This PDF file contains the front matter associated with SPIE Proceedings Volume 9864, including the Title Page, Copyright information, Table of Contents, and Conference Committee listing.

Detection of low concentrations of bacteria in food samples is a challenging process. Key to this process is the separation of the target from the food matrix. We demonstrate magnetic beads and magnetic micro-cilia based microfluidic mixing and capture, which are particularly useful for pre-concentrating the target. The first method we demonstrate makes use of magnetic microbeads held on to NiFe discs on the surface of the substrate. These beads are rotated around the magnetic discs by rotating the external magnetic field. The second method we demonstrate shows the use of cilia which extends into the fluid and is manipulated by a rotating external field. Magnetic micro-features were fabricated by evaporating NiFe alloy at room temperature, on to patterned photoresist. The high magnetic permeability of NiFe allows for maximum magnetic force on the features. The magnetic features were actuated using an external rotating magnet up to frequencies of 50Hz. We demonstrate active mixing produced by the microbeads and the cilia in a microchannel. Also, we demonstrate the capture of target species in a sample using microbeads.

Detection of fresh bruises in apples remains a challenging task due to the absence of visual symptoms and significant chemical alterations of fruit tissues during the initial stage after the fruit have been bruised. This paper reports on a new structured-illumination reflectance imaging (SIRI) technique for enhanced detection of fresh bruises in apples. Using a digital light projector engine, sinusoidally-modulated illumination at the spatial frequencies of 50, 100, 150 and 200 cycles/m was generated. A digital camera was then used to capture the reflectance images from ‘Gala’ and ‘Jonagold’ apples, immediately after they had been subjected to two levels of bruising by impact tests. A conventional three-phase demodulation (TPD) scheme was applied to the acquired images for obtaining the planar (direct component or DC) and amplitude (alternating component or AC) images. Bruises were identified in the amplitude images with varying image contrasts, depending on spatial frequency. The bruise visibility was further enhanced through post-processing of the amplitude images. Furthermore, three spiral phase transform (SPT)-based demodulation methods, using single and two images and two phase-shifted images, were proposed for obtaining AC images. Results showed that the demodulation methods greatly enhanced the contrast and spatial resolution of the AC images, making it feasible to detect the fresh bruises that, otherwise, could not be achieved by conventional imaging technique with planar or uniform illumination. The effectiveness of image enhancement, however, varied with spatial frequency. Both 2-image and 2-phase SPT methods achieved the performance similar to that by conventional TPD. SIRI technique has demonstrated the capability of detecting fresh bruises in apples, and it has the potential as a new imaging modality for enhancing food quality and safety detection.

The main goals of this study are to investigate the potential of absorption coefficient for the prediction of water contents in ‘Yuanhuang’ pear and analyze the relationship between the shelf-life and bulk optical properties in the range of 900-1050 nm. An automated integrating sphere (AIS) system was used to measure the total reflectance, total transmittance of pear flesh tissues in visible-Near infrared (Vis-NIR) range. These two measurements were used to estimate the absorption coefficient μ_a and reduced scattering coefficient μ'_s of pear samples by using an inverse adding doubling (IAD) light propagation model. The detection accuracy of the AIS system was verified by using both liquid (Intralipid-20% as scatterer) and solid phantom (TiO₂ as scatterer, carbon black as absorber). The relative error of measurement of μ'_s of liquid phantom with four different concentration (0.5%,1%,1.5%,2%) at 632.8 nm, 751 nm, 833 nm are less than 10% except for 2% concentration at 833 nm, and the relative error of measurement μ_a and μ'_s of solid phantom at 525.4 nm, 632.1 nm, 710.3 nm and 780.1 nm are less than 5% except for the μ_a at 525.4 nm. A total of 140 samples were used to conduct the moisture measurement, and drying method was used. Predictive models for moisture content from μ_a data were constructed using partial least squares regression (PLSR). The coefficient of correlation of calibration set (R_c) and validation set (R_p) were 0.50 and 0.45 respectively. The relationship between the shelf-life and optical properties was analyzed by dividing pear samples into three categories according to the actual shelf-life, and calculating classification accuracy by using actual and calculated shelf-life grade.

