This PDF file contains the front matter associated with SPIE Proceedings Volume 8721, including the Title Page, Copyright Information, Table of Contents, and the Conference Committee listing.

This research developed a Raman chemical imaging method for detecting multiple adulterants in skim milk powder. Ammonium sulfate, dicyandiamide, melamine, and urea were mixed into the milk powder as chemical adulterants in the concentration range of 0.1–5.0%. A Raman imaging system using a 785-nm laser acquired hyperspectral images in the wavenumber range of 102–2538 cm^–1 for a 25×25 mm² area of each mixture. A polynomial curve-fitting method was used to correct fluorescence background in the Raman images. An image classification method was developed based on single-band fluorescence-free images at unique Raman peaks of the adulterants. Raman chemical images were created to visualize identification and distribution of the multiple adulterant particles in the milk powder. Linear relationship was found between adulterant pixel number and adulterant concentration, demonstrating the potential of the Raman chemical imaging for quantitative analysis of the adulterants in the milk powder.

Early detection of bruises on apples is important for an automatic apple sorting system. A hyperspectral imaging system with the wavelength range of 400 to 1000nm was built for detecting bruises happened in an hour on ‘Fuji’ apples. Principal components analysis (PCA) was conducted on the hyperspecrtral images and the principal components (PC) images were compared. Three effective wavelengths 780, 850 and 960nm were determined using the weighing coefficients plot of the best PC image. Then, a multi-spectral imaging system with three bands 780, 850 and 960nm in the near-infrared range was developed. The system was consisted of two beamsplitters at 805 and 900nm, two bandpass filters and halogen tungsten lamp, and three CCD cameras. Images of 20 intact and 20 bruised apples were acquired. PCA was conducted on the three-band images of each apple and the best PC image was selected for bruise detection. A bruise detection algorithm based on the PC images and a global threshold method was developed. Results show that 90% of the bruised apples are correctly recognized.

The objectives of this research were to develop a rapid non-destructive method to evaluate the edible quality of chilled pork. A total of 42 samples were packed in seal plastic bags and stored at 4°C for 1 to 21 days. Reflectance spectra were collected from visible/near-infrared spectroscopy system in the range of 400nm to 1100nm. Microbiological, physicochemical and organoleptic characteristics such as the total viable counts (TVC), total volatile basic-nitrogen (TVB-N), pH value and color parameters L* were determined to appraise pork edible quality. Savitzky-Golay (SG) based on five and eleven smoothing points, Multiple Scattering Correlation (MSC) and first derivative pre-processing methods were employed to eliminate the spectra noise. The support vector machines (SVM) and partial least square regression (PLSR) were applied to establish prediction models using the de-noised spectra. A linear correlation was developed between the VIS/NIR spectroscopy and parameters such as TVC, TVB-N, pH and color parameter L* indexes, which could gain prediction results with Rv of 0.931, 0.844, 0.805 and 0.852, respectively. The results demonstrated that VIS/NIR spectroscopy technique combined with SVM possesses a powerful assessment capability. It can provide a potential tool for detecting pork edible quality rapidly and non-destructively.

Efficient deboning is key to optimizing production yield (maximizing the amount of meat removed from a chicken frame while reducing the presence of bones). Many processors evaluate the efficiency of their deboning lines through manual yield measurements, which involves using a special knife to scrape the chicken frame for any remaining meat after it has been deboned. Researchers with the Georgia Tech Research Institute (GTRI) have developed an automated vision system for estimating this yield loss by correlating image characteristics with the amount of meat left on a skeleton. The yield loss estimation is accomplished by the system’s image processing algorithms, which correlates image intensity with meat thickness and calculates the total volume of meat remaining. The team has established a correlation between transmitted light intensity and meat thickness with an R² of 0.94. Employing a special illuminated cone and targeted software algorithms, the system can make measurements in under a second and has up to a 90-percent correlation with yield measurements performed manually. This same system is also able to determine the probability of bone chips remaining in the output product. The system is able to determine the presence/absence of clavicle bones with an accuracy of approximately 95 percent and fan bones with an accuracy of approximately 80%. This paper describes in detail the approach and design of the system, results from field testing, and highlights the potential benefits that such a system can provide to the poultry processing industry.

