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- Front Matter: Volume 7676
- NIR Sensing
- Optical Sensing
- Hyperspectral Imaging for Food Evaluation
- Online Hyperspectral Application
- Biosensors and Pathogen Detection
- Remote Sensing
- Poster Session
Front Matter: Volume 7676
Front Matter: Volume 7676
Show abstract
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.
NIR Sensing
Influence of temperature on visible and near-infrared spectra and the predictive ability of multivariate models
Show abstract
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.
Near-infrared hyperspectral imaging for quality analysis of agricultural and food products
C. B. Singh,
D. S. Jayas,
J. Paliwal,
et al.
Show abstract
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.
Food quality assessment by NIR hyperspectral imaging
Martin B. Whitworth,
Samuel J. Millar,
Astor Chau
Show abstract
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.
UV/visible/near-infrared reflectance spectroscopic determination of cotton fiber and trash content in lint cotton waste
Show abstract
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.
Damage and quality assessment in wheat by NIR hyperspectral imaging
Show abstract
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.
Optical Sensing
Detection of organic residues on food processing equipment surfaces by spectral imaging method
Show abstract
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.
Effects of muscle structures on two-dimensional reflectance in beef muscles
G. Yao
Show abstract
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.
Hyperspectral Imaging for Food Evaluation
Single aflatoxin contaminated corn kernel analysis with fluorescence hyperspectral image
Show abstract
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.
Calibration of a fluorescence hyperspectral imaging system for agricultural inspection and detection
Show abstract
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.
Classification of fecal contamination on leafy greens by hyperspectral imaging
Show abstract
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.
Online Hyperspectral Application
Characterization of the optical properties of normal and defective pickling cucumbers and whole pickles
Show abstract
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.
Line-scan hyperspectral imaging for real-time poultry fecal detection
Show abstract
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.
Development of real-time line-scan hyperspectral imaging system for online agricultural and food product inspection
Show abstract
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.
Biosensors and Pathogen Detection
Rapid detection of total viable count of chilled pork using hyperspectral scattering technique
Show abstract
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.
Biophotonics determination of 6-benzylaminopurine (6-BAP) plant growth regulator using OFRR biosensor
Show abstract
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.
Development of highly sensitive handheld device for real-time detection of bacteria in food
Kewei Zhang,
Anxue Zhang,
Liling Fu,
et al.
Show abstract
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.
Micro-fabricated wireless biosensors for the detection of S. typhimurium in liquids
Show abstract
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.
Near-infrared microscopic methods for the detection and quantification of processed by-products of animal origin
Show abstract
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.
Remote Sensing
Remote sensing of canopy dynamics and biochemical variables estimation of fodder crops
Suchit K. Rai,
S. K. Das,
A. K. Rai
Show abstract
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.
Poster Session
Spatial variation of water and soil erosion in Donghe basin based on SWAT model
Show abstract
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%.
Technical system of land survey and monitoring and its future schemes in China
Show abstract
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.
The development of the geographic image cognition approach on studying land degradation
Show abstract
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.
Real-time near-infrared spectroscopic inspection system for adulterated sesame oil
Show abstract
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.
On-line determination of pork color and intramuscular fat by computer vision
Show abstract
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.
Quantitative analysis and detection of adulteration in pork using near-infrared spectroscopy
Show abstract
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.