This PDF file contains the front matter associated with SPIE Proceedings Volume 8027, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

Lycopene is a major carotenoid in tomatoes and its content varies considerably during postharvest ripening. Hence evaluating lycopene changes can be used to monitor the ripening of tomatoes. Raman chemical imaging technique is promising for mapping constituents of interest in complex food matrices. In this study, a benchtop point-scanning Raman chemical imaging system was developed to evaluate lycopene content in tomatoes at different maturity stages. The system consists of a 785 nm laser, a fiber optic probe, a dispersive imaging spectrometer, a spectroscopic CCD camera, and a two-axis positioning table. Tomato samples at different ripeness stages (i.e., green, breaker, turning, pink, light red, and red) were selected and cut before imaging. Hyperspectral Raman images were acquired from cross sections of the fruits in the wavenumber range of 200 to 2500 cm^-1 with a spatial resolution of 1 mm. The Raman spectrum of pure lycopene was measured as reference for spectral matching. A polynomial curve-fitting method was used to correct for the underlying fluorescence background in the Raman spectra of the tomatoes. A hyperspectral image classification method was developed based on spectral information divergence to identify lycopene in the tomatoes. Raman chemical images were created to visualize quantity and spatial distribution of the lycopene at different ripeness stages. The lycopene patterns revealed the mechanism of lycopene generation during the postharvest development of the tomatoes. The method and findings of this study form a basis for the future development of a Raman-based nondestructive approach for monitoring internal maturity of the tomatoes.

Due to its analytical ability and sensitivity to molecular vibrations, Raman spectroscopy provides valuable information of the secondary structure of proteins. Moreover, polarized Raman spectroscopy is shown to be a useful instrument to investigate the structural changes resulting from the aging and spoilage process of meat. In this work, polarized Raman spectra were measured on oriented cuts of pork and turkey. Fresh meat slices were stored at 5 °C and measured for a consecutive time period of 10 days. A 671 nm microsystem diode laser was used as excitation light source. The laser power at the sample was 50 mW and the integration time of each Raman spectrum was set to 5 seconds. Measurements were performed with a laser beam orientation perpendicular to the long axis of the muscle fibers. In that arrangement, the fibers were aligned either parallel or perpendicular to the polarization direction of the laser source. By using the statistical method of principal components analysis (PCA), a clear separation of the meat samples can be found for fresh meat according to the orientation (parallel or perpendicular) using the first two principal components. During the storage period, this separation subsequently vanishes due to the aging process and due to an increase of the microbial spoilage of the meat surface. For the latter effect, a time-dependent distinction of the Raman spectra is presented as well. Furthermore, specific changes of conformation-sensitive Raman bands were recognized, notably a decrease of the intensities of α-helical protein conformation.

The objective of our research was to use ATR-THz spectroscopy together with chemometric for quantitative study in food analysis. Glucose, fructose and sucrose are main component of sugar both in fresh and processed fruits. The use of spectroscopic-based method for sugar determination is well reported especially using visible, near infrared (NIR) and middle infrared (MIR) spectroscopy. However, the use of terahertz spectroscopy for sugar determination in fruits has not yet been reported. In this work, a quantitative study for sugars determination using attenuated total reflectance terahertz (ATR-THz) spectroscopy was conducted. Each samples of glucose, fructose and sucrose solution with different concentrations were prepared respectively and their absorbance spectra between wavenumber 20 and 450 cm^-1 (between 0.6 THz and 13.5 THz) were acquired using a terahertz-based Fourier Transform spectrometer (FARIS-1S, JASCO Co., Japan). This spectrometer was equipped with a high pressure of mercury lamp as light source and a pyroelectric sensor made from deuterated L-alanine triglycine sulfate (DLTGS) as detector. Each spectrum was acquired using 16 cm^-1 of resolution and 200 scans for averaging. The spectra of water and sugar solutions were compared and discussed. The results showed that increasing sugar concentration caused decreasing absorbance. The correlation between sugar concentration and its spectra was investigated using multivariate analysis. Calibration models for glucose, fructose and sucrose determination were developed using partial least squares (PLS) regression. The calibration model was evaluated using some parameters such as coefficient of determination (R²), standard error of calibration (SEC), standard error of prediction (SEP), bias between actual and predicted sugar concentration value and ratio prediction to deviation (RPD) parameter. The cross validation method was used to validate each calibration model. It is showed that the use of ATR-THz spectroscopy combined with appropriate chemometric can be a potential for a rapid determination of sugar concentrations.

