This PDF file contains the front matter associated with SPIE Proceedings Volume 8369, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

In this paper the results of polarization measurements of clear air and clouds brightness temperatures at 37GHz are presented. The results were obtained during the measurements carried out in Armenia from the measuring complex built under the framework of ISTC Projects A-872 and A-1524. The measurements were carried out at vertical and horizontal polarizations, under various angles of sensing by Ka-band combined scatterometric-radiometric system (ArtAr-37) developed and built by ECOSERV Remote Observation Centre Co.Ltd. under the framework of the above Projects. In the paper structural and operational features of the utilized system and the whole measuring complex will be considered and discussed as well.

A novel way to produce biomass estimation will offer possibilities for precision farming. Fertilizer prediction maps can be made based on accurate biomass estimation generated by a novel biomass estimator. By using this knowledge, a variable rate amount of fertilizers can be applied during the growing season. The innovation consists of light UAS, a high spatial resolution camera, and VTT's novel spectral camera. A few properly selected spectral wavelengths with NIR images and point clouds extracted by automatic image matching have been used in the estimation. The spectral wavelengths were chosen from green, red, and NIR channels.

There are a lot of methods to acquire multispectral images. Dynamic band selective and area-scan multispectral camera has not developed yet. This research focused on development of a filter exchangeable 3CCD camera which is modified from the conventional 3CCD camera. The camera consists of F-mounted lens, image splitter without dichroic coating, three bandpass filters, three image sensors, filer exchangeable frame and electric circuit for parallel image signal processing. In addition firmware and application software have developed. Remarkable improvements compared to a conventional 3CCD camera are its redesigned image splitter and filter exchangeable frame. Computer simulation is required to visualize a pathway of ray inside of prism when redesigning image splitter. Then the dimensions of splitter are determined by computer simulation which has options of BK7 glass and non-dichroic coating. These properties have been considered to obtain full wavelength rays on all film planes. The image splitter is verified by two line lasers with narrow waveband. The filter exchangeable frame is designed to make swap bandpass filters without displacement change of image sensors on film plane. The developed 3CCD camera is evaluated to application of detection to scab and bruise on Fuji apple. As a result, filter exchangeable 3CCD camera could give meaningful functionality for various multispectral applications which need to exchange bandpass filter.

Registration of multispectral images remains a challenging task due to the lack of stable feature points. Methods based on intensities are generally more robust for multi-modal image registration, but are computationally demanding or are restrictive to the transformation model allowed in the registration. This paper proposes a new registration framework which overcomes these drawbacks. The proposed method optimizes the location of a set of virtual landmarks in order to get robust and accurate registration.

In this research, a multispectral algorithm derived from hyperspectral line-scan fluorescence imaging under violet LED excitation was developed for the detection of frass contamination on mature tomatoes. The algorithm utilized the fluorescence intensities at two wavebands, 664 nm and 690 nm, for computation of the simple ratio function for effective detection of frass contamination. The contamination spots were created on the tomato surfaces using four concentrations of aqueous frass dilutions. The algorithms could detect more than 99% of the 0.2 g/ml and 0.1 g/ml frass contamination spots and successfully differentiated these spots from clean tomato surfaces. The results demonstrated that the simple multispectral fluorescence imaging algorithms based on violet LED excitation can be appropriate to detect frass on tomatoes in high-speed post-harvest processing lines.

We investigated the feasibility of visible and near-infrared (VNIR) hyperspectral imaging for rapid presumptive-positive screening of six representative non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) on spread plates of mixed cultures. Although the traditional culture method is still the "gold standard" for presumptive-positive pathogen screening, it is time-consuming, labor-intensive, not effective in testing large amount of food samples, and cannot completely prevent unwanted background microflora from growing together with target microorganisms on agar media. A previous study was performed using the data obtained from pure cultures individually inoculated on spot and/or spread plates in order to develop multivariate classification models differentiating each colony of the six non-O157 STEC serogroups and to optimize the models in terms of parameters. This study dealt with the validation of the trained and optimized models with a test set of new independent samples obtained from colonies on spread plates of mixed cultures. A new validation protocol appropriate to a hyperspectral imaging study for mixed cultures was developed. One imaging experiment with colonies obtained from two serial dilutions was performed. A total of six agar plates were prepared, where O45, O111 and O121 serogroups were inoculated into all six plates and each of O45, O103 and O145 serogroups was added into the mixture of the three common bacterial cultures. The number of colonies grown after 24-h incubation was 331 and the number of pixels associated with the grown colonies was 16,379. The best model found from this validation study was based on pre-processing with standard normal variate and detrending (SNVD), first derivative, spectral smoothing, and k-nearest neighbor classification (kNN, k=3) of scores in the principal component subspace spanned by 6 principal components. The independent testing results showed 95% overall detection accuracy at pixel level and 97% at colony level. The developed model was proven to be still valid even for the independent samples although the size of a test set was small and only one experiment was performed. This study was an important first step in validating and updating multivariate classification models for rapid screening of ground beef samples contaminated by non-O157 STEC pathogens using hyperspectral imaging.

