ReviewHyperspectral imaging – an emerging process analytical tool for food quality and safety control
Introduction
Food process control necessitates real-time monitoring at critical processing points. Fast and precise analytical methods are essential to ensure product quality, safety, authenticity and compliance with labelling. Traditional methods of food monitoring involving analytical techniques such as high performance liquid chromatography (HPLC) and mass spectrometry (MS) are time consuming, expensive and require sample destruction. Near infrared spectroscopy (NIRS) is well established as a non-destructive tool for multi-constituent quality analysis of food materials (Scotter, 1990). However, the inability of NIR spectrometers to capture internal constituent gradients within food products may lead to discrepancies between predicted and measured composition. Furthermore, spectroscopic assessments with relatively small point-source measurements do not contain spatial information, which is important to many food inspection applications (Ariana, Lu, & Guyer, 2006).
Recent advances in computer technology have led to the development of imaging systems capable of identifying quality problems rapidly on the processing line, with the minimum of human intervention (Brosnan and Sun, 2004, Du and Sun, 2004). Red–Green–Blue (RGB) colour vision systems find widespread use in food quality control for the detection of surface defects and grading operations (Chao et al., 1999, Daley et al., 1993, Throop et al., 1993). However, conventional colour cameras are poor identifiers of surface features sensitive to wavebands other than RGB, such as low but potentially harmful concentrations of animal faeces on foods (Liu et al., 2007, Park et al., 2006). To overcome this, multispectral imaging systems have been developed to combine images acquired at a number (usually 3–4) of narrow wavebands, sensitive to features of interest on the object. Compared with conventional analytical methods such as HPLC, multispectral imaging systems can perform non-destructive analyses in a fraction of the time required (Malik, Poonacha, Moses, & Lodder, 2001).
Section snippets
Hyperspectral imaging
Hyperspectral imaging, known also as chemical or spectroscopic imaging, is an emerging technique that integrates conventional imaging and spectroscopy to attain both spatial and spectral information from an object. It was originally developed for remote sensing applications (Goetz, Vane, Solomon, & Rock, 1985) but has since found application in such diverse fields as astronomy (Hege et al., 2003, Wood et al., 2002), agriculture (Monteiro et al., 2007, Smail et al., 2006, Uno et al., 2005),
Applications of hyperspectral imaging to food quality and safety
Hyperspectral imaging is a powerful tool for the identification of key wavebands in the development of online automated multispectral imaging systems. Consequently, it finds widespread use in research for the development of multispectral inspection tools. Hyperspectral imaging, like other spectroscopy techniques, can be carried out in reflectance, transmission or fluorescence modes. While the majority of published research on hyperspectral imaging has been performed in reflectance mode,
Limitations
HSI is a powerful platform technology for food process monitoring. Currently, however, there are two major barriers to its widespread adoption in the food industry. The first is the high purchase cost of HSI systems: since this technology is emerging as a tool for food quality evaluation, there are few commercial suppliers. It is anticipated that future technological developments in HSI systems for the pharmaceutical industry will promote the manufacture of low cost systems suitable for food
Conclusions
Hyperspectral imaging (HSI) is an emerging tool for food quality and safety analysis; the spatial feature of HSI enables characterisation of complex heterogeneous samples, while the spectral feature allows for the identification of a wide range of multi-constituent surface and sub-surface features. Due to the current high cost of HSI systems, most food related HSI research has been geared towards identification of important wavebands for the development of low cost multispectral imaging
Acknowledgement
The authors would like to acknowledge the funding of the Irish Government Department of Agriculture and Food under the Food Institutional Research Measure (FIRM).
References (69)
- et al.
Hyperspectral reflectance imaging for detection of bruises on pickling cucumbers
Computers and Electronics in Agriculture
(2006) - et al.
Improving quality inspection of food products by computer vision – a review
Journal of Food Engineering
(2004) - et al.
Identification of the pigment responsible for the blue fluorescence band in the laser induced fluorescence (LIF) spectra of green plants, and the potential use of this band in remotely estimating rates of photosynthesis
Remote Sensing of Environment
(1991) - et al.
Machine vision technology for agricultural applications
Computers and Electronics in Agriculture
(2002) - et al.
Application of fluorescence spectroscopy and chemometrics in the evaluation of processed cheese during storage
Journal of Dairy Science
(2003) - et al.
Recent developments in the applications of image processing techniques for food quality evaluations
Trends in Food Science & Technology
(2004) - et al.
Bacterial identification by near-infrared chemical imaging of food-specific cards
Food Microbiology
(2005) - et al.
Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry
Journal of Food Engineering
(2007) - et al.
Raman spectroscopy and chemical imaging for quantification of filtered waterborne bacteria
Journal of Microbiological Methods
(2006) - et al.
Discrimination of black walnut shell and pulp in hyperspectral fluorescence imagery using Gaussian kernel function approach
Journal of Food Engineering
(2007)
A review of the analytical methods coupled with chemometric tools for the determination of the quality and identity of dairy products
Food Chemistry
Development of simple algorithms for the detection of fecal contaminants on apples from visible/near infrared hyperspectral reflectance imaging
Journal of Food Engineering
Development of simple algorithm for the detection of chilling injury in cucumbers from visible/near-infrared hyperspectral imaging
Applied Spectroscopy
Hyperspectral scattering for assessing peach fruit firmness
Biosystems Engineering
Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations
Journal of Food Engineering
Prediction of sweetness and amino acid content in soybean crops from hyperspectral imagery
ISPRS Journal of Photogrammetry and Remote Sensing
Non-destructive measurement of bitter pit in apple fruit using NIR hyperspectral imaging
Postharvest Biology and Technology
Hyperspectral laser-induced fluorescence imaging for assessing apple fruit quality
Postharvest Biology and Technology
Performance of hyperspectral imaging system for poultry surface fecal contaminant detection
Journal of Food Engineering
Contaminant classification of poultry hyperspectral imagery using a spectral angle mapper algorithm
Biosystems Engineering
Pork quality and marbling level assessment using a hyperspectral imaging system
Journal of Food Engineering
NIR spectrometry for counterfeit drug detection: a feasibility study
Analytica Chimica Acta
Infrared hyperspectral imaging for qualitative analysis of pharmaceutical solid forms
Analytica Chimica Acta
Use of near infrared spectroscopy in the food industry with particular reference to its applications to on/in-line food processes
Food Control
Chemical imaging of intact seeds with NIR focal plane array assists plant breeding
Vibrational Spectroscopy
Theory and applications of fluorescence spectroscopy in food research
Trends in Food Science & Technology
Artificial neural networks to predict corn yield from Compact Airborne Spectrographic Imager data
Computers and Electronics in Agriculture
Detecting bruises on ‘Golden Delicious’ apples using hyperspectral imaging with multiple wavebands
Biosystems Engineering
Combination of chemometric tools and image processing for bruise detection on apples
Computers and Electronics in Agriculture
Color image classification systems for poultry viscera inspection
Applied Engineering in Agriculture
A novel integrated PCA and FLD method on hyperspectral image feature extraction for cucumber chilling damage inspection
Transactions of ASAE
Single-kernel maize analysis by near-infrared hyperspectral imaging
Transactions of the ASAE
Poultry grading/inspection using color imaging
Proceedings of the SPIE
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