This PDF file contains the front matter associated with SPIE Proceedings Volume 7315, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

In this paper, we report a wireless magnetoelastic (ME) biosensor with phage as the bio-recognition probe for real time detection of Salmonella typhimurium. The ME biosensor was constructed by immobilizing filamentous phage that specifically binds with S. typhimurium onto the surface of a strip-shaped ME particle. The ME sensor oscillates with a characteristic resonance frequency when subjected to a time varying magnetic field. Binding between the phage and antigen (bacteria) causes a shift in the sensor's resonance frequency. Sensors with different dimensions were exposed to various known concentrations of S. typhimurium ranging from 5 x10¹ to 5 x 10⁸ cfu/ml. The detection limit of the ME sensors was found to improve as the size of the sensor became smaller. The detection limit was found to improve from 161 Hz/decade (2mm length sensors) to 1150 Hz/decade (500 μm length sensors). The stability of the ME biosensor was investigated by storing the sensor at different temperatures (25, 45, and 65 °C), and then evaluating the binding activity of the stored biosensor after exposure to S. typhimurium solution (5 x 10⁸ cfu/ml). The results showed that the phage-coated biosensor is robust. Even after storage in excess of 60 days at 65 °C, the phage-coated sensors have a greater binding affinity than the best antibody coated sensors stored for 1 day at 45 °C. The antibody coated sensors showed near zero binding affinity after 3 days of storage at 65 °C.

Biosensor designs are emerging at a significant rate and play an increasingly important role in foodborne pathogen detection. Conducting polymers are excellent tools for the fabrication of biosensors and polypyrrole has been used in the detection of biomolecules due to its unique properties. The prime intention of this paper was to pioneer the design and fabrication of a single-strand (ss) DNA biosensor for the detection of the Bacillus cereus (B.cereus) group species. Growth of B. cereus, results in production of several highly active toxins. Therefore, consumption of food containing >10⁶ bacteria/gm may results in emetic and diarrhoeal syndromes. The most common source of this bacterium is found in liquid food products, milk powder, mixed food products and is of particular concern in the baby formula industry. The electrochemical deposition technique, such as cyclic voltammetry, was used to develop and test a model DNA-based biosensor on a gold electrode electropolymerized with polypyrrole. The electrically conducting polymer, polypyrrole is used as a platform for immobilizing DNA (1μg) on the gold electrode surface, since it can be more easily deposited from neutral pH aqueous solutions of pyrrolemonomers. The average current peak during the electrodeposition event is 288μA. There is a clear change in the current after hybridization of the complementary oligonucleotide (6.35μA) and for the noncomplementary oligonucleotide (5.77μA). The drop in current after each event was clearly noticeable and it proved to be effective.

We present a compact portable chip-based capillary electrophoresis system that employs capacitively coupled contactless conductivity detection (C⁴D) operating at 4 MHz as an alternative detection method compared to the commonly used optical detection based on laser-induced fluorescence. Emphasis was put on system integration and industrial manufacturing technologies for the system. Therefore, the disposable chip for this system is fabricated out of PMMA using injection molding; the electrodes are screen-printed or thin-film electrodes. The system is designed for the measurement of small ionic species like Li⁺, Na⁺, K⁺, SO₄^2- or NO₃^- typically present in foods like milk and mineral water as well as acids e.g. in wine.

We have developed a sandwiched immuno assay to detect sensitively Shiga-like toxins (SLTs) produced by Escherichia coli O157:H7. The method involved the capture of toxins by specific immuno magnetic beads followed by tagging the toxins with peroxidase-labeled anti E. coli O157:H7 antibody. Upon addition of proper substrate, peroxidase induced luminescence was used to measure the presence of SLTs. We have previously demonstrated that co-incubation of shiga toxin (SLT) producing E. coli O157:H7 with certain other bacteria can inhibit toxin production but does not affect the growth of the E. coli. We show here that media in which the cells have grown been centrifuged from (conditioned media) have similar effects on cell growth and SLT production. Adjusting the pH and adding nutrients to the conditioned media did not have any effect on the reduction of SLT produced. Bacteria communicate with each other via secreted sensing molecules. Several types of the molecules have been identified. However, the mechanisms of control remain to be established. This pattern for bacteria growth and toxin production is also observed when quorum-sensing molecules of homoserine lactone and indole are added to the media prior to inoculation.