A hyperspectral scattering technique was investigated for predicting the total viable counts (TVC) of pork in the article. Fresh pork was purchased from a local market and stored at 4°C for 1-15 days. Totally 35 samples were used in the experiment and 2-4 samples were taken out randomly each day for collecting hyperspectral images and reference microbiological tests. Gompertz function was applied to fit the scattering profiles of pork and Teflon, and the fitting results were pretty good in the spectral range of 470-1010 nm. Both individual parameters and integrated parameters were explored to develop the multi-linear regression models for predicting pork TVC, and the results indicated that individual Gompertz parameter α was superior to other individual parameters, while the integrated parameters can perform better. The best result for predicting pork TVC was achieved by the form of (α, β, ε), with the RCV of 0.963. The study demonstrated that hyperspectral scattering technique combined with Gompertz function was potential for rapid determination of pork TVC, and would be a valid tool for monitoring the quality and safety attributes of meat in the future.

Naturally occurring Aspergillus flavus strains can be either toxigenic or atoxigenic, indicating their ability to produce aflatoxin or not, under specific conditions. Corn contaminated with toxigenic strains of A. flavus can result in great losses to the agricultural industry and pose threats to public health. Past research showed that fluorescence hyperspectral imaging could be a potential tool for rapid and non-invasive detection of aflatoxin contaminated corn. The objective of the current study was to assess, with the use of a hyperspectral sensor, the difference in fluorescence emission between corn kernels inoculated with toxigenic and atoxigenic inoculums of A. flavus. Corn ears were inoculated with AF13, a toxigenic strain of A. flavus, and AF38, an atoxigenic strain of A. flavus, at dough stage of development and harvested 8 weeks after inoculation. After harvest, single corn kernels were divided into three groups prior to imaging: control, adjacent, and glowing. Both sides of the kernel, germplasm and endosperm, were imaged separately using a fluorescence hyperspectral imaging system. It was found that the classification accuracies of the three manually designated groups were not promising. However, the separation of corn kernels based on different fungal inoculums yielded better results. The best result was achieved with the germplasm side of the corn kernels. Results are expected to enhance the potential of fluorescence hyperspectral imaging for the detection of aflatoxin contaminated corn.

Monitoring for the appearance of volatile organic compounds emitted by plants which correspond to time of first insect attack can be used to detect the early stages of insect infestation. This paper reports a chemical sensor array consisting of polymer based chemiresistor sensors that could detect insect infestation effectively. The sensor array consists of sensors with micro electronically fabricated interdigitated electrodes, and twelve different types of electro active polymer layers. The sensor array was cheap, easy to fabricate, and could be used easily in agricultural fields. The polymer array was found to be sensitive to a variety of volatile organic compounds emitted by plants including γ-terpinene α-pinene, pcymene, farnesene, limonene and cis-hexenyl acetate. The sensor array was not only able to detect but also distinguish between these compounds. The twelve sensors produced a resistance change for each of the analytes detected, and each of these responses together produced a unique fingerprint, enabling to distinguish among these chemicals.

