A new fluorescent sandwich method for the detection of Escherichia coli O157:H7 was developed. Strepavidin coated magnetic beads and fluorescence beads reacted with biotinylated anti E. coli O157 antibodies to form the immuno magnetic beads (IMB) and immuno fluorescence beads (IFB), respectively. The E. coli bacteria captured by IMB were further labeled with IFB to form IMB_M-(E. coliO157:H7)_N-IFB_O sandwich complexes where the subscripts M, N and O were integral numbers. Using broth cultured E. coli O157:H7, the sandwich method was able to detect the bacteria at the level of ~ 10³to 10⁴ CFU/mL. Known quantity of freshly cultured E. coli O157:H7 cells were added to ground beef obtained from local markets. The bacteria in inoculated beef patties were enriched in EC broth containing novobiocin. After enriched for 4 h at 40 °C, the developed IMB-IFB method was applied to detect the presence of E. coli O157:H7. The results demonstrated that the developed method could detect the presence of 1 CFU of E. coli O157:H7 per gram of ground beef.

A mammalian cell-based optical biosensor was built to detect pathogenic Listeria and Bacillus species. This sensor measures the ability of the pathogens to infect and induce cytotoxicity on hybrid lymphocyte cell line (Ped-2E9) resulting in the release of alkaline phosphatase (ALP) that can be detected optically using a portable spectrophotometer. The Ped-2E9 cells were encapsulated in collagen gel matrices and grown in 48-well plates or in specially designed filtration tube units. Toxin preparations or bacterial cells were introduced and ALP release was assayed after 3-5 h. Pathogenic L. monocytogenes strains or the listeriolysin toxins preparation showed cytotoxicity ranging from 55% - 92%. Toxin preparations (~20 μg/ml) from B. cereus strains showed 24 - 98% cytotoxicity. In contrast, a non-pathogenic L. innocua (F4247) and a B. substilis induced only 2% and 8% cytotoxicity, respectively. This cell-based detection device demonstrates its ability to detect the presence of pathogenic Listeria and Bacillus species and can potentially be used onsite for food safety or in biosecurity application.

Electrical lysis of biological cells on a microfluidic platform has been raising a lot of interests due to its applications in rapid recovering intracellular contents without introducing lytic agents. In this study, we demonstrated a simple microfluidic device which lysed green fluorescent protein (GFP) expressing E. coli cells under continuous DC voltage while cells flowed through. The cell lysis only happened in a defined section of a microfluidic channel due to the local field amplification by geometric modification. The geometric modification also effectively decreased the required voltage for lysis by several folds. We found that a local field strength of 1500V/cm was required for lysis of nearly 100% of E. coli cells. This lysis field strength was considerably lower than the value reported in the literature, possibly due to the longer duration of the field. The lysis was witnessed by plate count and fluorescence spectroscopy. The devices were fabricated using low-cost soft lithography with channel widths considerably larger than the cell size to avoid clogging and ensure stable performance. Our tool will be ideal for high throughput processing of a large number of cells. Furthermore, the application of continuous DC field makes it straightforward to couple our cell lysis device with on-chip electrophoresis to realize the integration of cell pretreatment and chemical analysis. In principle, the same approach can also be applied for the lysis of mammalian cells and for the electroporation and transfection.

A phase-fluorometric oxygen sensor system has been developed, which allows non-destructive measurement of residual oxygen levels in sealed containers such as packaged foods. It operates with disposable solid-state sensors incorporated in each pack, and a portable detector which interrogates with the sensors through a (semi)transparent packaging material. The system has been optimized for packaging applications and validated in small and medium scale trials with different types of food, including MAP hams, cheese, convenience foods, smoked fish, bakery. It has demonstrated high efficiency in monitoring package integrity, oxygen profiles in packs, performance of packaging process and many other research and quality control tasks, allowing control of 100% of packs. The low-cost batch-calibrated sensors have demonstrated reliability, safety, stability including direct contact with food, high efficiency in the low oxygen range. Another system, which also employs the fluorescence-based oxygen sensing approach, provides rapid assessment of microbial contamination (total viable counts) in complex samples such as food homogenates, industrial waste, environmental samples, etc. It uses soluble oxygen-sensitive probes, standard microtitter plates and fluorescence measurements on conventional plate reader to monitor growth of aerobic bacteria in small test samples (e.g. food homogenates) via their oxygen respiration. The assay provides high sample through put, miniaturization, speed, and can serve as alternative to the established methods such as agar plate colony counts and turbidimetry.

Understanding the dynamics of the global carbon cycle requires an accurate determination of the spatial and temporal distribution of photosynthetic CO₂ uptake by terrestrial vegetation. Stress factors may cause sub-optimal photosynthetic function resulting in down-regulation (i.e., reduced rate of photosynthesis). Photosynthetic down-regulation is related to changes in the apparent spectral reflectance of leaves. Present approaches to determine ecosystem carbon exchange rely on meteorological data as inputs to models that predict the relative photosynthetic function in response to environmental conditions inducing stress (e.g., drought, high/low temperatures). This study examines the determination of ecosystem photosynthetic light use efficiency (LUE) from satellite observations, through measurement of vegetation spectral reflectance changes associated with physiologic stress responses. This approach is possible using the Moderate-Resolution Spectroradiometer (MODIS) on Terra to provide frequent, narrow-band measurements of high radiometric accuracy. Data from reflective MODIS ocean bands were used over land to calculate the Photochemical Reflectance Index (PRI), an index that is sensitive to reflectance changes near 531nm associated with vegetation stress responses exhibited by photosynthetic pigments. MODIS PRI values were compared with LUE calculated from values of CO₂ flux measured at the overpass time at a flux tower located in a Douglas fir forest on Vancouver Island in Canada. Preliminary results show a relationship between MODIS PRI and LUE when using MODIS observations in the backscattering direction. These results compare well to previous work at a boreal aspen forest suggesting this approach may be generally useful.

Aflatoxin contaminated corn is dangerous for domestic animals when used as feed and cause liver cancer when consumed by human beings. Therefore, the ability to detect A. flavus and its toxic metabolite, aflatoxin, is important. The objective of this study is to measure A. flavus growth using hyperspectral technology and develop spectral signatures for A. flavus. Based on the research group's previous experiments using hyperspectral imaging techniques, it has been confirmed that the spectral signature of A. flavus is unique and readily identifiable against any background or surrounding surface and among other fungal strains. This study focused on observing changes in the A. flavus spectral signature over an eight-day growth period. The study used a visible-near-infrared hyperspectral image system for data acquisition. This image system uses focal plane pushbroom scanning for high spatial and high spectral resolution imaging. Procedures previously developed by the research group were used for image calibration and image processing. The results showed that while A. flavus gradually progressed along the experiment timeline, the day-to-day surface reflectance of A. flavus displayed significant difference in discreet regions of the wavelength spectrum. External disturbance due to environmental changes also altered the growth and subsequently changed the reflectance patterns of A. flavus.

