Special Issue "Application of Hyperspectral Imaging for Nondestructive Measurement II"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".

Deadline for manuscript submissions: 18 February 2022.

Special Issue Editor

Prof. Dr. Byoung-Kwan Cho
E-Mail Website
Guest Editor
Nondestructive Bio-Sensing Laboratory, Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon, 34134, Korea
Interests: hyperspectral imaging; spectral analysis; chemometrics; nondestructive sensing

Special Issue Information

Dear Colleagues,

Hyperspectral imaging (HSI) technology has recently emerged as a powerful analytical technique, using vibrational spectroscopy for nondestructive quality measurement of various materials. Previously described spectroscopic analytical methods (Vis/NIR, MIR, fluorescence, Raman, etc.) are well-established, non-invasive analytical techniques for the analysis of materials. However, these techniques are point-based scanning techniques and only examine a relatively small area of a specimen; thus, these techniques are unable to provide spatial information, which is important for many material inspection applications. The sample analysis is also more convenient and comparatively fast with the hyperspectral imaging technique, due to a large number of samples being analyzed at the same time rather than the single sampling technique used by the other spectroscopic methods. Furthermore, HSI has instrumental flexibility and can be used to collect hyperspectral data for specimens with different sizes and shapes. In addition, the spectral region collected, spatial resolution, and field of view (FOV) can be adjusted depending on the application. With these advantages and flexibility, hyperspectral imaging has been successfully adopted in a variety of research and industry environments.

This Special Issue, the second on this topic, focuses on the latest research and developments of hyperspectral imaging in nondestructive measurement applications. Accordingly, papers that demonstrate novel hyperspectral imaging technology concepts for nondestructive measurement are sought. These include papers dealing with theoretical analyses and laboratory and field studies in various industries, such as agriculture, food, pharmaceutical, natural science, etc.

We would like to invite you to submit original research papers for the “Application of Hyperspectral Imaging for Nondestructive Measurement II” Special Issue.

Prof. Dr. Byoung-Kwan Cho
Guest Editor

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Keywords

  • hyperspectral imaging
  • multispectral imaging
  • chemical imaging
  • spectral imaging
  • nondestructive measurement
  • quality evaluation
  • sorting technique

Published Papers (6 papers)