Fluorescence imaging technique has been widely used for quality and safety measurements of agro-food materials. Fluorescence emission intensities of target materials are influenced by wavelengths of excitation sources. Hence, selection of a proper excitation wavelength is an important factor in differentiating target materials effectively. In this study, optimal fluorescence excitation wavelength was determined on the basis of fluorescence emission intensity of defect and sound areas of cherry tomatoes. The result showed that fluorescence responses of defect and sound surfaces of cherry tomatoes were most significantly separated with the excitation light wavelength range between 400 and 410 nm. Fluorescence images of defect cherry tomatoes were acquired with the LEDs with the central wavelength of 410 nm as the excitation source to verify the detection efficiency of cherry tomato defects. The resultant fluorescence images showed that the defects were discriminated from sound areas on cherry tomatoes with above 98% accuracy. This study shows that high power LEDs as the excitation source for fluorescence imaging are suitable for defect detection of cherry tomatoes.

For sanitation inspection in food processing environment, fluorescence imaging can be a very useful method because many organic materials reveal unique fluorescence emissions when excited by UV or violet radiation. Although some fluorescence-based automated inspection instrumentation has been developed for food products, there remains a need for devices that can assist on-site inspectors performing visual sanitation inspection of the surfaces of food processing/handling equipment. This paper reports the development of an inexpensive handheld imaging device designed to visualize fluorescence emissions and intended to help detect the presence of fecal contaminants, organic residues, and bacterial biofilms at multispectral fluorescence emission bands. The device consists of a miniature camera, multispectral (interference) filters, and high power LED illumination. With WiFi communication, live inspection images from the device can be displayed on smartphone or tablet devices. This imaging device could be a useful tool for assessing the effectiveness of sanitation procedures and for helping processors to minimize food safety risks or determine potential problem areas. This paper presents the design and development including evaluation and optimization of the hardware components of the imaging devices.

An important part of the quality assurance of meat is the estimation of germs in the meat exudes. The kind and the number of the germs in the meat affect the medical risk for the consumer of the meat. State-of-the-art analyses of meat are incubator test procedures. The main disadvantages of such incubator tests are the time consumption, the necessary equipment and the need of special skilled employees. These facts cause in high inspection cost. For this reason a new method for the quality assurance is necessary which combines low detection limits and less time consumption. One approach for such a new method is fluorescence microscopic imaging. The germs in the meat exude are caught in special membranes by body-antibody reactions. The germ typical signature could be enhanced with fluorescent chemical markers instead of reproduction of the germs. Each fluorescent marker connects with a free germ or run off the membrane. An image processing system is used to detect the number of fluorescent particles. Each fluorescent spot should be a marker which is connected with a germ. Caused by the small object sizes of germs, the image processing system needs a high optical magnification of the camera. However, this leads to a small field of view and a small depth of focus. For this reasons the whole area of the membrane has to be scanned in three dimensions. To minimize the time consumption, the optimal path has to be found. This optimization problem is influenced by features of the hardware and is presented in this paper. The traversing range in each direction, the step width, the velocity, the shape of the inspection volume and the field of view have influence on the optimal path to scan the membrane.

This paper presents a new device and method for the in-situ detection of Salmonella Typhimurium on tomato surfaces. This real-time in-situ detection was accomplished with phage-based magnetoelastic (ME) biosensors on fresh food surfaces. The E2 phage from a landscape phage library serves as the bio-recognition element that has the capability of binding specifically with S. Typhimurium. This mass-sensitive ME biosensor is wirelessly actuated into mechanical resonance by an externally applied time-varying magnetic field. When the biosensor binds with S. Typhimurium, the mass of the sensor increases, resulting in a decrease in the sensor's resonant frequency. Until now, ME sensors had to be collected from the tomato surface where they are exposed to S. Typhimurium and inserted into a measurement coil for the detection of the bacterium. In contrast, the newly designed test device allows the whole detection process to take place directly on the tomato. Changes in resonant frequency over time due to the accumulation of S. Typhimurium on the sensor were measured and are presented. Real-time in-situ detection of 20 minutes was achieved. In addition, this new methodology effectively decreases the measurement error and enables the simultaneous detection of multiple pathogens.