Fluorescence of foliage in the laboratory has proven more rigorous than reflectance for correlation to plant physiology. Especially useful are emissions produced from two stable red and far-red chlorophyll fluorescence (ChlF) peaks centered at 685 nm and 735 nm. Methods have been developed elsewhere to extract steady state solar induced fluorescence (SIF) from apparent reflectance of vegetation canopies/landscapes using the Fraunhofer Line Depth (FLD) principal. Our study utilized these methods in conjunction with field-acquired high spectral resolution canopy reflectance spectra obtained in 2004 and 2005 over corn crops and small tree plots of three deciduous species (red maple, tulip poplar, sweet gum). Leaf level measurements were also made of foliage which included ChlF, photosynthesis, and leaf constituents (photosynthetic pigment, carbon (C), and nitrogen (N) contents). As part of ongoing experiments, measurements were made on N application plots within corn (280, 140, 70, and 0 kg N/ha) and tree (0, 37.5, 75, 112.5, 150 kg N /ha) sites at the USDA/Agriculture Research Service in Beltsville, MD. SIF intensities for ChlF were derived directly from canopy reflectance spectra in specific narrow- band regions associated with atmospheric oxygen absorption features centered at 688 and 760 nm. The red/far-red SIF ratio (SIFratio) derived from these field reflectance spectra successfully discriminated foliar pigment ratios altered by N application rates in both corn crops. This ratio was also positively correlated to the C/N ratio at leaf and canopy levels, for the available corn data (e.g., 2004). No consistent N treatment or species differences in SIF were detected in the tree foliage, but additional 2005 data are forthcoming. This study has relevance to future passive satellite remote sensing approaches to monitoring C dynamics from space.

An unsupervised method for plant species identification was developed which uses computer extracted individual whole leaves from color images of crop canopies. Green canopies were isolated from soil/residue backgrounds using a modified Excess Green and Excess Red separation method. Connected components of isolated green regions of interest were changed into pixel fragments using the Gustafson-Kessel fuzzy clustering method. The fragments were reassembled as individual leaves using a genetic optimization algorithm and a fitness method. Pixels of whole leaves were then analyzed using the elliptic Fourier shape and Haralick's classical textural feature analyses. A binary template was constructed to represent each selected leaf region of interest. Elliptic Fourier descriptors were generated from a chain encoding of the leaf boundary. Leaf template orientation was corrected by rotating each extracted leaf to a standard horizontal position. This was done using information provided from the first harmonic set of coefficients. Textural features were computed from the grayscale co-occurrence matrix of the leaf pixel set. Standardized leaf orientation significantly improved the leaf textural venation results. Principle component analysis from SAS ^(R) was used to select the best Fourier descriptors and textural indices. Indices of local homogeneity, and entropy were found to contribute to improved classification rates. A SAS classification model was developed and correctly classified 83% of redroot pigweed, 100% of sunflower 83% of soybean, and 73% of velvetleaf species. An overall plant species correct classification rate of 86% was attained.

Brassica chinensis var parachinensis was grown in a recirculating water culture system until the '6-leaf stage' when the plants were separated into two groups: a 'Control' group where plant growth was continued in complete nutrient solution and a '-Ca' group in which the plants were grown in calcium-deficient nutrient solution. Leaf reflectance data was collected daily for eight days, starting from the day before the two treatments were imposed. No visual difference was found between 'Control' and '-Ca' groups during the experimental period. Total calcium content in '-Ca' plants decreased significantly from about 20,000 ppm to steady-state levels at 5,000 ppm by Day 5 while leaf chlorophyll levels in both 'Control' and '-Ca' were relatively similar. However, as the plants matured in the two nutrient solutions, the position of the red edge inflection point (REIP defined as the maximum first derivative of the reflectance spectrum in the 680 nm to 750 nm region) in 'Control' plants shifted towards longer wavelengths, while that in the '-Ca' plants remained relatively unchanged. Good correlation was found between Δ[Ca] and Δ[REIP] of 'Control' and '-Ca' plants. Our results showed that monitoring REIP shifts can provide invaluable spectral cues for pre-visual diagnosis of calcium deficiency in plants.

The objectives of this study were to characterize leaf reflectance spectra of tomato leaves damaged by leaf miner and to determine those leaf reflectance wavelengths that were most responsive to plant damage caused by the pest. Near infrared (NIR) Spectral characteristics of single tomato leaves at various levels of infestation by the leaf miner, were measured and analyzed using a spectrometer. Tomato leaf damage was classified into five scales, i.e., 0 (no damage), 1 (light damaged), 2 (10-25% damaged), 3 (more than 25% damaged), and 4 (severe damaged), based on the scale of infestation displayed on the surfaces of plant parts. Spectral parameter such as reflectance sensitivity was used to find the optimal wavelengths to determining and evaluating the damage level. Results showed that there were significant differences in reflectance among infestations at wavelengths of 1450nm and 1900 nm particularly. The determining coefficients (R²) for a linear relationship were 0.98 and 0.91 for the spectral-infestation levels relations. Thus, both of these wavelengths were good indicators of leaf senescence caused by the leaf miner.

Researchers at the Georgia Tech Research Institute designed a vision inspection system for poultry kill line sorting with the potential for process control at various points throughout a processing facility. This system has been successfully operating in a plant for over two and a half years and has been shown to provide multiple benefits. With the introduction of HACCP-Based Inspection Models (HIMP), the opportunity for automated inspection systems to emerge as viable alternatives to human screening is promising. As more plants move to HIMP, these systems have the great potential for augmenting a processing facilities visual inspection process. This will help to maintain a more consistent and potentially higher throughput while helping the plant remain within the HIMP performance standards. In recent years, several vision systems have been designed to analyze the exterior of a chicken and are capable of identifying Food Safety 1 (FS1) type defects under HIMP regulatory specifications. This means that a reliable vision system can be used in a processing facility as a carcass sorter to automatically detect and divert product that is not suitable for further processing. This improves the evisceration line efficiency by creating a smaller set of features that human screeners are required to identify. This can reduce the required number of screeners or allow for faster processing line speeds. In addition to identifying FS1 category defects, the Georgia Tech vision system can also identify multiple "Other Consumer Protection" (OCP) category defects such as skin tears, bruises, broken wings, and cadavers. Monitoring this data in an almost real-time system allows the processing facility to address anomalies as soon as they occur. The Georgia Tech vision system can record minute-by-minute averages of the following defects: Septicemia Toxemia, cadaver, over-scald, bruises, skin tears, and broken wings. In addition to these defects, the system also records the length and width information of the entire chicken and different parts such as the breast, the legs, the wings, and the neck. The system also records average color and miss- hung birds, which can cause problems in further processing. Other relevant production information is also recorded including truck arrival and offloading times, catching crew and flock serviceman data, the grower, the breed of chicken, and the number of dead-on- arrival (DOA) birds per truck. Several interesting observations from the Georgia Tech vision system, which has been installed in a poultry processing plant for several years, are presented. Trend analysis has been performed on the performance of the catching crews and flock serviceman, and the results of the processed chicken as they relate to the bird dimensions and equipment settings in the plant. The results have allowed researchers and plant personnel to identify potential areas for improvement in the processing operation, which should result in improved efficiency and yield.