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Research

Article
Near-Infrared Hyperspectral Imaging (NIR-HSI) for Nondestructive Prediction of Anthocyanins Content in Black Rice Seeds
Appl. Sci. 2021, 11(11), 4841; https://doi.org/10.3390/app11114841 - 25 May 2021
Viewed by 784
Abstract
Anthocyanins are an important micro-component that contributes to the quality factors and health benefits of black rice. Anthocyanins concentration and compositions differ among rice seeds depending on the varieties, growth conditions, and maturity level at harvesting. Chemical composition-based seeds inspection on a real-time, [...] Read more.
Anthocyanins are an important micro-component that contributes to the quality factors and health benefits of black rice. Anthocyanins concentration and compositions differ among rice seeds depending on the varieties, growth conditions, and maturity level at harvesting. Chemical composition-based seeds inspection on a real-time, non-destructive, and accurate basis is essential to establish industries to optimize the cost and quality of the product. Therefore, this research aimed to evaluate the feasibility of near-infrared hyperspectral imaging (NIR-HSI) to predict the content of anthocyanins in black rice seeds, which will open up the possibility to develop a sorting machine based on rice micro-components. Images of thirty-two samples of black rice seeds, harvested in 2019 and 2020, were captured using the NIR-HSI system with a wavelength of 895–2504 nm. The spectral data extracted from the image were then synchronized with the rice anthocyanins reference value analyzed using high-performance liquid chromatography (HPLC). For comparison, the seed samples were ground into powder, which was also captured using the same NIR-HSI system to obtain the data and was then analyzed using the same method. The model performance of partial least square regression (PLSR) of the seed sample developed based on harvesting time, and mixed data revealed the model consistency with R2 over 0.85 for calibration datasets. The best prediction models for 2019, 2020, and mixed data were obtained by applying standard normal variate (SNV) pre-processing, indicated by the highest coefficient of determination (R2) of 0.85, 0.95, 0.90, and the lowest standard error of prediction (SEP) of 0.11, 0.17, and 0.16 mg/g, respectively. The obtained R2 and SEP values of the seed model were comparable to the result of powder of 0.92–0.95 and 0.09–0.15 mg/g, respectively. Additionally, the obtained beta coefficients from the developed model were used to generate seed chemical images for predicting anthocyanins in rice seed. The root mean square error (RMSE) value for seed prediction evaluation showed an acceptable result of 0.21 mg/g. This result exhibits the potential of NIR-HSI to be applied in a seed sorting machine based on the anthocyanins content. Full article
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Article
Influence of Altitude and Image Overlap on Minimum Mapping Size of Chemical in Non-Destructive Trace Detection Using Hyperspectral Remote Sensing
Appl. Sci. 2021, 11(6), 2586; https://doi.org/10.3390/app11062586 - 14 Mar 2021
Viewed by 529
Abstract
The increasing threat of explosives is a serious issue affecting socio-economy of many countries at multiple levels, such as public security, unused arable land, closing of trade routes, isolation of villages, and these act as a hindrance in the development of the country. [...] Read more.
The increasing threat of explosives is a serious issue affecting socio-economy of many countries at multiple levels, such as public security, unused arable land, closing of trade routes, isolation of villages, and these act as a hindrance in the development of the country. Activities using explosives have increased in the last two decades making it a global threat that is challenging humanity. In this study, different chemicals such as Ammonium Nitrate (AN), Trinitrotoluene (TNT) and C4 along with soil as the background material were used for trace detection. The aim of this study was to determine an altitude for the sensor and to identify the minimum mapping size of the chemical at which the model can achieve 70% accuracy. To determine the altitude and minimum size of the chemical that can be detected in the acceptable range of accuracy, several experiments were performed in real ground conditions. This study focuses on the applicability of the proposed method in the real world. In the first set of experiments, the altitude of the sensor was varied from 40 cm to 150 cm and the accuracy of the model was determined. From the analysis, it was found that the model achieved more than 75% accuracy at an altitude of 90 cm with an image overlap of 70%. In the second set of experiments, the minimum size of chemical sample was varied from 0.25 cm to 1 cm. The accuracy of the model was more than 70% when the minimum sample size was 0.5 cm or greater. For various altitude determined, the speed of the vehicle was calculated. Therefore, to implement hyperspectral imaging system on the unarmed vehicle for real application, the suggested altitude and speed of the sensor should be around 90 cm and 10.5 cm/s at which detection limit would be equal or more than 0.5 cm with accuracy greater than 70%. Full article
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Article
Development of Fluorescence Imaging Technique to Detect Fresh-Cut Food Organic Residue on Processing Equipment Surface
Appl. Sci. 2021, 11(1), 458; https://doi.org/10.3390/app11010458 - 05 Jan 2021
Viewed by 833
Abstract
With increasing public demand for ready-to-eat fresh-cut food products, proper sanitation of food-processing equipment surfaces is essential to mitigate potential contamination of these products to ensure safe consumption. This study presents a sanitation monitoring technique using hyperspectral fluorescence images to detect fruit residues [...] Read more.
With increasing public demand for ready-to-eat fresh-cut food products, proper sanitation of food-processing equipment surfaces is essential to mitigate potential contamination of these products to ensure safe consumption. This study presents a sanitation monitoring technique using hyperspectral fluorescence images to detect fruit residues on food-processing equipment surfaces. An algorithm to detect residues on the surfaces of 2B-finished and #4-finished stainless-steel, both commonly used in food processing equipment, was developed. Honeydew, orange, apple, and watermelon were selected as representatives since they are mainly used as fresh-cut fruits. Hyperspectral fluorescence images were obtained for stainless steel sheets to which droplets of selected fruit juices at six concentrations were applied and allowed to dry. The most significant wavelengths for detecting juice at each concentration were selected through ANOVA analysis. Algorithms using a single waveband and using a ratio of two wavebands were developed for each sample and for all the samples combined. Results showed that detection accuracies were better for the samples with higher concentrations. The integrated algorithm had a detection accuracy of 100% and above 95%, respectively, for the original juice up to the 1:20 diluted samples and for the more dilute 1:50 to 1:100 samples, respectively. The results of this study establish that using hyperspectral imaging, even a small residual quantity that may exist on the surface of food processing equipment can be detected and that sanitation monitoring and management is possible. Full article