Coastal saltmarsh and their constituent components and processes are of an interest scientifically due to their ecological function and services. However, heterogeneity and seasonal dynamic of the coastal wetland system makes it challenging to map saltmarshes with remotely sensed data. This study selected four important saltmarsh species Pragmitis australis, Sporobolus virginicus, Ficiona nodosa and Schoeloplectus sp. as well as a Mangrove and Pine tree species, Avecinia and Casuarina sp respectively. High Spatial Resolution Worldview-2 data and Coarse Spatial resolution Landsat 8 imagery were selected in this study. Among the selected vegetation types some patches ware fragmented and close to the spatial resolution of Worldview-2 data while and some patch were larger than the 30 meter resolution of Landsat 8 data. This study aims to test the effectiveness of different classifier for the imagery with various spatial and spectral resolutions. Three different classification algorithm, Maximum Likelihood Classifier (MLC), Support Vector Machine (SVM) and Artificial Neural Network (ANN) were tested and compared with their mapping accuracy of the results derived from both satellite imagery. For Worldview-2 data SVM was giving the higher overall accuracy (92.12%, kappa =0.90) followed by ANN (90.82%, Kappa 0.89) and MLC (90.55%, kappa = 0.88). For Landsat 8 data, MLC (82.04%) showed the highest classification accuracy comparing to SVM (77.31%) and ANN (75.23%). The producer accuracy of the classification results were also presented in the paper.

Turmeric is well known for its medicinal value and is often used in Asian cuisine. Economically motivated contamination of turmeric by chemicals such as metanil yellow has been repeatedly reported. Although traditional technologies can detect such contaminants in food, high operational costs and operational complexities have limited their use to the laboratory. This study used Fourier Transform Raman Spectroscopy (FT-Raman) and Fourier Transform - Infrared Spectroscopy (FT-IR) to identify metanil yellow contamination in turmeric powder. Mixtures of metanil yellow in turmeric were prepared at concentrations of 30%, 25%, 20%, 15%, 10%, 5%, 1% and 0.01% (w/w). The FT-Raman and FT-IR spectral signal of pure turmeric powder, pure metanil yellow powder and the 8 sample mixtures were obtained and analyzed independently to identify metanil yellow contamination in turmeric. The results show that FT-Raman spectroscopy and FT-IR spectroscopy can detect metanil yellow mixed with turmeric at concentrations as low as 1% and 5%, respectively, and may be useful for non-destructive detection of adulterated turmeric powder.

Highly ordered hexagonally patterned Ag-nanorod (Ag-NR) arrays for surface-enhanced Raman scattering (SERS) detection of unhealthy chemical residues in food oil was reported, which was obtained by sputtering Ag on the alumina nanotip arrays stuck out of conical-pore anodic aluminum oxide (AAO) templates. SERS measurements demonstrate that the as-fabricated large-scale Ag-nanostructures can serve as highly sensitive and reproducible SERS substrates for detection of trace amount of chemicals in oil with the lower detection limits of 2×10^-6 M for thiram and 10^-7 M for rhodamine B, showing the potential of application of SERS in rapid trace detection of pesticide residues and illegal additives in food oils.

This paper presented a method for subsurface food inspection using a newly developed line-scan spatially offset Raman spectroscopy (SORS) technique. A 785 nm laser was used as a Raman excitation source. The line-shape SORS data was collected in a wavenumber range of 0–2815 cm^-1 using a detection module consisting of an imaging spectrograph and a CCD camera. A layered sample, which was created by placing a plastic sheet cut from the original container on top of cane sugar, was used to test the capability for subsurface food inspection. A whole set of SORS data was acquired in an offset range of 0–36 mm (two sides of the laser) with a spatial interval of 0.07 mm. Raman spectrum from the cane sugar under the plastic sheet was resolved using self-modeling mixture analysis algorithms, demonstrating the potential of the technique for authenticating foods and ingredients through packaging. The line-scan SORS measurement technique provides a new method for subsurface inspection of food safety and quality.

