This PDF file contains the front matter associated with SPIE Proceedings Volume 7676, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

When vibrational spectra are measured on- or in-line for process analytical or control purposes, the spectra may fluctuate in response due to fluctuations in environmental conditions, such as temperature or humidity that must be taken into consideration when developing calibration models. In this paper, the influence of temperature fluctuations on visible and near-infrared (Vis/NIR) spectra and their effect on the predictive power of calibration models, partial least squares (PLS), principal component regression (PCR) and stepwise multiple linear regression (SMLR) was studied. The sample was peach. Soluble solids content in peach was detected. The results shows influence of temperature on Vis/NIR spectra of the peach exists. The overall results sufficiently demonstrate that the performances of the same method, PLS, PCR or SMLR are similar, no matter what the data are at different temperatures.

Agricultural and food processing industries are always looking to implement real-time quality monitoring techniques as a part of good manufacturing practices (GMPs) to ensure high-quality and safety of their products. Near-infrared (NIR) hyperspectral imaging is gaining popularity as a powerful non-destructive tool for quality analysis of several agricultural and food products. This technique has the ability to analyse spectral data in a spatially resolved manner (i.e., each pixel in the image has its own spectrum) by applying both conventional image processing and chemometric tools used in spectral analyses. Hyperspectral imaging technique has demonstrated potential in detecting defects and contaminants in meats, fruits, cereals, and processed food products. This paper discusses the methodology of hyperspectral imaging in terms of hardware, software, calibration, data acquisition and compression, and development of prediction and classification algorithms and it presents a thorough review of the current applications of hyperspectral imaging in the analyses of agricultural and food products.

Near infrared reflectance (NIR) spectroscopy is well established in the food industry for rapid compositional analysis of bulk samples. NIR hyperspectral imaging provides new opportunities to measure the spatial distribution of components such as moisture and fat, and to identify and measure specific regions of composite samples. An NIR hyperspectral imaging system has been constructed for food research applications, incorporating a SWIR camera with a cooled 14 bit HgCdTe detector and N25E spectrograph (Specim Ltd, Finland). Samples are scanned in a pushbroom mode using a motorised stage. The system has a spectral resolution of 256 pixels covering a range of 970-2500 nm and a spatial resolution of 320 pixels covering a swathe adjustable from 8 to 300 mm. Images are acquired at a rate of up to 100 lines s^-1, enabling samples to be scanned within a few seconds. Data are captured using SpectralCube software (Specim) and analysed using ENVI and IDL (ITT Visual Information Solutions). Several food applications are presented. The strength of individual absorbance bands enables the distribution of particular components to be assessed. Examples are shown for detection of added gluten in wheat flour and to study the effect of processing conditions on fat distribution in chips/French fries. More detailed quantitative calibrations have been developed to study evolution of the moisture distribution in baguettes during storage at different humidities, to assess freshness of fish using measurements of whole cod and fillets, and for prediction of beef quality by identification and separate measurement of lean and fat regions.

Lint cleaning at cotton processing facilities is performed in order to remove the non-lint materials with minimal fiber damage. The resultant waste contains some degree of cotton fiber having good equal qualities, and hence is of great concern for operating cost. Traditional methods for measuring non-lint trash are labor intensive and time consuming. UV / visible / NIR technique was examined for its feasibility in determining the portions of cotton fiber and trash. Overall result indicated that NIR prediction was limited to screening purpose for probable reasons as heterogeneous trash distribution, relatively small sampling, and gravimetric reference method.