A prototype of on-line system developed by ourselves was used to non-destructively inspect orange quality. This system includes three main parts: machine vision part for fruit external quality detection, visible and near infrared (Vis-NIR) spectroscopy part for fruit internal quality detection, and weighing part for fruit weight detection. Fruit scrolled on the roller in the machine vision part, while stopped scrolling before entering the Vis-NIR spectroscopy part. Therefore, fruit positions and directions were inconsistent for spectra acquisition. This paper was aimed to study the influence of fruit detection orientation on spectra variation and model estimation performance using the on-line system. The system was configured to operate at typical grader speeds (0.27m/s or approximately three fruit per second) and detect the light transmitted through oranges. Stepwise multi linear regression models were developed for fruit with consistent directions and inconsistent directions in the wavelength range of 600-950 nm, and gave reasonable calibration correlations R²=0.89-0.92 and low cross validation errors (RMSECV=0.44-0.56%). The calibration model with spherical samples only turned out the best prediction results, which has lowest RMSEP of 0.56%-0.63% for different fruit orientations. It can be seen from the study that fruit shape would influence the fruit orientation for spectra aqcuiring of spherical samples after scrolling, and would further influence the modeling resutls. It is better to acquire spectra and establish models for sampels with different shapes separately and then applying them based on shape detection resutls to improve the soluble solid content (SSC) prediction accuracy.

Currently, inspection of wheat in the United States for grade and class is performed by human visual analysis. This is a time consuming operation typically taking several minutes for each sample. Digital imaging research has addressed this issue over the past two decades, with success in recognition of differing wheat classes, and distinguishing wheat from non-wheat species. Detection of wheat kernel defects, either by damage or disease, has been a greater challenge. A study has been undertaken that uses high-speed black and white imaging at 10-bit photometric resolution to detect damaged kernels one kernel at a time. The system, composed of hardware (camera, lighting, power supplies, and data acquisition card), software (LabVIEW and MATLAB), and analytical (MATLAB and SAS) components, is designed to a) capture images of free-falling kernels at opposing angles through the use of optical grade mirrors, b) parameterize the images and, c) perform classification. The system operates with a 1/30,000 second exposure time though with restrictions on image transfer rate (60 Hz) and image processing routines for feature extraction (currently conducted offline). Fifty samples of hard red and white wheat subjected to weather related damage during plant development were used in this study. Parametric (linear discriminant analysis) and non-parametric (k-nearest neighbor) classification models were tested to determine the image features that best foster recognition of the damage conditions of mold, sprout, and black tip. The morphological features used in classification included area, projected volume, perimeter, elliptical eccentricity, and major and minor axis lengths. Textural features from calculated gray level co-occurrence matrices (including contrast, correlation, energy, and homogeneity), are also under consideration though not reported herein. So far, our results indicate that with as few as three image parameters, classification (damaged vs. sound) levels approach 85 to 90 percent accuracy. Information learned from this study is intended to lead to the streamlining of feature extraction in image-based high speed sorting.

A fast imaging system that can reveal internal sample structures is important for research and quality controls of seeds. Optical coherence tomography (OCT) is a non-invasive optical imaging technique that can acquire high speed, high resolution depth-resolved images in scattering samples. It has found numerous applications in studying various biological tissues and other materials in vivo. A few studies have reported the use of OCT in studying seed morphology. However, 3D imaging of internal seed structure has not been reported before. In this study, we used a frequency domain OCT system to image tomato seeds. The system has a central wavelength of 844nm with a 46.8 nm FWHM bandwidth. The requirement for depth scan was eliminated by using a Fourier domain implementation. The B-scan imaging speed was limited by the spectroscopic imaging CCD at 52 kHz. The calibrated system has a 6.7μm depth resolution and a 15.4μm lateral resolution. Our results show that major seed structures can be clearly visualized in OCT images.

Evaluating onions quality using optical techniques is challenging because the presence of outer dry skin and the layered structure of onion fleshy tissues. To better understand the light propagation in onions, the optical properties of dry skin and fleshy tissues from two cultivars were measured at 632.8 nm by using a single integrating sphere based system. Onion tissues were cut into 30 mm square pieces and sandwiched by Borofloat glass slides. The total diffuse reflectance, the total transmittance, and the collimated transmittance of the onion samples were measured by an integrating sphere system with a VIS-NIR spectrometer. The absorption coefficient (μ_a), the reduced scattering coefficient (μ_s'), and the anisotropy coefficient (g) of onion tissue samples were estimated using the inverse adding-doubling method based on the measured spectra. The light propagation in onion tissues were modeled based on the calculated optical parameters using Monte Carlo simulations. The results indicated that onion tissues are high albedo biological media. Onion dry skins have much higher absorption and reduced scattering coefficients than onion fleshy tissues. Comparisons between the two onion cultivars showed that the optical properties of onions could vary with cultivars. The results of this study can be used to develop appropriate optical approaches for the onion quality inspection.