The need for routine, non-destructive chemical screening of agricultural products is increasing due to the health hazards to animals and humans associated with intentional and unintentional contamination of foods. Melamine, an industrial additive used to increase flame retardation in the resin industry, has recently been used to increase the apparent protein content of animal feed, of infant formula, as well as powdered and liquid milk in the dairy industry. Such contaminants, even at regulated levels, pose serious health risks. Chemical imaging technology provides the ability to evaluate large volumes of agricultural products before reaching the consumer. In this presentation, recent advances in chemical imaging technology that exploit Raman, fluorescence and near-infrared (NIR) are presented for the detection of contaminants in agricultural products.

A microsystem based Raman sensor system for the in situ control of meat was realized. As excitation laser source a compact external cavity diode laser (ECDL) emitting at 671.0 nm mounted on a micro optical bench with a total dimension of (13 x 4 x 1) mm³ is implemented. An output power of 200 mW, a stable emission at 671.0 nm, and a narrow spectral width of about 80 pm, i.e. 2 cm^-1, were measured. The device is well suited for Raman measurements of liquid and solid samples. The devices parameters and the stability will be reviewed. The micro-system laser device is implemented into a specifically laboratory prototype, including an optical bench with a diameter of 25 mm and a length of 170 mm. The probe is coupled fiber-optically to a polychromator with CCD detector for rapid spectral analysis. The Raman probe is characterized and first Raman measurements of porcine musculus longissimus dorsi through the package will be presented. The usefulness of Raman spectroscopy will be discussed with a view of integrating the sensor in a handheld laser scanner for food control.

Due to the narrow linewidth signals and its fingerprinting nature, Raman spectra provide information about the molecular structure and composition of the samples. In this paper, the applicability of Raman spectroscopy is shown for the in-situ characterization of the aging of meat. Miniaturized diode lasers are utilized as light sources with excitation wavelengths of 671 nm and 785 nm with a view to the development of a portable field device for meat. As test sample, musculus longissimus dorsi from pork was taken. The chops were stored refrigerated at 5 °C and Raman spectra were measured daily from slaughter up to three weeks. Throughout the entire period of one month, the Raman spectra preserve the basic spectral features identifying the samples as meat. More specific, the spectra exhibit gradual changes of the Raman signals and they show a time-dependent modification of the background signal which arises from a laser-induced fluorescence (LIF). To analyze the time-correlation of the complex spectra, multivariate statistical methods are employed. By means of principal components analysis (PCA) a distinction of spectra is found on the time scale between day 8 and 10. This corresponds to the transition from ripened meat to meat at and beyond the limit of inedibility. After ca. 10 days of storage at 5 °C the microbial load is overwhelming and LIF increases. The results of the Raman measurements depending on the storage time of meat are discussed in the context of reference analyses which have been performed in parallel.

In order to maximize the utility of the optical scattering technology in the area of bacterial colony identification, it is necessary to have a thorough understanding of how bacteria species grow into different morphological aggregation and subsequently function as distinctive optical amplitude and phase modulators to alter the incoming Gaussian laser beam. In this paper, a 2-dimentional reaction-diffusion (RD) model with nutrient concentration, diffusion coefficient, and agar hardness as variables is investigated to explain the correlation between the various environmental parameters and the distinctive morphological aggregations formed by different bacteria species. More importantly, the morphological change of the bacterial colony against time is demonstrated by this model, which is able to characterize the spatio-temporal patterns formed by the bacteria colonies over their entire growth curve. The bacteria population density information obtained from the RD model is mathematically converted to the amplitude/phase modulation factor used in the scalar diffraction theory which predicts the light scattering patterns for bacterial colonies. The conclusions drawn from the RD model combined with the scalar diffraction theory are useful in guiding the design of the optical scattering instrument aiming at bacteria colony detection and classification.