A label-free bioaffinity sensor working in terahertz (THz) region with a nitrocellulose membrane filter was demonstrated, which is based on the resonant transmission phenomenon and the dip in the spectra of the metal mesh device. By using this sensor, we succeeded in the highly sensitive detection of small amounts of protein avidin-biotin complex. A distinct change of transmittance caused by shift of the transmission dip was observed for 8 ng/mm² (74 fmol) of horseradish peroxidase (HRP) labeled avidin. The sensing method has broad utility for many reactions on the membrane filter as a simple and rapid sensor.

Hyperspectral microscope imaging (HMI) method which provides both spatial and spectral information can be effective for foodborne pathogen detection. The AOTF-based hyperspectral microscope imaging method can be used to characterize spectral properties of biofilm formed by Salmonella enteritidis as well as Escherichia coli. The intensity of spectral imagery and the pattern of spectral distribution varied with system parameters (integration time and gain) of HMI system. The preliminary results demonstrated determination of optimum parameter values of HMI system and the integration time must be no more than 250 ms for quality image acquisition from biofilm formed by S. enteritidis. Among the contiguous spectral imagery between 450 and 800 nm, the intensity of spectral images at 498, 522, 550 and 594 nm were distinctive for biofilm; whereas, the intensity of spectral images at 546 nm was distinctive for E. coli. For more accurate comparison of intensity from spectral images, a calibration protocol, using neutral density filters and multiple exposures, need to be developed to standardize image acquisition. For the identification or classification of unknown food pathogen samples, ground truth regions-of-interest pixels need to be selected for "spectrally pure fingerprints" for the Salmonella and E. coli species.

This paper presents the direct detection of Salmonella typhimurium on shell eggs using a phage-based magnetoelastic (ME) biosensor. The ME biosensor consists of a ME resonator as the sensor platform and E2 phage as the biorecognition element that is genetically engineered to specifically bind with Salmonella typhimurium. The ME biosensor, which is a wireless sensor, vibrates with a characteristic resonant frequency under an externally applied magnetic field. Multiple sensors can easily be remotely monitored. Multiple measurement and control sensors were placed on the shell eggs contaminated by Salmonella typhimurium solutions with different known concentrations. The resonant frequency of sensors before and after the exposure to the spiked shell eggs was measured. The frequency shift of the measurement sensors was significantly different than the control sensors indicating Salmonella contamination. Scanning electron microscopy was used to confirm binding of Salmonella to the sensor surface and the resulting frequency shift results.

This paper presents an investigation into the use of magnetoelastic biosensors for the rapid detection of Salmonella typhimurium on fresh spinach leaves. The biosensors used in this investigation were comprised of a strip-shaped, goldcoated sensor platform (2 mm-long) diced from a ferromagnetic, amorphous alloy and a filamentous fd-tet phage which specifically binds with S. typhimurium. After surface blocking with bovine serum albumin, these biosensors were, without any preceding sample preparation, directly placed on wet spinach leaves inoculated with various concentrations of S. typhimurium. Upon contact with cells, the phage binds S. typhimurium to the sensor thereby increasing the total mass of the sensor. This change in mass causes a corresponding decrease in the sensor's resonant frequency. After 25 min, the sensors were collected from the leaf surface and measurements of the resonant frequency were performed immediately. The total assay time was less than 30 min. The frequency changes for measurement sensors (i.e., phageimmobilized) were found to be statistically different from those for control sensors (sensors without phage), down to 5 × 10⁶ cells/ml. The detection limit may be improved by using smaller, micron-sized sensors that will have a higher probability of contacting Salmonella on the rough surfaces of spinach leaves.

