Listeria monocytogenes and Salmonella are two major foodborne pathogens of significant concern. Two optical evanescent wave immunosensors were evaluated for detection: Antibody-coupled fiber-optic biosensor and a surface plasmon resonant (SPR) immunosensor. In the fiber-optic sensor, polyclonal antibodies for the test organisms were immobilized on polystyrene fiber wave -guides using streptavidin - biotin chemistry. Cyanine 5 -labeled monoclonal antibodies C11E9 (for L. monocytogenes) and SF-11 (for Salmonella Enteritidis) were used to generate a specific fluorescent signal. Signal acquisition was performed by launching a laser-light (635 nm) from an Analyte-2000. This immunosensor was able to detect 10³ - 10⁹ cfu/ml of L. monocytogenes or 10⁶-10⁹ cfu/ml of Salmonella Enteritidis and the assays were conducted at near real-time with results obtained within one hour of sampling. The assays were specific and showed signal even in the presence of other microorganisms such as E. coli, Enterococcus faecalis or Salmonella Typhimurium. In the SPR system, IAsys instrument (resonant mirror sensor) was used. Monoclonal antibody-C11E9 was directly immobilized onto a carboxylate cuvette. Whole Listeria cells at various concentrations did not yield any signal while surface protein extracts did. Crude protein extracts from L. monocytogenes and L. innocua had average binding responses of around 150 arc sec (0.25 ng/mm²), which was significantly different from L. grayi, L. ivanovii, or L. welshimeri with average responses of <48 arc sec. Both fiber-optic and SPR sensors show promise in near real-time detection of foodborne L. monocytogenes and Salmonella Enteritidis.

Commercially available alfalfa seeds were inoculated with low levels (~ 4 CFU/g) of pathogenic bacteria. The inoculated seeds were then allowed to sprout in sterile tap water at 22°C. After 48 hours, the irrigation water and sprouts were separately transferred to bovine heart infusion (BHI) media. The microbes in the BHI samples were allowed to grow for 4 hours at 37°C and 160 rpm. Specific immunomagnetic beads (IMB) were then applied to capture the E.coli O157 and/or Salmonella in the growth media. Separation and concentration of IMB-captured pathogens were achieved using magnetic separators. The captured E. coli O157:H7 and Salmonella spp were further tagged with europium (Eu) labeled anti-E. coli O157 antibodies and samarium (Sm) labeled anti-Salmonella antibodies, respectively. After washing, the lanthanide labels were extracted out from the complexes by specific chelators to form strongly fluorescent chelates. The specific time-resolved fluorescence (TRF) associated with Eu or Sm was measured to estimate the extent of capture of the E. coli O157 and Salmonella, respectively. The results indicated that the approach could detect E. coli O157 and Salmonella enterica from the seeds inoculated with ~ 4 CFU/g of the pathogens. Non-targeted bacteria, e.g., Aeromonas and Citrobacter exhibited essentially no cross reactivity. Since the pathogen detection from the sprouts was achieved within 6 hours, the developed methodology could be use as a rapid, sensitive and specific screening process for E. coli O157 and Salmonella enterica in this popular salad food.

Antimicrobial peptides (AMPs) have been discovered in insects, mammals, reptiles, and plants to protect against microbial infection. Many of these peptides have been isolated and studied exhaustively to decipher the molecular mechanisms that impart protection against infectious bacteria, fungi, and viruses. Unfortunately, the molecular mechanisms are still being debated within the scientific community but valuable clues have been obtained through structure/function relationship studies¹. Biophysical studies have revealed that cecropins, isolated from insects and pigs, exhibit random structure in solution but undergo a conformational change to an amphipathic α-helix upon interaction with a membrane surface². The lack of secondary structure in solution results in an extremely durable peptide able to survive exposure to high temperatures, organic solvents and incorporation into fibers and films without compromising antibacterial activity. Studies to better understand the antimicrobial action of cecropins and other AMPs have provided insight into the importance of peptide sequence and structure in antimicrobial activities. Therefore, enhancing our knowledge of how peptide structure imparts function may result in customized peptide sequences tailored for specific applications such as targeted cell delivery systems, novel antibiotics and food preservation additives. This review will summarize the current state of knowledge with respect to cell binding and antimicrobial activity of AMPs focusing primarily upon cecropins.

Modern agriculture depends on pesticides to curb infestations, increase crop yield and to produce the quantity and quality of food demanded by today's society. However, potential pesticide residue contamination of food is of critical concern to the food industry and the regulators responsible for health and safety. For example, many pesticides kill insects by attacking the central nervous system, and the use of these pesticides above the EPA set tolerance levels (from 0.1 to 50 ppm) could pose a threat to humans, in particular infants. Unfortunately, rapid, chemical analysis of pesticide residues is unavailable, and only a very small fraction of foods are inspected. The greatest concern is food imported from nations that simply ignore US regulations. In an effort to address this need, we have been developing a simple device to collect residues from food surfaces, perform a rapid chemical separation, and detect and identify pesticides by surface-enhanced Raman spectroscopy (SERS). Capillaries are coated with a metal-doped sol-gel that both separates chemicals and generates SER spectra when irradiated. SERS of pesticides at ppm concentrations, and a preliminary product to aid inspectors is presented.