Fusarium head blight is a fungal disease that affects the world's small grains, such as wheat and barley. Attacking the spikelets during development, the fungus causes a reduction of yield and grain of poorer processing quality. It also is a health concern because of the secondary metabolite, deoxynivalenol, which often accompanies the fungus. While chemical methods exist to measure the concentration of the mycotoxin and manual visual inspection is used to ascertain the level of Fusarium damage, research has been active in developing fast, optically based techniques that can assess this form of damage. In the current study a near-infrared (1000-1700 nm) hyperspectral image system was assembled and applied to Fusarium-damaged kernel recognition. With anticipation of an eventual multispectral imaging system design, 5 wavelengths were manually selected from a pool of 146 images as the most promising, such that when combined in pairs or triplets, Fusarium damage could be identified. We present the results of two pairs of wavelengths [(1199, 1474 nm) and (1315, 1474 nm)] whose reflectance values produced adequate separation of kernels of healthy appearance (i.e., asymptomatic condition) from kernels possessing Fusarium damage.

Organic residues on equipment surfaces in poultry processing plants can generate cross contamination and increase the risk of unsafe food for consumers. This research was aimed to investigate the potential of LED-induced fluorescence imaging technique for rapid inspection of organic residues on poultry processing equipment surfaces. High-power blue LEDs with a spectral output at 410 nm were used as the excitation source for a line-scanning hyperspectral imaging system. Common chicken residue samples including fat, blood, and feces from ceca, colon, duodenum, and small intestine were prepared on stainless steel sheets. Fluorescence emission images were acquired from 120 samples (20 for each type of residue) in the wavelength range of 500-700 nm. LED-induced fluorescence characteristics of the tested samples were determined. PCA (principal component analysis) was performed to analyze fluorescence spectral data. Two SIMCA (soft independent modeling of class analogy) models were developed to differentiate organic residues and stainless steel samples. Classification accuracies using 2-class ('stainless steel' and 'organic residue') and 4-class ('stainless steel', 'fat', 'blood', and 'feces') SIMCA models were 100% and 97.5%, respectively. An optimal single-band and a band-pair that are promising for rapid residue detection were identified by correlation analysis. The single-band approach using the selected wavelength of 666 nm could generate false negative errors for chicken blood inspection. Two-band ratio images using 503 and 666 nm (F503/F666) have great potential for detecting various chicken residues on stainless steel surfaces. This wavelength pair can be adopted for developing a LED-based hand-held fluorescence imaging device for inspecting poultry processing equipment surfaces.

Our recent studies found that the optical reflectance in skeletal muscle had a unique geometric pattern. This pattern can be characterized using a set of five parameters extracted from each image including two shape parameters, two spatial intensity decay gradients along and across the muscle fibers and the total scattering intensity. We previously found that some of these optical parameters may have the capability to predict beef tenderness. In this data analysis study, we investigated the effects of sarcomere length, college content, and proteolysis on the two-dimensional reflectance in beef muscles. The results may help better understand the capabilities of using optical methods to assess meat tenderness.

Aflatoxins are toxic secondary metabolites of the fungi Aspergillus flavus and Aspergillus parasiticus, among others. Aflatoxin contaminated corn is toxic to domestic animals when ingested in feed and is a known carcinogen associated with liver and lung cancer in humans. Consequently, aflatoxin levels in food and feed are regulated by the Food and Drug Administration (FDA) in the US, allowing 20 ppb (parts per billion) limits in food and 100 ppb in feed for interstate commerce. Currently, aflatoxin detection and quantification methods are based on analytical tests including thin-layer chromatography (TCL) and high performance liquid chromatography (HPLC). These analytical tests require the destruction of samples, and are costly and time consuming. Thus, the ability to detect aflatoxin in a rapid, nondestructive way is crucial to the grain industry, particularly to corn industry. Hyperspectral imaging technology offers a non-invasive approach toward screening for food safety inspection and quality control based on its spectral signature. The focus of this paper is to classify aflatoxin contaminated single corn kernels using fluorescence hyperspectral imagery. Field inoculated corn kernels were used in the study. Contaminated and control kernels under long wavelength ultraviolet excitation were imaged using a visible near-infrared (VNIR) hyperspectral camera. The imaged kernels were chemically analyzed to provide reference information for image analysis. This paper describes a procedure to process corn kernels located in different images for statistical training and classification. Two classification algorithms, Maximum Likelihood and Binary Encoding, were used to classify each corn kernel into "control" or "contaminated" through pixel classification. The Binary Encoding approach had a slightly better performance with accuracy equals to 87% or 88% when 20 ppb or 100 ppb was used as classification threshold, respectively.