This work demonstrated a direct detection of Salmonella on fresh food produce using groups of magnetoelastic biosensors. The magnetoelastic biosensors were coated with E2 phage, which specifically binds with S. typhimurium. The resonance frequency of the biosensor is measured using a pulse excitation system, which allows simultaneous detection of multiple sensors. Multiple measurement and control biosensors were placed on fresh food surfaces that had been spiked with a known amount of Salmonella. Binding with bacteria was allowed to occur for 30 minutes in a humid air environment. The resonance frequencies of the groups of biosensors were then measured to determine the amount of bound bacteria. By using a statistical experimental design and by taking the average of repeated measurements, possible detection errors are decreased. By using multiple sensors at each site of interest, a higher portion of the contaminated surface has contact with biosensors, allowing for more complete information on the food produce surface. Results from SEM pictures of the sensor surface agree with the sensor frequency response results.

Surface Enhanced Raman Scattering (SERS) can detect the pathogen in rapid and accurate. In SERS weak Raman scattering signals are enhanced by many orders of magnitude. In this study silver metal with biopolymer was used. Silver encapsulated biopolymer polyvinyl alcohol nano-colloid was prepared and deposited on stainless steel plate. This was used as metal substrate for SERS. Salmonella typhimurium a common food pathogen was selected for this study. Salmonella typhimurium bacteria cells were prepared in different concentrations in cfu/mL. Small amount of these cells were loaded on the metal substrate individually, scanned and spectra were recorded using confocal Raman microscope. The cells were exposed to laser diode at 785 nm excitation and object 50x was used to focus the laser light on the sample. Raman shifts were obtained from 400 to 2400 cm^-1. Multivariate data analysis was carried to predict the concentration of unknown sample using its spectra. Concentration prediction gave an R² of 0.93 and standard error of prediction of 0.21. The results showed that it could be possible to find out the Salmonella cells present in a low concentration in food samples using SERS.

To experimentally analyze the morphological characteristics and to predict the resulting scattering patterns of different bacterial colonies, an optical morphology analyzer was constructed based on a laser confocal displacement meter to simultaneously obtain the optical properties of colonies. The profile data was accurately captured using the confocal laser triangulation technology and the transmitted light was collected by a photodiode circuit. The analog signals were read into a data acquisition board in parallel for off-line signal processing. This approach showed promising results for differentiation of micro-colonies in the range of 100~300 μm based on the morphological differences among different species using light scattering.

Aflatoxin is produced by the fungus Aspergillus flavus when the fungus invades developing corn kernels. Because of its potent toxicity, the levels of aflatoxin are regulated by the Food and Drug Administration (FDA) in the US, allowing 20 ppb (parts per billion) limits in food, and feed intended for interstate commerce. Currently, aflatoxin detection and quantification methods are based on analytical tests. These tests require the destruction of samples, can be costly and time consuming, and often rely on less than desirable sampling techniques. Thus, the ability to detect aflatoxin in a rapid, non-invasive way is crucial to the corn industry in particular. This paper described how narrow-band fluorescence indices were developed for aflatoxin contamination detection based on single corn kernel samples. The indices were based on two bands extracted from full wavelength fluorescence hyperspectral imagery. The two band results were later applied to two large sample experiments with 25 g and 1 kg of corn per sample. The detection accuracies were 85% and 95% when 100 ppb threshold was used. Since the data acquisition period is significantly lower for several image bands than for full wavelength hyperspectral data, this study would be helpful in the development of real-time detection instrumentation for the corn industry.

In this paper, a method for detection of popcorn kernels infected by a fungus is developed using image processing. The method is based on two dimensional (2D) mel and Mellin-cepstrum computation from popcorn kernel images. Cepstral features that were extracted from popcorn images are classified using Support Vector Machines (SVM). Experimental results show that high recognition rates of up to 93.93% can be achieved for both damaged and healthy popcorn kernels using 2D mel-cepstrum. The success rate for healthy popcorn kernels was found to be 97.41% and the recognition rate for damaged kernels was found to be 89.43%.