A hyperspectral line-scan imaging system for automated inspection of wholesome and diseased chickens was developed and demonstrated. The hyperspectral imaging system consisted of an electron-multiplying charge-coupled-device (EMCCD) camera and an imaging spectrograph. The system used a spectrograph to collect spectral measurements across a pixel-wide vertical linear field of view through which moving chicken carcasses passed. After a series of image calibration procedures, the hyperspectral line-scan images were collected for chickens on a laboratory simulated processing line. From spectral analysis, four key wavebands for differentiating between wholesome and systemically diseased chickens were selected: 413 nm, 472 nm, 515 nm, and 546 nm, and a reference waveband, 622 nm. The ratio of relative reflectance between each key wavelength and the reference wavelength was calculated as an image feature. A fuzzy logic-based algorithm utilizing the key wavebands was developed to identify individual pixels on the chicken surface exhibiting symptoms of systemic disease. Two differentiation methods were built to successfully differentiate 72 systemically diseased chickens from 65 wholesome chickens.

Most cutting and deboning operations in meat processing require accurate cuts be made to obtain maximum yield and ensure food safety. This is a significant concern for purveyors of deboned product. This task is made more difficult by the variability that is present in most natural products. The specific application of interest in this paper is the production of deboned poultry breast. This is typically obtained from a cut of the broiler called a 'front half' that includes the breast and the wings. The deboning operation typically consists of a cut that starts at the shoulder joint and then continues along the scapula. Attentive humans with training do a very good job of making this cut. The breast meat is then removed by pulling on the wings. Inaccurate cuts lead to poor yield (amount of boneless meat obtained relative to the weight of the whole carcass) and increase the probability that bone fragments might end up in the product. As equipment designers seek to automate the deboning operation, the cutting task has been a significant obstacle to developing automation that maximizes yield without generating unacceptable levels of bone fragments. The current solution is to sort the bone-in product into different weight ranges and then to adjust the deboning machines to the average of these weight ranges. We propose an approach for obtaining key cut points by extrapolation from external reference points based on the anatomy of the bird. We show that this approach can be implemented using a stereo imaging system, and the accuracy in locating the cut points of interest is significantly improved. This should result in more accurate cuts and with this concomitantly improved yield while reducing the incidence of bones. We also believe the approach could be extended to the processing of other species.

Foreign materials such as metal slivers and stones in packed food are listed safety hazards, which could lead to severe health problems. In this paper, a real time X-ray imaging inspection method is investigated for foreign material detection in chili packages. A new image segmentation method combining edge detection and region growing was successfully applied to address the challenges due to the uneven thickness of chili package.

Recent research showed that spectral scattering is useful for assessing the firmness of apple fruit. This paper reports the development of a laser-based multispectral imaging prototype for real-time detection of apple fruit firmness. The prototype consisted of a common aperture multispectral imaging unit, a multi-laser unit, and a belt conveyor, which was able to capture and process spectral scattering images for up to two fruit/s. The multispectral imaging system was tested for detecting the firmness of 'Golden Delicious' and 'Red Delicious' apples when they were moving on the conveyor belt at an imaging speed of one fruit for every two seconds. The original scattering images were corrected by using the newly developed methods of removing noise pixels and incorporating fruit size into the calculation of the scattering distance and intensity. The corrected scattering images were reduced to one-dimensional scattering profiles by radial averaging. The scattering profiles were fitted with a Lorentzian distribution function of four parameters. Multi-linear regression models were developed using the four Lorentzian parameters for the four wavelengths for each apple cultivar, and the models were then used to predict the firmness of validation apples. The multispectral imaging system achieved good firmness predictions with values for the correlation coefficient of 0.85 for 'Golden Delicious' and 0.86 for 'Red Delicious'. The laser-based multispectral imaging system is fast and relatively easy to implement, and it has the potential to meet the requirement for online sorting and grading of apple fruit.

High moisture extrusion using twin-screw extruders shows great promise of producing meat analog products with vegetable proteins. The resulting products have well defined fiber formations; resemble real meat in both visual appearance and taste sensation. Developing reliable non-destructive techniques to quantify the textural properties of extrudates is important for quality control in the manufacturing process. In this study, we developed an image processing technique to automatically characterize sample fiber formation using digital imaging. The algorithm is based on statistical analysis of Hough transform. This objective method can be used as a standard method for evaluating other non-invasive methods. We have compared the fiber formation indices measured using this technique and a non-invasive fluorescence polarization method and obtained a high correlation.

Optical chemical sensors and biosensors are attracting research interest in applications such as environmental monitoring and biomedical diagnostics. Structured Integrated Optical Waveguide is one solution to reduce the reader's cost and size. The principle is the capture of fluorescence emitted by Qdots at the surface of a rib waveguide, which collects then guides it at the end-face of the chip to be detected. However, fluorescence coupling into a waveguide is still not easy to predict as it depends on fluorophore's environment and dipole's orientation and location. We report here the validation of a simple theory concerning optimization of optical waveguide's thickness considering a fluorophore's position. Optimisation of coupling power between a dipole and a guided mode can be simplified by the optimisation of the guided mode's intensity ratio integrated in the 5 nm region over the guide's core surface (where QDots are supposed to settle) divided by the whole guided intensity. A model has been developed from the work of Marcuse¹: coupled power is proportional to the square of the electrical field of the guided wave. As a result, this model gives an optimal core's thickness and efficiency of coupling depends on polarisation. Moreover, FDTD simulations do complete this study. Three thicknesses have been therefore experimentally deposited: 100 nm, 125 nm and 150 nm. To conclude, experimentation corresponds to the model. A new, sensitive and potentially low cost portable transducer for the analysis of all kinds of biomolecular affinity systems has been developed and validated.

Chlorophyll fluorescence has been researched for assessing fruit post-harvest quality and condition. The objective of this preliminary research was to investigate the potential of fluorescence spectroscopy for measuring apple fruit quality. Ultraviolet (UV) and blue light was used as an excitation source for inducing fluorescence in apples. Fluorescence spectra were measured from 'Golden Delicious' (GD) and 'Red Delicious' (RD) apples by using a visible/near-infrared spectrometer after one, three, and five minutes of continuous UV/blue light illumination. Standard destructive tests were performed to measure fruit firmness, skin and flesh color, soluble solids and acid content from the apples. Calibration models for each of the three illumination time periods were developed to predict fruit quality indexes. The results showed that fluorescence emission decreased steadily during the first three minutes of UV/blue light illumination and was stable within five minutes. The differences were minimal in the model prediction results based on fluorescence data at one, three or five minutes of illumination. Overall, better predictions were obtained for apple skin chroma and hue and flesh hue with values for the correlation coefficient of validation between 0.80 and 0.90 for both GD and RD. Relatively poor predictions were obtained for fruit firmness, soluble solids content, titrational acid, and flesh chroma. This research demonstrated that fluorescence spectroscopy is potentially useful for assessing selected quality attributes of apple fruit and further research is needed to improve fluorescence measurements so that better predictions of fruit quality can be achieved.