Salmonella is a major cause of foodborne disease outbreaks resulting from the consumption of contaminated food products in the United States. This paper reports the development of a hyperspectral imaging technique for detecting and differentiating two of the most common Salmonella serotypes, Salmonella Enteritidis (SE) and Salmonella Typhimurium (ST), from background microflora that are often found in poultry carcass rinse. Presumptive positive screening of colonies with a traditional direct plating method is a labor intensive and time consuming task. Thus, this paper is concerned with the detection of differences in spectral characteristics among the pure SE, ST, and background microflora grown on brilliant green sulfa (BGS) and xylose lysine tergitol 4 (XLT4) agar media with a spread plating technique. Visible near-infrared hyperspectral imaging, providing the spectral and spatial information unique to each microorganism, was utilized to differentiate SE and ST from the background microflora. A total of 10 classification models, including five machine learning algorithms, each without and with principal component analysis (PCA), were validated and compared to find the best model in classification accuracy. The five machine learning (classification) algorithms used in this study were Mahalanobis distance (MD), k-nearest neighbor (kNN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM). The average classification accuracy of all 10 models on a calibration (or training) set of the pure cultures on BGS agar plates was 98% (Kappa coefficient = 0.95) in determining the presence of SE and/or ST although it was difficult to differentiate between SE and ST. The average classification accuracy of all 10 models on a training set for ST detection on XLT4 agar was over 99% (Kappa coefficient = 0.99) although SE colonies on XLT4 agar were difficult to differentiate from background microflora. The average classification accuracy of all 10 models on a validation set of chicken carcass rinses spiked with SE or ST and incubated on BGS agar plates was 94.45% and 83.73%, without and with PCA for classification, respectively. The best performing classification model on the validation set was QDA without PCA by achieving the classification accuracy of 98.65% (Kappa coefficient=0.98). The overall best performing classification model regardless of using PCA was MD with the classification accuracy of 94.84% (Kappa coefficient=0.88) on the validation set.

Melamine (2,4,6-triamino-1,3,5-triazine) contamination of food has become an urgent and broadly recognized issue for which rapid and accurate identification methods are needed by the food industry. In this study, the feasibility and effectiveness of near-infrared (NIR) hyperspectral imaging was investigated for detecting melamine in milk powder. Hyperspectral NIR images (144 bands spanning from 990 to 1700 nm) were acquired for Petri dishes containing samples of milk powder mixed with melamine at various concentrations (0.02% to 1%). Spectral bands that showed the most significant differences between pure milk and pure melamine were selected, and two-band difference analysis was applied to the spectrum of each pixel in the sample images to identify melamine particles in milk powders. The resultant images effectively allowed visualization of melamine particle distributions in the samples. The study demonstrated that NIR hyperspectral imaging techniques can qualitatively and quantitatively identify melamine adulteration in milk powders.

This paper reports the development of a multispectral algorithm, using the line-scan hyperspectral imaging system, to detect fecal contamination on leafy greens. Fresh bovine feces were applied to the surfaces of washed loose baby spinach leaves. A hyperspectral line-scan imaging system was used to acquire hyperspectral fluorescence images of the contaminated leaves. Hyperspectral image analysis resulted in the selection of the 666 nm and 688 nm wavebands for a multispectral algorithm to rapidly detect feces on leafy greens, by use of the ratio of fluorescence intensities measured at those two wavebands (666 nm over 688 nm). The algorithm successfully distinguished most of the lowly diluted fecal spots (0.05 g feces/ml water and 0.025 g feces/ml water) and some of the highly diluted spots (0.0125 g feces/ml water and 0.00625 g feces/ml water) from the clean spinach leaves. The results showed the potential of the multispectral algorithm with line-scan imaging system for application to automated food processing lines for food safety inspection of leafy green vegetables.

Aflatoxin is a mycotoxin produced mainly by Aspergillus flavus (A.flavus) and Aspergillus parasitiucus fungi that grow naturally in corn. Very serious health problems such as liver damage and lung cancer can result from exposure to high toxin levels in grain. Consequently, many countries have established strict guidelines for permissible levels in consumables. Conventional chemical-based analytical methods used to screen for aflatoxin such as thin-layer chromatography (TLC) and high performance liquid chromatography (HPLC) are time consuming, expensive, and require the destruction of samples as well as proper training for data interpretation. Thus, it has been a continuing effort within the research community to find a way to rapidly and non-destructively detect and possibly quantify aflatoxin contamination in corn. One of the more recent developments in this area is the use of spectral technology. Specifically, fluorescence hyperspectral imaging offers a potential rapid, and non-invasive method for contamination detection in corn infected with toxigenic A.flavus spores. The current hyperspectral image system is designed for scanning flat surfaces, which is suitable for imaging single or a group of corn kernels. In the case of a whole corn cob, it is preferred to be able to scan the circumference of the corn ear, appropriate for whole ear inspection. This paper discusses the development of a hyperspectral imaging system for whole corn ear imaging. The new instrument is based on a hyperspectral line scanner using a rotational stage to turn the corn ear.