The Cucumber Green Mottle Mosaic Virus (CGMMV) is a globally distributed plant virus. CGMMV-infected plants exhibit severe mosaic symptoms, discoloration, and deformation. Therefore, rapid and early detection of CGMMV infected seeds is very important for preventing disease damage and yield losses. Raman spectroscopy was investigated in this study as a potential tool for rapid, accurate, and nondestructive detection of infected seeds. Raman spectra of healthy and infected seeds were acquired in the 400 cm-1 to 1800 cm-1 wavenumber range and an algorithm based on partial least-squares discriminant analysis was developed to classify infected and healthy seeds. The classification model’s accuracies for calibration and prediction data sets were 100% and 86%, respectively. Results showed that the Raman spectroscopic technique has good potential for nondestructive detection of virus-infected seeds.

The structural, cognitive and visual development of the human brain and retina strictly require long-chain polyunsaturated fatty acids (LC-PUFA). Excluding water, the mammalian brain is about 60% lipid. One of the great unanswered questions with respect to biological science in general is the absolute necessity of the LC-PUFA docosahexaenoic acid (DHA; 22:6n-3) in these fast signal processing tissues. A lipid of the same chain length with just one less diene group, docosapentaenoic acid (DPA; 22:5n-6) is fairly abundant in terrestrial food chains yet cannot substitute for DHA. Gradient Temperature Raman spectroscopy (GTRS) applies the temperature gradients utilized in differential scanning calorimetry to Raman spectroscopy, providing a straightforward technique to identify molecular rearrangements that occur near and at phase transitions. Herein we apply GTRS to DPA, and DHA from -100 to 20°C. 20 Mb three-dimensional data arrays with 1°C increments and first/second derivatives allows complete assignment of solid, liquid and transition state vibrational modes, including low intensity/frequency vibrations that cannot be readily analyzed with conventional Raman. DPA and DHA show significant spectral changes with premelting (-33 and -60°C, respectively) and melting (-27 and -44°C, respectively). The CH2-(HC=CH)-CH2 moieties are not identical in the second half of the DHA and DPA structures. The DHA molecule contains major CH2 twisting (1265 cm^-1) with no noticeable CH₂ bending, consistent with a flat helical structure with small pitch. Further modeling of neuronal membrane phospholipids must take into account this structure for DHA, which would be configured parallel to the hydrophilic head group line.

A near infrared bottled liquid scanner has been developed for security check at airports for anti-terrorism. A compact handheld liquid scanner has been developed using an LED as a light source, instead of a halogen lamp. An LED has much smaller size, longer life time and lower power consumption than those of the lamp. However, it has narrower wave band. Here, we tried to use LEDs to scan liquids and showed the possibility that LEDs can be used as a light source of liquid detector.

This paper presents a method for detection of a few pathogenic bacteria and determination of live versus dead cells. The method combines wireless phage-coated magnetoelastic (ME) biosensors and a surface-scanning dectector, enabling real-time monitoring of the growth of specific bacteria in a nutrient broth. The ME biosensor used in this investigation is composed of a strip-shaped ME resonator upon which an engineered bacteriophage is coated to capture a pathogen of interest. E2 phage with high binding affinity for Salmonella Typhimurium was used as a model study. The specificity of E2 phage has been reported to be 1 in 105 background bacteria. The phage-coated ME biosensors were first exposed to a low-concentration Salmonella suspension to capture roughly 300 cells on the sensor surface. When the growth of Salmonella in the broth occurs, the mass of the biosensor increases, which results in a decrease in the biosensor's resonant frequency. Monitoring of this mass- induced resonant frequency change allows for real-time detection of the presence of Salmonella. Detection of a few bacteria is also possible by growing them to a sufficient number. The surface-scanning detector was used to measure resonant frequency changes of 25 biosensors sequentially in an automated manner as a function of time. This methodology offers direct, real-time detection, quantification, and viability determination of specific bacteria. The rate of the sensor's resonant frequency change was found to be largely dependent on the number of initially bound cells and the efficiency of cell growth.