The U. S. Department of Agriculture, Agricultural Research Service has been developing a method and system to detect fecal contamination on processed poultry carcasses with hyperspectral and multispectral imaging systems. The patented method utilizes a three step approach to contaminant detection. Spectra of homogenous samples of feces, ingesta (undigested food particles), and skin were first collected. Then those spectra were evaluated with multivariate analysis techniques to identify significant wavelength regions for further analysis. Hyperspectral data were then collected on contaminated poultry carcasses and information learned from the spectroscopic data was used to aide in hyperspectral data analysis. Finally, the results of the hyperspectral data were used to identify a few optimum wavelengths for use in a real-time multispectral imaging system. In this work, two techniques for developing spectral datasets and algorithms for classifying surface contaminants on poultry carcasses were explored. The first consisted of a scanning monochrometer that measured the average spectra of uncontaminated breast skin and fecal and ingesta contaminants. The second technique used regions of interest (ROI) from a hyperspectral image to collect spatially averaged spectra. Comparison of the spectra from each instrument showed variations in the spectra collected from similar samples. There was an offset of absorption values between the two instruments and the hyperspectral imaging system had better resolution at higher absorption wavelengths. Although both systems were calibrated prior to measuring, there was also a slight shift in absorption peaks between the two systems. Both techniques were able to classify contaminated skin from uncontaminated skin in a full cross-validated test set with better than 99% accuracy. However, when the classification model developed from the monochrometer spectra was applied to whole-carcass hyperspectral images, numerous common carcass features, such as exposed meat and wing-shadowed skin, were wrongly identified as false positives. Since spectra of entire poultry carcasses were available in the original hyperspectral dataset, the hyperspectral ROI technique allowed researchers to easily add the spectra of these false positives to the calibration dataset. New partial least squares regression models with meat and skin shadow spectra resulted in different principal component loadings and improved classification models. The classification model with the combined ROI spectra from skin, feces, ingesta, meat, and skin shadows gave a classification accuracy of 99.5%. When this model was compared to the original model developed from the monochrometer dataset on a few hyperspectral images of contaminated carcasses, fewer false positives were classified with the hyperspectral ROI model without sacrificing the accuracy of contaminant detection. Further research must be done to fully characterize the accuracy of the model.

Successful differentiation of normal chicken livers from septicemic chicken livers was demonstrated using visible/near-infrared (Vis/NIR) spectral data subjected to principal component analysis and then fed into a feedforward back-propagation neural network. The study used 300 fresh chicken livers, 150 collected from normal chicken carcasses and 150 collected from chicken carcasses diagnosed with the septicemica/toxemia (septox) condition as defined for condemnation under U.S. Department of Agriculture (USDA) standards for food safety. Using a training set of 200 samples and testing set of 100 samples, the best neural network model demonstrated a classification accuracy of 98% for normal samples and 94% for septicemia/toxemia samples. These results show that Vis/NIR spectral methods have potential for use in chicken liver inspection as part of automated online systems for food safety inspection. Liver abnormalities are identifying characteristics of the septox condition; consequently, liver screening would be extremely useful as part of an automated inspection system to meet USDA food safety requirements for poultry. Automated inspection systems capable of real-time on-line operation are currently being developed, and spectroscopic liver inspection is potential tool that could be implemented as part of such systems to help poultry processors increase production while meeting food safety inspection requirements.

A visible/near-infrared spectroscopic system for high-speed on-line poultry carcass inspection was developed and demonstrated. The inspection system, which was an area scanning system designed to measure the interactance spectra of poultry carcasses in the visible to near-infrared regions, consisted of a fiber optic probe, a spectrograph, a spectroscopic charge coupled device detector, a quartz tungsten halogen light source, an industrial computer, and inhouse developed software modules. On-line trials of the visible/near-infrared chicken inspection system were conducted during a 5-day period in a poultry processing plant in Athens, Georgia. Spectra (431-943 nm) of 450 wholesome and 426 unwholesome chicken carcasses were measured. The instrument measured the spectra of veterinarian-selected carcasses on a processing line running at speeds of 140 and 180 birds per minute. Results showed this visible/near-infrared system can be used to differentiate between wholesome and unwholesome poultry carcasses at high speeds. For the 140 bird per minute line speed, the best model achieved classification accuracies of 95% for wholesome and 92% for unwholesome birds. For the 180 bird per minute line speed, the best model achieved classification accuracies of 94% and 92% for wholesome and unwholesome birds, respectively. The system is ready to be implemented for operation on high speed poultry processing lines.

The use of spectral sensing has gained acceptance as a rapid means for nondestructive inspection of postharvest food produce. Current technologies generally use color or a single wavelength camera technology. The applicability and sensitivity of these techniques can be expanded through the use of multiple wavelengths. Reflectance in the Vis/NIR is the prevalent spectral technique. Fluorescence, compared to reflectance, is regarded as a more sensitive technique due to its dynamic responses to subtle changes in biological entities. Our laboratory has been exploring fluorescence as a potential means for detection of quality and wholesomeness of food products. Applications of fluorescence sensing require an understanding of the spectral characteristics emanating from constituents and potential contaminants. A number of factors affecting fluorescence emission characteristics are discussed. Because of relatively low fluorescence quantum yield from biological samples, a system with a powerful pulse light source such as a laser coupled with a gated detection device is used to harvest fluorescence, in the presence of ambient light. Several fluorescence sensor platforms developed in our laboratory, including hyperspectral imaging, and laser-induced fluorescence (LIF) and steady-state fluorescence imaging systems with multispectral capabilities are presented. We demonstrate the potential uses of recently developed fluorescence imaging platforms in food safety inspection of apples contaminated with animal feces.