Attribute of apple according to geographical origin is often recognized and appreciated by the consumers. It is usually an important factor to determine the price of a commercial product. Hyperspectral imaging technology and supervised pattern recognition was attempted to discriminate apple according to geographical origins in this work. Hyperspectral images of 207 Fuji apple samples were collected by hyperspectral camera (400-1000nm). Principal component analysis (PCA) was performed on hyperspectral imaging data to determine main efficient wavelength images, and then characteristic variables were extracted by texture analysis based on gray level co-occurrence matrix (GLCM) from dominant waveband image. All characteristic variables were obtained by fusing the data of images in efficient spectra. Support vector machine (SVM) was used to construct the classification model, and showed excellent performance in classification results. The total classification rate had the high classify accuracy of 92.75% in the training set and 89.86% in the prediction sets, respectively. The overall results demonstrated that the hyperspectral imaging technique coupled with SVM classifier can be efficiently utilized to discriminate Fuji apple according to geographical origins.

Apple is the world largest produced and consumed fruit item. At the same time, apple ranks number one among the fruit item contaminated with pesticide. This research focuses on development of laboratory based self-developed software and hardware for detection of commercially available organophosphorous pesticide (chlorpyrifos) in apple surface. A laser light source of 785nm was used to excite the sample, and Raman spectroscopy assembled with CCD camera was used for optical data acquisition. A hardware system was designed and fabricated to clamp and rotate apple sample of varying size maintaining constant working distance between optical probe and sample surface. Graphical Users Interface (GUI) based on LabView platform was developed to control the hardware system. The GUI was used to control the Raman system including CCD temperature, exposure time, track height and track centre, data acquisition, data processing and result prediction. Different concentrations of commercially available 48% chlorpyrifos pesticide solutions were prepared and gently placed in apple surface and dried. Raman spectral data at different points from same apple along the equatorial region were then acquired. The results show that prominent peaks at 341cm^-1, 632cm^-1 and 680 cm^-1 represent the pesticide residue. The laboratory based experiment was able to detect pesticide solution of 20ppm within 3 seconds. A linear relation between Raman intensity and pesticide residue was developed with accuracy of 97.8%. The result of the research is promising and thus is a milestone for developing industrially desired real time, non-invasive pesticide residue detection technology in future.

Quality attributes of fresh meat will influence nutritional value and consumers' purchasing power. The aim of the research was to develop a prototype for real-time detection of quality in meat. It consisted of hardware system and software system. A VIS/NIR spectrograph in the range of 350 to 1100 nm was used to collect the spectral data. In order to acquire more potential information of the sample, optical fiber multiplexer was used. A conveyable and cylindrical device was designed and fabricated to hold optical fibers from multiplexer. High power halogen tungsten lamp was collected as the light source. The spectral data were obtained with the exposure time of 2.17ms from the surface of the sample by press down the trigger switch on the self-developed system. The system could automatically acquire, process, display and save the data. Moreover the quality could be predicted on-line. A total of 55 fresh pork samples were used to develop prediction model for real time detection. The spectral data were pretreated with standard normalized variant (SNV) and partial least squares regression (PLSR) was used to develop prediction model. The correlation coefficient and root mean square error of the validation set for water content and pH were 0.810, 0.653, and 0.803, 0.098 respectively. The research shows that the real-time non-destructive detection system based on VIS/NIR spectroscopy can be efficient to predict the quality of fresh meat.

Apple is one of the highly consumed fruit item in daily life. However, due to its high damage potential and massive influence on taste and export, the quality of apple has to be detected before it reaches the consumer’s hand. This study was aimed to develop a hardware and software unit for real-time detection of apple bruises based on machine vision technology. The hardware unit consisted of a light shield installed two monochrome cameras at different angles, LED light source to illuminate the sample, and sensors at the entrance of box to signal the positioning of sample. Graphical Users Interface (GUI) was developed in VS2010 platform to control the overall hardware and display the image processing result. The hardware-software system was developed to acquire the images of 3 samples from each camera and display the image processing result in real time basis. An image processing algorithm was developed in Opencv and C++ platform. The software is able to control the hardware system to classify the apple into two grades based on presence/absence of surface bruises with the size of 5mm. The experimental result is promising and the system with further modification can be applicable for industrial production in near future.

