Demonstration of the Salmonella Typhimurium detection system was shown utilizing a quartz crystal microbalance (QCM) biosensor and signal enhancement by gold nanoparticles. In this study, a benchtop system of a QCM biosensor was utilized for the detection of Salmonella Typhimurium. It was designed with a peristaltic pump system to achieve immobilization of antibodies, detection of Salmonella, and the addition of gold nanoparticles to the sensor. As a series of biochemical solutions were introduced to the surface, the proposed system was able to track the changes in the resonant frequency which were proportional to the variations of mass on the sensor. For antibody immobilization, polyclonal antibodies were immobilized via self-assembled monolayers to detect Salmonella O-antigen. Subsequently, Salmonella Typhimurium was detected by antibodies and the average frequency before and after detecting Salmonella was compared. The highest frequency shifts were −26.91 Hz for ${10}^9 \mathrm{CFU}/\mathrm{mL}$ while the smallest frequency shift was −3.65 Hz corresponding to ${10}^3 \mathrm{CFU}/\mathrm{mL}$. For the specificity tests, non-Salmonella samples such as E. coli, Listeria, and Staphylococcus resulted in low cross-reactivity. For signal amplification, biotinylated antibodies reacted to Salmonella followed by streptavidin—100 nm AuNPs through biotin-avidin interaction. The frequency shifts of ${10}^3 \mathrm{CFU}/\mathrm{mL}$ showed −28.04 Hz, and consequently improved the limit of detection.

Hand-hygiene is a critical component for safe food handling. In this paper, we apply an iterative engineering process to design a hand-hygiene action detection system to improve food-handling safety. We demonstrate the feasibility of a baseline RGB-only convolutional neural network (CNN) in the restricted case of a single scenario; however, since this baseline system performs poorly across scenarios, we also demonstrate the application of two methods to explore potential reasons for its poor performance. This leads to the development of our hierarchical system that incorporates a variety of modalities (RGB, optical flow, hand masks, and human skeleton joints) for recognizing subsets of hand-hygiene actions. Using hand-washing video recorded from several locations in a commercial kitchen, we demonstrate the effectiveness of our system for detecting hand hygiene actions in untrimmed videos. In addition, we discuss recommendations for designing a computer vision system for a real application.

A majority of foodborne illnesses result from inappropriate food handling practices. One proven practice to reduce pathogens is to perform effective hand-hygiene before all stages of food handling. In this paper, we design a multi-camera system that uses video analytics to recognize hand-hygiene actions, with the goal of improving hand-hygiene effectiveness. Our proposed two-stage system processes untrimmed video from both egocentric and third-person cameras. In the first stage, a low-cost coarse classifier efficiently localizes the hand-hygiene period; in the second stage, more complex refinement classifiers recognize seven specific actions within the hand-hygiene period. We demonstrate that our two-stage system has significantly lower computational requirements without a loss of recognition accuracy. Specifically, the computationally complex refinement classifiers process less than 68% of the untrimmed videos, and we anticipate further computational gains in videos that contain a larger fraction of non-hygiene actions. Our results demonstrate that a carefully designed video action recognition system can play an important role in improving hand hygiene for food safety.

Despite ongoing efforts to mitigate foodborne pathogen contamination, outbreaks associated with the consumption of fresh produce persist. This research evaluated the efficacy of three sanitizers in reducing various microorganisms on the surfaces of fresh cucumbers, broccoli, and lettuce under retail crisping conditions. Specifically, this study examined the ability of peracetic acid (PAA; 80 mg/L), hypochlorous acid (FAC; 50 mg/L free available chlorine), and accelerated hydrogen peroxide (AHP; 500 mg/L) to reduce pathogens (0.79-2.41 log CFU/g) and natural microbiota, including aerobic bacteria (0.54-2.55 log CFU/g) and yeasts and molds (0.34-1.58 log CFU/g), when applied for 1 min at both 5 and 25 °C. Results showed that all antimicrobial washes significantly reduced pathogens at both temperatures (p < 0.05). Cucumbers treated with the washing solutions exhibited a greater reduction in E. coli O157:H7 (up to 3.72 log CFU/g), S. typhimurium (up to 2.07 log CFU/g), and aerobic bacteria (up to 2.55 log CFU/g) compared to broccoli and lettuce (p < 0.05). This study also investigated the sanitizers' ability to prevent cross-contamination during the retail crisping process of lettuce heads (contact time: 1.5, 3, and 5 min). PAA and FAC significantly reduced cross-contamination with E. coli O157:H7 and S. typhimurium after 3 min of immersion. PAA also significantly reduced L. monocytogenes cross-contamination in batch 2 after 3 min of contact time (p < 0.05). In contrast, immersion of lettuce heads in water alone (WTC) was ineffective at reducing foodborne pathogens and cross-contamination risks. Highlighting the significance of postharvest antimicrobial washes in reducing the contamination and cross-contamination of fresh produce with foodborne pathogens at the retail level.

Amylose-free and wild-type cassava starches were fermented for up to 30 days and oven- or sun-dried. The specific volume (ν) after baking was measured in native and fermented starches. The average ν (across treatments) for waxy starch was 3.5 times higher than that in wild-type starches (17.6 vs. 4.8 cm³ g⁻¹). The best wild-type starch (obtained after fermentation and sun-drying) had considerably poorer breadmaking potential than native waxy cassava (8.4 vs. 16.4 cm³ g⁻¹, respectively). The best results were generally obtained through the synergistic combination of fermentation (for about 10–14 days) and sun-drying. Fermentation reduced viscosities and the weight average molar mass led to denser macromolecules and increased branching degree, which are linked to a high loaf volume. The absence of amylose, however, was shown to be a main determinant as well. Native waxy starch (neutral in taste, gluten-free, and considerably less expensive than the current alternatives to cassava) could become a new ingredient for the formulation of clean label-baked or fried expanded products.

Lateral flow assays (LFAs) are extensively utilized in point-of-care diagnostics due to their affordability, simplicity, and rapid time-to-results. However, their low sensitivity remains a significant limitation, particularly for detecting foodborne pathogens at concentrations below regulatory thresholds. This study evaluated two distinct sensing modalities—photothermal speckle imaging and colorimetric line intensity analysis—for their potential to enhance the sensitivity of commercially available LFAs. Photothermal imaging quantified refractive index shifts induced by plasmonic heating of gold nanoparticles, while colorimetric analysis used smartphone-acquired images processed with machine learning. The photothermal method achieved a limit of detection (LOD) of 2.13 × 10⁵ CFU/mL, while the colorimetric approach, using a logistic regression model with LASSO regularization, achieved an LOD of 10⁵ CFU/mL. While both approaches demonstrated detection thresholds comparable to traditional visual interpretation, the colorimetric method provided an added advantage by enabling quantitative prediction of bacterial concentration through regression modeling. With further optimization of each sensing method, these findings demonstrate the feasibility of improving unmodified commercial LFAs through optical and computational enhancements, offering a promising pathway toward the development of portable biosensing systems for real-time food safety monitoring.