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Application of Hyperspectral Imaging for Nondestructive Measurement

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

Deadline for manuscript submissions: closed (30 April 2020) | Viewed by 35623

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Prof. Dr. Byoung-Kwan Cho

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Nondestructive Bio-Sensing Laboratory, Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Korea
Interests: hyperspectral imaging; spectral analysis; chemometrics; nondestructive sensing
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Special Issue Information

Dear Colleagues,

Hyperspectral imaging technology has recently emerged as a powerful analytical technique that uses vibrational spectroscopy for nondestructive quality measurement of various materials. The previously described spectroscopic analytical methods (Vis/NIR, MIR, Fluorescence, Raman spectroscopies, 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 that is important for many material inspection applications. Sample analysis is also more convenient and fast compared with the hyperspectral imaging technique because a large number of samples are analyzed at the same time, instead of 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 focuses on the latest research and development 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, foods, pharmaceutics, natural science, etc.

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

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Published Papers (9 papers)

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Research

18 pages, 7305 KiB

Open AccessArticle

Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant Factories

by Tiejun Zhao, Akimasa Nakano, Yasunaga Iwaski and Hiroki Umeda

Appl. Sci. 2020, 10(13), 4665; https://doi.org/10.3390/app10134665 - 6 Jul 2020

Cited by 21 | Viewed by 3704

Abstract

Irrigation management continues to be an important issue for tomato cultivation, especially in plant factories. Accurate and timely assessment of tomato leaf water status is a key factor in enabling appropriate irrigation, which can save nutrition solution and labor. In recent decades, hyperspectral imaging has been widely used as a nondestructive measurement method in agriculture to obtain plant biological information. The objective of this research was to establish an approach to obtain the tomato leaf water status—specifically, the relative water content (WC) and equivalent water thickness (MC)—for five different tomato cultivars in real time by using hyperspectral imaging. The normalized difference vegetation index (NDVI) and two-band vegetation index (TBI) analyses were performed on the tomato leaf raw relative reflection (RAW), the inversion-logarithm relative reflection (LOG), and the first derivative of relative reflection (DIFF) from wavelengths of 900 nm to 1700 nm. The best regression model for WC assessment was obtained by TBI regression using DIFF at wavelengths of 1410 nm and 1520 nm, and the best regression model for MC assessment was obtained by NDVI regression using RAW at wavelengths of 1300 nm and 1310 nm. Higher model performance was obtained with MC assessment than with WC assessment. The results will help improve our understanding of the relationship between hyperspectral reflectance and leaf water status. Full article

(This article belongs to the Special Issue Application of Hyperspectral Imaging for Nondestructive Measurement)

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14 pages, 2469 KiB

Open AccessArticle

Prediction of Soluble Solids Content in Green Plum by Using a Sparse Autoencoder

by Luxiang Shen, Honghong Wang, Ying Liu, Yang Liu, Xiao Zhang and Yeqi Fei

Appl. Sci. 2020, 10(11), 3769; https://doi.org/10.3390/app10113769 - 29 May 2020

Cited by 18 | Viewed by 2399

Abstract

The soluble solids content (SSC) affects the flavor of green plums and is an important parameter during processing. In recent years, the hyperspectral technology has been widely used in the nondestructive testing of fruit ingredients. However, the prediction accuracy of most models can hardly be improved further. The rapid development of deep learning technology has established the foundation for the improvement of building models. A new hyperspectral imaging system aimed at measuring the green plum SSC is developed, and a sparse autoencoder (SAE)–partial least squares regression (PLSR) model is combined to further improve the accuracy of component prediction. The results of the experiment show that the SAE–PLSR model, which has a correlation coefficient of 0.938 and root mean square error of 0.654 for the prediction set, can achieve better performance for the SSC prediction of green plums than the three traditional methods. In this paper, integration approaches have combined three different pretreatment methods with PLSR to predict the SSC in green plums. The SAE–PLSR model has shown good prediction performance, indicating that the proposed SAE–PLSR model can effectively detect the SSC in green plums. Full article