The symmetrical β−conformer of endosulfan has the identical chemical composition of the asymmetrical α−conformer, and both have very different melting/boiling points. The α− and β−isomer however have markedly different Raman spectra at each of 50°C, 75°C and 100°C. Moreover, the commercially available Raman spectra of the 60/40 (α−/β−) mixture at the same temperatures is discrete from either a- and b-isomer alone. Previous research demonstrated that at a- boiling point 110°C, β−conformer partially converts to α−. DSC curves of mixtures suggest thermal interactions and conformational changes occurs in BOTH α− and β−isomer at temperatures even 60°C lower than the liquid/gas phase transition.

This study presents the use of Raman chemical imaging for the screening of dry milk powder for the presence of chemical contaminants and Raman spectroscopy for quantitative assessment of chemical contaminants in liquid milk. For image-based screening, melamine was mixed into dry milk at concentrations (w/w) between 0.2% and 10.0%, and images of the mixtures were analyzed by a spectral information divergence algorithm. Ammonium sulfate, dicyandiamide, and urea were each separately mixed into dry milk at concentrations (w/w) between 0.5% and 5.0%, and an algorithm based on self-modeling mixture analysis was applied to these sample images. The contaminants were successfully detected and the spatial distribution of the contaminants within the sample mixtures was visualized using these algorithms. Liquid milk mixtures were prepared with melamine at concentrations between 0.04% and 0.30%, with ammonium sulfate and with urea at concentrations between 0.1% and 10.0%, and with dicyandiamide at concentrations between 0.1% and 4.0%. Analysis of the Raman spectra from the liquid mixtures showed linear relationships between the Raman intensities and the chemical concentrations. Although further studies are necessary, Raman chemical imaging and spectroscopy show promise for use in detecting and evaluating contaminants in food ingredients.

In security, it is an important issue to analyze hazardous materials in sealed bottles. Particularly, prompt nondestructive checking of sealed liquid bottles in a very short time at the checkpoints of crowded malls, stadiums, or airports is of particular importance to prevent probable terrorist attack using liquid explosives. Aiming to design and fabricate a detector for liquid explosives, we have used linearly focused Raman spectroscopy to analyze liquid materials in transparent or semi-transparent bottles without opening their caps. Continuous lasers with 532 nm wavelength and 58 mW/130 mW beam energy have been used for the Raman spectroscopy. Various hazardous materials including flammable liquids and explosive materials have successfully been distinguished and identified within a couple of seconds. We believe that our technique will be one of suitable methods for fast screening of liquid materials in sealed bottles.

Real time and accurate measurement of sub-surface soil moisture and nutrients is critical for agricultural and environmental studies. This paper presents a novel on-board solution for a robust, accurate and self-calibrating soil moisture and nutrient sensor with inbuilt wireless transmission and reception capability that makes it ideally suited to act as a node in a network spread over a large area. The sensor works on the principle of soil impedance measurement by comparing the amplitude and phase of signals incident on and reflected from the soil in proximity of the sensor. Accuracy of measurements is enhanced by considering a distributed transmission line model for the on-board connections. Presence of an inbuilt self-calibrating mechanism which operates on the standard short-open-load (SOL) technique makes the sensor independent of inaccuracies that may occur due to variations in temperature and surroundings. Moreover, to minimize errors, the parasitic impedances of the board are taken into account in the measurements. Measurements of both real and imaginary parts of soil impedance at multiple frequencies gives the sensor an ability to detect variations in ionic concentrations other than soil moisture content. A switch-controlled multiple power mode transmission and reception is provided to support highly energy efficient medium access control.¹

Black ginseng is produced by steaming a ginseng root followed by drying repeatedly 9 times during the process and it is changed to be black color, so it is known that a black ginseng has more contents of saponins than red ginseng. However a fake black ginseng which is produced to be black color at high temperature in a short period of time generate carcinogenic benzo[a]pyrene(BaP) through the process. In this year, maximum residue level(MRL) for BaP was established to 2 ug/kg in black ginseng and more sensitive method was developed to quantitatively analyze the BaP by high performance liquid chromatography (HPLC) coupling with florescence detector and tandem mass spectrometry (atmospheric pressure chemical ionization-MS/MS). Chromatographic separation was performed on a Supelcosil™ LC-PAH column (3 μm, 3 mm x 50 mm). Mobile phase A was water and mobile phase B was acetonitrile. BaP was exactly separated from other 15 polycyclic aromatic hydrocarbons (PAHs) which have been selected as priority pollutants by the US Environmental Protection Agency (EPA). Linearity of detection was in the range of 0.2~20 μg/kg and limit of detection (LOD) for BaP was lower than 0.1 μg/kg, limit of quantification (LOQ) was 0.2 μg/kg. The recovery of Bap was 92.54%±6.3% in black ginseng.