This work was carried out in the framework of the LIFE RESAFE Project (LIFE12 ENV/IT/000356) “Innovative fertilizer from urban waste, bio-char and farm residues as substitute of chemical fertilizers”. The aim of RESAFE project is the production of a new fertilizer from waste for agricultural practices. The new fertilizer was tested on 5 different crops during field trials carried out in Spain: barley, corn, tomato, potato and melon. For each crop six different treatments were applied and compared to verify the quality of RESAFE fertilizer. Soil samples were collected at the beginning and at the end of the experiment. The possibility to apply hyperspectral imaging (HSI) to perform soil evolution monitoring and characterization in respect to the fertilizer utilization and quality of the resulting crops was investigated. Soil samples were acquired by HSI in the near infrared field (1000–1700 nm) and on the same samples classical chemical analyses were carried out with reference to total nitrogen, total organic carbon, C/N ratio, total organic matter. Hyperspectral data were analyzed adopting a chemometric approach through application of Principal Component Analysis (PCA) for exploratory purposes and Partial Least Squares Analysis (PLS) for estimation of chemical parameters. The results showed as the proposed hardware and software integrated architecture allows to implement low cost and easy to use analytical procedures able to quantitatively assess soil chemical-physical attributes according to different fertilization strategies, in respect of different environmental conditions and selected crops.

Black Sigatoka (BS) is a banana plant disease caused by the fungus Mycosphaerella fijiensis. BS symptoms can be observed at late infection stages. By that time, BS has probably spread to other plants. In this paper, we present our current work on building an hyper-spectral (HS) imaging system aimed at in-vivo detection of BS pre-symptomatic responses in banana leaves. The proposed imaging system comprises a motorized stage, a high-sensitivity VIS-NIR camera and an optical spectrograph. To capture images of the banana leaf, the stage's speed and camera's frame rate must be computed to reduce motion blur and to obtain the same resolution along both spatial dimensions of the resulting HS cube. Our continuous leaf scanning approach allows imaging leaves of arbitrary length with minimum frame loss. Once the images are captured, a denoising step is performed to improve HS image quality and spectral profile extraction.

Nowadays food inspection and evaluation is becoming significant public issue, therefore robust, fast, and environmentally safe methods are studied instead of human visual assessment. Optical sensing is one of the potential methods with the properties of being non-destructive and accurate. As a remote sensing technology, hyperspectral imaging (HSI) is being successfully applied by researchers because of having both spatial and detailed spectral information about studied material. HSI can be used to inspect food quality and safety estimation such as meat quality assessment, quality evaluation of fish, detection of skin tumors on chicken carcasses, and classification of wheat kernels in the food industry. In this paper, we have implied an experiment to detect fat ratio in ground meat via Support Vector Data Description which is an efficient and robust one-class classifier for HSI. The experiments have been implemented on two different ground meat HSI data sets with different fat percentage. Addition to these implementations, we have also applied bagging technique which is mostly used as an ensemble method to improve the prediction ratio. The results show that the proposed methods produce high detection performance for fat ratio in ground meat.

Visual inspection is the most commonly used method for detecting quarantine pests in agricultural cargo items at ports. For example, solid wood packing material (SWPM) at ports may be a pathway for wood pests and is a frequent item of inspection at ports. The inspection process includes examination of the external surface of the item and often destructive sampling to detect internal pest targets. There are few tools available to inspectors to increase the efficiency of inspection and reduce the labor involved. Ion mobility spectrometry (IMS) has promise as an aid for inspection. Because pests emit volatile organic compounds (VOCs) such as hormone like substances, Ion Mobility Spectrometry (IMS) was investigated for possible utility for detecting pests during inspection. SWPM is a major pest pathway in trade, and fumigation of many kinds of wood, including SWPM, with methyl bromide (MeBr) following a published schedule¹ is regularly conducted for phytosanitary reasons prior to shipment to the United States. However, the question remains as to how long the methyl bromide remains in the wood samples after fumigation such that it could act as an interferent to the detection of pest related VOC emissions. This work investigates the capability of ion mobility spectrometry to detect the presence of residual methyl bromide in fumigated maple and poplar wood samples at different times post fumigation up to 118 days after fumigation. Data show that MeBr can be detected in the less dense poplar wood up to 118 days after fumigation while MeBr can be detected in the denser maple wood 55 days after fumigation.

