S. typhimurium. The off-rate values for the interactions of these two aptamers to the OMPs were estimated as 5.2 × 10-3 and 7.4 × 10-3 s-1, respectively, by the AFM dynamic force microscopy (DFS). The force and extension values form DFS measurements were used to distinguish the two aptamers. The surface membrane model was proposed to explain the complex correlations among force and extension values. Next, these two aptamers were used in the bulk solution detections of S. typhimurium. The gold chips in SPR experiments were modified with carboxymethylated-dextran (CD), followed by aptamers immobilization, to reduce the non-specific binding signals. The limit of detection (LOD) was determined as 3 × 104 CFU mL-1. CONCLUSIONS: The AFM single-molecule study revealed detailed information about the unbinding force and extension of the aptamer in complex biological reactions. The careful analysis of the experimental results provide better understanding of the molecular mechanism of OMPs reactions. The single-molecule measurements are helpful in evaluating the specificity of binding reagents, such as aptamers, in bulk solution detections. The protocols used in the SPR detections can be expanded into the label-free detections of other bacterial pathogens.