Proper chemical analyses of materials in sealed containers are important for quality control purpose. Although it is feasible to detect chemicals at top surface layer, it is relatively challenging to detect objects beneath obscuring surface. This study used spatially offset Raman spectroscopy (SORS) method to detect urea, ibuprofen and acetaminophen powders contained within one or more (up to eight) layers of gelatin capsules to demonstrate subsurface chemical detection and identification. A 785 nm point-scan Raman spectroscopy system was used to acquire spatially offset Raman spectra for an offset range of 0 to 10 mm from the surfaces of 24 encapsulated samples, using a step size of 0.1 mm to obtain 101 spectral measurements per sample. With increasing offset distance, the fraction of information from the deeper subsurface material increased compared to that from the top surface material. The series of measurements was analyzed to differentiate and identify the top surface and subsurface materials. Containing mixed contributions from the powder and capsule, the SORS of each sample was decomposed using self modeling mixture analysis (SMA) to obtain pure component spectra of each component and corresponding components were identified using spectral information divergence values. Results show that SORS technique together with SMA method has a potential for non-invasive detection of chemicals at deep subsurface layer.
Turmeric is well known for its medicinal value and is often used in Asian cuisine. Economically motivated contamination of turmeric by chemicals such as metanil yellow has been repeatedly reported. Although traditional technologies can detect such contaminants in food, high operational costs and operational complexities have limited their use to the laboratory. This study used Fourier Transform Raman Spectroscopy (FT-Raman) and Fourier Transform - Infrared Spectroscopy (FT-IR) to identify metanil yellow contamination in turmeric powder. Mixtures of metanil yellow in turmeric were prepared at concentrations of 30%, 25%, 20%, 15%, 10%, 5%, 1% and 0.01% (w/w). The FT-Raman and FT-IR spectral signal of pure turmeric powder, pure metanil yellow powder and the 8 sample mixtures were obtained and analyzed independently to identify metanil yellow contamination in turmeric. The results show that FT-Raman spectroscopy and FT-IR spectroscopy can detect metanil yellow mixed with turmeric at concentrations as low as 1% and 5%, respectively, and may be useful for non-destructive detection of adulterated turmeric powder.
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.
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.
Fecal contamination of produce is a food safety issue associated with pathogens such as Escherichia coli that can easily
pollute agricultural products via animal and human fecal matters. Outbreaks of foodborne illnesses associated with
consuming raw fruits and vegetables have occurred more frequently in recent years in the United States. Among fruits,
strawberry is one high-potential vector of fecal contamination and foodborne illnesses since the fruit is often consumed
raw and with minimal processing. In the present study, line-scan LED-induced fluorescence imaging techniques were
applied for inspection of fecal material on strawberries, and the spectral characteristics and specific wavebands of
strawberries were determined by detection algorithms. The results would improve the safety and quality of produce
consumed by the public.
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.
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).
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.
This paper reported the development of hyperspectral fluorescence imaging system using ultraviolet-A excitation (320-400 nm) for detection of bovine fecal contaminants on the abaxial and adaxial surfaces of romaine lettuce and baby
spinach leaves. Six spots of fecal contamination were applied to each of 40 lettuce and 40 spinach leaves. In this study,
the wavebands at 666 nm and 680 nm were selected by the correlation analysis. The two-band ratio, 666 nm / 680 nm, of
fluorescence intensity was used to differentiate the contaminated spots from uncontaminated leaf area. The proposed
method could accurately detect all of the contaminated spots.
Previously, we showed that two- and three-band color-mixing techniques could be used to achieve results optically
equivalent to two- and three-band ratios that are normally implemented using multispectral imaging systems, for
enhancing identification of single target types against a background and for separation of multiple targets by color or
contrast. In this paper, a prototype of a wavelength-changeable two- and three-band color-mixing device is presented
and its application is demonstrated. The wavelength-changeable device uses changeable central wavelength bandpass
filters and various filter arrangements. The experiments showed that a color-mixing technique implemented in a pair of
binoculars coupled with an imager could greatly enhance target identification of color-blindness test cards with hidden
numbers and figures as the targets. Target identification of color blindness cards was greatly improved by using twoband
color-mixing with filters at 620 nm and 650 nm, which were selected based on the criterion of uniform background.
Target identification of a different set of color blindness test cards was also improved using three-band color-mixing
with filters at 450 nm, 520 nm, and 632 nm, which were selected based on the criterion of maximum chromaticness
difference. These experiments show that color-mixing techniques can significantly enhance electronic imaging and
During in-plant testing of a hyperspectral line-scan imaging system, images were acquired of wholesome and
systemically diseased chickens on a commercial processing line moving at a speed 70 birds per minute. A fuzzy logic
based algorithm using four key wavelengths, 468 nm, 501 nm, 582 nm, 629 nm, was developed using image data from
the validation set of images of 543 wholesome and 66 systemically diseased chickens. A classification method using the
fuzzy logic based algorithm was then tested on the testing set of images of 457 wholesome and 37 systemically diseased
chickens, as well as 80 systemically diseased chickens that were imaged off-shift during breaks between normal
processing shifts of the chicken plant. The classification method correctly identified 89.7% of wholesome chicken
images and 98.5% of systemically diseased chicken images in the validation set. For the testing data set, the method
correctly classified 96.7 % of 457 wholesome chicken images and 100% of 37 systemically diseased chicken images.
The 80 images acquired off-shift were also 100% correctly identified.
