A multimodal sensing system was developed for automated and intelligent food safety inspection. The system uses two pairs of lasers and spectrometers at 785 and 1064 nm to realize dual-band Raman measurement. Automated sampling can be conducted using a XY moving stage for solid, powder, and liquid samples in customized well plates or randomly scattered in standard Petri dishes (e.g., bacterial colonies). Three LED lights (white backlight, UV ring light, and white ring light) and two color cameras are used for machine vision measurements of samples in the Petri dishes (e.g., transmission, fluorescence, and color). Real-time image processing and motion control techniques are used to fulfill automated sample counting, positioning, sampling, and synchronization functions. System software was developed with integrated AI functions able to identify and label interesting targets instantly. The system capability was demonstrated by an example application for rapid identification of five common foodborne bacteria. Using a machine learning model based on a linear support vector machine, a classification accuracy of 98.6% was achieved using Raman spectra collected from bacterial colonies grown on nutrient nonselective agar in Petri dishes. The system is compact and portable (30×45×35 cm3) that can be used for biological and chemical food safety inspection in regulatory and industrial applications.
Outbreaks of foodborne illness due to pathogenic bacteria have been identified worldwide and have been associated with the consumption of contaminated agricultural products. The main objective of this research is to develop a rapid method for pathogen detection using Raman spectroscopy (RS). Direct detection in culture media and surface-enhanced Raman scattering (SERS) were used to identify Escherichia coli, Escherichia coli O157:H7, Salmonella spp., Listeria monocytogenes, Staphylococcus aureus, Bacillus cereus, and Bacillus thuringiensis. Bacterial isolates were cultured on selective media for 24 h at 37°C or 30°C and then tested with RS. A portable 785 nm point-scan Raman system was developed at ARS USDA for this purpose and multiple laser current and exposure times were tested to establish optimal conditions. Seven nanoparticles and three substrates were evaluated for optimal bacterial detection using label-free SERS. Raman peaks were very weak in direct detection and the bacteria were not identified using direct or SERS approaches. However, two gold nanoparticles consistently showed SERS peaks at 878.9, 1086, and 1455 cm-1 and relative differences in Raman intensity were observed among each of the tested bacteria. This method can be used to lay a foundation for future research such as SERS combined with chemometric analysis and label-based SERS approaches.
Bacterial biofilm formed by pathogens on fresh produce surfaces is a food safety concern because the complex extracellular matrix in the biofilm structure reduces the reduction and removal efficacies of washing and sanitizing processes such as chemical or irradiation treatments. Therefore, a rapid and nondestructive method to identify pathogenic biofilm on produce surfaces is needed to ensure safe consumption of fresh, raw produce. This research aimed to evaluate the feasibility of hyperspectral fluorescence imaging for detecting Escherichia.coli (ATCC 25922) biofilms on baby spinach leaf surfaces. Samples of baby spinach leaves were immersed and inoculated with five different levels (from 2.6x104 to 2.6x108 CFU/mL) of E.coli and stored at 4°C for 24 h and 48 h to induce biofilm formation. Following the two treatment days, individual leaves were gently washed to remove excess liquid inoculums from the leaf surfaces and imaged with a hyperspectral fluorescence imaging system equipped with UV-A (365 nm) and violet (405 nm) excitation sources to evaluate a spectral-image-based method for biofilm detection. The imaging results with the UV-A excitation showed that leaves even at early stages of biofilm formations could be differentiated from the control leaf surfaces. This preliminary investigation demonstrated the potential of fluorescence imaging techniques for detection of biofilms on leafy green surfaces.
Current meat inspection in slaughter plants, for food safety and quality attributes including potential fecal contamination, is conducted through by visual examination human inspectors. A handheld fluorescence-based imaging device (HFID) was developed to be an assistive tool for human inspectors by highlighting contaminated food and food contact surfaces on a display monitor. It can be used under ambient lighting conditions in food processing plants. Critical components of the imaging device includes four 405-nm 10-W LEDs for fluorescence excitation, a charge-coupled device (CCD) camera, optical filter (670 nm used for this study), and Wi-Fi transmitter for broadcasting real-time video/images to monitoring devices such as smartphone and tablet. This study aimed to investigate the effectiveness of HFID in enhancing visual detection of fecal contamination on red meat, fat, and bone surfaces of beef under varying ambient luminous intensities (0, 10, 30, 50 and 70 foot-candles). Overall, diluted feces on fat, red meat and bone areas of beef surfaces were detectable in the 670-nm single-band fluorescence images when using the HFID under 0 to 50 foot-candle ambient lighting.
Black ginseng is produced by steaming a ginseng root followed by drying repeatedly 9 times during the process and it is changed to be black color, so it is known that a black ginseng has more contents of saponins than red ginseng. However a fake black ginseng which is produced to be black color at high temperature in a short period of time generate carcinogenic benzo[a]pyrene(BaP) through the process. In this year, maximum residue level(MRL) for BaP was established to 2 ug/kg in black ginseng and more sensitive method was developed to quantitatively analyze the BaP by high performance liquid chromatography (HPLC) coupling with florescence detector and tandem mass spectrometry (atmospheric pressure chemical ionization-MS/MS). Chromatographic separation was performed on a Supelcosil™ LC-PAH column (3 μm, 3 mm x 50 mm). Mobile phase A was water and mobile phase B was acetonitrile. BaP was exactly separated from other 15 polycyclic aromatic hydrocarbons (PAHs) which have been selected as priority pollutants by the US Environmental Protection Agency (EPA). Linearity of detection was in the range of 0.2~20 μg/kg and limit of detection (LOD) for BaP was lower than 0.1 μg/kg, limit of quantification (LOQ) was 0.2 μg/kg. The recovery of Bap was 92.54%±6.3% in black ginseng.
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