Fluorescence can be a sensitive method for detecting food contaminants. Of particular interest is detection of fecal contamination as feces is the source of many pathogenic organisms. Feces generally contain chlorophyll a and related compounds due to ingestion of plant materials, and these compounds can readily be detected using fluorescence techniques. Described is a fluorescence-imaging system consisting primarily of a UV light source, an intensified camera with a six-position filter wheel, and software for controlling the system and automatically analyzing the resulting images. To validate the system, orchard apples artificially contaminated with dairy feces were used in a "hands-on" public demonstration. The contamination sites were easily identified using automated edge detection and threshold detection algorithms. In addition, by applying feces to apples and then washing sets of apples at hourly intervals, it was determined that five h was the minimum contact time that allowed identification of the contamination site after the apples were washed. There are many potential uses for this system, including studying the efficacy of apple washing systems.

In this paper, the feasibility is investigated to improve discrimination between different defect and diseases on raw French fries with multispectral imaging. Four different potato cultivars are selected from which French Fries are cut. Both multispectral images and RGB color images are classified with linear Bayes normal classifier and a support vector classifier. The effect of applying different preprocessing techniques on the spectra prior to classification was also investigated. The classification result are compared with both RGB images and the full spectra classification results. Experimental results indicate that the support vector classifier gives the best performance for both multispectral and RGB color images and is less preprocessing dependent. The multispectral image classification results outperform the RGB color classification results with a factor 15 at best. An explorative multispectral analysis also shows that latent defects can be detected with multispectral imaging, in contrast with traditional color imaging.

A computer vision system was established to explore a method for citrus maturity detection. The surface color information and the ratio of total soluble solid to titratable acid (TSS/TA) were used as maturity indexes of citrus. The spectral reflectance properties with different color were measured by UV-240 ultraviolet and visible spectrophotometer. The biggest discrepancy of gray levels between citrus pixels and background pixels was in blue component image by image background segmentation. Dynamic threshold method for background segmentation had best result in blue component image. Methods for citrus image color description were studied. The citrus spectral reflectance experiments showed that green surface and saffron surface of citrus were of highest spectral reflectance at the wavelength of 700nm, the difference between them reached to maximum, about 53%, and the image acquired at this wavelength was of more color information for maturity detection. A triple-layer feed forward network was established to map citrus maturity from the hue frequency sequence by the mean of artificial neural network. After training, the network mapper was used to detect the maturity of the test sample set, which was composed of 252 Weizhang citrus with different maturity. The identification accuracy of mature citrus reached 79.1%, that of immature citrus was 63.6%, and the mean identification accuracy was 77.8%. This study suggested that it is feasible to detect citrus maturity non-invasively by using the computer vision system and hue frequency sequence method.

This paper describes a novel approach for detection of foreign materials in deboned poultry patties based on real-time imaging technologies. Uneven thickness of poultry patties could lead to a significant classification error in a typical X-ray imaging system, and we addressed this issue successfully by fusing laser range imaging (3D imaging) into the x-ray inspection system. In order for this synergic technology to work effectively for on-line industrial applications, the vision system should be able to identify various physical contaminations automatically and have viable real-time capabilities. To meet these challenges, a rule-based approach was formulated under a unified framework for detection of diversified subjects, and a multithread scheme was developed for real-time image processing. Algorithms of data fusion, feature extraction and pattern classification of this approach are described in this paper. Detection performance and overall throughput of the system are also discussed.

This paper presents the research results of the performance of classification methods for hyperspectral poultry imagery to identify fecal and ingesta contaminants on the surface of broiler carcasses. A pushbroom line-scan hyperspectral imager was used to acquire hyperspectral data with 512 narrow bands covered from 400 to 900 nm wavelengths. Three different feces from digestive tracts (duodenum, ceca, colon), and ingesta were used as contaminants. These contaminants were collected from the broiler carcasses fed by corn, milo, and wheat with soybean meals. For the selection of optimum classifier, various widely used supervised classification methods (parallelepiped, minimum distance, Mahalanobis distance, maximum likelihood, spectral angle mapper, and binary coding) were investigated. The classification accuracies ranged from 62.94% to 92.27%. The highest classification accuracy for identifying contaminants for corn fed carcasses was 92.27% with spectral angle mapper classifier. While, the classification accuracy was 82.02% with maximum likelihood method for milo fed carcasses and 91.16% accuracy was obtained for wheat fed carcasses when same classification method was used. The mean classification accuracy obtained in this study for classifying fecal and ingesta contaminants was 90.21%.

We consider a feature selection method to detect skin tumors on chicken carcasses using hyperspectral reflectance data. This allows for faster data collection than does fluorescence data. A chicken skin tumor is an ulcerous lesion region surrounded by a region of thickened-skin. Detection of chicken tumors is a difficult detection problem because the tumors vary in size and shape; some tumors appear on the side of the chicken. In addition, different areas of normal chicken skin have a variety of hyperspectral response variations, some of which are very similar to the spectral responses of tumors. Similarly, different tumors and different parts of a tumor have different spectral responses. Thus, proper classifier training is needed and many false alarms are expected. Since the spectral responses of the lesion and the thickened-skin regions of tumors are considerably different, we train our feature selection algorithm to detect lesion regions and to detect thickened-skin regions separately; we then process the resultant images and we fuse the two HS detection results to reduce false alarms. Our new forward selection and modified branch and bound algorithm is used to select a small number of λ spectral features that are useful for discrimination. Initial results show that our method offers promise for a good tumor detection rate and a low false alarm rate.