Mycotoxins are toxic secondary metabolites produced by fungi. They have been demonstrated to cause various health problems in humans, including immunosuppression and cancer. A class of mycotoxins, aflatoxins, has been studied extensively because they have caused many deaths particularly in developing countries. Chili pepper is also prone to aflatoxin contamination during harvesting, production and storage periods. Chemical methods to detect aflatoxins are quite accurate but expensive and destructive in nature. Hyperspectral and multispectral imaging are becoming increasingly important for rapid and nondestructive testing for the presence of such contaminants. We propose a compact machine vision system based on hyperspectral imaging and machine learning for detection of aflatoxin contaminated chili peppers. We used the difference images of consecutive spectral bands along with individual band energies to classify chili peppers into aflatoxin contaminated and uncontaminated classes. Both UV and halogen illumination sources were used in the experiments. The significant bands that provide better discrimination were selected based on their neural network connection weights. Higher classification rates were achieved with fewer numbers of spectral bands. This selection scheme was compared with an information-theoretic approach and it demonstrated robust performance with higher classification accuracy.

This research developed and evaluated the multispectral algorithms derived from hyperspectral line-scan fluorescence imaging under violet LED excitation for detection of fecal contamination on Golden Delicious apples. The algorithms utilized the fluorescence intensities at four wavebands, 680 nm, 684 nm, 720 nm, and 780 nm, for computation of simple functions for effective detection of contamination spots created on the apple surfaces using four concentrations of aqueous fecal dilutions. The algorithms detected more than 99% of the fecal spots. The effective detection of feces showed that a simple multispectral fluorescence imaging algorithm based on violet LED excitation may be appropriate to detect fecal contamination on fast-speed apple processing lines.

Freshness of pork is an important quality attribute, which can vary greatly in storage and logistics. The specific objectives of this research were to develop a hyperspectral imaging system to predict pork freshness based on quality attributes such as total volatile basic-nitrogen (TVB-N), pH value and color parameters (L*,a*,b*). Pork samples were packed in seal plastic bags and then stored at 4°C. Every 12 hours. Hyperspectral scattering images were collected from the pork surface at the range of 400 nm to 1100 nm. Two different methods were performed to extract scattering feature spectra from the hyperspectral scattering images. First, the spectral scattering profiles at individual wavelengths were fitted accurately by a three-parameter Lorentzian distribution (LD) function; second, reflectance spectra were extracted from the scattering images. Partial Least Square Regression (PLSR) method was used to establish prediction models to predict pork freshness. The results showed that the PLSR models based on reflectance spectra was better than combinations of LD "parameter spectra" in prediction of TVB-N with a correlation coefficient (r) = 0.90, a standard error of prediction (SEP) = 7.80 mg/100g. Moreover, a prediction model for pork freshness was established by using a combination of TVB-N, pH and color parameters. It could give a good prediction results with r = 0.91 for pork freshness. The research demonstrated that hyperspectral scattering technique is a valid tool for real-time and nondestructive detection of pork freshness.

Bruise damage on pears is one of the most crucial internal quality factors that needs to be detected in postharvest quality sorting processes. Development of sensitive detection methods for the defects including fruit bruise is necessary to ensure accurate quality assessment. Infra-red imaging techniques in the 1000 nm to 1700 nm has good potentials for identifying and detecting bruises since bruises result in the rupture of internal cell walls due to defects on agricultural materials. In this study, feasibility of hyperspectral infra-red (1000 - 1700 nm) imaging technique for the detection of bruise damages underneath the pear skin was investigated. Pear bruises, affecting the quality of fruits underneath the skin, are not easily discernable by using conventional imaging technique in the visible wavelength ranges. Simple image combination methods as well as multivariate image analyses were explored to develop optimal image analysis algorithm to detect bruise damages of pear. Results demonstrated good potential of the infra-red imaging techniques for detection of bruises damages on pears.

