scholarly journals Use of infrared hyperspectral imaging as an aid for paint identification

2016 ◽  
Author(s):  
A. Polak ◽  
T. Kelman ◽  
P. Murray ◽  
S. Marshall ◽  
D. Stothard ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Imaging System ◽  
Classification Problem ◽  
Extensive Study ◽  
Electromagnetic Spectrum ◽  
New Techniques ◽  
Art Authentication ◽  
Infrared Spectral ◽  
Non Destructive

Art authentication is a complicated process that often requires the extensive study of high value objects. Although a series of non-destructive techniques is already available for art scientists, new techniques, extending current possibilities, are still required. In this paper, the use of a novel mid-infrared tunable imager is proposed as an active hyperspectral imaging system for art work analysis. The system provides access to a range of wavelengths in the electromagnetic spectrum (2500–3750 nm) which are otherwise difficult to access using conventional hyperspectral imaging (HSI) equipment. The use of such a tool could be beneficial if applied to the paint classification problem and could help analysts map the diversity of pigments within a given painting. The performance of this tool is demonstrated and compared with a conventional, off-the-shelf HSI system operating in the near infrared spectral region (900–1700 nm). Various challenges associated with laser-based imaging are demonstrated and solutions to these challenges as well as the results of applying classification algorithms to datasets captured using both HSI systems are presented. While the conventional HSI system provides data in which more pigments can be accurately classified, the result of applying the proposed laser-based imaging system demonstrates the validity of this technique for application in art authentication tasks.

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 and rapid discrimination of hard-to-cook beans using hyperspectral imaging

2018 ◽  
pp. 7.1-7.8
Author(s):  
Lina Diaz-Contreras ◽  
Chyngyz Erkinbaev ◽  
Jitendra Paliwal
Keyword(s):  
Hyperspectral Imaging ◽  
Nutritional Value ◽  
Near Infrared ◽  
Storage Conditions ◽  
Imaging Systems ◽  
Electromagnetic Spectrum ◽  
Rapid Discrimination ◽  
Spectral Ranges ◽  
And Control ◽  
Non Destructive

Dry beans stored under sub-optimal conditions tend to develop hard-to-cook (HTC) defect, which extends the cooking time making them less palatable while reducing their nutritional value. The current methods of identifying HTC beans are time-consuming, destructive, and unreliable. A rapid non-destructive inspection technique for pre-screening beans could help identify and discard HTC beans prior to processing. To this end, the potential of hyperspectral imaging technique covering the entire visible to near infrared (NIR) spectral range (400‒2500 nm) was evaluated for rapid and non-destructive identification of HTC beans. The HTC phenomenon was artificially induced in healthy white beans using two different combinations of suboptimal storage conditions of temperature and relative humidity (35℃, 75% RH for 45 days and 60℃, 75% RH for 10 days). Subsequently, the beans were cooked for specified durations and their hardness measured using a texture analyzer. The HTC and control (i.e. easy-to-cook (ETC)) beans were scanned with push-broom hyperspectral imaging systems. Results indicate that both sets of storage conditions rendered the beans HTC but the phenomenon induced by the two different methods was detected in different spectral ranges using hyperspectral imaging. Wavelengths across the entire visible and NIR ranges of electromagnetic spectrum were found useful in detecting HTC as beans stored at 35℃ and 75% RH for 45 days were identified mainly in the 1000‒2500 nm range and those stored at 60℃ and 75% RH for 10 days were identified in the 400‒1000 nm region. The degree of HTC defect could not be ascertained using this technique and requires further investigation.

scholarly journals Prediction of Douglas-Fir Lumber Properties: Comparison between a Benchtop Near-Infrared Spectrometer and Hyperspectral Imaging System

2018 ◽  
Vol 8 (12) ◽  
pp. 2602 ◽  
Author(s):  
Laurence Schimleck ◽  
Joseph Dahlen ◽  
Seung-Chul Yoon ◽  
Kurt Lawrence ◽  
Paul Jones
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Imaging System ◽  
Nir Spectroscopy ◽  
Physical And Mechanical Properties ◽  
Douglas Fir ◽  
Modulus Of Rupture ◽  
Visual Grade ◽  
Fit Statistics ◽  
Infrared Spectrometer

Near-infrared (NIR) spectroscopy and NIR hyperspectral imaging (NIR-HSI) were compared for the rapid estimation of physical and mechanical properties of No. 2 visual grade 2 × 4 (38.1 mm by 88.9 mm) Douglas-fir structural lumber. In total, 390 lumber samples were acquired from four mills in North America and destructively tested through bending. From each piece of lumber, a 25-mm length block was cut to collect diffuse reflectance NIR spectra and hyperspectral images. Calibrations for the specific gravity (SG) of both the lumber (SGlumber) and 25-mm block (SGblock) and the lumber modulus of elasticity (MOE) and modulus of rupture (MOR) were created using partial least squares (PLS) regression and their performance checked with a prediction set. The strongest calibrations were based on NIR spectra; however, the NIR-HSI data provided stronger predictions for all properties. In terms of fit statistics, SGblock gave the best results, followed by SGlumber, MOE, and MOR. The NIR-HSI SGlumber, MOE, and MOR calibrations were used to predict these properties for each pixel across the transverse surface of the scanned samples, allowing SG, MOE, and MOR variation within and among rings to be observed.

scholarly journals Chemometric Tools for NIRS and NIR Hyperspectral Imaging

2012 ◽  
Vol 69 (1) ◽  
Author(s):  
Laura M. DALE ◽  
André THEWIS ◽  
Ioan ROTAR ◽  
Juan A. FERNANDEZ PIERNA ◽  
Christelle BOUDRY ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Agricultural Sector ◽  
Spatial Information ◽  
Imaging System ◽  
Agricultural Products ◽  
Chemical Information ◽  
Special Focus ◽  
Large Area ◽  
Analytical Tools

Nowadays in agriculture, new analytical tools based on spectroscopic technologies are developed. Near Infrared Spectroscopy (NIRS) is a well known technology in the agricultural sector allowing the acquisition of chemical information from the samples with a large number of advantages, such as: easy to use tool, fast and simultaneous analysis of several components, non-polluting, noninvasive and non destructive technology, and possibility of online or field implementation. Recently, NIRS system was combined with imaging technologies creating the Near Infrared Hyperspectral Imaging system (NIR-HSI). This technology provides simultaneously spectral and spatial information from an object. The main differences between NIR-HSI and NIRS is that many spectra can be recorded simultaneously from a large area of an object with the former while with NIRS only one spectrum was recorded for analysis on a small area. In this work, both technologies are presented with special focus on the main spectrum and images analysis methods. Several qualitative and quantitative applications of NIRS and NIR-HSI in agricultural products are listed. Developments of NIRS and NIR-HSI will enhance progress in the field of agriculture by providing high quality and safe agricultural products, better plant and grain selection techniques or compound feed industry’s productivity among others.

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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