Vitamin C is considered an important nutrition component of fruits, especially of kiwifruit. Traditional destructive method for vitamin C measurement is very complex and fussy. Near Infrared (NIR)spectroscopy is a promising technique for nondestructive measurement of fruit internal qualities, such as soluble solid content (SSC), valid acidity (VA). The objective of this research was to study the potential of NIR diffuse reflectance spectroscopy as a way for nondestructive measurement of vitamin C content in "Qinmei" kiwifruit. NIR spectral data were collected in the spectral range of 800-2500 nm with different combinations of resolution (4 cm^-1, 16 cm^-1 and 32 cm^-1) and scan number (32, 64 and 128). Statistical models were developed using partial least square (PLS) method. The combination with resolution of 4 cm^-1 and scan number of 64 gave the best result when all samples were used in calibration sample set. Then two spectral pretreatments multiplicative signal correction (MSC) and standard normal variate (SNV), and three kinds of mathematical treatment of original spectra, first derivative spectra and second derivative spectra were discussed. The PLS model of second derivative spectra using SNV pretreatment turned out better prediction results: correlation coefficient (r) of 0. 93, root mean square error of calibration (RMSEC) of 9.24 mg/100g and root mean square error of prediction (RMSEP) of 10.3 mg/100g. The results of this study showed that NIR diffuse reflectance spectroscopy could be used for kiwifruit vitamin C prediction. The higher the resolution, the better the results, but longer time will be taken, which may not be suitable for on-line use. Therefore, further research still needs to be done.

Chufa (Eleocharis tuberose Schult) is a special local product in south China. It is both vegetable and fruit. Near infrared spectroscopy was widely used for fruit and vegetable quality evaluation. The objective of this research was to study whether Chufa MT-firmness can be nondestructively measured by NIR technology and chemometrics methods. Two hundreds and thirty-nine samples were collected from two different cultivate regions and in each region three plots were chosen. NIR spectral data were acquired in the spectral region between 800 nm and 2500 nm using Nicolet FT-NIR spectrometer. Firmness was detected by a biomaterial universal testing machine. Chemometrics methods of PLS, PCR and SMLR were applied to establish statistical models for establishing the relationship between Chufa NIR spectra and MT-firmness in three different spectral regions of 800-2500 nm, 830-1250 nm and 860-1090 nm. The PLS model educed better results than PCR and SMLR models. And for the three spectral regions, the full spectral region of 800-2500 nm was better than other two. The correlation coefficient (r), root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP) and root mean square error of cross validation (RMSECV) of the PLS model in the range of 800-2500 nm were 0.74, 4.96 N, 5.63 N and 5.38 N respectively.

Emerging concerns about safety and security in current mass production of food products necessitate rapid and reliable inspection for contaminant-free products. Diluted fecal residues on poultry processing plant equipment surface, not easily discernable from water by human eye, are contamination sources for poultry carcasses. Development of sensitive detection methods for fecal residues is essential to ensure safe production of poultry carcasses. Hyperspectral imaging techniques have shown good potential for detecting of the presence of fecal and other biological substances on food and processing equipment surfaces. In this study, use of high spatial resolution hyperspectral reflectance and fluorescence imaging (with UV-A excitation) is presented as a tool for selecting a few multispectral bands to detect diluted fecal and ingesta residues on materials used for manufacturing processing equipment. Reflectance and fluorescence imaging methods were compared for potential detection of a range of diluted fecal residues on the surfaces of processing plant equipment. Results showed that low concentrations of poultry feces and ingesta, diluted up to 1:100 by weight with double distilled water, could be detected using hyperspectral fluorescence images with an accuracy of 97.2%. Spectral bands determined in this study could be used for developing a real-time multispectral inspection device for detection of harmful organic residues on processing plant equipment.

Meat grading has always been a research topic because of large variations among meat products. Many subjective assessment methods with poor repeatability and tedious procedures are still widely used in meat industry. In this study, a hyperspectral-imaging-based technique was developed to achieve fast, accurate, and objective determination of pork quality attributes. The system was able to extract the spectral and spatial characteristics for simultaneous determination of drip loss and pH in pork meat. Two sets of six significant feature wavelengths were selected for predicting the drip loss (590, 645, 721, 752, 803 and 850 nm) and pH (430, 448, 470, 890, 980 and 999 nm). Two feed-forward neural network models were developed. The results showed that the correlation coefficient (r) between the predicted and actual drip loss and pH were 0.71, and 0.58, respectively, by Model 1 and 0.80 for drip loss and 0.67 for pH by Model 2. The color levels of meat samples were also mapped successfully based on a digitalized Meat Color Standard.

Ornamental stones are usually utilized for many purposes, ranging from structural to aesthetic ones. In this wide range of utilization, many different industrial sectors are involved. For all of them it is very important, at a different level, that these materials satisfy not only specific physical-chemical-mechanical requirements, but also some attributes that are much more difficult to quantify, that is those attributes strictly related to the final pictorial aspect of the stone manufactured goods. Stone pictorial-aesthetic characteristics are strongly influenced by stone surface status, that is by the surfaces reflectance properties. Such a property depends from stone compositional-textural characteristics and from the working procedures applied. The first set of attributes are related to stone mineral composition and their micro/macro arrangement, the others are related to the tools utilized and the actions applied in terms of operation sequence and workers knowledge-expertise. Each stone and each macro-operation carried out lead to a stone product whose finishing has to follow a specific rule: "optimal" polishing procedures for a stone can lead to very poor results for others. The study was addressed to evaluate the possibility to introduce a new hyperspectral imaging based approach to quantify the level of polishing of stone products and, at the same time, trying to perform also a pictorial-aesthetic characterization trough the identification of natural and/or working defects.

Contaminated soil characterization represents one of the primary key-factors to evaluate when reclamation strategies have to be designed and applied. 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 hyperspectral imaging based techniques to perform fast and reliable tests able to identify and quantify specific soil characteristics of primary importance in soil reclamation. The proposed approach, methodologically very simple to apply, for its flexibility could be profitably utilized also for other applications as those linked to agricultural soil monitoring.

