Application Of Hyperspectral Imaging Technology Research Articles

Applications of hyperspectral imaging technology in the food industry

Applications of hyperspectral imaging technology in the food industry

Research and application of intelligent hyperspectral analysis technology for Chinese materia medica

With the development of imaging technology and artificial intelligence, hyperspectral imaging technology provides a fast, non-destructive, intelligent, and precise new method for the analysis of Chinese materia medica(CMM). This paper summarized the methods and applications of hyperspectral imaging technology combined with intelligent analysis technology in the field of CMM in recent years, focusing on the acquisition and preprocessing of hyperspectral data, intelligent analysis methods of hyperspectral data, and practical cases of these technologies in the field of CMM. Hyperspectral data of CMM can provide spectral information with nanometer-level resolution and rich spatial texture information simultaneously. This paper summarized the acquisition process, including black-and-white board calibration and region-of-interest extraction, and preprocessing methods including smoothing, differentiation, scale-space, and scattering correction. The feature extraction methods in terms of spectral, spatial, color, and texture were briefly described, and common modeling methods were summarized. Finally, this paper reviewed the research cases of the application of the above methods to the fields of CMM, such as authenticity identification, origin tracing, variety recognition, year identification, sulfur fumigation degree determination, and quantitative measurement.

Research progress on the application of hyperspectral imaging techniques in tea science

AbstractHyperspectral imaging technology combines two‐dimensional imaging and spectral technology, which can simultaneously obtain spatial and spectral information of the object to be measured and is an advanced technical method. With the development of science and technology, the detection of tea has also been continuously improved, and it has developed in the direction of being nondestructive, fast, real‐time, and accurate. In this paper, the principle of hyperspectral imaging technology is introduced, and according to research on hyperspectral imaging technology in the nondestructive rapid detection of tea in the past 5 years, the application of hyperspectral imaging technology in the detection of tea biochemical components, accurate classification, determination of mildew degree, and stress monitoring and the application progress in planting production management are analyzed. Additionally, the main challenges existing in the current research are analyzed, and future application prospects are proposed to provide a reference for the application and promotion of hyperspectral imaging technology in the actual production of tea.

土壤有机质含量的高光谱成像预测研究

Soil nutrient content is an important index to evaluate the growing environment of crops. Rapid access to soil nutrient information is an important requirement for the development of modern precision agriculture, while the detection of soil organic matter content is a necessary condition for understanding the basic soil fertility and implementing crop precision cultivation. In this paper, the soil of rural fields in the southeast of Shanxi Province before sowing was taken as the research object. 111 soil samples to be tested were collected. After the process of drying, impurity removal and grinding, the hyperspectral data of the Region of interest (ROI) of the samples were collected, and then the chemical determination of soil organic matter content was conducted. The original spectral data matrix was pretreated by numerical transformation operations, such as arithmetic mean, average deviation, 1st derivation, natural logarithm and mixed multiplication, and a Partial least square regression (PLSR) quantitative analysis model was established. In these models, the obtained prediction set RP value under the pretreatment of F(A)*ln(AD) was the highest, reaching 0.8859. For spectral data preprocessed by F(A)* Ln (AD), the Competitive adaptive reweighted sampling (CARS) algorithm and Random frog (RF) algorithm were used to select key variables. The PLSR model was established by using F(A)* Ln (AD)&CARS data processing method, and the RP value was increased to 0.9545. The prediction results can accurately reflect the real content of soil organic matter. The results of this study can provide theoretical support for the application of hyperspectral imaging technology in the determination of soil organic matter content, and provide a reference for the rapid detection of other soil components.

Application of hyperspectral imaging technology to digitally protect murals in the Qutan temple

Hyperspectral imaging technology is a research hotspot in the field of cultural heritage protection. It can be used to quickly and noninvasively obtain detailed spectral information from the surfaces of cultural relics of different categories. We can intuitively analyse pigment compositions, line characteristics, painting skills and patterns using spectral information. Hyperspectral imaging has high scientific significance and application value for the protection, restoration and research of ancient murals and other cultural relics. In this study, a mural from Daheitian hall in the Qutan temple, Qinghai Province, China, was used as a sample. The hyperspectral data were acquired and analysed for several purposes. Pigment spectral matching and abundance inversion were carried out to obtain the pigment distribution. These data were enhanced by continuum removal and histogram stretching to obtain hidden information. The dark channel prior, Criminisi and Retinex methods were used to virtually restore the image of the mural. The results indicated that by using hyperspectral imaging data, the constructed pure pigment spectrum library and suitable approaches, the types and distributions of mural pigments can be quantitatively analysed, and the lines in murals can be extracted. Hyperspectral images are helpful for identifying information hidden by pigments or surface materials. Mural images can be enhanced, and hidden information can be highlighted using enhancement methods, such as continuum removal and histogram linear stretching. In addition, hyperspectral imaging data have unique advantages in the restoration of mural images, and the combination of defogging methods and image inpainting algorithms can realize the virtual restoration of mural images. In brief, hyperspectral imaging technology was found to have a highly favourable effect on pigment analysis, line extraction, information enhancement, hidden information extraction and the virtual restoration of ancient murals.