Bones continue to be a problem of concern for the poultry industry. Most further processed products begin with the requirement for raw material with minimal bones. The current process for generating deboned product requires systems for monitoring and inspecting the output product. The current detection systems are either people palpitating the product or X-ray systems. The current performance of these inspection techniques are below the desired levels of accuracies and are costly. We propose a technique for monitoring bones that conduct the inspection operation in the deboning the process so as to have enough time to take action to reduce the probability that bones will end up in the final product. This is accomplished by developing active cones with built in illumination to backlight the cage (skeleton) on the deboning line. If the bones of interest are still on the cage then the bones are not in the associated meat. This approach also allows for the ability to practice process control on the deboning operation to keep the process under control as opposed to the current system where the detection is done post production and does not easily present the opportunity to adjust the process. The proposed approach shows overall accuracies of about 94% for the detection of the clavicle bones.

Ensuring meat is fully cooked is an important food safety issue for operations that produce "ready to eat" products. In order to kill harmful pathogens like Salmonella, all of the product must reach a minimum threshold temperature. Producers typically overcook the majority of the product to ensure meat in the most difficult scenario reaches the desired temperature. A difficult scenario can be caused by an especially thick piece of meat or by a surge of product into the process. Overcooking wastes energy, degrades product quality, lowers the maximum throughput rate of the production line and decreases product yield. At typical production rates of 6000lbs/hour, these losses from overcooking can have a significant cost impact on producers. A wide area 3D camera coupled with a thermal camera was used to measure the thermal mass variability of chicken breasts in a cooking process. Several types of variability are considered including time varying thermal mass (mass x temperature / time), variation in individual product geometry and variation in product temperature. The automatic identification of product arrangement issues that affect cooking such as overlapping product and folded products is also addressed. A thermal model is used along with individual product geometry and oven cook profiles to predict the percentage of product that will be overcooked and to identify products that may not fully cook in a given process.

High moisture extrusion technology is capable of producing meat analogs which assemble real meat. Since visual and textural properties are the key factor for consumer acceptance, assessing fiber formation in extruded products is important for producing quality meat analogs with a great texture. Recently, we developed a photon migration method to assess fiber formation in meat analogs. In this paper, we present an implementation of this method in a real time scanning system. Acquired images were processed to characterize the fiber formation. This system provides a fast, non destructive means to determine the fiber formation in meat analogs.

Because Thai Hom Mali, also known as Thai Dawk Mali (KDML105), rice is very popular and its price is high compared to other Thai rice varieties, there is an increase in mixing KDML105 milled and unmilled rice grains with other rice varieties, leading to unqualified KDML105 milled rice products for export and unqualified KDML105 unmilled rice seeds for next plants. Instead of using traditional time- and energy- consuming procedures such as alkaline spreading value and pasting property tests, this paper proposes a fast refractometry-based method to analyze ground milled rice grains dissolved in an alkaline solution. Our idea comes from the fact that due to differences in the amount of amylose content in each rice variety, the refractive index of the milled rice powder dissolved in an alkaline solution can be used to distinguish the desired KDML105 rice from others. In our approach, only 0.1 grams of milled rice powder is ground, it is then dissolved in a 10% potassium hydroxide, and its refractive index is investigated. Our experiment using a temperature-controlled optical refractometer and four Thai rice varieties (KDML105, Pathumthani1, Chainat1, and a Thai sticky rice) shows that the milled KDML105 rice can be distinguished from the remaining three rice varieties with a total false error rate of 6.7% and the required measurement time of < 20 seconds. Key advantages include simplicity, moderate accuracy, and less waste produced.