This study investigated the potential of Raman chemical imaging for simultaneously detecting multiple adulterants in milk powder. Potential chemical adulterants, including ammonium sulfate, dicyandiamide, melamine, and urea, were together mixed into nonfat dry milk in the concentration range of 0.1%-5.0% for each adulterant. A benchtop point-scan Raman imaging system using a 785-nm laser was assembled to acquire hyperspectral images in the wavenumber range of 102-2538 cm^-1. Each mixture was imaged in an area of 25×25 mm² with a spatial resolution of 0.25 mm. Selfmodeling mixture analysis (SMA) was used to extract pure component spectra, by which the four types of the adulterants were identified at all concentration levels based on their spectral information divergence values to the reference spectra. Raman chemical images were created using the contribution images from SMA, and their use to effectively visualize identification and spatial distribution of the multiple adulterant particles in the dry milk was demonstrated.

The potential of Raman spectroscopy in the analysis of low concentration organic contaminants on apples' surface was evidenced in this study. Chlorpyrifos, an organophosphorus pesticide, was used as a probe for this purpose. The characteristic peaks of fingerprints of pesticide on an aluminum substrate and apple fruit cuticle without pesticide residue were acquired first. Then a concentration range of chlorpyrifos (commercial products at 40%) solutions were made using deionised and distilled water. Single 100 μL droplets of the chlorpyrifos solutions were placed gently on apple fruit cuticles and left to dry before analysis. Through comparative analysis of the Raman spectra data collected, 341, 632 and 1237cm^-1 were identified to detect the chlorpyrifos pesticide residue on apple surface. Based on the relationship between the Raman intensity of the most prominent peak at around 632cm^-1 and the pesticide concentrations, the limit of detection of ordinary Raman spectrum for chlorpyrifos was estimated to be 48ppm.

The identification of food products and the detection of adulteration are of global interest for food safety and quality control. We present a non-invasive in-situ approach for the differentiation of meat from selected animal species using microsystem diode laser based shifted excitation Raman difference spectroscopy (SERDS) at 671 nm and 785 nm. In that way, the fingerprint Raman spectra can be used for identification without a disturbing fluorescence background masking Raman signals often occurring in the investigation of biological samples. Two miniaturized SERDS measurement heads including the diode laser and all optical elements are fiber-optically coupled to compact laboratory spectrometers. To realize two slightly shifted excitation wavelengths necessary for SERDS the 671 nm laser (spectral shift: 0.7 nm, optical power: 50 mW) comprises two separate laser cavities each with a volume Bragg grating for frequency selection whereas the 785 nm light source (spectral shift: 0.5 nm, optical power: 110 mW) is a distributed feedback laser. For our investigations we chose the most consumed meat types in the US and Europe, i.e. chicken and turkey as white meat as well as pork and beef as red meat species. The applied optical powers were sufficient to detect meat Raman spectra with integration times of 10 seconds pointing out the ability for a rapid discrimination of meat samples. Principal components analysis was applied to the SERDS spectra to reveal spectral differences between the animals suitable for their identification. The results will be discussed with respect to specific characteristics of the analyzed meat species.

Foodborne diseases resulting from Campylobacter, Escherichia, Listeria, Salmonella, Shigella and Vibrio species affect as many as 76 million persons in the United States each year, resulting in 325,000 hospitalizations and 5,000 deaths. The challenge to preventing distribution and consumption of contaminated foods lies in the fact that just a few bacterial cells can rapidly multiply to millions, reaching infectious doses within a few days. Unfortunately, current methods used to detect these few cells rely on lengthy growth enrichment steps that take a similar amount of time (1 to 4 days). Consequently, there is a critical need for an analyzer that can rapidly extract and detect foodborne pathogens in 1-2 hours (not days), at 100 colony forming units per gram of food, and with a specificity that differentiates from indigenous microflora, so that false alarms are eliminated. In an effort to meet this need, we have been developing a sample system that extracts such pathogens from food, selectively binds these pathogens, and produces surface-enhanced Raman spectra (SERS). Here we present preliminary SERS measurements of Listeria and Salmonella.

