Reference: Patrick M. Mehl, Yud-Ren Chen, Moon S. Kim, Diane E. Chan (2004) Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations. Journal of Food Engineering, 61, 67–81 IMPACT FACTOR: 3.851 NAAS RATING: 9.10 Debora A.P. Forchetti, Ronei J. Poppi (2016) Use of NIR hyperspectral imaging and multivariate curve resolution (MCR) for detection and quantification of adulterants in milk powder. LWT - Food Science and Technology, 1-7 IMPACT FACTOR: 3.455 NAAS RATING: 8.33

1. HYPERSPECTRAL AND MULTISPECTRAL IMAGING IN FOOD
ANALYSIS
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. Hyperspectral imaging (HSI) is emerging as a non-destructive tool for
multi-constituent quality analysis of food materials. It has been regarded as a smart and promising
analytical tool for analyses conducted in research, control, and industries. Hyperspectral imaging is a
technique that generates a spatial map of spectral variation, making it a useful tool in many applications.
The use of hyperspectral imaging for both automatic target detection and recognizing its analytical
composition is relatively new and is an amazing area of research.
The present studies demonstrate the capabilities of hyperspectral imaging for real time applications in
industries. Developing proper precise and calibrated standard spectra helps in touching the unprecedented
capabilities to measure, inspect, sort, and grade food products effectively and efficiently. It integrates
conventional imaging and spectroscopy to attain both spatial and spectral information from the food
sample and uses it as fingerprint to characterize its biochemical composition, thus enabling the
visualization of the constituents of the food sample at pixel level. As a result, hyperspectral imagery
provides the potential for more accurate and detailed information extraction than is possible with any other
type of technology for the food industry.
Hyperspectral image data; also known as hypercube consist of several congruent images representing
intensities at different wavelength bands composed of vector pixels (voxels) containing two-dimensional
spatial information (of ‘m’ rows and ‘n’ columns) as well as spectral information (of ‘K’ wavelengths).
These data (hypercube) can provide physical and/or chemical information of a material under test. This
information can include physical and geometric observations of size, orientation, shape, color, and texture,
as well as chemical/molecular information such as water, fat, proteins, and other hydrogen-bonded
constituents.
The HSI technique is still in its developmental stage and due to the current high cost most food related
HSI research has been geared towards identification of important wavebands for the development of low
cost multispectral imaging systems. However, judging by the continuing emphasis on process analytical
technologies to provide accurate, rapid, nondestructive analysis of foodstuffs, it is likely that hyperspectral
imaging will be increasingly adopted for safety and quality control in the food industry. Future
developments in HSI equipment manufacture, such as lower purchase costs and improvements in
processing speed, will encourage more widespread utilization of this emerging platform technology.
Reference:
Patrick M. Mehl, Yud-Ren Chen, Moon S. Kim, Diane E. Chan (2004) Development of hyperspectral imaging technique
for the detection of apple surface defects and contaminations. Journal of Food Engineering, 61, 67–81
IMPACT FACTOR: 3.851 NAAS RATING: 9.10
Debora A.P. Forchetti, Ronei J. Poppi (2016) Use of NIR hyperspectral imaging and multivariate curve resolution (MCR)
for detection and quantification of adulterants in milk powder. LWT - Food Science and Technology, 1-7
IMPACT FACTOR: 3.455 NAAS RATING: 8.33