An online line-scan imaging system was developed for differentiation of wholesome and systemically diseased chickens. The hyperspectral imaging system used in this research can be directly converted to multispectral operation and would provide the ideal implementation of essential features for data-efficient high-speed multispectral classification algorithms. The imaging system consisted of an electron-multiplying charge-coupled-device (EMCCD) camera and an imaging spectrograph for line-scan images. The system scanned the surfaces of chicken carcasses on an eviscerating line at a poultry processing plant in December 2005. A method was created to recognize birds entering and exiting the field of view, and to locate a Region of Interest on the chicken images from which useful spectra were extracted for analysis. From analysis of the difference spectra between wholesome and systemically diseased chickens, four wavelengths of 468 nm, 501 nm, 582 nm and 629 nm were selected as key wavelengths for differentiation. The method of locating the Region of Interest will also have practical application in multispectral operation of the line-scan imaging system for online chicken inspection. This line-scan imaging system makes possible the implementation of multispectral inspection using the key wavelengths determined in this study with minimal software adaptations and without the need for cross-system calibration.
Several of visible and NIR bands were sought to explore the potential for the classification of fecal / ingesta ("F/I")
objectives from rubber belt and stainless steel ("RB/SS") backgrounds. Spectral features of "F/I" objectives and
"RB/SS" backgrounds showed large differences in both visible and NIR regions, due to the diversity of their chemical
compositions. Such spectral distinctions formed the basis on which to develop simple three-band ratio algorithms for the
classification analysis. Meanwhile, score-score plots from principal component analysis (PCA) indicated the obvious
cluster separation between "F/I" objectives and "RB/SS" backgrounds, but the corresponding loadings did not show any
specific wavelengths for developing effective algorithms. Furthermore, 2-class soft independent modeling of class
analogy (SIMCA) models were developed to compare the correct classifications with those from the ratio algorithms.
Results indicated that using ratio algorithms in the visible or NIR region could separate "F/I" objectives from "RB/SS"
backgrounds with a success rate of over 97%.
Hyperspectral images of cucumbers were acquired before and during cold storage treatments as well as during subsequent room temperature (RT) storage to explore the potential for the detection of chilling induced damage in whole cucumbers. Region of interest (ROI) spectral features of chilling injured areas, resulting from cold storage treatments at 0°C or 5°C, showed a reduction in reflectance intensity during multi-day post chilling periods of RT storage. Large spectral differences between good-smooth skins and chilling injured skins occurred in the 700-850 nm visible/NIR region. A number of data processing methods, including simple spectral band algorithms, second difference, and principal component analysis (PCA), were attempted to discriminate the ROI spectra of good cucumber skins from those of chilling injured skins. Results revealed that using either a dual-band ratio algorithm (Q811/756) or a PCA model from a narrow spectral region of 733-848 nm could detect chilling injured skins with a success rate of over 90%. Furthermore, the dual-band algorithm was applied to the analysis of images of cucumbers at different conditions, and the resultant images showed more correct identification of chilling injured spots than other processing methods.
Successful differentiation of normal chicken livers from septicemic chicken livers was demonstrated using
visible/near-infrared (Vis/NIR) spectral data subjected to principal component analysis and then fed into a feedforward
back-propagation neural network. The study used 300 fresh chicken livers, 150 collected from normal
chicken carcasses and 150 collected from chicken carcasses diagnosed with the septicemica/toxemia (septox)
condition as defined for condemnation under U.S. Department of Agriculture (USDA) standards for food safety.
Using a training set of 200 samples and testing set of 100 samples, the best neural network model demonstrated a
classification accuracy of 98% for normal samples and 94% for septicemia/toxemia samples. These results show
that Vis/NIR spectral methods have potential for use in chicken liver inspection as part of automated online systems
for food safety inspection. Liver abnormalities are identifying characteristics of the septox condition; consequently,
liver screening would be extremely useful as part of an automated inspection system to meet USDA food safety
requirements for poultry. Automated inspection systems capable of real-time on-line operation are currently being
developed, and spectroscopic liver inspection is potential tool that could be implemented as part of such systems to
help poultry processors increase production while meeting food safety inspection requirements.
We examined fecal contamination on apples, as part of on-going food safety research, with the use ofthe recently developed hyperspectral imaging system that has a spectralrange spanning the VIS to NIR region ofthe spectrum from 400 to 900 nm. Both reflectance and fluorescence techniques for detection ofexogenous fecal contamination on four apple varieties, 'Red Delicious', 'Gala', 'Fuji' and 'Golden Delicious' were evaluated. Thick patches and thin, transparent smear spots offresh dairy cow manure were empirically created on these apples to emulate fecal contamination. In addition, these spots were created on sun-exposed side and shaded side surfaces to account for natural color variations due to environmental growth conditions and ripeness. Spectral features from both reflectance and fluorescence spectra of samples including fecal contaminated spots were evaluated to determine wavelengths where minima, maxima, and plateau occur. Images at these wavelengths were used to create combinations ofsimple two band ratios, second differences, normalized differences, and absorption depth images. Preliminary results ofthese simplistic multispectralapproaches indicatedthatthe reflectance method can differentiate thickpatches ofmanure from regions of normal apple surfaces using a two NIR band ratio (R8501R800) with a simple threshold. However, for the detection ofthin manure spots, the reflectance methodmay require more complicated image processing approaches. Fluorescence techniques with a simple two band ratio (F6801F450) differentiated normal apple surfaces from contaminated spots regardless of apple skin coloration and thickness of manure treatments. These results will be further refmed to develop a rapid on-line multispectral detection system.