Non-O157:H7 Shiga toxin-producing Escherichia coli (STEC) strains such as O26, O45, O103, O111, O121 and O145 are recognized as serious outbreak to cause human illness due to their toxicity. A conventional microbiological method for cell counting is laborious and needs long time for the results. Since optical detection method is promising for realtime, in-situ foodborne pathogen detection, acousto-optical tunable filters (AOTF)-based hyperspectral microscopic imaging (HMI) method has been developed for identifying pathogenic bacteria because of its capability to differentiate both spatial and spectral characteristics of each bacterial cell from microcolony samples. Using the AOTF-based HMI method, 89 contiguous spectral images could be acquired within approximately 30 seconds with 250 ms exposure time. From this study, we have successfully developed the protocol for live-cell immobilization on glass slides to acquire quality spectral images from STEC bacterial cells using the modified dry method. Among the contiguous spectral imagery between 450 and 800 nm, the intensity of spectral images at 458, 498, 522, 546, 570, 586, 670 and 690 nm were distinctive for STEC bacteria. With two different classification algorithms, Support Vector Machine (SVM) and Sparse Kernel-based Ensemble Learning (SKEL), a STEC serotype O45 could be classified with 92% detection accuracy.

In order to cost-effectively and rapidly detect bacterial food contamination in the field, the potential usefulness of phage-coated magnetoelastic (ME) biosensors has been recently reported. These biosensors are freestanding, mass-sensitive biosensors that can be easily batch-fabricated, thereby reducing the fabrication cost per sensor to a fraction of a cent. In addition, the biosensors can be directly placed on fresh produce surfaces and used to rapidly monitor possible bacterial food contamination without any preceding sample preparation. Previous investigations showed that the limit of detection (LOD) with millimeter-scale ME biosensors was fairly low for fresh produce with smooth surfaces (e.g., tomatoes and shell eggs). However, the LOD is anticipated to be dependent on the size of the biosensors as well as the topography of produce surfaces of interest. This paper presents an investigation into the use of micron-scale, phage-coated ME biosensors for the enhanced detection of Salmonella Typhimurium on fresh spinach leaves.

Analysis of DNA samples of Salmonella serotypes were performed using FT-IR spectrometer by placing directly in contact with a diamond attenuated total reflection (ATR) crystal. Spectra were recorded from 4000 cm^-1 to 525 cm^-1 wavenumber with the resolution of 4 cm^-1 and data spacing of 1.928 cm^-1. Collected spectra were subtracted from the background spectra of empty diamond crystal surface. Principal Component Analysis (PCA) was conducted at four different spectral regions to differentiate the different serotypes of Salmonella on the basis of difference in their spectral features of DNA structure macromolecules. PCA was used to show the natural clusters in the data set and to describe the difference between the sample clusters. At the region 1800 - 1200 cm^-1, PC1 distinguished 93 % and PC2 distinguished 7 % of the serotypes. Therefore, maximum classification of 100 % in total was obtained at this region. For all the Salmonella serotypes, the frequency between 1000-1150 cm^-1 and 1170 -1280 cm^-1 had higher loading values which showed their significant contribution in the serotype classification.

An impedance biosensor was designed, fabricated and tested for detection of viable Escherichia coli O157:H7 in food samples. This device consists of interdigitated microelectrode array (IDEA) fabricated using thin layer of sputtered gold, embedded under a polydimethylsiloxane (PDMS) microchannel. The array of electrodes is designed to detect viable EColi in different food products. The active surface area of the detection array was modified using goat anti-E.coli polyclonal IgG antibody. Contaminated food samples were tested by infusing the supernatant containing bacteria over the IDEA's, through the microchannel. Antibody-antigen binding on the electrodes results in impedance change. Four serial concentrations of E.coli contaminated food samples (3x10² CFUmL^-1 to 3x10⁵ CFUmL^-1) were tested. The biosensor successfully detected the E.coli samples, with the lower detection limit being 3x10³ CFUmL^-1 (up to 3cells/μl). Comparing the test results with an IDEA impedance biosensor without microchannel (published elsewhere) indicates that this biosensor have two order of magnitude times higher sensitivity. The proposed biosensor provides qualitative and quantitative detection, and potentially could be used for detection of other type of bacteria by immobilizing the specific type of antibody.