Automated detection of defects on freshly harvested pickling cucumbers will help the pickle industry provide higher quality pickle products and reduce potential economic losses. Research was conducted on using a hyperspectral imaging system for detecting defects on pickling cucumbers caused by mechanical stress. A near-infrared hyperspectral imaging system was used to capture both spatial and spectral information from cucumbers in the spectral region of 900 - 1700 nm. The system consisted of an imaging spectrograph attached to an InGaAs camera with line-light fiber bundles as an illumination source. Cucumber samples were subjected to two forms of mechanical loading, dropping and rolling, to simulate stress caused by mechanical harvesting. Hyperspectral images were acquired from the cucumbers over time periods of 0, 1, 2, 3, and 6 days after mechanical stress. Hyperspectral image processing methods, including principal component analysis and wavelength selection, were developed to separate normal and mechanically injured cucumbers. Results showed that reflectance from normal or non-bruised cucumbers was consistently higher than that from bruised cucumbers. The spectral region between 950 and 1350 nm was found to be most effective for bruise detection. The hyperspectral imaging system detected all mechanically injured cucumbers immediately after they were bruised. The overall detection accuracy was 97% within two hours of bruising and it was lower as time progressed. Lower detection accuracies for the prolonged times after bruising were attributed to the self- healing of the bruised tissue after mechanical injury. This research demonstrated that hyperspectral imaging is useful for detecting mechanical injury on pickling cucumbers.

Absorption and reduced scattering coefficients are two fundamental optical properties for turbid biological materials. This paper presents the technique and method of using hyperspectral diffuse reflectance for fast determination of the optical properties of fruit and vegetable juices and milks. A hyperspectral imaging system was used to acquire spatially resolved steady-state diffuse reflectance over the spectral region between 530 and 900 nm from a variety of fruit and vegetable juices (citrus, grapefruit, orange, and vegetable) and milks with different fat levels (full, skim and mixed). The system collected diffuse reflectance in the source-detector separation range from 1.1 to 10.0 mm. The hyperspectral reflectance data were analyzed by using a diffusion theory model for semi-infinite homogeneous media. The absorption and reduced scattering coefficients of the fruit and vegetable juices and milks were extracted by inverse algorithms from the scattering profiles for wavelengths of 530-900 nm. Values of the absorption and reduced scattering coefficient at 650 nm were highly correlated to the fat content of the milk samples with the correlation coefficient of 0.990 and 0.989, respectively. The hyperspectral imaging technique can be extended to the measurement of other liquid and solid foods in which light scattering is dominant.

In this paper, color-mixing applications for food safety and quality was studied, including two-color mixing and three-color mixing. It was shown that the chromaticness of the visual signal resulting from two- or three-color mixing is directly related to the band ratio of light intensity at the two or three selected wavebands. An optical visual device using color mixing to implement the band ratio criterion was presented. Inspection through human vision assisted by an optical device that implements the band ratio criterion would offer flexibility and significant cost savings as compared to inspection with a multispectral machine vision system that implements the same criterion. Example applications of this optical color mixing technique were given for the inspection of chicken carcasses with various diseases and for the detection of chilling injury in cucumbers. Simulation results showed that discrimination by chromaticness that has a direct relation with band ratio can work very well with proper selection of the two or three narrow wavebands. This novel color mixing technique for visual inspection can be implemented on visual devices for a variety of applications, ranging from target detection to food safety inspection.

A real time machine vision system for fruit quality inspection was developed, which consists of rollers, an encoder, a lighting chamber, a TMS-7DSP CCD camera (PULNIX Inc.), a computer (P4 1.8G, 128M) and a set of grading controller. The system was made for size detecting of fruit, and then sorting fruits into 3 groups by the skin color: red group, yellow group, and green group which was immaturity. Color model for segmenting fruits from background and classing fruits into different groups was discussed. RGB color model was used to segment fruits from background, an equation of red component and blue component was used to segment the figure of relationship between red and blue component into two zones, which represent background and a fruit respectively. And then HIS color model was introduced to class fruits into three groups, Hue component was used as the optimum feature for this objective because that there were less overlap on this component of the three groups.100 navel orange was used to class by their skin color, total error was 2.1%.

A combined X-ray/laser 3D imaging technology has been developed for bone fragment and foreign material detection in boneless poultry products. In this paper, various methods of pattern classification including neural network and statistical approaches are applied to the poultry images obtained by the combined imaging system, and the classification performances are compared and analyzed.

An important task in hyperspectral data processing is to reduce the redundancy of the spectral and spatial information without losing any valuable details that are needed for the subsequent detection, discrimination and classification processes. Band selection and combination not only serves as the first step of hyperspectral data processing that leads to a significant decrease in computational complexity in the successive procedures, but also a research tool for determining optimal spectra requirements for different online applications. In order to uniquely characterize the materials of interest, band selection criteria for optimal band was defined. An integrated PCA and Fisher linear discriminant (FLD) method has been developed based on the criteria that used for hyperspectral feature band selection and combination. This method has been compared with other feature extraction and selection methods when applied to detect apple defects, and the performance of each method was evaluated and compared based on the detection results.

In many hyperspectral applications, it is desirable to extract the texture features for pattern classification. Texture refers to replications, symmetry of certain patterns. In a set of hyperspectral images, the differences of image textures often imply changes in the physical and chemical properties on or underneath the surface. In this paper, we utilize Gabor wavelet based texture analysis method for textural pattern extraction, and combined with integrated PCA-FLD method for hyperspectral band selection in the application of classifying chilling damaged cucumbers from normal ones. The classification performances are compared and analyzed.

The artificial neural networks (ANNs) have been used successfully in applications such as pattern recognition, image processing, automation and control. However, majority of today's applications of ANNs is back-propagate feed-forward ANN (BP-ANN). In this paper, back-propagation artificial neural networks (BP-ANN) were applied for modeling soluble solid content (SSC) of intact pear from their Fourier transform near infrared (FT-NIR) spectra. One hundred and sixty-four pear samples were used to build the calibration models and evaluate the models predictive ability. The results are compared to the classical calibration approaches, i.e. principal component regression (PCR), partial least squares (PLS) and non-linear PLS (NPLS). The effects of the optimal methods of training parameters on the prediction model were also investigated. BP-ANN combine with principle component regression (PCR) resulted always better than the classical PCR, PLS and Weight-PLS methods, from the point of view of the predictive ability. Based on the results, it can be concluded that FT-NIR spectroscopy and BP-ANN models can be properly employed for rapid and nondestructive determination of fruit internal quality.