(This article belongs to the Special Issue Application of Hyperspectral Imaging for Nondestructive Measurement)

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11 pages, 1194 KiB

Open AccessArticle

Raman Spectral Analysis for Quality Determination of Grignard Reagent

by Rahul Joshi, Ritu Joshi, Changyeun Mo, Mohammad Akbar Faqeerzada, Hanim Z. Amanah, Rudiati Evi Masithoh, Moon S. Kim and Byoung-Kwan Cho

Appl. Sci. 2020, 10(10), 3545; https://doi.org/10.3390/app10103545 - 20 May 2020

Cited by 3 | Viewed by 2852

Abstract

Grignard reagent is one of the most popular materials in chemical and pharmaceutical reaction processes, and requires high quality with minimal adulteration. In this study, Raman spectroscopic technique was investigated for the rapid determination of toluene content, which is one of the common adulterants in Grignard reagent. Raman spectroscopy is the most suitable spectroscopic method to mitigate moisture and CO₂ interference in the molecules of Grignard reagent. Raman spectra for the mixtures of toluene and Grignard reagent with different concentrations were analyzed with a partial least square regression (PLSR) method. The combination of spectral wavebands in the prediction model was optimized with a variables selection method of variable importance in projection (VIP). The results obtained from the VIP-based PLSR model showed the reliable performance of Raman spectroscopy for predicting the toluene concentration present in Grignard reagent with a correlation coefficient value of 0.97 and a standard error of prediction (SEP) of 0.71%. The results showed that Raman spectroscopy combined with multivariate analysis could be an effective analytical tool for rapid determination of the quality of Grignard reagent. Full article

(This article belongs to the Special Issue Application of Hyperspectral Imaging for Nondestructive Measurement)

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13 pages, 3163 KiB

Open AccessArticle

Development of Visible/Near-Infrared Hyperspectral Imaging for the Prediction of Total Arsenic Concentration in Soil

by Lifei Wei, Yangxi Zhang, Ziran Yuan, Zhengxiang Wang, Feng Yin and Liqin Cao

Appl. Sci. 2020, 10(8), 2941; https://doi.org/10.3390/app10082941 - 24 Apr 2020

Cited by 4 | Viewed by 3280

Abstract

Soil total arsenic (TAs) contamination caused by human activities—such as mining, smelting, and agriculture—is a problem of global concern. Visible/near-infrared (VNIR), X-ray fluorescence spectroscopy (XRF), and laser-induced breakdown spectroscopy (LIBS) do not need too much sample preparation and utilization of chemicals to evaluate total arsenic (TAs) concentration in soil. VNIR with hyperspectral imaging has the potential to predict TAs concentration in soil. In this study, 59 soil samples were collected from the Daye City mining area of China, and hyperspectral imaging of the soil samples was undertaken using a visible/near-infrared hyperspectral imaging system (wavelength range 470–900 nm). Spectral preprocessing included standard normal variate (SNV) transformation, multivariate scatter correction (MSC), first derivative (FD) preprocessing, and second derivative (SD) preprocessing. Characteristic bands were then identified based on Spearman’s rank correlation coefficients. Four regression models were used for the modeling prediction: partial least squares regression (PLSR) (R² = 0.71, RMSE = 0.48), support vector machine regression (SVMR) (R² = 0.78, RMSE = 0.42), random forest (RF) (R² = 0.78, RMSE = 0.42), and extremely randomized trees regression (ETR) (R² = 0.81, RMSE = 0.38). The prediction results were compared with the results of atomic fluorescence spectrometry methods. In the prediction results of the models, the accuracy of ETR using FD preprocessing was the highest. The results confirmed that hyperspectral imaging combined with Spearman’s rank correlation with machine learning models can be used to estimate soil TAs content. Full article

(This article belongs to the Special Issue Application of Hyperspectral Imaging for Nondestructive Measurement)

