The production of contaminant free fresh fruit and vegetables is needed to reduce foodborne illnesses and related costs.
Leafy greens grown in the field can be susceptible to fecal matter contamination from uncontrolled livestock and wild
animals entering the field. Pathogenic bacteria can be transferred via fecal matter and several outbreaks of E.coli
O157:H7 have been associated with the consumption of leafy greens. This study examines the use of hyperspectral
fluorescence imaging coupled with multivariate image analysis to detect fecal contamination on Spinach leaves
(Spinacia oleracea). Hyperspectral fluorescence images from 464 to 800 nm were captured; ultraviolet excitation was
supplied by two LED-based line light sources at 370 nm. Key wavelengths and algorithms useful for a contaminant
screening optical imaging device were identified and developed, respectively. A non-invasive screening device has the
potential to reduce the harmful consequences of foodborne illnesses.
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.
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.
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.
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.