Cuticle cracks on tomatoes are potential sites of pathogenic infection that may cause deleterious consequences both to consumer health and to fresh and fresh-cut produce markets. The feasibility of hyperspectral near-infrared imaging technique in the spectral range of 1000 nm to 1700 nm was investigated for detecting defects on tomatoes. Spectral information obtained from the regions of interest on both defect areas and sound areas were analyzed to determine some an optimal waveband ratio that could be used for further image processing to discriminate defect areas from the sound tomato surfaces. Unsupervised multivariate analysis method, such as principal component analysis, was also explored to improve detection accuracy. Threshold values for the optimized features were determined using linear discriminant analysis. Results showed that tomatoes with defects could be differentiated from the sound ones, with an overall accuracy of 94.4%. The spectral wavebands and image processing algorithms determined in this study could be used for multispectral inspection of defects tomatoes.

Fruit fly infestation can be a serious problem in pickling cucumber production. In the United States and many other countries, there is zero tolerance for fruit flies in pickled products. Currently, processors rely on manual inspection to detect and remove fruit fly-infested cucumbers, which is labor intensive and also prone to error due to human fatigue and the difficulty of visually detecting infestation that is hidden inside the fruit. In this research, a laboratory hyperspectral imaging system was used to detect fruit fly-infested pickling cucumbers. Hyperspectral reflectance (450-740 nm) and transmittance (740-1,000 nm) images were acquired simultaneously for 329 normal (infestation free) and fruit flyinfested pickling cucumbers of three size classes with the mean diameters of 16.8, 22.1, and 27.6 mm, respectively. Mean spectra were extracted from the hyperspectral image of each cucumber, and they were then corrected for the fruit size effect using a diameter correction equation. Partial least squares discriminant analyses for the reflectance, transmittance and their combined data were performed for differentiating normal and infested pickling cucumbers. With reflectance mode, the overall classification accuracies for the three size classes and mixed class were between 82% and 88%, whereas transmittance achieved better classification results with the overall accuracies of 88%-93%. Integration of reflectance and transmittance did not result in noticeable improvements, compared to transmittance mode. Overall, the hyperspectral imaging system performed better than manual inspection, which had an overall accuracy of 75% and decreased significantly for smaller size cucumbers. This research demonstrated that hyperspectral imaging is potentially useful for detecting fruit fly-infested pickling cucumbers.

The objective of this research was to measure the absorption (μ_a) and reduced scattering coefficients (μ_s') of peaches, using a hyperspectral imaging-based spatially-resolved method, for their maturity/quality assessment. A newly developed optical property measuring instrument was used for acquiring hyperspectral reflectance images of 500 'Redstar' peaches. μ_a and μ_s' spectra for 515-1,000 nm were extracted from the spatially-resolved reflectance profiles using a diffusion model coupled with an inverse algorithm. The absorption spectra of peach fruit presented several absorption peaks around 525 nm for anthocyanin, 620 nm for chlorophyll-b, 675 nm for chlorophyll-a, and 970 nm for water, while μ_s' decreased consistently with the increase of wavelength for most of the tested samples. Both μ_a and μ_s' were correlated with peach firmness, soluble solids content (SSC), and skin and flesh color parameters. Better prediction results for partial least squares models were obtained using the combined values of μ_a and μ_s' (i.e., μ_a × μ_s' and μ_eff) than using μ_a or μ_s', where μ_eff = [3 μ_a (μ_a + μ_s')]^1/2 is the effective attenuation coefficient. The results were further improved using least squares support vector machine models with values of the best correlation coefficient for firmness, SSC, skin lightness and flesh lightness being 0.749 (standard error of prediction or SEP = 17.39 N), 0.504 (SEP = 0.92 °Brix), 0.898 (SEP = 3.45), and 0.741 (SEP = 3.27), respectively. These results compared favorably to acoustic and impact firmness measurements with the correlation coefficient of 0.639 and 0.631, respectively. Hyperspectral imaging-based spatially-resolved technique is useful for measuring the optical properties of peach fruit, and it also has good potential for assessing fruit maturity/quality attributes.