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15 pages, 6573 KiB

Open AccessArticle

Hyperspectral Imaging and Hierarchical PLS-DA Applied to Asbestos Recognition in Construction and Demolition Waste

by Giuseppe Bonifazi, Giuseppe Capobianco and Silvia Serranti

Appl. Sci. 2019, 9(21), 4587; https://doi.org/10.3390/app9214587 - 28 Oct 2019

Cited by 26 | Viewed by 4755

Abstract

Asbestos-Containing Materials (ACMs) are hazardous and prohibited to be sold or used as recycled materials. In the past, asbestos was widely used, together with cement, to produce “asbestos cement-based” products. During the recycling process of Construction and Demolition waste (C&DW), ACM must be collected and deposited separately from other wastes. One of the main aims of the recycling strategies applied to C&DW was thus to identify and separate ACM from C&DW (e.g., concrete and brick). However, to obtain a correct recovery of C&DW materials, control methodologies are necessary to evaluate the quality and the presence of harmful materials, such as ACM. HyperSpectral Imaging (HSI)-based sensing devices allow performing the full detection of materials constituting demolition waste. ACMs are, in fact, characterized by a spectral response that nakes them is different from the “simple” matrix of the material/s not embedding asbestos. The described HSI quality control approach is based on the utilization of a platform working in the short-wave infrared range (1000–2500 nm). The acquired hyperspectral images were analyzed by applying different chemometric methods: Principal Component Analysis for data exploration and hierarchical Partial Least-Square-Discriminant Analysis (PLS-DA) to build classification models. Following this approach, it was possible to set up a repeatable, reliable and efficient technique able to detect ACM presence inside a C&DW flow stream. Results showed that it is possible to discriminate and identify ACM inside C&DW. The recognition is potentially automatic, non-destructive and does not need any contact with the investigated products. Full article

(This article belongs to the Special Issue Application of Hyperspectral Imaging for Nondestructive Measurement)

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15 pages, 2590 KiB

Open AccessArticle

Rapid Classification of Wheat Grain Varieties Using Hyperspectral Imaging and Chemometrics

by Yidan Bao, Chunxiao Mi, Na Wu, Fei Liu and Yong He

Appl. Sci. 2019, 9(19), 4119; https://doi.org/10.3390/app9194119 - 2 Oct 2019

Cited by 91 | Viewed by 6407

Abstract

The classification of wheat grain varieties is of great value because its high purity is the yield and quality guarantee. In this study, hyperspectral imaging combined with the chemometric methods was applied to explore and implement the varieties classification of wheat seeds. The hyperspectral images of all the samples covering 874–1734 nm bands were collected. Exploratory analysis was first carried out while using principal component analysis (PCA) and linear discrimination analysis (LDA). Spectral preprocessing methods including standard normal variate (SNV), multiplicative scatter correction (MSC), and wavelet transform (WT) were introduced, and their effects on discriminant models were studied to eliminate the interference of instrumental and environmental factors. PCA loading, successive projections algorithm (SPA), and random frog (RF) were applied to extract feature wavelengths for redundancy elimination owing to the possibility of existing redundant spectral information. Classification models were developed based on full wavelengths and feature wavelengths using LDA, support vector machine (SVM), and extreme learning machine (ELM). This optimal model was finally utilized to generate visualization map to observe the classification performance intuitively. When comparing with other models, ELM based on full wavelengths achieved the best accuracy up to 91.3%. The overall results suggested that hyperspectral imaging was a potential tool for the rapid and accurate identification of wheat varieties, which could be conducted in large-scale seeds classification and quality detection in modern seed industry. Full article

(This article belongs to the Special Issue Application of Hyperspectral Imaging for Nondestructive Measurement)