Fluorescence hyperspectral imaging is increasingly being used for food quality inspection and detection of potential food safety concerns. The flexible nature of a self-scanning pushbroom hyperspectral imager lends itself to these kinds of applications, among others. To increase the use of this technique there has been a tendency to use low cost off-the-shelf hyperspectral sensors which are typically not radiometrically calibrated. To ensure that these systems are optimized for response and repeatability, it is imperative that the systems be both radiometrically and spectrally calibrated specifically for fluorescence imaging. Fluorescence imaging provides several challenges such as low signal, stray light and a low signal dynamic range that are improved with careful radiometric calibration. A radiometric and spectral approach that includes flat fielding and the conversion of digital number responses to radiance for calibrating this imaging system and other types of hyperspectral imagers is described in this paper. Results show that this method can be adopted for calibrating fluorescence and reflective hyperspectral imaging systems in the visible and near infra-red domains.

This paper reported the development of hyperspectral fluorescence imaging system using ultraviolet-A excitation (320-400 nm) for detection of bovine fecal contaminants on the abaxial and adaxial surfaces of romaine lettuce and baby spinach leaves. Six spots of fecal contamination were applied to each of 40 lettuce and 40 spinach leaves. In this study, the wavebands at 666 nm and 680 nm were selected by the correlation analysis. The two-band ratio, 666 nm / 680 nm, of fluorescence intensity was used to differentiate the contaminated spots from uncontaminated leaf area. The proposed method could accurately detect all of the contaminated spots.

Internal defect in pickling cucumbers can cause bloater damage during brining, which lowers the quality of final pickled products and results in economic loss for the pickle industry. Hence it is important to have an effective optical inspection system for detection and segregation of defective pickling cucumbers. This research was intended to measure the spectral absorption and scattering properties of normal and internally defective pickling cucumbers and whole pickles, using hyperspectral imaging-based spatially-resolved technique. Spatially-resolved hyperspectral scattering images were acquired from 50 freshly harvested 'Journey' pickling cucumbers in the summer of 2008. The cucumbers were then subjected to rolling under mechanical load to induce internal damage. The damaged cucumbers were imaged again one hour and one day after the mechanical stress treatment. In addition, 20 whole pickles each of normal and defective (bloated) class were also measured by following the same procedure as that for pickling cucumbers. Spectra of the absorption and reduced scattering coefficients for pickling cucumbers and whole pickles were extracted from the spatially-resolved scattering profiles, using an inverse algorithm for a diffusion theory model, for the spectral range of 700-1,000 nm. It was found that within one hour after mechanical damage, changes in the absorption and reduced scattering coefficients for the cucumbers were minimal. One day after mechanical damage, the absorption coefficient for the cucumbers increased noticeably for the wavelengths of 700-920 nm, whereas the reduced scattering coefficient decreased more significantly for the wavelengths of 700-1,000 nm. Overall mechanical damage had greater impact on the scattering properties than on the absorption properties. After brining, pickles became translucent and scattering was greatly diminished. Thus the diffusion theory model was no longer valid for determining the optical properties of whole pickles. This research suggests that effective defect detection may be achieved by enhancing scattering features in the optical evaluation of cucumbers.

The ARS multispectral imaging system with three-band common aperture camera was able to inspect fecal contaminants in real-time mode during poultry processing. Recent study has demonstrated several image processing methods including binning, cuticle removal filter, median filter, and morphological analysis in real-time mode could remove false positive errors. The ARS research groups and their industry partner are now merging the fecal detection and systemically disease detection systems onto a common platform using line-scan hyperspectral imaging system. This system will aid in commercialization by creating one hyperspectral imaging system with user-defined wavelengths that can be installed in different locations of the processing line to solve significant food safety problems. Therefore, this research demonstrated the feasibility of line-scan hyperspectral imaging system in terms of processing speed and detection accuracy for a real-time, on-line fecal detection at current processing speed (140 birds per minute) of commercial poultry plant. The newly developed line-scan hyperspectral imaging system could improve Food Safety Inspection Service (FSIS)'s poultry safety inspection program significantly.

