Food safety is a great public concern, and outbreaks of food-borne illnesses can lead to disturbance to the society. Consequently, fast and nondestructive methods are required for sensing the safety situation of produce. As an emerging technology, hyperspectral imaging has been successfully employed in food safety inspection and control. After presenting the fundamentals of hyperspectral imaging, this paper provides a comprehensive review on its application in determination of physical, chemical, and biological contamination on food products. Additionally, other studies, including detecting meat and meat bone in feedstuffs as well as organic residue on food processing equipment, are also reported due to their close relationship with food safety control. With these applications, it can be demonstrated that miscellaneous hyperspectral imaging techniques including near-infrared hyperspectral imaging, fluorescence hyperspectral imaging, and Raman hyperspectral imaging or their combinations are powerful tools for food safety surveillance. Moreover, it is envisaged that hyperspectral imaging can be considered as an alternative technique for conventional methods in realizing inspection automation, leading to the elimination of the occurrence of food safety problems at the utmost.

Application of Hyperspectral Imaging in Food Safety Inspection and Control: A Review

The requirements of reliability, expeditiousness, accuracy, consistency, and simplicity for quality assessment of food products encouraged the development of non-destructive technologies to meet the demands of consumers to obtain superior food qualities. Hyperspectral imaging is one of the most promising techniques currently investigated for quality evaluation purposes in numerous sorts of applications. The main advantage of the hyperspectral imaging system is its aptitude to incorporate both spectroscopy and imaging techniques not only to make a direct assessment of different components simultaneously but also to locate the spatial distribution of such components in the tested products. Associated with multivariate analysis protocols, hyperspectral imaging shows a convinced attitude to be dominated in food authentication and analysis in future. The marvellous potential of the hyperspectral imaging technique as a non-destructive tool has driven the development of more sophisticated hyperspectral imaging systems in food applications. The aim of this review is to give detailed outlines about the theory and principles of hyperspectral imaging and to focus primarily on its applications in the field of quality evaluation of agro-food products as well as its future applicability in modern food industries and research.

Principles and Applications of Hyperspectral Imaging in Quality Evaluation of Agro-Food Products: A Review

Hyperspectral imaging which combines imaging and spectroscopic technology is rapidly gaining ground as a non-destructive, real-time detection tool for food quality and safety assessment. Hyperspectral imaging could be used to simultaneously obtain large amounts of spatial and spectral information on the objects being studied. This paper provides a comprehensive review on the recent development of hyperspectral imaging applications in food and food products. The potential and future work of hyperspectral imaging for food quality and safety control is also discussed.

Recent Developments in Hyperspectral Imaging for Assessment of Food Quality and Safety

During the last two decades, a number of methods have been developed to objectively measure meat quality attributes. Hyperspectral imaging technique as one of these methods has been regarded as a smart and promising analytical tool for analyses conducted in research and industries. Recently there has been a renewed interest in using hyperspectral imaging in quality evaluation of different food products. The main inducement for developing the hyperspectral imaging system is to integrate both spectroscopy and imaging techniques in one system to make direct identification of different components and their spatial distribution in the tested product. By combining spatial and spectral details together, hyperspectral imaging has proved to be a promising technology for objective meat quality evaluation. The literature presented in this paper clearly reveals that hyperspectral imaging approaches have a huge potential for gaining rapid information about the chemical structure and related physical properties of all types of meat. In addition to its ability for effectively quantifying and characterizing quality attributes of some important visual features of meat such as color, quality grade, marbling, maturity, and texture, it is able to measure multiple chemical constituents simultaneously without monotonous sample preparation. Although this technology has not yet been sufficiently exploited in meat process and quality assessment, its potential is promising. Developing a quality evaluation system based on hyperspectral imaging technology to assess the meat quality parameters and to ensure its authentication would bring economical benefits to the meat industry by increasing consumer confidence in the quality of the meat products. This paper provides a detailed overview of the recently developed approaches and latest research efforts exerted in hyperspectral imaging technology developed for evaluating the quality of different meat products and the possibility of its widespread deployment.

Meat Quality Evaluation by Hyperspectral Imaging Technique: An Overview

Computer vision has emerged as a useful alternative to manual expert grading of meat in recent years. Manual grading by experts has a number of essential flaws that can be effectively mitigated with computer vision technology. Computer vision technology is a simple and affordable alternative that, while requiring some expertise, is not excessively technologically demanding. Computer vision technology has shown key attributes of flexibility and ease of compatibility allowing a wide range of meat quality assessment challenges to be successfully tackled. The simplest approaches involving visible light imaging and explicit statistical modelling have proven adequate on the vast majority of occasions. On the other hand, for difficult tasks, more expensive non-visible wavelength imaging and implicit statistical modelling are required. In addition, fully automatic image segmentation still remains a difficult problem in many instances, although image processing is becoming more powerful particularly as computationally demanding texture characterisation algorithms become more viable as computation speeds increase. Finally training and testing a computer vision system will require considerable groundwork as a substantial amount of image and independent meat quality data will be required for good model calibration and validation.