White mushrooms were subjected to mechanical injury by controlled shaking in a plastic box at 400 rpm for different times (0, 60, 120, 300 and 600 s). Immediately after shaking, hyperspectral images were obtained using two pushbroom line-scanning hyperspectral imaging instruments, one operating in the wavelength range of 400 - 1000 nm with spectroscopic resolution of 5 nm, the other operating in the wavelength range of 950 - 1700 nm with spectroscopic resolution of 7 nm. Different spectral and spatial pretreatments were investigated to reduce the effect of sample curvature on hyperspectral data. Algorithms based on Chemometric techniques (Principal Component Analysis and Partial Least Squares Discriminant Analysis) and image processing methods (masking, thresholding, morphological operations) were developed for pixel classification in hyperspectral images. In addition, correlation analysis, spectral angle mapping and scaled difference of sample spectra were investigated and compared with the chemometric approaches.

Spectral scattering is useful for assessing the firmness and soluble solids content (SSC) of apples because it provides an effective means for characterizing light scattering in the fruit. This research compared three methods for quantifying the spectral scattering profiles acquired from 'Golden Delicious' apples using a hyperspectral imaging system for the spectral region of 500-1000 nm. The first method relied on a diffusion theory model to describe the scattering profiles, from which the absorption and reduced scattering coefficients were obtained. The second method utilized a four-parameter Lorentzian function, an empirical model, to describe the scattering profiles. And the third method was calculation of mean reflectance from the scattering profiles for a scattering distance of 10 mm. Calibration models were developed, using multi-linear regression (MLR) and partial least squares (PLS), relating function parameters for each scattering characterization method to the fruit firmness and SSC of 'Golden Delicious' apples. The diffusion theory model gave poorer prediction results for fruit firmness and SSC (the average values of r obtained with PLS were 0.837 and 0.664 respectively for the validation samples). Lorentzian function and mean reflectance performed better than the diffusion theory model; their average r values for PLS validations were 0.860 and 0.852 for firmness and 0.828 and 0.842 for SSC respectively. The mean reflectance method is recommended for firmness and SSC prediction because it is simple and much faster for characterizing spectral scattering profiles for apples.

The use of hyperspectral technology in the NIR for food quality monitoring is discussed. An example of the use of hyperspectral diffuse reflectance scanning and post-processing with a chemometric model shows discrimination between four pharmaceutical samples comprising Aspirin, Acetaminophen, Vitamin C and Vitamin D.

A feasibility study was conducted on the use of hyperspectral imaging to differentiate sound wheat kernels from those with the fungal condition called black point or black tip. Individual kernels of hard red spring wheat were loaded in indented slots on a blackened machined aluminum plate. Damage conditions, determined by official (USDA) inspection, were either sound (no damage) or damaged by the black tip condition alone. Hyperspectral imaging was separately performed under modes of reflectance from white light illumination and fluorescence from UV light (~380 nm) illumination. By cursory inspection of wavelength images, one fluorescence wavelength (531 nm) was selected for image processing and classification analysis. Results indicated that with this one wavelength alone, classification accuracy can be as high as 95% when kernels are oriented with their dorsal side toward the camera. It is suggested that improvement in classification can be made through the inclusion of multiple wavelength images.

The technical challenges of applying hyperspectral imaging techniques to on-line real-time food monitoring is discussed. System optimization must be applied to the design of the hyperspectral imaging spectrograph, the choice and operation of the imaging detector, the design of the illumination system and finally the development of software algorithms to correctly quantify the hyperspectral images. The signal to noise limitation of hyperspectral detection is discussed with particular emphasis on the detection of moving objects at high measurement bandwidths. An example is given of the development of a simple but accurate algorithm for the detection and discrimination of rust particles on leaves.

This paper presents a new method for detecting poultry skin tumors based on serial feature fusion in hyperspectral images. First, some transform methods, including principal component analysis, discrete wavelet transform and band ratio method, are used to generate largely independent datasets in the hyperspectral fluorescence images. Then, the kernel discriminant analysis is utilized to extract features from each represented dataset for the purpose of classification; another set of features are extracted from hyperspectral reflectance images by using kernel discriminant analysis. Finally, new fused features are made by combining aforementioned features. The experimental result based on the proposed method shows the better performance in detecting tumors compared with previous works.