This paper reports a recent development of a line-scan hyperspectral imaging system for real-time multispectral imaging applications in agricultural and food industries. The hyperspectral imaging system consisted of a spectrograph, an EMCCD camera, and application software. The real-time multispectral imaging with the developed system was possible due to (1) data binning, especially a unique feature of the EMCCD sensor allowing the access to non-contiguous multispectral bands, (2) an image processing algorithm designed for real-time multispectral imaging, and (3) the design and implementation of the real-time application software. The imaging system was developed as a poultry inspection instrument determining the presence of surface feces on poultry carcasses moving at commercial poultry processing line speeds up to 180 birds per minute. The imaging system can be easily modifiable to solve other real-time inspection/sorting problems. Three wavelengths at 517 nm, 565 nm and 802 nm were selected for real-time fecal detection imaging. The fecal detection algorithm was based on dual band ratios of 565nm/517nm and 802nm/517nm followed by thresholding. The software architecture was based on a ping pong memory and a circular buffer for the multitasking of image grabbing and processing. The software was written in Microsoft Visual C++. An image-based internal triggering (i.e. polling) algorithm was developed to determine the start and end positions of birds. Twelve chickens were used for testing the imaging system at two different speeds (140 birds per minute and 180 bird per minute) in a pilot-scale processing line. Four types of fecal materials (duodenum, ceca, colon and ingesta) were used for the evaluation of the detection algorithm. The software grabbed and processed multispectral images of the dimension 118 (line scans) x 512 (height) x 3 (bands) pixels obtained from chicken carcasses moving at the speed up to 180 birds per minute (a frame rate 286 Hz). Intensity calibration, detection algorithm, displaying and saving were performed within the real-time deadlines.

A rapid nondestructive measurement method for determining the total viable count of chilled pork was studied. Chilled pork samples were purchased from supermarket and then stored in refrigerator at 4°C. Every 24 hours, hyperspectral images were collected from the chilled pork samples in 400-1100nm region, in parallel total viable counts were obtained by classical microbiological plating methods. The 3-parameter modified lorentzian distribution function was applied to fit the scattering profiles of all samples and the fitting results were satisfactorily high in region 470-943 nm. Then the parameters extracted were used to establish PLSR models. The prediction results for the parameter a, b, c, b×c are 0.945, 0.918, 0.919, 0.935 respectively. The study show that the hyperspectral technology can accurately tracks the increase of total viable count of chilled pork during 2-14 days storage at 4°C, and so indicate it a valid tool for assessing the quality and safety properties of chilled pork rapidly and nondestructively in the future.

The identification of pesticide and 6-benzylaminopurine (6-BAP) plant growth regulator was carried out using a label-free opto-fluidic ring resonator (OFRR) biosensor. The OFRR sensing platform is a recent advancement in opto-fluidic technology that integrates photonic sensing technology with microfluidics. It features quick detection time, small sample volume, accurate quantitative and kinetic results. The most predominant advantage of the OFRR integrated with microfluidics is that we can potentially realize the multi-channel and portable biosensor that detects numerous analytes simultaneously. Antisera for immunoassay were raised in rabbits against the 6-BAP-BSA conjugate. Using the immunization protocol and unknown cytokinin reacting with same antibody, comparable sensitivity and specificity were obtained. 6-BAP antibody was routinely used for cytokinin analysis. A sensitive and simple OFRR method with a good linear relationship was developed for the determination of 6-BAP. The detection limit was also examined. The biosensor demonstrated excellent reproducibility when periodically exposed to 6-BAP.