A sequential method for estimating the optical properties of two-layer media with spatially-resolved diffuse reflectance was proposed and validated using Monte Carlo-generated reflectance profiles. The relationship between the penetration depth of detected photons and source-detector separation was first studied. Photons detected at larger source-detector separations generally penetrated deeper into the medium than those detected at small source-detector separations. The effect of each parameter (i.e., the absorption and reduced scattering coefficients (μ_a and μ_s′) of each layer, and the thickness of top layer) on reflectance was investigated. It was found that the relationship between the optical properties and thickness of top layer was a critical factor in determining whether photons would have sufficient interactions with the top layer and also penetrate into the bottom layer. The constraints for the proposed sequential estimation method were quantitatively determined by the curve fitting procedure coupled with error contour map analyses. Results showed that the optical properties of top layer could be determined within 10% error using the semi-infinite diffusion model for reflectance profiles with properly selected start and end points, when the thickness of top layer was larger than two times its mean free path (mfp’). And the optical properties of the bottom layer could be estimated within 10% error by the two-layer diffusion model, when the thickness of top layer was less than 16 times its mfp’. The proposed sequential estimation method is promising for improving the estimation of the optical properties of two-layer tissues from the same spatially-resolved reflectance.

The goal of this project was to construct a cart and a mounting system that would allow a hyperspectral laser-induced fluorescence imaging system (HLIFIS) to be used to detect fecal material in produce fields. Fecal contaminated produce is a recognized food safety risk. Previous research demonstrated the HLIFIS could detect fecal contamination in a laboratory setting. A cart was designed and built, and then tested to demonstrate that the cart was capable of moving at constant speeds or at precise intervals. A mounting system was designed and built to facilitate the critical alignment of the camera’s imaging and the laser’s illumination fields, and to allow the HLIFIS to be used in both field and laboratory settings without changing alignments. A hardened mount for the Powell lens that is used to produce the appropriate illumination profile was also designed, built, and tested.

Potatoes are one of the major food crops in the world. Potato black heart is a kind of defect that the surface is intact while the tissues in skin become black. This kind of potato has lost the edibleness, but it’s difficult to be detected with conventional methods. A nondestructive detection system based on the machine vision technology was proposed in this study to distinguish the normal and black heart of potatoes according to the different transmittance of them. The detection system was equipped with a monochrome CCD camera, LED light sources for transmitted illumination and a computer. Firstly, the transmission images of normal and black heart potatoes were taken by the detection system. Then the images were processed by algorithm written with VC++. As the transmitted light intensity was influenced by the radial dimension of the potato samples, the relationship between the grayscale value and the potato radial dimension was acquired by analyzing the grayscale value changing rule of the transmission image. Then proper judging condition was confirmed to distinguish the normal and black heart of potatoes after image preprocessing. The results showed that the nondestructive system built coupled with the processing methods was accessible for the detection of potato black heart at a considerable accuracy rate. The transmission detection technique based on machine vision is nondestructive and feasible to realize the detection of potato black heart.