Sand deposition is the major problem of Nepalese rivers and it causes substantial impact to different sectors including hydropower generation, natural resource management, and many others. Due to the typical nature of soil and sand of Nepalese mountains it has almost become impossible to predict and manage the upcoming natural disasters and hazards. Sand deposition in rivers affect landslides, aquatic life of rives, environmental disorders and many others. Sedimentation causes not only disasters but also reduces the overall efficiency of hydropower generation units as well. A systematic approach to the problem has been identified in this work. Sand particles are collected from the erosion sensitive power plants and its digital images have been acquired. Software has been developed on MATLAB 6.5 platform to extract the exact shape of sand particles collected. These shapes have further been analyzed by artificial neural network. This network has been first trained for the known input and known output. After that it is trained for unknown input and known output. Finally these networks can recognize any shape given to it and gives the shape which is nearest to the seven predefined shape. The software is trained for seven types of shapes with shape number 1 to 7 in increasing number of sharp edges. The shape with shape number seven is having large number of sharp edges and considered as most erosive where as shape with shape number one is having round edges and considered as least erosive.

Sugar content (SC) and soluble solids content (SSC) are very important factors of navel orange internal quality and can be measured non-invasively by ultraviolet-visible spectroscopy techniques. The feasibility and methods of ultraviolet-visible spectroscopic techniques for rapid quantifying SC and SSC of navel orange fresh juices was investigated by its spectral transmittance. A total 55 juice samples were used to develop the calibration and prediction models. Different spectra correction algorithms (constant, multiplicative signal correction (MSC) and standard normal variate (SNV) were compared in our work. Three different kinds of mathematical spectra treatments (original, first derivative and second derivative) of spectra in the range of 200-800 nm and two kinds of reference standards were also investigated. Three kinds of models including partial least square regression (PLSR), stepwise multiple linear regression (SMLR) and principle component regression (PCR) were evaluated for the determination of SC and SSC in navel orange juice. Calibration models based on the different spectral ranges were also compared. Performance of different models was assessed in terms of root mean square errors of prediction (RMSEP) and correlation coefficient (r) of prediction set of samples. The correlation coefficients of calibration models for SC and SSC were 0.965 and 0.961, the correlation coefficients of prediction models for SC and SSC were 0.857 and 0.888, and the corresponding RMSEP were 0.562 and 0.492 respectively. The results show that ultraviolet-visible transmittance technique is a feasible method for non-invasive estimation of fruit juice SC and SSC.

The objective of this research is to develop algorithm to recognize clipped stigma traces in rice seeds using image processing. At first, the micro-configuration of clipped stigma traces was observed with electronic scanning microscope. Then images of rice seeds were acquired with a color machine vision system. A digital image-processing algorithm based on morphological operations and Hough transform was developed to inspect the occurrence of clipped stigma traces. Five varieties of Jinyou402, Shanyou10, Zhongyou207, Jiayou and you3207 were evaluated. The algorithm was implemented with all image sets using a Matlab 6.5 procedure. The results showed that the algorithm achieved an average accuracy of 96%. The algorithm was proved to be insensitive to the different rice seed varieties.

Further optimization of the agricultural growth process and quality control of perishable food which can be fruits and vegetables as well as every kind of meat or milk product requires new approaches for the sensitive front end. One possibility is reflectance or fluorescence spectroscopy in a wide wavelength range. By now broad usage is hindered by costs, size and performance of existing systems. MOEMS scanning gratings for spectrometers and translational mirrors for Fourier Transform spectroscopy enable small robust systems working in a range from 200nm to 5μm. Both types use digital signal processors (DSPs) capable to compute the spectra and execute complex evaluation and decision algorithms. The MOEMS chips are realized by anisotropic etching of a silicon on insulator (SOI) substrate. First the backside silicon and buried oxide is removed by a wet process then the front side structure is realized by dry etching. Depending on the bearing springs a silicon plate up to 3 x 3 mm² wide and typically 30μm thick can be driven resonantly to rotational or translational movement. Combined with additional optical components and appropriate detectors handheld Czerny-Turner or Fourier Transform spectrometers have been realized and tested. Results of first measurements of reflection spectroscopy on model substances have been performed with both system types in the NIR range. Measurements on real objects like tomatoes or apples are intended for a wider wavelength range. Future systems may contain displays and light sources as well as data storage cards or additional interfaces.

Chitinous materials are important sources for bio-medical applications, and the process monitoring is one of key factors for the quality control of products. In this study, chitin and chitosan in suspension form were analyzed using near infrared (NIR) spectroscopy. Two models including multiple linear regression (MLR), modified partial least square regression (MPLSR) were adopted for studying the degree of deacetylation (DD) of chitinous materials in order to assure a better quality monitoring and control for chitosan production. During the process of the deacetylation, the real-time measurements of suspension were conducted. The MPLSR model with second derivative spectra in the range of 600-1000 and 1400-1500 nm yielded the best results, which were r_c=0.991, SEC=0.019, RESC=1.4%, r_p=0.990, SEP=0.022, RSEP=3.4%, RPD=9.4. The NIR measurements of DD status of chitinous suspension could be achieved by using the MLR and MPLSR models developed in this study. It provides great application potentials to the real-time and on-line quality monitoring of deacetylation process for the production of chitosan.

Near infrared (NIR) spectroscopy is a promising technique for nondestructive measurement of farm products quality measurement and information acquisition. The objective of this research was to study the potential of NIR diffuse reflectance spectroscopy as a way for nondestructive measurement of the water content of tomato leaves. A total of 120 leaves were collected as experimental materials, 80 of them were used to form a calibration data set. In order to set up a calibration model, NIR spectral data were collected in the spectral region between 800 nm and 2500 nm by NIR spectrometer of Nicolet Corporation, and water content of tomato leaves by a drying chest, four different mathematical treatments were used in spectrums processing: different wavelength range, baseline correction, smoothing, first and second derivative. Depending on data preprocessing and PLS analysis, we can get best prediction model when we select original spectra by baseline correction at full wavelength range (800-2500nm), the best model of water content has a root mean square error of prediction (RMSEP) of 1.91, a root mean square error of calibration (RMSEC) of 0.731 and a calibration correlation coefficient (R) value of 0.96265. It is conclude that the FTNIR method with Smart Near-IR UpDRIFT accessory can accurate estimate the water content in tomato leaves.

An objective and reliable method for meat quality measurement will benefit both consumers and meat industry. Among various techniques, optical methods have the advantage of being fast, flexible, inexpensive and nondestructive, which are important characteristics for online quality control. Although there have been great progress in this area, many results are inconsistent and controversial because of the lack of fundamental understanding of in light-meat interactions. Optical measurements on meat tissues are affected by both meat scattering and absorption properties. In the project, a method based on diffuse approximation solution of light transport in tissue was used to derive meat scattering and absorption coefficients. Differentiating muscle scattering properties from absorption properties are important for muscle characterization because they represent distinctly different aspects of muscle physical and chemical components. Our preliminary results showed that scattering coefficients can detect variations in beef steak tenderness. This new technique is promising to be used as an indicator for beef tenderness. However, a more extensive study with larger sample population will be necessary to fully test the capability of using optical scattering for beef tenderness characterization.