Incompletely closed glumes, germ and disease are three characteristics of hybrid rice seed. Image-processing algorithms developed to detect these seed characteristics were presented in this paper. The rice seed used for this study involved five varieties of Jinyou402, Shanyou10, Zhongyou207, Jiayou and IIyou. The algorithms were implemented with a 5*600 images set, a 4*400 images set and the other 5*600 images set respectively. The image sets included black background images, white background images and both sides images of rice seed. Results show that the algorithm for inspecting seeds with incompletely closed glumes based on Radon Transform achieved an accuracy of 96% for normal seeds, 92% for seeds with fine fissure and 87% for seeds with unclosed glumes, the algorithm for inspecting germinated seeds on panicle based on PCA and ANN achieved n average accuracy of 98% for normal seeds, 88% for germinated seeds on panicle and the algorithm for inspecting diseased seeds based on color features achieved an accuracy of 92% for normal and healthy seeds, 95% for spot diseased seeds and 83% for severe diseased seeds.

The need exists to improve sensitivity of detection of toxic pollutants and pathogenic microorganisms, ensuring food and water safety. Developing methods that would increase antibody binding surface area and/or improve the sampling process by specifically concentrating the analyte of interest from the diluted extracted food sample would increase the chances of finding and detecting food pathogens and their toxins. Our approach to improve sensitivity was to generate high surface nanofibrous membranes with covalently attached molecular recognition elements (MREs, e.g. antibodies and peptides) for the selective capture of target analytes through the use of electrospinning. Electrospinning is a process by which high static voltages are used to produce an interconnected membrane-like web of small fibers with diameters ranging from 50-1000 nanometers. These nanofibrous membranes can have surface areas approximately one to two orders of magnitude higher than those found in continuous films. The association of MREs with electrospun fibers presents the opportunity for developing both biosensor detection platforms with increased surface area and membrane concentrators. It is expected that the available surface area demonstrated by this technique will provide increased sensitivity, capture efficiency and fast response time in sensing applications. Antibodies and peptide-based receptors were selectively immobilized onto these nanoporous membranes for bioaffinity capture. Initial results involving fluorescent and chemiluminescent imaging for quantifying attachment and activity in association with the electrospinning process will be discussed.

We present the spectroscopic study of milk with soy and discuss using that for express-analysis. In the non-elastic light scattering spectra the luminescence band are found in visible region. A peak of the band is dramatic changed with changing soy concentration in milk. the nature of phenomenon is discussed. The obtained results can be used for instrumental express-analysis soy in milk by modified the laser device <MIG> have constructed for determining fat and protein in milk.

Incompletely closed glumes, germ on panicle and disease are three characteristics of hybrid rice seed, which are actual reasons of poor seed quality. To find how many and which categories should be classified to meet the demand of produce actually, the effects of various degree of incompletely closed glumes, germ on panicle and disease on ratio of germination in changed storage periods were studied with standard germination rate test. An electronic scanning microscope was used for micro-observation and measurement. Then the possibility of automation inspection was tested with a machine vision system. The measures of increasing quality of hybrid rice seed were discussed in the paper at last. In the light of the periods of treatment and the classification of characteristics, difference steps should be taken. Before storage, Seeds with germ or severe disease should be rejected at first. Then seeds with incompletely closed glumes or spot disease might be stored separately for a shorter time in dried condition and treated with antisepsis before using for some special fields with lower quality demand. The seeds with fine fissure between glumes should be stored in a strictly controlled condition separately and inspected before use, just like other normal and healthy seeds.

Surface roughness is an important physical property of soil in agricultural applications. It is a key parameter affecting the optical reflectance of bare soils, which can be computed from imagery acquired with airborne or space-based remotesensing devices. Accurate ground-truth roughness data need to be collected before a correct computational interpretation can be made. This paper presents the development of a real-time, geo-referenced, ground-based imaging system that produces quantitative ground truth information of soil surface roughness. The system applies Fourier transform profilometry (FTP) to an image of a soil area under study to obtain relative height data of the surface. Then it computes parameters such as root mean square (RMS) and correlation length as measures of roughness. Measurement experiments have been carried out successfully both under simulated conditions in the laboratory and in the field. The results show that the system is capable of generating reliable ground-truth soil surface roughness information. In comparison with other approaches, this developed system is fast, efficient and inexpensive.

Soil characteristics in agriculture represent one of the primary key-factors affecting soil productivity and quality of the produced products. Soil characterization are conventionally performed adopting integrated physical-chemical analyses based on soil portion (samples) directly collected in situ. Such an approach is obviously time consuming. In this work is examined the possibility offered by digital imaging based spectrophotometric techniques in order to perform fast and reliable tests able to identify and quantify specific soil characteristics of primary importance in horticulture. The proposed approach is very simple to apply and for its flexibility can be profitably utilized also for other applications (i.e. environmental monitoring) where soil reclamation plays a pre-eminent role.