Single kernels of durum wheat have been analyzed by hyperspectral imaging (HSI). Such an approach is based on the utilization of an integrated hardware and software architecture able to digitally capture and handle spectra as an image sequence, as they results along a pre-defined alignment on a surface sample properly energized. The study was addressed to investigate the possibility to apply HSI techniques for classification of different types of wheat kernels: vitreous, yellow berry and fusarium-damaged. Reflectance spectra of selected wheat kernels of the three typologies have been acquired by a laboratory device equipped with an HSI system working in near infrared field (1000-1700 nm). The hypercubes were analyzed applying principal component analysis (PCA) to reduce the high dimensionality of data and for selecting some effective wavelengths. Partial least squares discriminant analysis (PLS-DA) was applied for classification of the three wheat typologies. The study demonstrated that good classification results were obtained not only considering the entire investigated wavelength range, but also selecting only four optimal wavelengths (1104, 1384, 1454 and 1650 nm) out of 121. The developed procedures based on HSI can be utilized for quality control purposes or for the definition of innovative sorting logics of wheat.

Dried fruits products present different market values according to their quality. Such a quality is usually quantified in terms of freshness of the products, as well as presence of contaminants (pieces of shell, husk, and small stones), defects, mould and decays. The combination of these parameters, in terms of relative presence, represent a fundamental set of attributes conditioning dried fruits humans-senses-detectable-attributes (visual appearance, organolectic properties, etc.) and their overall quality in terms of marketable products. Sorting-selection strategies exist but sometimes they fail when a higher degree of detection is required especially if addressed to discriminate between dried fruits of relatively small dimensions and when aiming to perform an "early detection" of pathogen agents responsible of future moulds and decays development. Surface characteristics of dried fruits can be investigated by hyperspectral imaging (HSI). In this paper, specific and "ad hoc" applications addressed to propose quality detection logics, adopting a hyperspectral imaging (HSI) based approach, are described, compared and critically evaluated. Reflectance spectra of selected dried fruits (hazelnuts) of different quality and characterized by the presence of different contaminants and defects have been acquired by a laboratory device equipped with two HSI systems working in two different spectral ranges: visible-near infrared field (400-1000 nm) and near infrared field (1000-1700 nm). The spectra have been processed and results evaluated adopting both a simple and fast wavelength band ratio approach and a more sophisticated classification logic based on principal component (PCA) analysis.

Microbially Induced Corrosion (MIC) occurs at metal surfaces and is associated with microorganisms and their metabolic activities. These microbes can coexist as biofilms, growing as synergistic communities (consortia) that are able to affect electrochemical processes, both cathodic and anodic, often through co-operative metabolism. Recent research has revealed the role of "quorum sensing" molecules in control of microbial activities such as biofilm formation. In this paper, we propose the detection of quorum sensing molecules as a means of detecting bacterial contamination prior to the onset on biofilm formation. Further we outline the development of an E. coli cell based sensor for detection of the quorum sensing molecule Autoinducer-2 (AI-2).

In this research, four chemicals, urea, ammonium sulfate, dicyandiamide, and melamine, were mixed into liquid nonfat milk at concentrations starting from 0.1% to a maximum concentration determined for each chemical according to its maximum solubility, and two Raman spectrometers-a commercial Nicolet Raman system and an in-house Raman Chemical Imaging (RCI) system-were used to acquire Raman shift spectra for these mixture samples. These chemicals are potential adulterants that could be used to artificially elevate protein measurements of milk products evaluated by the Kjeldahl method. Baseline subtraction was employed to eliminate milk intensity, and the normalized Raman intensity was calculated from the specific Raman shift from the spectrum of solid chemical. Linear relationships were found to exist between the normalized Raman intensity and chemical concentrations. The linear regression coefficients (R²) ranged from 0.9111 to 0.998. Although slightly higher R² values were calculated for regressions using spectral intensities measured by the Nicolet system compared to those using measurements from the RCI system, the results from the two systems were similar and comparable. A very low concentration of melamine (400 ppm) in milk was also found to be detectable by both systems. Raman sensitivity of Nicolet Raman system was estimated from normalized Raman intensity and slope of regression line in this study. Chemicals (0.2%) were dissolved in milk and detected the normalized Raman intensity. Melamine was found to have the highest Raman sensitivity, with the highest values for normalized Raman intensity (0.09) and regression line slope (57.04).