To ensure the safety of food, a detection device, which can detect/monitor the present of bacteria in a real-time manner and can be easily used for in-field tests, is highly desirable. Recently, magnetostrictive particles (MSPs) as a new type of high-performance biosensor have been developed. The detection of various bacteria and spores in food with high sensitivity has already been experimentally demonstrated. To fully use the technique for food safety, two miniaturized interrogation systems based on frequency-domain and time-domain technique are developed to fabricate a handheld detection device. The detection of Salmonella typhimurium (S. typhimurium) in liquid using a time-domain based interrogation system was demonstrated.

Food borne illnesses from the ingestion of S. typhimurium have been of primary concern due to their common occurrence in food products of daily consumption. In this paper, micron size, magnetoelastic (ME) biosensors for the detection of S. typhimurium were fabricated and tested in liquid solutions containing known concentrations of S. typhimurium cells. The biosensors are comprised of a ME sensor platform and immobilized bio-molecular recognition element (E2 phage) that has been engineered to bind the S. typhimurium multi-valently. The micron size ME sensor platforms were manufactured using microelectronics fabrication techniques. Phage was engineered at Auburn University and immobilized onto all surfaces of the sensor. The ME biosensor oscillates with a characteristic resonance frequency when subjected to a time varying magnetic field. Binding between the phage and bacteria, adds mass to the sensor that causes a shift in the sensor's resonance frequency. Sensors with the dimension of 500x100x4 μm were exposed to S. typhimurium with increasing known concentrations ranging from 5 x10¹ to 5 x 10⁷ cfu/ml. The ME biosensor exhibited high sensitivity and a detection limit better than 50 cfu/ml.

Since the BSE crisis, researches concern mainly the detection, identification, and quantification of meat and bone meal with an important focus on the development of new analytical methods. Microscopic based spectroscopy methods (NIR microscopy - NIRM or/and NIR hyperspectral imaging) have been proposed as complementary methods to the official method; the optical microscopy. NIR spectroscopy offers the advantage of being rapid, accurate and independent of human analyst skills. The combination of an NIR detector and a microscope or a camera allows the collection of high quality spectra for small feed particles having a size larger than 50 μm. Several studies undertaken have demonstrated the clear potential of NIR microscopic methods for the detection of animal particles in both raw and sediment fractions. Samples are sieved and only the gross fraction (superior than 250 μm) is investigated. Proposed methodologies have been developed to assure, with an acceptable level of confidence (95%), the detection of at least one animal particle when a feed sample is adulterated at a level of 0.1%. NIRM and NIR hyperspectral imaging are running under accreditation ISO 17025 since 2005 at CRA-W. A quantitative NIRM approach has been developed in order to fulfill the new requirements of the European commission policies. The capacities of NIRM method have been improved; only the raw fraction is analyzed, both the gross and the fine fractions of the samples are considered, and the acquisition parameters are optimized (the aperture, the gap, and the composition of the animal feed). A mapping method for a faster collection of spectra is also developed. The aim of this work is to show the new advances in the analytical methods developed in the frame of the feed ban applied in Europe.

Non-destructive monitoring and diagnosis of plant nitrogen (N) concentration status is necessary for precision in N management. Leaf -N and chlorophyll (Chl) concentration of fodder crops are important indicators of plant N status. Studies were conducted to determine the relationship between canopy hyperspectral reflectance (325 to 1075 nm) and Chl or N concentration in field grown fodder crops [bajra (Pennisetum typhoides, sorghum (Sorghum bicolor L.) in Kharif season and oat (Avena sativa) in Rabi season] without and with recommended dose of nitrogen of different crops. Nitrogen fertilizer application mainly affected leaf reflectance at 575 and 623 nm in sorghum, 565 and 657 nm in bajra and 563 and 716 nm in oat. The reflectance ratio at R₅₈₁/R₃₉₇ (R²=0.46**) and R₆₁₉/R₄₆₂ nm (R²=0.79***) had the highest correlation with sorghum and bajra leaf N concentration respectively with greatest R² values. However in oat single reflectance at R₅₄₂ (R²=0.53**) had the highest correlation with leaf N concentration. Similarly, sorghum, bajra and oat leaf Chl concentration were highly correlated with R₆₇₇/R₅₂₇ (R²=0.63**), R₆₈₈/R₄₀₉ (R²=0.71***) and R₆₉₅ (R²=0.56** ), respectively. A linear relationship was found between sorghum leaf N and a simple ratio at R₅₈₁/R₃₉₇ (Intercept=8.85, slope=-2.64, R²=0.44). Bajra leaf N concentration was associated closely with ratio of R₆₁₉/ R₄₆₂, (R²= 0.78***). Oat leaf N concentration could be best estimate through single reflectance at R₆₉₅ (Slope=-0.48, Intercept=0.15; R²=0.56). Similarly sorghum, bajra and oat leaf Chl could be best-estimated using reflectance ratio of R₆₇₇/R₅₂₇, R₆₁₅/R₄₁₁ and R₆₉₅, respectively. Thus our results suggest that spectral reflectance measurements hold promise for the assessment of some physiological parameter at the leaf level real time monitoring of sorghum and bajra N status and N fertilizer management.