Near-infrared (NIR) spectroscopy has become a very popular technique for the non-invasive assessment of intact fruit. This work presents an application of a low-cost commercially available NIR spectrometer for the estimation of soluble solids content (SSC) of Chinese citrus. The configuration for the spectra acquisition was used (diffuse transmittance), using a custom-designed contact optical fiber probe. Samples of Chinese citrus in deferent orchard, collected over the 2005 harvest seasons, were analyzed for soluble solids content (Brix). Partial least squares calibration models, obtained from several preprocessing techniques (smoothing, multiplicative signal correction, standard normal variate, etc), were compared. Also, the short-wave (SW-NIR) spectral regions were used. Performance of different models was assessed in terms of root mean square of cross-validation, root mean square of prediction (RMSEP) and R for a validation set of samples. RMSEP of 0.538 with R = 0.896 indicate that it is possible to estimate Chinese citrus SSC (Brix value), by using a portable spectrometer.

The SPR biosensor was sensitive to the presence of Salmonella Typhimurium in chicken carcass. The selectivity of the SPR biosensor was assayed using a series of antibody concentrations and dilution series of the organism. The SPR biosensor was specific to Salmonella Typhimurium at concentrations of 10⁶ CFU/ml. Initial results show potential for its application for pathogenic bacteria monitoring.

A machine vision system for quality inspection of bulk rice seeds has been developed in this research. This system is designed to inspect rice seeds on a rotating disk with a CCD camera. The seeds scattering and positioning device on this system, under continuous feeding condition, reaches 85% fill-ratio of the number of holes on the disk. Combining morphological and color characteristics gave a highly acceptable classification. The high classification accuracies obtained using a small number of features indicate the potential of the algorithm for on-line inspection of germinated rice seeds in industrial environment. The overall average classification accuracy among the four categories was above 90%. This paper presents the significant elements of the computer vision system and emphasizes the important aspects of the image processing technique.

Chlorophyll content in leaves is one of the important internal information for predicting plants growth status. In this study, we use near infrared (NIR) spectroscopy technique to predict chlorophyll content in pepper leaves. Calibration models were created from spectral and constituent measurements, chlorophyll content measured by a SPAD-502 chlorophyll meter, 74 samples served as the calibration sets and 16 samples served as the validation sets. Partial least squares (PLS) and principal component regression (PCR) analysis technique were used to develop the prediction models, and four different mathematical treatments were used in spectrums processing: smoothing, baseline correction, different wavelength range, first and second derivative. When we use PLS analysis and select spectra with second derivate, we can get high correlation efficient and low RMSEC value, but big difference between RMSEC and RMSEP. The best calibration model when we delete four outlier samples, when we process spectra with second derivate at full wavelength, we can get highest correlation coefficient (r=0.97537), a relative lower RMSEC value (2.33), and a small difference between RMSEC (2.33) and RMSEP (5.49). Result showed that NIR technique is a non-destructive way; it can acquire chlorophyll content in pepper leaves quickly and conveniently.

Genetic algorithms (GAs) are used to implement an automated wavelength selection procedure for use in building multivariate calibration models based on partial least squares regression. The GAs also allows the number of latent variables used in constructing the calibration models to be optimized along with the selection of the wavelengths. This method was applied to fundamental study of non-destructive measurement of intact fruit quality with Fourier transform near infrared spectroscopy (FT-NIR). The experiments tested in this method are sugar content, titratable acidity and valid acidity. The optimal configurations for the GAs were investigated for each data set through experimental design techniques. Despite the complexity of the spectral data, the GA procedure was found to perform well (RMSEP=0.395, 0.0195, 0.0087 for SC, TA and pH respectively), leading to calibration models that significantly outperform those based on full spectrum analyses (RMSEP=0.512, 0.0198, 0.0111for SC, TA and pH respectively). In addition, a significant reduction in the number of spectral points required to build the models is realized and all of the numbers of wavelengths for building the models can reduce by 84.4%. It is instructive for the further study of the theory of non-destructive measurement of the fruit internal quality with FT-NIR spectroscopy.

A new method is proposed to eliminate the varying background and noise simultaneously for multivariate calibration of Fourier transform near infrared (FT-NIR) spectral signals. An ideal spectrum signal prototype was constructed based on the FT-NIR spectrum of fruit sugar content measurement. The performances of wavelet based threshold de-noising approaches via different combinations of wavelet base functions were compared. Three families of wavelet base function (Daubechies, Symlets and Coiflets) were applied to estimate the performance of those wavelet bases and threshold selection rules by a series of experiments. The experimental results show that the best de-noising performance is reached via the combinations of Daubechies 4 or Symlet 4 wavelet base function. Based on the optimization parameter, wavelet regression models for sugar content of pear were also developed and result in a smaller prediction error than a traditional Partial Least Squares Regression (PLSR) mode.

Near infrared (NIR) spectroscopy is an instrumental method widely used for rapid and nondestructive detection of internal qualities of agricultural products. Statistical modeling is a very important and difficult process in NIR detection to establish the relationship between spectral information and interested index. Classical multivariate calibration methods such as partial least square regression (PLSR), principle component regression (PCR) and stepwise multi linear regression (SMLR) were often used for modeling. In this study, besides these algorithms, another mixed algorithm was adopted for establishing a nonlinear model of NIR spectra and MT-firmness of pears. The mixed algorithm was combined with SMLR and artificial neural network (ANN). Compared the classical multivariate calibration methods of PLSR, PCR and SMLR, the modeling results using PLSR method of original spectra were much better than the results using derivative spectra and the other two methods: r=0.88, RMSEC=3.79 N of calibration and r=0.83, RMSEP=4.35 N of validation. The mixed algorithm also performed better than SMLR and PCR, but was a bit worse than PLSR: r=0.85, RMSEC=4.15 N of calibration and r=0.82, RMSEP=4.67 N of validation. The results indicated that fruit NIR spectra could be used for MT-firmness prediction when proper algorithm was chosen, however, further study on statistic modeling are still needed to improve the predicting performance.

Nondestructive method of measuring soluble solids content (SSC) of kiwifruit was developed by Fourier transform near infrared (FT-NIR) reflectance and fiber optics. Also, the models describing the relationship between SSC and the NIR spectra of the fruit were developed and evaluated. To develop the models several different NIR reflectance spectra were acquired for each fruit from a commercial supermarket. Different spectra correction algorithms (standard normal variate (SNV), multiplicative signal correction (MSC)) were used in this work. The relationship between laboratory SSC and FT-NIR spectra of kiwifruits were analyzed via principle component regression (PCR) and partial least squares (PLS) regression method using TQ 6.2.1 quantitative software (Thermo Nicolet Co., USA). Models based on the different spectral ranges were compared in this research. The first derivative and second derivative were applied to all measured spectra to reduce the effects of sample size, light scattering, noise of instrument, etc. Different baseline correction methods were applied to improve the spectral data quality. Among them the second derivative method after baseline correction produced best noise removing capability and to obtain optimal calibration models. Total 480 NIR spectra were acquired from 120 kiwifruits and 90 samples were used to develop the calibration model, the rest samples were used to validate the model. Developed PLS model, which describes the relationship between SSC and NIR spectra, could predict SSC of 84 unknown samples with correlation coefficient of 0.9828 and SEP of 0.679 Brix.