Meat is the necessary source of essential nutrients for people including protein, fat, and so on. The discrimination of meat species and the determination of meat authenticity have been an important issue in the meat industry. The objective of this study is to realize the fast and accurate identification of three main red meats containing beef, lamb and pork by using near-infrared hyperspectral imaging (HSI) technology. After acquiring the hyperspectral images of meat samples, the calibration of acquired images and selection of the region of interest (ROI) were carried out. Then spectral preprocessing method of standard normal variate correction (SNV) was used to reduce the light scattering and random noise before the spectral analysis. Finally, characteristic wavelengths were extracted by principal component analysis (PCA), and the Fisher linear discriminant method was applied to establish Fisher discriminant functions to identify the meat species. All the samples were collected from different batches in order to improve the coverage of the models. In addition to the validation of sample itself in train set and cross validation, three different meat samples were sliced at the size of 2cm×2cm×2 cm approximately and were spliced together in one interface to be scanned by HSI system. The acquired hyperspectral data was applied to further validate the discriminant model. The results demonstrated that the near-infrared hyperspectral imaging technology could be applied as an effective, rapid and non-destructive discrimination method for main red meats.

There are many preferences expressing the quality of pork: color, pH, especially TVB-N content. Different quality pork has different spectral feature (in range of 400 to 1000nm). To detect quality attributes of pork easily, real-time, nondestructively, a portable device based on Vis/NIR spectral technique was developed. The device is mainly made up of four units: light source, spectrometer, controller and display screen. After hardware platform established, reflectance spectra of 44 samples were collected from this system. And their physicochemical characteristics such as color parameters, pH value and the content of total volatile basic-nitrogen (TVB-N) were measured in standard methods. Spectrum data acquired were processed by Savitzky-Golay filter(S-G) for noise removal, and then operated by standard normal variable transformation (SNV) for baseline drifts relieving. The partial least squares regression (PLSR) was used to build prediction models for L*, a*, b* pH* and TVB-N content, which could gain good prediction results with Rp of 0.92, 0.91, 0.92, 0.95 and 0.96 respectively. The results demonstrated that this device could be a promising tool applied to detecting pork quality attributes portably, real-time and nondestructively.

Meat freshness is directly related to the health of consumers, and total volatile basic nitrogen (TVB-N) content is an important reference index for evaluating pork freshness. This paper attempted to measure TVB-N content for assessing pork meat freshness using a new self-developed portable and low cost detection device designed by ourselves basing on near infrared technique. The front-end part of this device was an integrated detection component containing a mini probe which was about 5cm in diameter circle. In the signal acquiring component, silicon photodiode detector was embedded in the center of light source in probe and spectral response range was 400-1100nm to receive diffuse light from pork meat surface in mini probe. The main circuits in this device included stabilized current supply circuit which was used to provide a stable power supply for each LED light source in probe and signal processing circuit which was utilized to complete signal amplification and A/D conversion, In addition, another vital function of the signal processing circuit was to analysis detection signals from mini probe in the detection component. For verifying this device performance, 58 pork samples with different freshness attributes and Multiple Linear Regression (MLR) mathematical method and ratio data processing algorithm were employed to build pork TVB-N content prediction model, and comparing with results from raw data model, the correlation coefficient of prediction and validation of TVB-N were 0.8027 and 0.7291 respectively, and the accuracy of predicting pork freshness was about 78.6%. This work demonstrates that it has the potential in nondestructive detection of TVB-N content in pork meat using this device, which can simplify related instruments design structure and reduce their development cost in future.

Raman spectroscopy technique has proven to be a reliable method for qualitative detection of chemical contaminants in food ingredients and products. For quantitative imaging-based detection, each contaminant particle in a food sample must be detected and it is important to determine the necessary spatial resolution needed to effectively detect the contaminant particles. This study examined the effective spatial resolution required for detection of maleic acid in tapioca starch and benzoyl peroxide in wheat flour. Each chemical contaminant was mixed into its corresponding food powder at a concentration of 1% (w/w). Raman spectral images were collected for each sample, leveled across a 45 mm x 45 mm area, using different spatial resolutions. Based on analysis of these images, a spatial resolution of 0.5mm was selected as effective spatial resolution for detection of maleic acid in starch and benzoyl peroxide in flour. An experiment was then conducted using the 0.5mm spatial resolution to demonstrate Raman imaging-based quantitative detection of these contaminants for samples prepared at 0.1%, 0.3%, and 0.5% (w/w) concentrations. The results showed a linear correlation between the detected numbers of contaminant pixels and the actual concentrations of contaminant.