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Article
Classification of Rice and Starch Flours by Using Multiple Hyperspectral Imaging Systems and Chemometric Methods
Appl. Sci. 2020, 10(19), 6724; https://doi.org/10.3390/app10196724 - 25 Sep 2020
Viewed by 762
Abstract
(1) Background: The general use of food-processing facilities in the agro-food industry has increased the risk of unexpected material contamination. For instance, grain flours have similar colors and shapes, making their detection and isolation from each other difficult. Therefore, this study is aimed [...] Read more.
(1) Background: The general use of food-processing facilities in the agro-food industry has increased the risk of unexpected material contamination. For instance, grain flours have similar colors and shapes, making their detection and isolation from each other difficult. Therefore, this study is aimed at verifying the feasibility of detecting and isolating grain flours by using hyperspectral imaging technology and developing a classification model of grain flours. (2) Methods: Multiple hyperspectral images were acquired through line scanning methods from reflectance of visible and near-infrared wavelength (400–1000 nm), reflectance of shortwave infrared wavelength (900–1700 nm), and fluorescence (400–700 nm) by 365 nm ultraviolet (UV) excitation. Eight varieties of grain flours were prepared (rice: 4, starch: 4), and the particle size and starch damage content were measured. To develop the classification model, four multivariate analysis methods (linear discriminant analysis (LDA), partial least-square discriminant analysis, support vector machine, and classification and regression tree) were implemented with several pre-processing methods, and their classification results were compared with respect to accuracy and Cohen’s kappa coefficient obtained from confusion matrices. (3) Results: The highest accuracy was achieved as 97.43% through short-wavelength infrared with normalization in the spectral domain. The submission of the developed classification model to the hyperspectral images showed that the fluorescence method achieves the highest accuracy of 81% using LDA. (4) Conclusions: In this study, the potential of non-destructive classification of rice and starch flours using multiple hyperspectral modalities and chemometric methods were demonstrated. Full article
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Article
Online Application of a Hyperspectral Imaging System for the Sorting of Adulterated Almonds
Appl. Sci. 2020, 10(18), 6569; https://doi.org/10.3390/app10186569 - 20 Sep 2020
Cited by 4 | Viewed by 992
Abstract
Almonds are nutrient-rich nuts. Due to their high level of consumption and relatively high price, their production is targeted for illegal practices, with the intention of earning more profit. The most common adulterants are based on superficial matching, and as an adulterant, the [...] Read more.
Almonds are nutrient-rich nuts. Due to their high level of consumption and relatively high price, their production is targeted for illegal practices, with the intention of earning more profit. The most common adulterants are based on superficial matching, and as an adulterant, the apricot kernel is comparatively inexpensive and almost identical in color, texture, odor, and other physicochemical characteristics to almonds. In this study, a near-infrared hyperspectral imaging (NIR-HSI) system in the wavelength range of 900–1700 nm synchronized with a conveyor belt was used for the online detection of added apricot kernels in almonds. A total of 448 samples from different varieties of almonds and apricot kernels (112 × 4) were scanned while the samples moved on the conveyor belt. The spectral data were extracted from each imaged nut and used to develop a partial least square discrimination analysis (PLS-DA) model coupled with different preprocessing techniques. The PLS-DA model displayed over a 97% accuracy for the validation set. Additionally, the beta coefficient obtained from the developed model was used for pixel-based classification. An image processing algorithm was developed for the chemical mapping of almonds and apricot kernels. Consequently, the obtained model was transferred for the online sorting of seeds. The online classification system feedback had an overall accuracy of 85% for the classification of nuts. However, the model presented a relatively low accuracy when evaluated in real-time for online application, which might be due to the rough distribution of samples on the conveyor belt, high speed, delaying time in suction, and lighting variations. Nevertheless, the developed online prototype (NIR-HSI) system combined with multivariate analysis exhibits strong potential for the classification of adulterated almonds, and the results indicate that the system can be effectively used for the high-throughput screening of adulterated almond nuts in an industrial environment. Full article
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Article
Geographical Origin Discrimination of White Rice Based on Image Pixel Size Using Hyperspectral Fluorescence Imaging Analysis
Appl. Sci. 2020, 10(17), 5794; https://doi.org/10.3390/app10175794 - 21 Aug 2020
Viewed by 616
Abstract
Geographical origin discrimination of white rice is an important endeavor in preventing illegal distribution of white rice and regulating and standardizing food safety and quality assurance. The aim of this study was to develop a method for geographical origin discrimination between South Korean [...] Read more.
Geographical origin discrimination of white rice is an important endeavor in preventing illegal distribution of white rice and regulating and standardizing food safety and quality assurance. The aim of this study was to develop a method for geographical origin discrimination between South Korean and Chinese rice using a hyperspectral fluorescence imaging technique and multivariate analysis. Hyperspectral fluorescence images of South Korean and Chinese rice samples were obtained in the wavelength range of 420 nm to 780 nm with intervals of 4.8 nm using 365 nm wavelength ultraviolet-A excitation light. Partial least squares discriminant analysis models were developed and applied to the acquired image to determine the geographical origins of the rice samples. In addition, various pre-processing techniques were applied to improve the discrimination accuracy. Accordingly, the pixel size of the hyperspectral image was determined. The results revealed that the optimum pixel size of the hyperspectral image that was above 7 mm × 7 mm showed a high discrimination accuracy. Moreover, the geographical origin discrimination model that applied the first-order derivative achieved a high discrimination accuracy of 98.89%. The results of this study showed that hyperspectral fluorescence imaging technology can be used to quickly and accurately discriminate the geographical origins of white rice. Full article
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