The Donghe basin is one of the typical regions with serious water and soil erosion in Kaixian county of Chongqing city. In this study the spatial distribution variation of water and soil erosion in Donghe basin is analyzed based on a comprehensive method that integrates SWAT model with a Geographic Information System and remote sensing technology. SWAT is a physical based model that requires specific information about weather, soil properties, topography, vegetation, and land management practices occurring within the watershed. Firstly, with the Donghe basin as a study area, using the spatially distributed and mechanism-based SWAT model, the distributed hydrological and sediment model are developed to simulate the runoff and sediment production of Donghe basin. Then the model is calibrated and validated against observed runoff and sediment data from 2003 to 2004, the validated result shows a deterministic coefficient of 0.93. Finally the spatial distribution of the water and soil erosion of Donghe is analyzed. The results show that the mean sediment production of Donghe basin is 30.7 t/ha•a, the maximum sediment production of its sub-watersheds is 212.7 t/ha•a, and the minimum is 0.3 t/ha•a. There is an obviously clustering feature of sub-watersheds distribution with different sediment production level. The area of the high erosion, strong erosion and violent erosion account for 30% of the whole basin area, the other soil erosion area occupy 70%.

China's land resources are extremely scarce. There is a pressing need for building the technical system of land survey and monitoring for detail knowledge about the current situation of land use, land value and land property right of each piece of land in the whole country for land use and management. Many works of land survey and monitoring in China have been finished for providing references directly for the macro decision-making and making the national economic and social development planning. However, there were limits in integrity, systemization, and standardization, and some of works about survey and monitoring were carried out in the early period and their results were not updated in time. The purpose of the paper is to establish the framework of systemized technical system of land survey and monitoring for guidance of future national work of land survey and monitoring. The study was through comparing and analyzing the past and ongoing projects of land survey and monitoring. Results indicated that the technical system is constituted by 5 sub-systems. The system will integrate land survey and monitoring, land evaluation, and information sharing service into a whole. The regional arrangement of land survey and monitoring was proposed. Seven implementation regions of land survey and monitoring were divided, including the northeastern region, eastern coastal region, central region, southwestern region, northwestern region, Xinjiang Urgur region, and Qinghai-Tibetan region. The survey and monitoring objectives and contents in each region are different. The zoning is for guidance of the future project arrangement about national land survey and monitoring in China based on land resources background and economic development demands.

For the extraction of land degradation information we should use not only information on climate, soil, vegetation, physiognomy, land use and its productivities, but also the knowledge and methodologies of geosciences. It is of importance to study some conceptual issues about geographic image cognition (GEOIC) on studying land degradation. The study is to discuss some conceptual issues and the theoretical background of the approach of geographic image cognition (GEOIC) on studying land degradation for building its methodological framework. Some issues concerning the approach of GEOIC on studying land degradation, especially the factors of impacting human's visual cognition, were discussed. The results indicated that the GEOIC is the objectification cognition on remote sensing images and multi-source information using geo-knowledge. As an integrated approach, it is the extension of the methodology of OBIA. The key objective of the GEOIC on studying land degradation is to simulate the function and process of the visual interpretation by experts, and extract spatial features, spatial object and spatial pattern of land degradation under the cognition mode of feature-object-pattern from remote sensing images and multi-source information. The methodology of the GEOIC is realized through the segmentation of geo-objects or meaningful image objects using remote sensing information, geographic information, vegetation, soil, and other ancillary information with geosciences knowledge and intelligence.