Determination of freshness is an important issue for rice quality. Near infrared (NIR) spectroscopy, a rapid nondestructive inspection method based on specific absorptions within a given range of wavelengths corresponding to the constituents in the sample, has been widely applied for evaluation of internal quality of agricultural products. Since NIR spectra of a mixture may be approximated as the linear addition of individual spectra of the constituents in the mixture, such a mixture spectrum thus can be regarded as 'blind sources' as the proportion of constituents in the samples remains unknown. A multiuse statistical approach, independent component analysis (ICA), is capable of disassembling the mixture signals of Gaussian distribution into non-Gaussian independent constituents, and (with assumption of independent constituent spectral response) can give a complete explanation about the property of constituents in the mixture. By example, a total of 180 white rice samples were collected from 6 crop seasons (from 2006 to 2010) for the purpose of developing an ICA NIR based procedure for rice freshness. , Values of pH were determined by a conventional (bromothymol blue methyl red) method. The calibration model of white rice yielded R_c = 0.939, SEC = 0.202, r_p = 0.803 and SEP = 0.233 using original full wavelength range (400 to 2498 nm) spectra and 5 independent components (ICs). Freshness of the white rice can be distinguished either visually by 3-dimensional diagram composed from ICs 2, 3 and 4, or statistically by a calibration model. The results show that ICA with NIR can quickly identify and effectively quantify the pH value in white rice with high predictability, and has the potential to be a useful tool for evaluating rice freshness.

Korla fragrant pears are small oval pears characterized by light green skin, crisp texture, and a pleasant perfume for which they are named. Anatomically, the calyx of a fragrant pear may be either persistent or deciduous; the deciduouscalyx fruits are considered more desirable due to taste and texture attributes. Chinese packaging standards require that packed cases of fragrant pears contain 5% or less of the persistent-calyx type. Near-infrared hyperspectral imaging was investigated as a potential means for automated sorting of pears according to calyx type. Hyperspectral images spanning the 992-1681 nm region were acquired using an EMCCD-based laboratory line-scan imaging system. Analysis of the hyperspectral images was performed to select wavebands useful for identifying persistent-calyx fruits and for identifying deciduous-calyx fruits. Based on the selected wavebands, an image-processing algorithm was developed that targets automated classification of Korla fragrant pears into the two categories for packaging purposes.

A nondestructive, real-time pungency measuring system with visible and near-infrared spectroscopy has been recently developed to measure capsaicinoids content in Korean red-pepper powder. One hundred twenty-five red-pepper powder samples produced from 11 regions in Republic of Korea were used for this investigation. The visible and near-infrared absorption spectra in the range from 450 to 950 nm were acquired and used for the development of prediction models of capsaicinoids contents in red-pepper powders without any chemical pretreatment to the samples. Partial Least Squares Regression (PLSR) models were developed to predict the regional capsaicinoids contents using the acquired absorption spectra. The chemical analysis of the total capsaicinoids (capsaicin and dihydrocapsaicin) was performed by a high performance liquid chromatographic (HPLC) method. The determination coefficient of validation (R_V ²) and the standard error of prediction (SEP) for the capsaicinoids content prediction model, for a representative region in this study, were 0.9585 and ±10.147 mg/100g, respectively.

Optical technology is an important and immerging technology for non-destructive and rapid detection of pork freshness. This paper studied on the possibility of using multispectral imaging technique and scattering characteristics to predict the freshness parameters of pork meat. The pork freshness parameters selected for prediction included total volatile basic nitrogen (TVB-N), color parameters (L *, a *, b *), and pH value. Multispectral scattering images were obtained from pork sample surface by a multispectral imaging system developed by ourselves; they were acquired at the selected narrow wavebands whose center wavelengths were 517,550, 560, 580, 600, 760, 810 and 910nm. In order to extract scattering characteristics from multispectral images at multiple wavelengths, a Lorentzian distribution (LD) function with four parameters (a: scattering asymptotic value; b: scattering peak; c: scattering width; d: scattering slope) was used to fit the scattering curves at the selected wavelengths. The results show that the multispectral imaging technique combined with scattering characteristics is promising for predicting the freshness parameters of pork meat.