Hyperspectral reconstruction from a single textile RGB image based on the generative adversarial network

Hyperspectral images are capable of significantly increasing the accuracy of textile color measurement because of their rich information. However, hyperspectral imaging generally requires expensive equipment and complex operations. If the hyperspectral information can be reconstructed based on a single RGB image, it can facilitate the widespread application of hyperspectral imaging technology, such as in textile color measurement. In this paper, a deep learning model was proposed for hyperspectral reconstruction of cotton and linen fabrics based on the conditional generative adversarial network. According to this model, the encoder–decoder structure and spatial pyramid convolution pooling operation were adopted to fuse multi-scale features for the prevention of mode collapse. Atrous convolution was introduced to increase the receptive field to adapt to the fabric texture information, and the hyperspectral information of the fabric from a single RGB image was reconstructed. The quantitative and qualitative tests verified that the method in this paper had good results. The root mean square error and peak signal-to-noise ratio were 0.0271 and 31.372, respectively, for reconstructed fabric hyperspectral images; the highest average color difference [Formula: see text] in the reconstructed hyperspectral colorimetry experiment was obtained as 2.755. Thus, the proposed method can meet the common application requirements of color measurement.

Review on the Application of Hyperspectral Imaging Technology of the Exposed Cortex in Cerebral Surgery.

The study of brain science is vital to human health. The application of hyperspectral imaging in biomedical fields has grown dramatically in recent years due to their unique optical imaging method and multidimensional information acquisition. Hyperspectral imaging technology can acquire two-dimensional spatial information and one-dimensional spectral information of biological samples simultaneously, covering the ultraviolet, visible and infrared spectral ranges with high spectral resolution, which can provide diagnostic information about the physiological, morphological and biochemical components of tissues and organs. This technology also presents finer spectral features for brain imaging studies, and further provides more auxiliary information for cerebral disease research. This paper reviews the recent advance of hyperspectral imaging in cerebral diagnosis. Firstly, the experimental setup, image acquisition and pre-processing, and analysis methods of hyperspectral technology were introduced. Secondly, the latest research progress and applications of hyperspectral imaging in brain tissue metabolism, hemodynamics, and brain cancer diagnosis in recent years were summarized briefly. Finally, the limitations of the application of hyperspectral imaging in cerebral disease diagnosis field were analyzed, and the future development direction was proposed.

Application of hyperspectral imaging technology in the rapid identification of microplastics in farmland soil

Microplastics (MPs) are emerging environmental pollutants and their accumulation in the soil can adversely affect the soil biota. This study aims to employ hyperspectral imaging technology for the rapid screening and classification of MPs in farmland soil. In this study, a total of 600 hyperspectral data are collected from 180 sets of farmland soil samples with a hyperspectral imager in the wavelength range of 369– 988 nm. To begin, the hyperspectral data are preprocessed by the Savitzky-Golay (S-G) smoothing filter and mean normalization. Second, principal component analysis (PCA) is used to minimize the dimensions of the hyperspectral data and hence the amount of data, making the subsequent model easier to construct. The cumulative contribution rate of the first three principal components is reached 98.37%, including the main information of the original spectral data. Finally, three models including decision tree (DT), support vector machine (SVM), and convolutional neural network (CNN) are established, all of which can achieve well classification effects on three MP polymers including polyethylene (PE), polypropylene (PP), and polyvinyl chloride (PVC) in farmland soil. By comparing the recognition accuracy of the three models, the classification accuracy of DT and SVM is 87.9% and 85.6%, respectively. The CNN model based on the S-G smoothing filter obtains the best prediction effect, the classification accuracy reaches 92.6%, exhibiting obvious advantages in classification effect. Altogether, these results show that the proposed hyperspectral imaging technique identifies the soil MPs rapidly and nondestructively, and provides an effective automated method for the detection of polymers, requiring only rapid and simple sample preparation.

Application of hyperspectral imaging technology for rapid identification of Ruditapes philippinarum contaminated by heavy metals.

