scholarly journals Non-Destructive and Rapid Variety Discrimination and Visualization of Single Grape Seed Using Near-Infrared Hyperspectral Imaging Technique and Multivariate Analysis

Molecules ◽  
2018 ◽  
Vol 23 (6) ◽  
pp. 1352 ◽  
Author(s):  
Yiying Zhao ◽  
Chu Zhang ◽  
Susu Zhu ◽  
Pan Gao ◽  
Lei Feng ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Imaging Technique ◽  
Grape Seed ◽  
Non Destructive

Applications of Visible and near-Infrared Hyperspectral Imaging for Non-Destructive Detection of the Agricultural Products

2011 ◽  
Vol 317-319 ◽  
pp. 909-914
Author(s):  
Ying Lan Jiang ◽  
Ruo Yu Zhang ◽  
Jie Yu ◽  
Wan Chao Hu ◽  
Zhang Tao Yin
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Imaging System ◽  
Imaging Technique ◽  
Agricultural Products ◽  
Quality Inspection ◽  
Hyperspectral Reflectance ◽  
Imaging And Spectroscopy ◽  
Non Destructive

Agricultural products quality which included intrinsic attribute and extrinsic characteristic, closely related to the health of consumer and the exported cost. Now, imaging (machine vision) and spectrum are two main nondestructive inspection technologies to be applied. Hyperspectral imaging, a new emerging technology developed for detecting quality of the food and agricultural products in recent years, combined techniques of conventional imaging and spectroscopy to obtain both spatial and spectral information from an objective simultaneously. This paper compared the advantage and disadvantage of imaging, spectrum and hyperspectral imaging technique, and provided a description to basic principle, feature of hyperspectral imaging system and calibration of hyperspectral reflectance images. In addition, the recent advances for the application of hyperspectral imaging to agricultural products quality inspection were reviewed in other countries and China.

scholarly journals Non-Destructive Detection Pilot Study of Vegetable Organic Residues Using VNIR Hyperspectral Imaging and Deep Learning Techniques

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2899
Author(s):  
Youngwook Seo ◽  
Giyoung Kim ◽  
Jongguk Lim ◽  
Ahyeong Lee ◽  
Balgeum Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Imaging System ◽  
Imaging Technique ◽  
Support Vector ◽  
Organic Residues ◽  
Efficient Detection ◽  
Non Destructive

Contamination is a critical issue that affects food consumption adversely. Therefore, efficient detection and classification of food contaminants are essential to ensure food safety. This study applied a visible and near-infrared (VNIR) hyperspectral imaging technique to detect and classify organic residues on the metallic surfaces of food processing machinery. The experimental analysis was performed by diluting both potato and spinach juices to six different concentration levels using distilled water. The 3D hypercube data were acquired in the range of 400–1000 nm using a line-scan VNIR hyperspectral imaging system. Each diluted residue in the spectral domain was detected and classified using six classification methods, including a 1D convolutional neural network (CNN-1D) and five pre-processing methods. Among them, CNN-1D exhibited the highest classification accuracy, with a 0.99 and 0.98 calibration result and a 0.94 validation result for both spinach and potato residues. Therefore, in comparison with the validation accuracy of the support vector machine classifier (0.9 and 0.92 for spinach and potato, respectively), the CNN-1D technique demonstrated improved performance. Hence, the VNIR hyperspectral imaging technique with deep learning can potentially afford rapid and non-destructive detection and classification of organic residues in food facilities.

scholarly journals Non-Destructive Detection of Bone Fragments Embedded in Meat Using Hyperspectral Reflectance Imaging Technique

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 4038 ◽  
Author(s):  
Jongguk Lim ◽  
Ahyeong Lee ◽  
Jungsook Kang ◽  
Youngwook Seo ◽  
Balgeum Kim ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Large Scale ◽  
Near Infrared ◽  
Imaging Technique ◽  
Chicken Breast ◽  
Hyperspectral Reflectance ◽  
Food Ingredients ◽  
Foreign Substance ◽  
Bone Fragments ◽  
Non Destructive

Meat consumption has shifted from a quantitative to a qualitative growth stage due to improved living standards and economic development. Recently, consumers have paid attention to quality and safety in their decision to purchase meat. However, foreign substances which are not normal food ingredients are unintentionally incorporated into meat. These should be eliminated as a hazard to quality or safety. It is important to find a fast, non-destructive, and accurate detection technique of foreign substance in the meat processing industry. Hyperspectral imaging technology has been regarded as a novel technology capable of providing large-scale imaging and continuous observation information on agricultural products and food. In this study, we considered the feasibility of the short-wave near infrared (SWIR) hyperspectral reflectance imaging technique to detect bone fragments embedded in chicken meat. De-boned chicken breast samples with thicknesses of 3, 6, and 9-mm and 5 bone fragments with lengths of about 20–30-mm are used for this experiment. The reflectance spectra (in the wavelength range from 987 to 1701-nm) of the 5 bone fragments embedded under the chicken breast fillet are collected. Our results suggested that these hyperspectral imaging technique is able to detect bone fragments in chicken breast, particularly with the use of a subtraction image (corresponding to image at 1153.8-nm and 1480.2-nm). Thus, the SWIR hyperspectral reflectance imaging technique can be potentially used to detect foreign substance embedded in meat.

Evaluation of melon drying using hyperspectral imaging technique in the near infrared region

LWT ◽  
2021 ◽  
Vol 143 ◽  
pp. 111092
Author(s):  
Jose Marcelino S. Netto ◽  
Fernanda A. Honorato ◽  
Patrícia M. Azoubel ◽  
Louise E. Kurozawa ◽  
Douglas F. Barbin
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Imaging Technique ◽  
Infrared Region ◽  
Near Infrared Region

Non-destructive evaluation of bacteria-infected watermelon seeds using visible/near-infrared hyperspectral imaging

2016 ◽  
Vol 97 (4) ◽  
pp. 1084-1092 ◽  
Author(s):  
Hoonsoo Lee ◽  
Moon S. Kim ◽  
Yu-Rim Song ◽  
Chang-Sik Oh ◽  
Hyoun-Sub Lim ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Non Destructive Evaluation ◽  
Non Destructive
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