Several nondestructive technologies have been developed for assessing the firmness and soluble solids content (SSC) of apples. Each of these technologies has its merits and limitations in predicting the two quality parameters. With the concept of multi-sensor data fusion, different sensors would work synergistically and complementarily to improve the quality prediction of apples. In this research, four sensing systems (i.e., an acoustic sensor, a bioyield firmness tester, a miniature near-infrared (NIR) spectrometer, and an online hyperspectral scattering system) were evaluated and combined for nondestructive prediction of firmness and SSC of 'Jonagold' (JG), 'Golden Delicious' (GD), and 'Delicious' (RD) apples. A total of 6,535 apples harvested in 2009 and 2010 were used for analysis. Each of the four sensors showed various degrees of ability to predict apple quality. Better predictions of the firmness and, in most cases, of the SSC were obtained using sensors fusion than using individual sensors, as measured by number of latent variables, correlation coefficient, and standard error of prediction (SEP). Results obtained from the two harvest seasons with the multi-sensor fusion approach were quite consistent, confirming the validity and robustness of the proposed approach. The SEPs for firmness measurement of JG, GD and RD using the best combination of two-sensor data were reduced by 13.3, 19.7 and 7.9% for the 2009 data and 16.0, 12.6 and 4.7% for the 2010 data; and using all four-sensor data by 21.8, 25.6 and 13.6% in 2009, and 14.9, 21.9, and 7.9% in 2010, respectively. For SSC prediction, using the two-sensor data (i.e., NIR and scattering) improved predictions for JG, GD and RD apples harvested in 2009, with their SEP values being reduced by 10.4, 6.6 and 6.8%, respectively. This research demonstrated that the fused systems provided more complete complementary information and, thus, were more powerful than individual sensors in prediction of apple quality.

Research was conducted to acquire knowledge of the ultraviolet and visible spectrums from 300 -800 nm of some common varieties of Japanese citrus, to investigate the best wave-lengths for fluorescence excitation and the resulting fluorescence wave-lengths and to provide a scientific background for the best quality fluorescent imaging technique for detecting surface defects of citrus. A Hitachi U-4000 PC-based microprocessor controlled spectrophotometer was used to measure the absorption spectrum and a Hitachi F-4500 spectrophotometer was used for the fluorescence and excitation spectrums. We analyzed the spectrums and the selected varieties of citrus were categorized into four groups of known fluorescence level, namely strong, medium, weak and no fluorescence.The level of fluorescence of each variety was also examined by using machine vision system. We found that around 340-380 nm LEDs or UV lamps are appropriate as lighting devices for acquiring the best quality fluorescent image of the citrus varieties to examine their fluorescence intensity. Therefore an image acquisition device was constructed with three different lighting panels with UV LED at peak 365 nm, Blacklight blue lamps (BLB) peak at 350 nm and UV-B lamps at peak 306 nm. The results from fluorescent images also revealed that the findings of the measured spectrums worked properly and can be used for practical applications such as for detecting rotten, injured or damaged parts of a wide variety of citrus.

Many researchers have been tried to find a rapid pungency measuring method for the capsaicinoids, the main component of spicy to overcome the disadvantages of the conventional HPLC measurement which is labor-intensive, time-consuming, and expensive. In this research, an on-line based pungency measuring system for red-pepper powder was developed using a UV/Visible/Near-Infrared spectrometer with the wavelength range of 400 ~ 1050 nm. The system was constructed with a charge-couple device(CCD) spectrometer, a reference measuring unit, and a sample transfer unit. Predetermined non-spicy red-pepper powder were mixed with spicy one (var. Chungyang) to produce samples with a wide range of spicy levels. Total 33 different samples with 11 spicy levels and three particle size(below 0.425 mm, 0.425 ~ 0.71 mm, 0.71 ~ 1.4 mm) were prepared for measurements. The Partial Least Square Regression Model (PLSR model) was developed to predict the capsaicinoids content with the obtained spectra using the developed pungency measuring system and compared with the results measured by HPLC. The best result of PLSR model (R² = 0.979, SEP = ± 6.56 mg%) was achieved for the spectra of red-pepper powders of the particle size below 1.4 mm with a pretreatment of smoothing with a 6.5 nm wavelength gap. The results show the potential of NIRS technique for non-destructive and on-line measurement of capsaicinoids content in red-pepper powder.