Spoilage in beef is the result of decomposition and the formation of metabolites caused by the growth and enzymatic activity of microorganisms. There is still no technology for the rapid, accurate and non-destructive detection of bacterially spoiled or contaminated beef. In this study, hyperspectral imaging technique was exploited to measure biochemical changes within the fresh beef. Fresh beef rump steaks were purchased from a commercial plant, and left to spoil in refrigerator at 8°C. Every 12 hours, hyperspectral scattering profiles over the spectral region between 400 nm and 1100 nm were collected directly from the sample surface in reflection pattern in order to develop an optimal model for prediction of the beef spoilage, in parallel the total viable count (TVC) per gram of beef were obtained by classical microbiological plating methods. The spectral scattering profiles at individual wavelengths were fitted accurately by a two-parameter Lorentzian distribution function. TVC prediction models were developed, using multi-linear regression, on relating individual Lorentzian parameters and their combinations at different wavelengths to log₁₀(TVC) value. The best predictions were obtained with r²= 0.96 and SEP = 0.23 for log₁₀(TVC). The research demonstrated that hyperspectral imaging technique is a valid tool for real-time and non-destructive detection of bacterial spoilage in beef.

Aflatoxin is a mycotoxin predominantly produced by Aspergillus flavus and Aspergillus parasitiucus fungi that grow naturally in corn, peanuts and in a wide variety of other grain products. Corn, like other grains is used as food for human and feed for animal consumption. It is known that aflatoxin is carcinogenic; therefore, ingestion of corn infected with the toxin can lead to very serious health problems such as liver damage if the level of the contamination is high. The US Food and Drug Administration (FDA) has strict guidelines for permissible levels in the grain products for both humans and animals. The conventional approach used to determine these contamination levels is one of the destructive and invasive methods that require corn kernels to be ground and then chemically analyzed. Unfortunately, each of the analytical methods can take several hours depending on the quantity, to yield a result. The development of high spectral and spatial resolution imaging sensors has created an opportunity for hyperspectral image analysis to be employed for aflatoxin detection. However, this brings about a high dimensionality problem as a setback. In this paper, we propose a technique that automatically detects aflatoxin contaminated corn kernels by using dual-band imagery. The method exploits the fluorescence emission spectra from corn kernels captured under 365 nm ultra-violet light excitation. Our approach could lead to a non-destructive and non-invasive way of quantifying the levels of aflatoxin contamination. The preliminary results shown here, demonstrate the potential of our technique for aflatoxin detection.

We used a portable hyperspectral fluorescence imaging system to evaluate biofilm formations on four types of food processing surface materials including stainless steel, polypropylene used for cutting boards, and household counter top materials such as formica and granite. The objective of this investigation was to determine a minimal number of spectral bands suitable to differentiate microbial biofilm formation from the four background materials typically used during food processing. Ultimately, the resultant spectral information will be used in development of handheld portable imaging devices that can be used as visual aid tools for sanitation and safety inspection (microbial contamination) of the food processing surfaces. Pathogenic E. coli O157:H7 and Salmonella cells were grown in low strength M9 minimal medium on various surfaces at 22 ± 2 °C for 2 days for biofilm formation. Biofilm autofluorescence under UV excitation (320 to 400 nm) obtained by hyperspectral fluorescence imaging system showed broad emissions in the blue-green regions of the spectrum with emission maxima at approximately 480 nm for both E. coli O157:H7 and Salmonella biofilms. Fluorescence images at 480 nm revealed that for background materials with near-uniform fluorescence responses such as stainless steel and formica cutting board, regardless of the background intensity, biofilm formation can be distinguished. This suggested that a broad spectral band in the blue-green regions can be used for handheld imaging devices for sanitation inspection of stainless, cutting board, and formica surfaces. The non-uniform fluorescence responses of granite make distinctions between biofilm and background difficult. To further investigate potential detection of the biofilm formations on granite surfaces with multispectral approaches, principal component analysis (PCA) was performed using the hyperspectral fluorescence image data. The resultant PCA score images revealed distinct contrast between biofilms and granite surfaces. This investigation demonstrated that biofilm formations on food processing surfaces, even for background materials with heterogeneous fluorescence responses, can be detected. Furthermore, a multispectral approach in developing handheld inspection devices may be needed to inspect surface materials that exhibit non-uniform fluorescence.