A real time machine vision system for fruit quality inspection was developed, which consists of rollers, an encoder, a lighting chamber, a TMS-7DSP CCD camera (PULNIX Inc.), a computer (P4 1.8G, 128M) and a set of grading controller. An image was binary, and the edge was detected with line-scanned based digit image description, and the MER was applied to detected size of the fruit, but failed. The reason for the result was that the test point with MER was different from which was done with vernier caliper. An improved method was developed, which was called as software vernier caliper. A line between weight O of the fruit and a point A on the edge was drawn, and then the crossed point between line OA and the edge was calculated, which was noted as B, a point C between AB was selected, and the point D on the other side was searched by a way to make CD was vertical to AB, by move the point C between point A and B, A new point D was searched. The maximum length of CD was recorded as an extremum value. By move point A from start to the half point on the edge, a serial of CD was gotten. 80 navel oranges were tested, the maximum error of the diameter was less than 1mm.

To evaluate the applicability of near infrared spectroscopy for determination of the five enological parameters (alcoholic degree, pH value, total acid and amino acid nitrogen, °Brix) of Chinese rice wine, transmission spectra were collected in the spectral range from 12500 to 3800 cm^-1 in a 1 mm path length rectangular quartz cuvette with air as reference at room temperature. Five calibration equations for the five parameters were established between the reference data and spectra by partial least squares (PLS) regression, separately. The best calibration results were achieved for the determination of alcoholic degree and °Brix. The RPD (ration of the standard deviation of the samples to the SECV) values of the calibration for both alcoholic degree and °Brix were higher than 3 (4.30 and 7.94, respectively), which demonstrated the robustness and power of the calibration models. The determination coefficients (R²) for alcoholic degree and °Brix were 0.987 and 0.991, respectively. The performance of pH, total acid and amino acid nitrogen was not as good as that of alcoholic degree and °Brix. The RPD values for the three parameters were 1.48, 1.85 and 1.82, respectively, and R2 values were 0.964, 0.970 and 0.971, respectively. In validation step, R2 value of the five parameters are all higher than 0.7, especially for alcoholic degree and °Brix (0.968 and 0.956, respectively). The results demonstrated that NIR spectroscopy could be used to predict the concentration of the five enological parameters in Chinese rice wine.

The attachment strength of Escherichia coli O157:H7, Salmonella Typhimurium, and Listeria monocytogenes to beef surface was evaluated. The effect of bacterial attachment strength on inactivation by HDP treatment was studied. Bacterial attachment was defined as loosely attached (analyzed first by running diluent over the beef cubes) and strongly attached (analyzed again rinsed cubes by pummeling for 2 min in diluent) to the beef surface. Most attachment of these three pathogens occurred within 10 min. There was no significant difference in attachment strength of pathogens during the first 30 minutes exposure time. Strongly attached bacteria to beef surface were inactivated by HDP at a greater rate and the reduction was significant for all three bacteria (0.52, 0.37, and 0.43 log₁₀ CFU/g for E. coli O157:H7, S. Typhimurium, and L. monocytogenes, respectively). No clear indications were obtained for sub-lethal damage of microorganisms surviving the HDP treatment. These findings disclose that HDP treatment is more lethal to pathogenic bacteria strongly attached to beef surface compared to those planktonic or loosely attached cells.

Shape is one of the major concerns and which is still a difficult problem in automated inspection and sorting of fruits. In this research, we proposed the multi-scale energy distribution (MSED) for object shape description, the relationship between objects shape and its boundary energy distribution at multi-scale was explored for shape extraction. MSED offers not only the mainly energy which represent primary shape information at the lower scales, but also subordinate energy which represent local shape information at higher differential scales. Thus, it provides a natural tool for multi resolution representation and can be used as a feature for shape classification. We addressed the three main processing steps in the MSED-based shape classification. They are namely, 1) image preprocessing and citrus shape extraction, 2) shape resample and shape feature normalization, 3) energy decomposition by wavelet and classification by BP neural network. Hereinto, shape resample is resample 256 boundary pixel from a curve which is approximated original boundary by using cubic spline in order to get uniform raw data. A probability function was defined and an effective method to select a start point was given through maximal expectation, which overcame the inconvenience of traditional methods in order to have a property of rotation invariants. The experiment result is relatively well normal citrus and serious abnormality, with a classification rate superior to 91.2%. The global correct classification rate is 89.77%, and our method is more effective than traditional method. The global result can meet the request of fruit grading.

The near infrared (NIR) method based on fibre-optic FT-NIR spectrometer was tested to determine soluble solids content (SSC) non-destructively in chufa (Eleocharis tuberose schult). A total of 240 chufas (120 of cv. 'Jinhua' and 120 of cv. 'Yongkang') sampled from eight positions in the different fields to increase variation in soluble solids content, were measured after 2-days storage and the measurements randomly assigned to a calibration data set and a prediction data set. Thus the calibration set and the prediction set represented exactly the same distribution. The calibration data set was used to select the wavelengths best correlated with Brix and different regression methods (partial least squares (PLS) regression and multiple linear regression (MLR)) that was applied to calculate the Brix value in the prediction data set. The most significant r (0.9056) was found with the first derivative of log (1/R) (where R reflectance), yielding standard error of calibration (SEC)=0.545 Brix, standard error of prediction (SEP)=0.632 Brix. Analysis of different methods performed on the actual and the predicted Brix showed PLS is better than MLR. This NIR method seems reliable for determining soluble solids contents of chufa non-destructively, and could prove useful for it.

An automatic method for identifying different variety of rice seeds using machine vision technology will be investigated and its system, consisting of an automatic inspection machine and an image-processing unit, was also developed. The system could continually present matrix-positioned rice seed to CCD cameras, singularize each rice seed image from the background. The inspection machine had scattering and positioning devices, a photographing station, a parallel discharging device, and a continuous conveyer belt with carrying holes for the rice seed. The rice seeds' image was achieved continuously by single chip controlled device. The line was stopped every one second for one second by the device. The camera took an image of simple seed when it stopped. Image analysis was carried out programmed by Visual C++ 6.0. Color features in RGB (red, green, blue) and color spaces were computed. A back-forward neural network was trained to identify rice seeds. Almost all 86.65% rice seeds were correctly identified. The correct classification rates for five rice varieties were: No.5 'Xiannong' of 99.99%, 'Jinyougui' of 99.93%,'You166' of 98.89%, No. 3 'Xiannong' of 82.82% and 'Medium you' 463 of 86.65%, respectively. Based on the results, it was concluded that the system was enough to use for inspection of varieties of different rice seed based on its appearance characters of seeds.