Shell eggs with microcracks are often undetected during egg grading processes. In the past, a modified pressure imaging system was developed to detect eggs with microcracks without adversely affecting the quality of normal intact eggs. The basic idea of the modified pressure imaging system was to apply a short burst of vacuum within a transparent chamber in order to cause a momentary and forced opening in the egg shell with a crack and thus to utilize the changes in image intensities during this process. The intensity changes from dark to bright in the shell surface were recorded by a highresolution digital camera and processed by an image ratio technique. The performance of the imaging system, however, was sometimes compromised by false readings due to motion of intact eggs relative to the camera. The uneven movement of the lid hinged on the chamber was considered as the main cause of motion errors. In this paper, a machine vision technique to compensate the motion errors was developed to reduce the false detection readings caused by motion of intact eggs. The developed motion compensation algorithm is based on motion estimation of individual eggs.

This paper described a control system of mobile navigation robot for precision spraying in greenhouse environment, which were composed of main control module, motor driving module, ultrasonic detecting module and wirless remote control module. The hard circuits of control system were built. The main control module used ARM7TDMI-S-based LPC2210 micro-processing controller. The motor driving module consisted of voltage amplifier circuit based SN74LS245N and DM74LS244N chips, RC filter circuit, and HM-YZ-30 DC brush motor driver. The ultrasonic detecting module consisted of four standard ultrasonic ranging modules which were arranged on the four sides around the mobile navigation robot, and used GM8125 chip to expand serial communication interfaces. An obstacle-avoiding strategy and its algorithm were proposed and the control programs of mobile navigation robot were programmed. The mobile navigation robot for spraying can realize the actions such as starting and stopping, forward and backward moving, accelerate and decelerate motion, and right and left turn. Finally, the functional experiments of the mobile navigation robot were conducted in the laboratory environment. The results showed that the ultrasonic detecting distance of the robot was 50.5mm-1832.0mm and detecting blind zone was less than 50mm, the ultrasonic detecting angle of individual ultrasonic detecting module of robot was similar to U-shaped and its vaule was about 45.66°, and the moving path of navigation robot was approximately linear.

This study presented a preliminary investigation into the use of macro-scale Raman chemical imaging for the screening of dry milk powder for the presence of chemical contaminants. Melamine was mixed into dry milk at concentrations (w/w) of 0.2%, 0.5%, 1.0%, 2.0%, 5.0%, 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 of (w/w) of 0.5%, 1.0%, 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. Although further studies are necessary, macro-scale Raman chemical imaging shows promise for use in detecting contaminants in food ingredients and may also be useful for authentication of food ingredients.

The physical and mechanical properties of baby spinach were investigated, including density, Young's modulus, fracture strength, and friction coefficient. The average apparent density of baby spinach leaves was 0.5666 g/mm³. The tensile tests were performed using parallel, perpendicular, and diagonal directions with respect to the midrib of each leaf. The test results showed that the mechanical properties of spinach are anisotropic. For the parallel, diagonal, and perpendicular test directions, the average values for the Young's modulus values were found to be 2.137MPa, 1.0841 MPa, and 0.3914 MPa, respectively, and the average fracture strength values were 0.2429 MPa, 0.1396 MPa, and 0.1113 MPa, respectively. The static and kinetic friction coefficient between the baby spinach and conveyor belt were researched, whose test results showed that the average coefficients of kinetic and maximum static friction between the adaxial (front side) spinach leaf surface and conveyor belt were 1.2737 and 1.3635, respectively, and between the abaxial (back side) spinach leaf surface and conveyor belt were 1.1780 and 1.2451 respectively. These works provide the basis for future development of a whole-surface online imaging inspection system that can be used by the commercial vegetable processing industry to reduce food safety risks.