Hyperspectral Imaging—A New Era of Applications in Non-Destructive Sensing of Meat Quality

NIR news ◽  
2012 ◽  
Vol 23 (6) ◽  
pp. 9-14 ◽  
Author(s):  
Gamal ElMasry ◽  
Da-Wen Sun ◽  
Mohammed Kamruzzaman ◽  
Douglas Barbin ◽  
Paul Allen
Keyword(s):  
Meat Quality ◽  
Hyperspectral Imaging ◽  
New Era ◽  
Non Destructive

HYPERSPECTRAL IMAGING TECHNIQUE AS A STATE OF ART TECHNOLOGY IN MEAT SCIENCE

2016 ◽  
Vol 2 (3) ◽  
pp. 127-137
Author(s):  
Hasan Ibrahim Kozan ◽  
Cemalettin Sariçoban ◽  
Hasan Ali Akyürek ◽  
Ahmet Ünver
Keyword(s):  
Meat Quality ◽  
Hyperspectral Imaging ◽  
Imaging Technique ◽  
Saturated Fat ◽  
Cholesterol Content ◽  
Quality Parameters ◽  
Meat Industry ◽  
Meat Safety ◽  
Non Destructive ◽  
Day By Day

Nowadays, the concern of meat consumption, safety and quality has been popular due to some health risks such coronary heart disease, stroke and diabetes caused by the content as saturated fat, cholesterol content and carcinogenic compounds, for consumers. The importance of the need of new non-destructive and fast meat analyze methods are increasing day by day.  For this, researchers have developed some methods to objectively measure the meat quality and meat safety as well as illness sources. Hyperspectral imaging technique is one of the most popular technology which combines imaging and spectroscopic technology. This technique is a non-destructive, real-time and easy-to-use detection tool for meat quality and safety assessment. It is possible to determine chemical structure and related physical properties of meat.It is clear that hyperspectral imaging technology can be automated for manufacturing in meat industry and all of data’s obtained from the hyperspectral images which represents the chemical quality parameters of meats in the process can be saved to database. 

scholarly journals The Performances of Hyperspectral Sensors for Proximal Sensing of Nitrogen Levels in Wheat

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4550
Author(s):  
Huajian Liu ◽  
Brooke Bruning ◽  
Trevor Garnett ◽  
Bettina Berger
Keyword(s):  
Hyperspectral Imaging ◽  
High Throughput ◽  
Management Practices ◽  
Leaf Tissue ◽  
Plant Phenotyping ◽  
Proximal Sensing ◽  
N Content ◽  
Hyperspectral Sensors ◽  
Wheat Leaves ◽  
Non Destructive

The accurate and high throughput quantification of nitrogen (N) content in wheat using non-destructive methods is an important step towards identifying wheat lines with high nitrogen use efficiency and informing agronomic management practices. Among various plant phenotyping methods, hyperspectral sensing has shown promise in providing accurate measurements in a fast and non-destructive manner. Past applications have utilised non-imaging instruments, such as spectrometers, while more recent approaches have expanded to hyperspectral cameras operating in different wavelength ranges and at various spectral resolutions. However, despite the success of previous hyperspectral applications, some important research questions regarding hyperspectral sensors with different wavelength centres and bandwidths remain unanswered, limiting wide application of this technology. This study evaluated the capability of hyperspectral imaging and non-imaging sensors to estimate N content in wheat leaves by comparing three hyperspectral cameras and a non-imaging spectrometer. This study answered the following questions: (1) How do hyperspectral sensors with different system setups perform when conducting proximal sensing of N in wheat leaves and what aspects have to be considered for optimal results? (2) What types of photonic detectors are most sensitive to N in wheat leaves? (3) How do the spectral resolutions of different instruments affect N measurement in wheat leaves? (4) What are the key-wavelengths with the highest correlation to N in wheat? Our study demonstrated that hyperspectral imaging systems with satisfactory system setups can be used to conduct proximal sensing of N content in wheat with sufficient accuracy. The proposed approach could reduce the need for chemical analysis of leaf tissue and lead to high-throughput estimation of N in wheat. The methodologies here could also be validated on other plants with different characteristics. The results can provide a reference for users wishing to measure N content at either plant- or leaf-scales using hyperspectral sensors.

scholarly journals Hyperspectral Imaging for Bloodstain Identification

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3045
Author(s):  
Maheen Zulfiqar ◽  
Muhammad Ahmad ◽  
Ahmed Sohaib ◽  
Manuel Mazzara ◽  
Salvatore Distefano
Keyword(s):  
Hyperspectral Imaging ◽  
Dna Analysis ◽  
Forensic Sciences ◽  
Blood Protein ◽  
Chemical Methods ◽  
Acrylic Paint ◽  
Spectral Bands ◽  
Nail Polish ◽  
Spatial Dimensions ◽  
Non Destructive