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11 pages, 2089 KiB

Open AccessArticle

Hyperspectral Reflectance Imaging Combined with Multivariate Analysis for Diagnosis of Sclerotinia Stem Rot on Arabidopsis Thaliana Leaves

by Jing Liang, Xiaoli Li, Panpan Zhu, Ning Xu and Yong He

Appl. Sci. 2019, 9(10), 2092; https://doi.org/10.3390/app9102092 - 21 May 2019

Cited by 6 | Viewed by 3008

Abstract

Sclerotinia stem rot (SSR) is one of the most destructive diseases in the world caused by Sclerotinia sclerotiorum (S. sclerotiorum), resulting in significant yield loss. Early and high-throughput detection would be critical to prevent SSR from spreading. This study aimed to propose a feasible method for SSR detection based on the hyperspectral imaging coupled with multivariate analysis. The performance of different detecting algorithms were compared by combining the extreme learning machine (ELM), K-nearest neighbor algorithm (KNN), linear discriminant analysis (LDA), naïve Bayes classifier (NB) and the support vector machine (SVM) with the random frog (RF), successive projection algorithm (SPA) and sequential forward selection (SFS). The similarity of selected optimal wavelengths by three different feature selection methods indicated a high correlation between selected wavelengths and SSR. Compared with KNN, LDA, NB, and SVM, three wavelengths (455, 671 and 747 nm) selected by SFS-CA combined with ELM could achieve relatively better results with the overall accuracy of 93.7% and the lowest false negative rate of 2.4%. These results demonstrated the potential of the presented method using hyperspectral reflectance imaging combined with multivariate analysis for SSR diagnosis. Full article

(This article belongs to the Special Issue Application of Hyperspectral Imaging for Nondestructive Measurement)

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15 pages, 5381 KiB

Open AccessArticle

Development of a Low-Cost Multi-Waveband LED Illumination Imaging Technique for Rapid Evaluation of Fresh Meat Quality

by Lalit Mohan Kandpal, Jayoung Lee, Jihoon Bae, Santosh Lohumi and Byoung-Kwan Cho

Appl. Sci. 2019, 9(5), 912; https://doi.org/10.3390/app9050912 - 4 Mar 2019

Cited by 13 | Viewed by 3870

Abstract

Determining the quality of meat has always been essential for the food industry because consumers prefer superior quality meat. Therefore, the food industry requires the development of a rapid and non-destructive method for meat-quality determination. Over the past few years, a number of techniques have been presented for monitoring meat–chemical attributes. However, most previous techniques are quite expensive, destructive, and require complex hardware to operate. Thus, in this work, we demonstrate a low-cost sensing technique (eliminating the expensive equipment and complicated design) for meat–chemical quality detection. The newly developed system was integrated with a low-cost monochrome camera and ordinary light-emitting diode (LED) light sources, with fifteen different wavebands ranging from 458 to 950 nm. The monochrome camera captures images of the meat sample across a spectral range from 458 to 950 nm using a single snapshot method. The chemical values (e.g., moisture, fat, and protein) were also determined using conventional methods. The collected images were combined to produce a multispectral data cube and to extract spectral data. Partial least squares (PLS) and support vector regression (SVR) modeling were used on the extracted spectra and chemical values. The developed models for meat samples displayed accurate chemical-component prediction (

R^{2}

> 0.80). Our model, based on a monochrome sensor using only fifteen wavebands, provided reasonable results compared with the previously developed expensive spectroscopic techniques. Therefore, this complementary metal-oxide semiconductor (CMOS) based multispectral sensing technique may have the potential to detect meat quality, thereby facilitating a simple, fast, and cost-effective method applicable to small-scale meat-processing industries. Full article

(This article belongs to the Special Issue Application of Hyperspectral Imaging for Nondestructive Measurement)

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17 pages, 15437 KiB

Open AccessArticle

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

by Laurence Schimleck, Joseph Dahlen, Seung-Chul Yoon, Kurt C. Lawrence and Paul David Jones

Appl. Sci. 2018, 8(12), 2602; https://doi.org/10.3390/app8122602 - 13 Dec 2018

Cited by 8 | Viewed by 4501

Abstract

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 (SG_lumber) and 25-mm block (SG_block) 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, SG_block gave the best results, followed by SG_lumber, MOE, and MOR. The NIR-HSI SG_lumber, 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. Full article

(This article belongs to the Special Issue Application of Hyperspectral Imaging for Nondestructive Measurement)

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