This paper introduces the use of aerial RGB digital photography to detect changes in soil structure at field level before vegetation appearance and in the early stages of crop development. Aerial digital photographs of a field trial were used as basis for soil structure mapping. Images were acquired right after sowing the crop, and image-processing routines made it possible to detect and map changes in soil structure. The detected pattern could be recognized in the growth pattern of the canopy a month later. The field trial used in this work was subdivided into areas receiving different amounts of fertilizer, and the aerial digital photographs clearly showed the detected soil variations to overpower the influence of the differences in fertilization.

Aerial hyperspectral imagery has been used to find the temporal relationship between image and corn yield. A total of five hyperspectral images were taken during the growing season. For each image, the optimal vegetation index was selected among many candidate vegetation indices. At the same time, the optimal band subset was selected to calculate the vegetation index. The optimal band subset has the minimum number of bands and represents the most significant image bands (or wavelength) for yield prediction. The optimization process used the EAVI (Evolutionary Algorithm based Vegetation Index generation) algorithm. Results showed that the EAVI algorithm generated the best vegetation index among many comparison indices for yield estimation. For image taken at different date, the algorithm selected a different optimal vegetation index and image bands. The most common sensitive wavelength identified was in the red edge at 700 nm and in the NIR region at 826 nm. This study showed that images taken from the beginning of full canopy coverage to the corn ear formation period provided the best and stable result for corn yield estimation. It is suggested that this period of time during the growing season would have great potential for remote sensing based corn yield prediction.

Several plant species release volatile organic compounds (VOCs) when under stresses such as herbivore feeding attack. The release of these plant-produced VOCs (i.e. terpenes) triggers the release of active biochemical defenses, which target the attacker. In some cases, the VOCs send cues to nearby carnivorous predators to attract them to the feeding herbivore. Volatile compounds are released both locally by damaged leaves and systemically by the rest of the plant. These compounds are released in large quantities, which facilitate detection of pests in the field by parasitoids. Detecting the plant’s VOC emissions as a function of various parameters (e.g. ambient temperature, atmospheric nitrogen levels, etc.) is essential to designing effective biological control systems. In addition these VOC releases may serve as early warning indicator of chemo-bio attacks. By combining Raman spectroscopy techniques with Laser Remote Sensing (LIDAR) systems, we are developing a Standoff detection system. Initial results indicate that is it possible to detect and differentiate between various terpenes, plant species, and other chemical compounds at distances greater than 12 meters. Currently, the system uses the 2nd harmonic of a Nd:YAG; however plans are underway to improve the Raman signal by moving the illumination wavelength into the solar-blind UV region. We report on our initial efforts of designing and characterizing this in a laboratory proof of concept system. We envision that this effort will lead to the design of a portable field-deployable system to rapidly characterize, with a high spatial resolution, large crops and other fields.

A plant health sensing system was developed for determining nitrogen status in plants. The system consists of a multi-spectral optical sensor and a data-acquisition and processing unit. The optical sensor’s light source provides modulated panchromatic illumination of a plant canopy with light-emitting diodes, and the sensor measures spectral reflectance through optical filters that partition the energy into blue, green, red, and near-infrared wavebands. Spectral reflectance of plants is detected in situ, at the four wavebands, in real time. The data-acquisition and processing unit is based on a single board computer that collects data from the multi-spectral sensor and spatial information from a global positioning system receiver. Spectral reflectance at the selected wavebands is analyzed, with algorithms developed during preliminary work, to determine nitrogen status in plants. The plant health sensing system has been tested primarily in the laboratory and field so far, and promising results have been obtained. This article describes the development, theory of operation, and test results of the plant health sensing system.

The development of precision farming requires new tools for plant nutritional stress monitoring. An operational fluorescence system has been designed for vegetation status mapping and stress detection at plant and field scale. The instrument gives relative values of fluorescence at different wavelengths induced by the two-excitation sources. Lightinduced fluorescence has demonstrated successful crop health monitoring and plant nutritional stress detection capabilities. The spectral response of the plants has first been measured with an hyperspectral imager using laser-induced fluorescence. A tabletop imaging fluorometer based on flash lamp technology has also been designed to study the spatial distribution of fluorescence on plant leaves. For field based non-imaging system, LED technology is used as light source to induce fluorescence of the plant. The operational fluorescence system is based on ultraviolet and blue LED to induce fluorescence. Four narrow fluorescence bands centered on 440, 520, 690 and 740nm are detected. The instrument design includes a modular approach for light source and detector. It can accommodate as many as four different light sources and six bands of fluorescence detection. As part of the design for field application, the instrument is compatible with a mobile platform equipped with a GPS and data acquisition system. The current system developed by Telops/GAAP is configured for potato crops fluorescence measurement but can easily be adapted for other crops. This new instrument offers an effective and affordable solution for precision farming.

Fuzzy excess green (ExG) crisp indices and clustering algorithms such as the Gustafson-Kessel (GK) have been successfully used for unsupervised classification of hidden and prominent regions of interest (ROI’s), namely green plants in crop color images against bare clay soil, corn residue and wheat residue, typical of the Great Plains. Each process can be enhanced with Zadeh (Z) and Gath-Geva (GG) fuzzy enhancement techniques. Enhanced indices and clusters can be then sorted by final degree of fuzziness, and recombined into labeled, false-color class images, which can be used as templates for further shape and textural analyses. ROI’s with the lowest degree of fuzziness were consistently found to be plant clusters according to foveated or prominence of the region size within the image. Clustering performance according to partition densities and hyper volume was also evaluated. These latter measures can be used to select the number of clusters and evaluate the computational time needed to find plant ROI’s with complex backgrounds under different lighting conditions. Enhanced GK clustering methods have performed very well and have identified plants in bare soil, corn residue plants , and wheat straw plants, well into the high 90 percentages, depending on plant age category and the relative proportion of plant size within the image. Improved clustering algorithms with textural finger printing could be potentially useful for unsupervised remote sensing, mapping, crop management, weed, and pest control for precision agriculture.

