A method using near infrared hyperspectral imaging to highlight the internal quality of apple fruit slices

2021 ◽  
Vol 175 ◽  
pp. 111497
Author(s):  
Weijie Lan ◽  
Benoit Jaillais ◽  
Catherine M.G.C. Renard ◽  
Alexandre Leca ◽  
Songchao Chen ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Apple Fruit ◽  
Internal Quality

Near infrared spectroscopy to predict internal quality of mangoes

2019 ◽  
pp. 289-294
Author(s):  
S.H.E.J. Gabriels ◽  
B. Brouwer ◽  
H. de Villiers ◽  
E. Westra ◽  
E.J. Woltering
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Internal Quality

Assessment of Internal Quality of Blueberry Using Hyperspectral Imaging

2012 ◽  
Author(s):  
Gabriel A Leiva-Valenzuela ◽  
Renfu Lu ◽  
José Miguel Aguilera
Keyword(s):  
Hyperspectral Imaging ◽  
Internal Quality

Fast inline tobacco classification by near-infrared hyperspectral imaging and support vector machine-discriminant analysis

2019 ◽  
Vol 11 (14) ◽  
pp. 1966-1975 ◽  
Author(s):  
Marcelo C. A. Marcelo ◽  
Frederico L. F. Soares ◽  
Jorge A. Ardila ◽  
Jailson C. Dias ◽  
Ricardo Pedó ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Classification Systems ◽  
Green Leaf ◽  
Support Vector ◽  
Chemical Characteristics ◽  
Tobacco Leaves

Classification systems are frequently used in tobacco Green Leaf Threshing (GLT) facilities to assess the chemical characteristics and quality of tobacco leaves.

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 Curing Assessment of Concrete with Hyperspectral Imaging

Materials ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 3848
Author(s):  
Lisa Ptacek ◽  
Alfred Strauss ◽  
Barbara Hinterstoisser ◽  
Andreas Zitek
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Strength Development ◽  
Contactless Method ◽  
Infrared Spectral ◽  
Young Concrete ◽  
Negative Effect ◽  
High Level ◽  
Destructive Measurement

The curing of concrete significantly influences the hydration process and its strength development. Inadequate curing leads to a loss of quality and has a negative effect on the durability of the concrete. Usually, the effects are not noticed until years later, when the first damage to the structure occurs because of the poor concrete quality. This paper presents a non-destructive measurement method for the determination of the curing quality of young concrete. Hyperspectral imaging in the near infrared is a contactless method that provides information about material properties in an electromagnetic wavelength range that cannot be seen with the human eye. Laboratory tests were carried out with samples with three different curing types at the age of 1, 7, and 27 days. The results showed that differences in the near infrared spectral signatures can be determined depending on the age of the concrete and the type of curing. The data was classified and analyzed by evaluating the results using k-means clustering. This method showed a high level of reliability for the differentiation between the different curing types and concrete ages. A recommendation for hyperspectral measurement and the evaluation of the curing quality of concrete could be made.

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