Sesame seed oil is popular and expensive in Korea and has been often mixed with other less expensive vegetable oils. The objective of this research is to develop an economical and rapid adulteration determination system for sesame seed oil mixed with other vegetable oils. A recently developed inspection system consists of a light source, a measuring unit, a spectrophotometer, fiber optics, and a data acquisition module. A near-infrared transmittance spectroscopic method was used to develop the prediction model using Partial Least Square (PLS). Sesame seed oil mixed with a range of concentrations of corn, or perilla, or soybean oil was measured in 8 mm diameter glass tubes. For the model development, a correlation coefficient value of 0.98 was observed for corn, perilla, and soybean oil mixtures with standard errors of correlation of 6.32%, 6.16%, and 5.67%, respectively. From the prediction model, the correlation coefficients of corn oil, perilla oil, and soybean oil were 0.98, 0.97 and 0.98, respectively. The Standard Error of Prediction (SEP) for corn oil, perilla oil, and soybean oil were 6.52%, 6.89% and 5.88%, respectively. The results indicated that this system can potentially be used as a rapid non-destructive adulteration analysis tool for sesame seed oil mixed with other vegetable oils.

In this study, the application potential of computer vision in on-line determination of CIE L*a*b* and content of intramuscular fat (IMF) of pork was evaluated. Images of pork chop from 211 pig carcasses were captured while samples were on a conveyor belt at the speed of 0.25 m•s-1 to simulate the on-line environment. CIE L*a*b* and IMF content were measured with colorimeter and chemical extractor as reference. The KSW algorithm combined with region selection was employed in eliminating the surrounding fat of longissimus dorsi muscle (MLD). RGB values of the pork were counted and five methods were applied for transforming RGB values to CIE L*a*b* values. The region growing algorithm with multiple seed points was applied to mask out the IMF pixels within the intensity corrected images. The performances of the proposed algorithms were verified by comparing the measured reference values and the quality characteristics obtained by image processing. MLD region of six samples could not be identified using the KSW algorithm. Intensity nonuniformity of pork surface in the image can be eliminated efficiently, and IMF region of three corrected images failed to be extracted. Given considerable variety of color and complexity of the pork surface, CIE L*, a* and b* color of MLD could be predicted with correlation coefficients of 0.84, 0.54 and 0.47 respectively, and IMF content could be determined with a correlation coefficient more than 0.70. The study demonstrated that it is feasible to evaluate CIE L*a*b* values and IMF content on-line using computer vision.

Authenticity is an important food quality criterion. Rapid methods for confirming authenticity or detecting adulteration are increasingly demanded by food processors and consumers. Near infrared (NIR) spectroscopy has been used to detect economic adulteration in pork . Pork samples were adulterated with liver and chicken in 10% increments. Prediction and quantitative analysis were done using raw data and pretreatment spectra. The optimal prediction result was achieved by partial least aquares(PLS) regression with standard normal variate(SNV) pretreatment for pork adulterated with liver samples, and the correlation coefficient(R value), the root mean square error of calibration(RMSEC) and the root mean square error of prediction (RMSEP) were 0.97706, 0.0673 and 0.0732, respectively. The best model for pork meat adulterated with chicken samples was obtained by PLS with the raw spectra, and the correlation coefficient(R value), RMSEP and RMSEC were 0.98614, 0.0525, and 0.122, respectively. The result shows that NIR technology can be successfully used to detect adulteration in pork meat adulterated with liver and chicken.