In this study, using the pepper leaves in facility agriculture as the experimental materials, research the application of near infrared spectrum analysis technique to obtain plant growth information. Chlorophyll content of plant leaves is useful information in describing and interpreting the performance of whole-plant systems grows under various conditions, and Near-infrared spectroscopy is a non-destructive analytical technique, which is widely used in farm production. In order to establish a model to predict the relationship between near infrared spectrum and leaf chlorophyll content, a chlorophyll meter and a near infrared spectrometer were used to get chlorophyll content and spectrum of pepper leaf respectively, after that, we use OMINIC and TQ software to acquire and process spectrums of leaves, and use partial least squares (PLS) technique to analyze the data we get by normal experimentation and near infrared spectrometer, set up a calibration model to predict the leaf chlorophyll content based on the characteristics of diffuse reflectance spectrums of pepper leaves. Result showed that NIR technique could acquire chlorophyll content in plant leaves conveniently and quickly. The best model of chlorophyll content has a root mean square error of prediction (RMSEP) of 2.44 and a calibration correlation coefficient (R²) value of 0.969.

Absorption spectroscopy and multi-angle scattering measurements in the visible spectral range are innovately used to analyze samples of extra virgin olive oils coming from selected areas of Tuscany, a famous Italian region for the production of extra virgin olive oil. The measured spectra are processed by means of the Principal Component Analysis method, so as to create a 3D map capable of clustering the Tuscan oils within the wider area of Italian extra virgin olive oils.

Hyperspectral (HS) data for the inspection of whole corn kernels for aflatoxin is considered. The high-dimensionality of HS data requires feature extraction or selection for good classifier generalization. For fast and inexpensive data collection, only several features (λ responses) can be used. These are obtained by feature selection from the full HS response. A new high dimensionality branch and bound (HDBB) feature selection algorithm is used; it is found to be optimum, fast and very efficient. Initial results indicate that HS data is very promising for aflatoxin detection in whole kernel corn.

Opto-electronic methods represent a potential to identify the presence of insect activities on or within agricultural commodities. Such measurements may detect actual insect presence or indirect secondary changes in the product resulting from past or present insect activities. Preliminary imaging studies have demonstrated some unique spectral characteristics of insect larvae on cherries. A detailed study on spectral characteristics of healthy and infested tart cherry tissue with and without larvae (Plum Curculio) was conducted for reflectance, transmittance and interactance modes for each of UV and visible/NIR light sources. The intensity of transmitted UV signals through the tart cherry was found to be weak; however, the spectral properties of UV light in reflectance mode has revealed some typical characteristics of larvae on healthy and infested tissue. The larvae on tissue were found to exhibit UV induced fluorescence signals in the range of 400-700 nm. Multi spectral imaging of the halved tart cherry has also corroborated this particular behavior of plum curculio larvae. The gray scale subtraction between corresponding pixels in these multi-spectral images has helped to locate the larvae precisely on the tart cherry tissue background, which otherwise was inseparable. The spectral characteristics of visible/NIR energy in transmittance and reflectance mode are capable of estimating the secondary effect of infestation in tart cherry tissue. The study has shown the shifting in peaks of reflected and transmitted signals from healthy and infested tissues and coincides with the concept of browning of tissue at cell level as a process of infestation. Interactance study has been carried out to study the possibility of coupling opto-electronic devices with the existing pitting process. The shifting of peaks has been observed for the normalized intensity of healthy and infested tissues. The study has been able to establish the inherent spectral characteristic of these tissues. It was found that there existed promising futuristic possibilities to use opto-electronic sensing to estimate the degree of secondary effect of insect activities within the tissue.

Multispectral imaging in reflectance and fluorescence modes combined with neural network analysis was used to classify various types of apple disorder from three apple varieties (Honey Crisp, Red Cort, and Red Delicious). Eighteen images from a combination of filter sets and three different imaging modes (reflectance, visible light induced fluorescence, and UV induced fluorescence) were acquired for each apple sample as a base for pixel-level classification into normal or disorder tissue. Two classification schemes, a 2-class and a multiple class, were developed and tested in this study. In the 2-class scheme, pixels were categorized into normal or disorder tissue, whereas in the multiple class scheme, pixels were categorized into normal, bitter pit, black rot, decay, soft scald, and superficial scald tissues. Results indicate that single variety training under the 2-class scheme yielded highest accuracy with total accuracy of 95, 97, and 100 % for Honey Crisp, Red Cort, or Red Delicious respectively. In the multiple-class scheme, the classification accuracy of Honey Crisp apple for normal, bitter pit, black rot, decay, and soft scald tissue was 94, 93, 97, 97, and 94 % respectively. Through variable selection analysis, in the 2-class scheme, fluorescence models yielded higher total classification accuracy compared to reflection models. For Red Cort and Red Delicious, models with only FUV yield more than 95% classification accuracy, demonstrating a potential of fluorescence to detect superficial scald. Several important wavelengths, including 680, 740, 905 and 940 nm, were identified from the filter combination analysis. The results indicate the potential of this technique to accurately recognize different types of disorder on apple.