We successfully demonstrated a multispectral remote sensing system based on our reported spectral imaging design. Dynamic spatial filters such as electronically selected slits were used to select desired bandpass spectrum at a Fourier plane of its optical system. Minimum 9 nm spectral resolution and 0.6° field of view has been achieved. In addition, compact prototype system packaging with a dimension of 17×11×8 inch has been attained. The real-time spectral imaging system capable of wide spectral band operation with simultaneous fine spectral resolution is particularly useful for a variety of defense, medical, and environmental monitoring applications.

As the Thai Dawk Mali (KDML105) rice variety is popular due to its high sensory after cook, there is an increase in mixing the KDML105 rice with other rice varieties that leads to unqualified KDML105 milled rice products for export and unqualified unmilled rice seeds for next plants. Instead of using traditional time consuming methods based on the disintegration of the rice kernel in an alkali solution and the inspection of rice cooked in boiling water, this paper proposes to analyze the milled rice powder dissolved in our alkali solution via a spectroscopic method. In our study, 0.1 g, 0.2 g, and 0.3 of milled rice powder from four Thai rice varieties, i.e., KDML105, Pathumthani1, Chainat1, and RD6, are selected. Then each milled rice sample is ground and then dissolved in a 10% potassium hydroxide (KOH) solution. At the specified minutes of dissolution, the relative optical transmission spectrum of the milled rice solution in a 500-800 nm wavelength is measured and only its first derivative is used for the identification of the KDML105 milled rice. We find that the use of 0.10 g of the milled rice powder dissolved in our KOH solution for 10 minutes provides the lowest false rejection rate of 15%, indicating that we have a faster approach with less amount of waste produced. With the 0.2-g milled rice powder, 5 minutes of dissolution is needed but with a slightly higher false rejection rate of 18.3%.

Early detection and classification of pathogenic bacteria species is crucial to food safety. The previous BARDOT (BActeria Rapid Detection by using Optical light scattering Technology) system is capable of classifying the bacterial colonies of around 1~1.5mm diameter within 24~36 hours of incubation. However, in order to further reduce the detection time and synchronize the detection operation with the bacterial cultivation, a micro-incubator is developed that not only grows bacteria at 37°C but also enables forward scatterometry. This new design feature enables us to continuously characterize the light scattering patterns of the bacterial colonies throughout their growing stages. Some experimental results from this new system are demonstrated and compared with the images obtained from phase contrast microscopy and a confocal displacement meter to show the possibility of earlier identification of bacteria species. Moreover, this paper also explains the updated optical and mechanical modules for the beam waist control to accommodate the smaller bacteria colony detection.

Computational burden due to high dimensionality of Hyperspectral images is an obstacle in efficient analysis and processing of Hyperspectral images. In this paper, we use Kernel Independent Component Analysis (KICA) for dimensionality reduction of Hyperspectraql images based on band selection. Commonly used ICA and PCA based dimensionality reduction methods do not consider non linear transformations and assumes that data has non-gaussian distribution. When the relation of source signals (pure materials) and observed Hyperspectral images is nonlinear then these methods drop a lot of information during dimensionality reduction process. Recent research shows that kernel-based methods are effective in nonlinear transformations. KICA is robust technique of blind source separation and can even work on near-gaussina data. We use Kernel Independent Component Analysis (KICA) for the selection of minimum number of bands that contain maximum information for detection in Hyperspectral images. The reduction of bands is basd on the evaluation of weight matrix generated by KICA. From the selected lower number of bands, we generate a new spectral image with reduced dimension and use it for hyperspectral image analysis. We use this technique as preprocessing step in detection and classification of poultry skin tumors. The hyperspectral iamge samples of chicken tumors used contain 65 spectral bands of fluorescence in the visible region of the spectrum. Experimental results show that KICA based band selection has high accuracy than that of fastICA based band selection for dimensionality reduction and analysis for Hyperspectral images.