Blood is key evidence to reconstruct crime scenes in forensic sciences. Blood identification can help to confirm a suspect, and for that reason, several chemical methods are used to reconstruct the crime scene however, these methods can affect subsequent DNA analysis. Therefore, this study presents a non-destructive method for bloodstain identification using Hyperspectral Imaging (HSI, 397–1000 nm range). The proposed method is based on the visualization of heme-components bands in the 500–700 nm spectral range. For experimental and validation purposes, a total of 225 blood (different donors) and non-blood (protein-based ketchup, rust acrylic paint, red acrylic paint, brown acrylic paint, red nail polish, rust nail polish, fake blood, and red ink) samples (HSI cubes, each cube is of size 1000 × 512 × 224, in which 1000 × 512 are the spatial dimensions and 224 spectral bands) were deposited on three substrates (white cotton fabric, white tile, and PVC wall sheet). The samples are imaged for up to three days to include aging. Savitzky Golay filtering has been used to highlight the subtle bands of all samples, particularly the aged ones. Based on the derivative spectrum, important spectral bands were selected to train five different classifiers (SVM, ANN, KNN, Random Forest, and Decision Tree). The comparative analysis reveals that the proposed method outperformed several state-of-the-art methods.

Meat Quality Evaluation by Hyperspectral Imaging Technique: An Overview

2012 ◽  
Vol 52 (8) ◽  
pp. 689-711 ◽  
Author(s):  
Gamal Elmasry ◽  
Douglas F. Barbin ◽  
Da-Wen Sun ◽  
Paul Allen
Keyword(s):  
Meat Quality ◽  
Hyperspectral Imaging ◽  
Quality Evaluation ◽  
Imaging Technique

Non-destructive assessment of cooking loss of fresh broiler breast fillets using shortwave infrared (SWIR) hyperspectral imaging

2018 ◽  
Author(s):  
Hongzhe Jiang ◽  
Wei Wang ◽  
Hong Zhuang ◽  
Seung-Chul Yoon ◽  
Yi Yang ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Cooking Loss ◽  
Shortwave Infrared ◽  
Broiler Breast ◽  
Non Destructive

scholarly journals Vis/NIR hyperspectral imaging for detection of hidden bruises on kiwifruits

2011 ◽  
Vol 29 (No. 6) ◽  
pp. 595-602 ◽  
Author(s):  
Q. Lü ◽  
M.-j. Tang ◽  
J.-r. Cai ◽  
J.-w. Zhao ◽  
S. Vittayapadung
Keyword(s):  
Hyperspectral Imaging ◽  
Hyperspectral Image ◽  
Detection System ◽  
Image Data ◽  
Principal Component ◽  
Quality Analysis ◽  
Economic Losses ◽  
Hyperspectral Image Data ◽  
Multispectral Detection ◽  
Non Destructive

It is necessary to develop a non-destructive technique for kiwifruit quality analysis because the machine injury could lower the quality of fruit and incur economic losses. Bruises are not visible externally owing to the special physical properties of kiwifruit peel.We proposed the hyperspectral imaging technique to inspect the hidden bruises on kiwifruit. The Vis/NIR (408–1117 nm) hyperspectral image data was collected. Multiple optimal wavelength (682, 723, 744, 810, and 852 nm) images were obtained using principal component analysis on the high dimension spectral image data (wavelength range from 600 nm to 900 nm). The bruise regions were extracted from the component images of the five waveband images using RBF-SVM classification. The experimental results showed that the error of hidden bruises detection on fruits by means of hyperspectral imaging was 12.5%. It was concluded that the multiple optimal waveband images could be used to constructs a multispectral detection system for hidden bruises on kiwifruits.

scholarly journals INFORMATION EXTRACTION IN TOMB PIT USING HYPERSPECTRAL DATA

2018 ◽  
Vol XLII-3 ◽  
pp. 2107-2111
Author(s):  
X. Yang ◽  
M. Hou ◽  
S. Lyu ◽  
S. Ma ◽  
Z. Gao ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Visual Experience ◽  
Hyperspectral Data ◽  
Shanxi Province ◽  
Bottom Surface ◽  
Archaeological Research ◽  
Reference Images ◽  
Research Findings ◽  
And Storage ◽  
Non Destructive

Hyperspectral data has characteristics of multiple bands and continuous, large amount of data, redundancy, and non-destructive. These characteristics make it possible to use hyperspectral data to study cultural relics. In this paper, the hyperspectral imaging technology is adopted to recognize the bottom images of an ancient tomb located in Shanxi province. There are many black remains on the bottom surface of the tomb, which are suspected to be some meaningful texts or paintings. Firstly, the hyperspectral data is preprocessing to get the reflectance of the region of interesting. For the convenient of compute and storage, the original reflectance value is multiplied by 10000. Secondly, this article uses three methods to extract the symbols at the bottom of the ancient tomb. Finally we tried to use morphology to connect the symbols and gave fifteen reference images. The results show that the extraction of information based on hyperspectral data can obtain a better visual experience, which is beneficial to the study of ancient tombs by researchers, and provides some references for archaeological research findings.

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