Firmness and sweetness are key quality attributes that determine the acceptability of apple fruit to the consumer. The objective of this research was to investigate a multispectral imaging system for simultaneous acquisition of multispectral scattering images from apple fruit to predict firmness and soluble solids content (SSC). A circular broadband light beam was used to generate light backscattering at the surface of apple fruit and scattering images were acquired, using a common aperture multispectral imaging system, from Red Delicious and Golden Delicious apple fruit for wavelengths at 680, 880, 905, and 940 nm. Scattering images were radially averaged to produce one-dimensional spectral scattering profiles, which were then input into a backpropagation neural network for predicting apple fruit firmness and SSC. It was found that the neural network performed best when 10 neurons and 20 epochs were used. With inputing three ratios of spectral profiles involving all four wavelengths, the neural network gave firmness predictions with the correlation (r) of 0.76 and the standard error for validation (SEV) of 6.2 N for Red Delicious apples and r=0.73 and SEV=8.9 N for Golden Delicious apples. Relatively good SSC predictions were obtained for both varieties with SEV=0.9° Brix.

To identify fruits on the tree and determine their locations are the key to harvest fruits by robots. The main features and applications of infrared thermal imaging were reviewed, and main methods to locate fruits on trees were compared. As the low identification rate of common machine vision system, a new method to identify the citrus in a tree canopy by means of infrared thermal imaging was put forward. About 45 infrared thermal images of citrus on trees were acquired from the citrus orchard. It was found that the different thermal distribution among citrus, leaves and branches was about 1^°C and these differences clearly appeared in the gray-level image, which could be easily used to segment the citrus from other parts in the image by using binary image at T=190. A multilayer-masks edge operator was used to extract edge of the image. The results indicated that it was possible to identify citrus on trees using infrared thermal imaging, and it was much easier than the methods presently used.

Obtaining clear images advantaged of improving the classification accuracy involves many factors, light source, lens extender and background were discussed in this paper. The analysis of rice seed reflectance curves showed that the wavelength of light source for discrimination of the diseased seeds from normal rice seeds in the monochromic image recognition mode was about 815nm for jinyou402 and shanyou10. To determine optimizing conditions for acquiring digital images of rice seed using a computer vision system, an adjustable color machine vision system was developed. The machine vision system with 20mm to 25mm lens extender produce close-up images which made it easy to object recognition of characteristics in hybrid rice seeds. White background was proved to be better than black background for inspecting rice seeds infected by disease and using the algorithms based on shape. Experimental results indicated good classification for most of the characteristics with the machine vision system. The same algorithm yielded better results in optimizing condition for quality inspection of rice seed. Specifically, the image processing can correct for details such as fine fissure with the machine vision system.

The increasing numbers of illnesses associated with foodborne pathogens such as Listeria monocytogenes and Escherichia coli O157:H7, has renewed concerns about food safety because of consumer preferences for minimally processed foods that offer convenience in availability and preparation. Accordingly, the need for better control of foodborne pathogens has been paramount in recent years. Mechanical removal of microorganisms from food can be accomplished by centrifugation, filtration, trimming and washing. Cleaning and sanitation strategies can be used for minimizing the access of microorganisms in foods from various sources. Other strategies for control of foodborne pathogens include established physical microbiocidal treatments such as ionizing radiation and heating. Research has continued to demonstrate that food irradiation is a suitable process to control and possibly eliminate foodborne pathogens, for example Listeria monocytogenes and Escherichia coli O157:H7, from a number of raw and cooked meat and poultry products. Heat treatment is the most common method in use today for the inactivation of microorganisms. Microorganisms can also be destroyed by nonthermal treatments, such as application of high hydrostatic pressure, pulsed electric fields, oscillating magnetic fields or a combination of physical processes such as heat-irradiation, or heat-high hydrostatic pressure, etc. Each of the non-thermal technologies has specific applications in terms of the types of food that can be processed. Both conventional and newly developed physical treatments can be used in combination for controlling foodborne pathogens and enhancing the safety and shelf life of foods. Recent research has focused on combining traditional preservation factors with emerging intervention technologies. However, many key issues still need to be addressed for combination preservation factors or technologies to be useful in the food industry to meet public demands for foods with enhanced safety, freshness and appeal. As a result of systematic study in these areas together with detailed assessment of technological performance of available preservatives and preservation technologies in real food formulations, new intervention processes and products are likely to be developed. The ultimate goal is to identify potential new approaches for the safer production of foods. The purpose of this presentation is to discuss key developmental activities concerning microbial reduction by intervention technologies.

Evaluation of internal quality is very indispensable in storing, circulating and processing of chicken-egg. The potential of the UV/VIS (200-1000nm) transmittance method to evaluate the freshness of intact chicken egg was examined. A total of 350 chicken-eggs were tested and one of 200 eggs were used to spectral measurement in the tracing test. The transmittance spectral characteristics of intact egg were also investigated. The correlation models between transmittance versus wavelengths and the storing time, freshness index (Haugh Unit and Yolk coefficient) and the storing time were established by linear regression analysis of SPSS10.0. The correlation coefficients were 0.86, 0.94, 0.99 and 0.98 respectively. The sensitive wavelength used to evaluate freshness was also found at 465nm and the correlation model between transmittance and freshness was built. The test results show that: Nondestructive evaluation freshness by transmittance properties is feasible in the range of 400 -600nm, while it is impossible to evaluate egg freshness in the range of 200-400nm owing to the low transmittance.