To achieve comprehensive online quality and safety inspection of fruits, whole-surface sample presentation and imaging regimes must be considered. Specifically, sample presentation method for round objects is under development to achieve effective whole-surface sample evaluation based on the use of a single hyperspectral line-scan imaging device. In this paper, a whole-surface round-object imaging method using hyperspectral line-scan imaging techniques is presented.

Hyperspectral imaging has been shown to be a powerful tool for nondestructive evaluation of biological samples. We recently developed a new line-scan-based shortwave infrared (SWIR) hyperspectral imaging system. Critical sensing components of the system include a SWIR spectrograph, an MCT (HgCdTe) array detector, and a custom-designed illumination source. The system has an effective imaging range from 900 nm to 2500 nm. In this paper, we present SWIR hyperspectral images of plant leaves and fruits, and preliminary SWIR image analysis results.

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.

Efforts were made to incorporate the phage Magnetoelastic (ME) biosensor in FDA's Spinach Soaking procedures according to FDA 2015 BAM method. Three soaking materials (Lactose broth, LB broth, and Peptone water) and various soaking times were investigated. Using merely 100 Salmonella cells spiked on the produce surfaces, the phage biosensors detected Salmonella within 5 hours when soaking tomatoes in LB broth as opposed to taking up to 24 hours. Salmonella was detected on spinach leaves within 7 hours. These phage ME biosensors provide a promising rapid detection platform using LB broth in FDA's soaking procedures while shortening the incubation period.

Many issues occur when microbial bacteria contaminates human food or water; it can be dangerous to the public. Determining how the microbial are growing, it can help experts determine how to prevent the outbreaks. Biofilms are a tightly group of microbial cells that grow on living surfaces or surrounding themselves. Though biofilms are not necessarily uniform; when there are more than one type of microbial bacteria that are grown, Raman mapping is performed to determine the growth patterns. Depending on the type of microbial bacteria, they can grow in various patterns such as symmetrical or scattered on the surface. The biofilms need to be intact in order to preclude and potentially figuring out the relative intensity of different components in a biofilm mixture. In addition, it is important to determine whether one biofilms is a substrate for another biofilm to be detected. For example, it is possible if layer B appears above layer A, but layer A doesn’t appear above layer B. In this case, three types of biofilms that are grown includes Listeria(L), Ralstonia(R), and a mixture of the two (LR). Since microbe deposits on metal surfaces are quite suitable, biofilms were grown on stainless steel surface slides. Each slide was viewed under a Raman Microscope at 100X and using a 532nm laser to provide great results and sharp peaks. The mapping of the laser helps determine how the bacteria growth, at which intensity the bacteria appeared in order to identify specific microbes to signature markers on biofilms.

An imaging device to detect fecal contamination in fresh produce fields could allow the producer avoid harvesting fecal contaminated produce. E.coli O157:H7 outbreaks have been associated with fecal contaminated leafy greens. In this study, in-field spectral profiles of bovine fecal matter, soil, and spinach leaves are compared. A common aperture imager designed with two identical monochromatic cameras, a beam splitter, and optical filters was used to simultaneously capture two-spectral images of leaves contaminated with both fecal matter and soil. The optical filters where 10 nm full width half maximum bandpass filters, one at 690 nm and the second at 710 nm. These were mounted in front of the object lenses. New images were created using the ratio of these two spectral images on a pixel by pixel basis. Image analysis results showed that the fecal matter contamination could be distinguished from soil and leaf on the ratio images. The use of this technology has potential to allow detection of fecal contamination in produce fields which can be a source of foodbourne illnesses. It has the added benefit of mitigating cross-contamination during harvesting and processing.

The pulsed lasers at wavelengths of 532 nm and 1064 nm were used as two beams of light for collinear double pulse laser induced breakdown spectroscopy (DP-LIBS). By changing the time sequence of two beams of different lasers, we studied the effect of the interval of two pulses of DP-LIBS on spectral signals compared with single pulsed (SP) LIBS.