Recent advances in the use of computer vision technology in the quality assessment of fresh meats

The contents of the upper digestive tract (i.e. crop, proventriculus and gizzard) may serve as a source of carcass contamination during broiler processing. The crop as been identified as a source of Salmonella and Campylobacter on contaminated carcasses and is more likely to rupture than the ceca during commercial evisceration. The objective of this study was to determine the effectiveness of hyperspectral imaging for detecting ingesta contamination spots varying in mass from the crop and gizzard. Pre-chilled broiler carcasses were collected from a commercial processing plant. Crop and gizzard contents were also aseptically collected and enumerated for Campylobacter, coliforms, E. coli and total aerobic bacteria. Broiler carcasses were imaged and then contaminated with a spot of known mass (10, 50, or 100 mg) of crop or gizzard contents. Carcasses were then re-imaged. The imaging system correctly detected 100% of the crop and gizzard contents regardless of the mass or spot size. However, not every pixel associated with a given spot (contaminant ground truth) was detected. Detection of crop and gizzard content contaminant ground truth pixels averaged 72 and 53%, respectively. The mean number of bacteria in the crop contents were as follows: E. coli 4.0 log, coliforms 4.1 log, and total aerobic bacteria 5.7 log CFU/g of crop contents. Crop contents in the current study were Campylobacter negative. Applying crop contents in the amounts of about 9, 54, and 231 mg resulted in significant (P<0.05) increases in all bacterial population measured, with the biggest increase being noted for total aerobic bacteria. Gizzard contents contained only 4.6 log CFU/g of total aerobic bacteria. The total added bacterial load from contamination with known amounts of crop and gizzard contents did not significantly increase whole carcass counts of all bacteria enumerated. Based on these counts and numbers of bacteria found in gizzard, carcass contamination with visible ingesta does not appear to significantly increase bacterial load.

Detection of Ingesta on Pre-Chilled Broiler Carcasses by Hyperspectral Imaging

Detection of fertility prior to incubation or recognition of absence of development during the first days of incubation could allow hatcheries to cull infertile or problem eggs, reducing incubator space, utility costs, and contamination from exploder eggs. This study was conducted to determine the feasibility of using a hyperspectral imaging system to detect changes in eggs related to fertility and development for both white shell eggs and brown eggs. In Experiment 1, 48 single comb white leghorn (SCWL) white shell eggs were obtained unincubated from a commercial hatchery in each of two replicate trials (n=96). On days 0, 1, 2, and 3 of incubation, 12 eggs were imaged using a hyperspectral camera system (wavelengths detected from 400-900nm) and a tungsten-halogen backlight. Each day the eggs were broken out for visual confirmation after imaging. Exposure time was 30 ms, and a ratio of transmittance images at wavelengths 576 and 655 nm was used to detect fertility and embryo development. On Day 3, 23 of 24 eggs were determined fertile at breakout; the imaging system accurately identified the one infertile and 21 of 23 fertile eggs. One egg was determined fertile at Day 0, none at Day 1, and 12 at Day 2. One infertile egg was incorrectly identified as fertile on Day 2. In Experiment 2, in each of two replicate trails 12 brown shell broiler chicken eggs were obtained unincubated, and imaged on days 0, 1, 2, and 3 (same eggs kept and imaged throughout each trial; n=24). Embryo development was then visually confirmed on Day 6. All eggs used were fertile, so the earliest point of embryo development was determined. Exposure time was increased to 250 ms and a transmission ratio of wavelengths used at 576 nm, and a range of wavelengths (682 +/13 nm, depending on the light transmission per egg). Detection rates by Day were: Day 1, 13/24 (54%); Day 2 14/24 (58%); and, Day 3, 20/24 (83%). Overall, the hyperspectral imaging system appears capable of detecting developing eggs on Day 3 of incubation at a 91% rate for white shell eggs, and an 83% rate for brown shell eggs.

/pdf/detection-of-fertility-and-early-development-of-hatching-1xnctdwfvq.pdf

Detection of fertility and early development of hatching eggs with hyperspectral imaging.

Detection of hatching and table egg defects using hyperspectral imaging.

Effect of Spectrally Mixed Hyperspectral Image Pixels on Detection of Cecal Contaminated Broiler Carcasses

A hyperspectral imaging system and a predictive modeling technique was evaluated for determining fertility and early embryo development of broiler chicken hatching eggs. Twenty-four broiler eggs were collected (12 fertile, 12 infertile) for each of 8 replicate trials (n = 192) and imaged on Days 0, 1, 2 and 3 of incubation for training and model validation. Three replications of 30 eggs each (fertile and infertile eggs randomly mixed) were collected and imaged as above for model verification (n = 90). Eggs were backlit and positioned below and vertical to the imaging system (lens, spectrograph and CCD camera). Spatial and spectral data from approximately 400-1000nm were collected for each egg on each day of incubation with refinement to 550-899nm. A Mahalanobis Distance (MD) supervised classifier was trained with spectral data from the first 5 replicate sets of eggs, then Principal Component Analysis (PCA) was performed. This model was applied to the next 3 sets for model validation and then to the three 30 egg sets for verification. Fertility was confirmed on Day 5 of incubation by candling and breakout. The MD/PCA model predictions for the 3 validation sets of eggs were: 71% accuracy for Day 0; 63% for Day 1, 65% for Day 2 and 83% for Day 3. For the 3 sets of verification eggs, the MD/PCA model accurately predicted 46/90 on Day 1 and 45/90 on Day 3. The data indicate that the particular MD/PCA model used is not appropriate for predicting fertility and early development.

/pdf/fertility-and-embryo-development-of-broiler-hatching-eggs-1wrijnv45t.pdf

Fertility and Embryo Development of Broiler Hatching Eggs Evaluated with a Hyperspectral Imaging and Predictive Modeling System

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