Some issues related to nondestructive evaluation of valid acidity in intact apples by means of Fourier transform near infrared (FTNIR) (800-2631nm) method were addressed. A relationship was established between the diffuse reflectance spectra recorded with a bifurcated optic fiber and the valid acidity. The data were analyzed by multivariate calibration analysis such as partial least squares (PLS) analysis and principal component regression (PCR) technique. A total of 120 Fuji apples were tested and 80 of them were used to form a calibration data set. The influence of data preprocessing and different spectra treatments were also investigated. Models based on smoothing spectra were slightly worse than models based on derivative spectra and the best result was obtained when the segment length was 5 and the gap size was 10. Depending on data preprocessing and multivariate calibration technique, the best prediction model had a correlation efficient (0.871), a low RMSEP (0.0677), a low RMSEC (0.056) and a small difference between RMSEP and RMSEC by PLS analysis. The results point out the feasibility of FTNIR spectral analysis to predict the fruit valid acidity non-destructively. The ratio of data standard deviation to the root mean square error of prediction (SDR) is better to be less than 3 in calibration models, however, the results cannot meet the demand of actual application. Therefore, further study is required for better calibration and prediction.

Fourier transform near infrared (FT-NIR) spectroscopy was tested as a non-destructive method to assess the sugar content (SC) and the valid acidity of intact peaches. Calibration models were created from spectral and constituent measurements. Data recorded from two sides of individual peach served as the calibration and the validation sets. Partial least squares (PLS) technique used to develop the prediction models by different data preprocessing. The best model for SC had a high correlation efficient (0.956), a low SEP (0.532), a low SEC (0.542), a SDR value of 3.34(>3.00), and also a small difference between SEP and SEC. The best model for valid acidity had a high correlation coefficient (0.948), a relatively low SEP (0.129), a relatively low SEC (0.124) and a SDR value of 2.68 (<3.00), and also a small difference between SEC and SEP. The results of this study suggest that FT-NIR method can be feasible to detect sugar content rapidly. However, the low acid content in the fruit might have caused the relative insensitivity for prediction valid acidity. Further work is required to optimize and implement this technique.

A near-infrared spectral reflectance system was developed and tested online to predict 14-day aged, cooked beef tenderness. A contact probe with a built-in tungsten-halogen light source supplied broadband light to the ribeye surface. Fiberoptics in the probe transmitted reflected light to a spectrometer with a spectral range of 400-2500 nm. In the first phase, steak samples (n=292) were brought from packing plants to the lab and scanned with the spectrometer. After scanning, samples were vacuum-packaged and aged for 14 days. They were then cooked in an impingement oven to an internal temperature of 70°C. Slice-shear force values were recorded for tenderness reference. In phase two, the spectrometer was modified for packing plant conditions. Spectral scans were obtained on-line on ribbed carcasses (n=276). A partial least square regression model was developed to predict tenderness scores from spectral reflectance. In phase three, the developed model was validated by scanning carcasses (n=200) on-line. The predicted shear-force values and samples were sent to the U.S. Meat Animal Research Center for third-party validation. At up to 70% certification levels, the system was able to successfully sort tough from tender carcasses.

Microbial contamination has become a mounting concern the last decade due to an increased emphasis of minimally processed food products specifically produce, and the recognition of foodborne pathogens such as Campylobacter jejuni, Escherichia coli O157:H7, and Listeria monocytogenes. This research investigates a detection approach utilizing bacteriophage pathogen specificity coupled with a bacterial bioluminescent bioreporter utilizing the quorum sensing molecule from Vibrio fischeri, N-(3-oxohexanoyl)-homoserine lactone (3-oxo-C6-HSL). The 3-oxo-C6-HSL molecules diffuse out of the target cell after infection and induce bioluminescence from a population of 3-oxo-C6-HSL bioreporters (ROLux). E. coli phage M13, a well-characterized bacteriophage, offers a model system testing the use of bacteriophage for pathogen detection through cell-to-cell communication via a LuxR/3-oxo-C6-HSL system. Simulated temperate phage assays tested functionality of the ROLux reporter and production of 3-oxo-C6-HSL by various test strains. These assays showed detection limits of 10²cfu after 24 hours in a varietry of detection formats. Assays incorporating the bacteriophage M13-luxI with the ROLux reporter and a known population of target cells were subsequently developed and have shown consistent detection limits of 10⁵cfu target organisms. Measurable light response from high concentrations of target cells was almost immediate, suggesting an enrichment step to further improve detection limits and reduce assay time.

The Food Safety and Inspection Service (FSIS) has implemented new procedures for meat and poultry establishments, egg products plants, and companies that manufacture and sell technology to official establishments to notify the Agency of new technology that they propose to use in meat and poultry establishments or egg products plants. If the new technology could affect FSIS regulations, product safety, inspection procedures, or the safety of Federal inspection program personnel, then the establishment or plant would need to submit a written protocol to the Agency. As part of this process, the submitter will be expected to conduct in-plant trials of the new technology. The submitter will need to provide data to FSIS throughout the duration of the in-plant trial for the Agency to examine. Data may take several forms: laboratory results, weekly or monthly summary production reports, and evaluations from inspection program personnel.