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 Rapid and Nondestructive Discrimination of Geographical Origins of Longjing Tea using Hyperspectral Imaging at Two Spectral Ranges Coupled with Machine Learning Methods

2020 ◽  
Vol 10 (3) ◽  
pp. 1173 ◽  
Author(s):  
Zhiqi Hong ◽  
Yong He
Keyword(s):  
Hyperspectral Imaging ◽  
Nutritional Value ◽  
Geographical Origin ◽  
Principal Component ◽  
Hyperspectral Images ◽  
Imaging Systems ◽  
Support Vector ◽  
Spectral Ranges ◽  
Tea Leaf ◽  
Non Destructive

Longjing tea is one of China’s protected geographical indication products with high commercial and nutritional value. The geographical origin of Longjing tea is an important factor influencing its commercial and nutritional value. Hyperspectral imaging systems covering the two spectral ranges of 380–1030 nm and 874–1734 nm were used to identify a single tea leaf of Longjing tea from six geographical origins. Principal component analysis (PCA) was conducted on hyperspectral images to form PCA score images. Differences among samples from different geographical origins were visually observed from the PCA score images. Support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) models were built using the full spectra at the two spectral ranges. Decent classification performances were obtained at the two spectral ranges, with the overall classification accuracy of the calibration and prediction sets over 84%. Furthermore, prediction maps for geographical origins identification of Longjing tea were obtained by applying the SVM models on the hyperspectral images. The overall results illustrate that hyperspectral imaging at both spectral ranges can be applied to identify the geographical origin of single tea leaves of Longjing tea. This study provides a new, rapid, and non-destructive alternative for Longjing tea geographical origins identification.

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.

Geometrical and spectral data stitching for combining hyperspectral imaging systems

2017 ◽  
Vol 65 (6) ◽  
Author(s):  
Fabian Stark ◽  
Maik Rosenberger ◽  
Paul-Gerald Dittrich ◽  
Rafael Celestre ◽  
Michael Hänsel ◽  
...  
Keyword(s):  
Image Registration ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Imaging Systems ◽  
Data Sets ◽  
Correction Algorithm ◽  
Optical Aberrations ◽  
Optical Aberration ◽  
Spectral Ranges

AbstractOne way to increase the amount of information acquired via hyperspectral imaging and therefore to increase the possibility of data analysis is combining the spatial and spectral information of hyperspectral data sets. The aforementioned data sets are obtained by cameras covering different spectral ranges. The purpose of this article is to develop an algorithm which is able to combine two data sets acquired by two hyperspectral pushbroom imagers, covering the visible (VIS) and the near infrared (NIR) wavelength range. Initially, the effect of optical aberrations, as well as errors via the image registration were examined. Subsequently a correction algorithm for both the optical aberration and the image registration is elaborated.

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

scholarly journals Near-infrared Spectroscopy and Hyperspectral Imaging for Sugar Content Evaluation in Potatoes over Multiple Growing Seasons

2020 ◽  
Author(s):  
Ahmed M Rady ◽  
Daniel E Guyer ◽  
Nicholas J Watson
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Multispectral Imaging ◽  
Sucrose Concentration ◽  
Sugar Content ◽  
Imaging Systems ◽  
Potato Tubers ◽  
Forward Selection ◽  
Concentration Data ◽  
Growing Seasons

Abstract Sugar content is one of the most important properties of potato tubers as it directly affects their processing and the final product quality, especially for fried products. In this study, data obtained from spectroscopic (interactance and reflectance) and hyperspectral imaging systems were used individually or fused to develop non-cultivar nor growing season-specific regression and classification models for potato tubers based on glucose and sucrose concentration. Data was acquired over three growing seasons for two potato cultivars. The most influential wavelengths were selected from the imaging systems using interval partial least squares for regression and sequential forward selection for classification. Hyperspectral imaging showed the highest regression performance for glucose with a correlation coefficient (ratio of performance to deviation) or r(RPD) of 91.8(2.41) which increased to 94%(2.91) when the data was fused with the interactance data. The sucrose regression results had the highest accuracy using data obtained from the interactance system with r(RPD) values of 74.5%(1.40) that increased to 84.4%(1.82) when the data was fused with the reflectance data. Classification was performed to identify tubers with either high or low sugar content. Classification performance showed accuracy values as high as 95% for glucose and 80.1% for sucrose using hyperspectral imaging, with no noticeable improvement when data was fused from the other spectroscopic systems. When testing the robustness of the developed models over different seasons, it was found that the regression models had r(RPD) values of 55(1.19)–90.3%(2.34) for glucose and 35.8(1.07)–82.2%(1.29) for sucrose. Results obtained in this study demonstrate the feasibility of developing a rapid monitoring system using multispectral imaging and data fusion methods for online evaluation of potato sugar content.

scholarly journals Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5481 ◽  
Author(s):  
Beatriz Martinez ◽  
Raquel Leon ◽  
Himar Fabelo ◽  
Samuel Ortega ◽  
Juan F. Piñeiro ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Brain Cancer ◽  
Near Infrared ◽  
Relevant Information ◽  
Support Vector ◽  
Algorithm Optimization ◽  
Spectral Bands ◽  
Tumor Identification ◽  
Spectral Ranges

Hyperspectral imaging (HSI) is a non-ionizing and non-contact imaging technique capable of obtaining more information than conventional RGB (red green blue) imaging. In the medical field, HSI has commonly been investigated due to its great potential for diagnostic and surgical guidance purposes. However, the large amount of information provided by HSI normally contains redundant or non-relevant information, and it is extremely important to identify the most relevant wavelengths for a certain application in order to improve the accuracy of the predictions and reduce the execution time of the classification algorithm. Additionally, some wavelengths can contain noise and removing such bands can improve the classification stage. The work presented in this paper aims to identify such relevant spectral ranges in the visual-and-near-infrared (VNIR) region for an accurate detection of brain cancer using in vivo hyperspectral images. A methodology based on optimization algorithms has been proposed for this task, identifying the relevant wavelengths to achieve the best accuracy in the classification results obtained by a supervised classifier (support vector machines), and employing the lowest possible number of spectral bands. The results demonstrate that the proposed methodology based on the genetic algorithm optimization slightly improves the accuracy of the tumor identification in ~5%, using only 48 bands, with respect to the reference results obtained with 128 bands, offering the possibility of developing customized acquisition sensors that could provide real-time HS imaging. The most relevant spectral ranges found comprise between 440.5–465.96 nm, 498.71–509.62 nm, 556.91–575.1 nm, 593.29–615.12 nm, 636.94–666.05 nm, 698.79–731.53 nm and 884.32–902.51 nm.

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