Human beings are confronted with a serious health hazard when ingesting Ruditapes philippinarum contaminated with heavy metals, and thus it is significantly necessary to identify heavy metal contaminated Ruditapes philippinarum. This study investigates the feasibility of hyperspectral imaging to identify heavy metal contamination in Ruditapes philippinarum rapidly. To reduce the effects of noise, four different spectral pretreatments were performed on the original spectra. To select characteristic wavebands for identification, four waveband selection algorithms based on neighbourhood rough set theory were proposed, namely, mutual information, consistency measure, dependency measure, and variable precision. The selected wavebands were input to an extreme learning machine to construct classification models. The results demonstrated that multiplicative scatter correction pretreatment was suitable for Ruditapes philippinarum hyperspectral imaging datasets. The identification models exhibited satisfactory performance to distinguish healthy Ruditapes philippinarum from those contaminated by both individual and multiple heavy metals. The identification results of Cd and Pb contaminated samples were more accurate than those of Cu and Zn contaminated samples. When the number of training samples decreased the identification performance decreased, but not significantly. The results showed that combined with pattern recognition analysis hyperspectral imaging technology can be used to distinguish healthy Ruditapes philippinarum samples from those contaminated by heavy metals, even with only a small number of training samples. This model is suitable for applications in analysing many shellfish rapidly and non-destructively.

Application and prospects of hyperspectral imaging and deep learning in traditional Chinese medicine in context of AI and industry 4.0

In the 21 st century, the rise of artificial intelligence(AI) marks the arrival of the intelligence era or the era of Industry 4.0. In addition to the rapid development of computer and electronic information science, machine learning, as the core intelligence of AI, provides a new methodology for the modernization of traditional Chinese medicine. The algorithms of machine learning include support vector machine(SVM), extreme learning machine(ELM), convolutional neural network(CNN), and recurrent neural network(RNN). The combination of machine learning algorithms and hyperspectral imaging analysis could be used for the identification of fake and inferior herbs, the origin of herbs and the content determination of bioactive ingredients in herbs, which has largely solved the difficulty in strictly controlling the quality of traditional Chinese medicine. The integration of high spectral imaging(HSI) and deep lear-ning will make the predicted results more reliable and suitable for analysis of great amounts of samples. This paper summarizes the application of hyperspectral imaging technology(HSI) and machine learning algorithms in the field of traditional Chinese medicine in recent years, focuses on the principles of hyperspectral imaging technology, preprocessing methods and deep learning algorithms, and gives the prospects of evolution of hyperspectral imaging technology in the field.

Hyperspectral imaging technology for quality and safety evaluation of horticultural products: A review and celebration of the past 20-year progress

• Reflectance, Transmittance, Fluorescence, Raman and integrated imaging is covered. • Instrumentation is discussed with special attention for real-time imaging. • The major steps in hyperspectral data analysis are discussed. • Overview of applications in external and internal quality evaluation. • Review of applications in food safety detection. In the past 20 years, hyperspectral imaging has been widely investigated as an emerging, promising technology for evaluating quality and safety of horticultural products. This technology has originated from remote sensing and joins the domains of machine vision and point spectroscopy to provide superior image segmentation for the detection of defects and contaminations, and to map the chemical composition. Thanks to the advancements in instrumentation and data analysis in the past two decades, hyperspectral imaging technology has evolved into a powerful nondestructive inspection tool and the scope of applications in postharvest quality and safety evaluation has expanded tremendously. In this article, different imaging modes (reflectance, transmittance, fluorescence and Raman) and their combinations, and the potential for real-time acquisition of hyperspectral images at industry relevant speeds are first discussed in terms of their advantages and disadvantages. Next reviewed are different data processing/analysis methods and associated steps from data pre-processing over the spectral and spatial domains to the actual model building and performance evaluation. An overview is then given of hyperspectral imaging applications for external quality and defect evaluation, internal quality and maturity assessment, and food safety detection of horticultural products. Finally, a brief discussion is presented on the challenges and opportunities in future development and application of hyperspectral imaging technology in food quality and safety evaluation of horticultural products.

Recognition of different Longjing fresh tea varieties using hyperspectral imaging technology and chemometrics

AbstractHyperspectral imaging technology was applied to detect and recognize six different varieties of Longjing fresh tea. The data contained image and spectral information at 370–1042 nm; color and texture features were the foci of the image research. Spectral pre‐processing was performed by multiplicative scatter correction (MSC) and standard normal variate (SNV), and then, we selected the corresponding position variable and vegetation indexes as spectral features. Representative features including the most information were chose by principal component analysis (PCA). A novel back propagation (BP) neural network, with a self‐generated number of hidden layer neurons, was proposed. Using spectral features, image features, and spectral image fusion features as input, three fresh tea recognition models were established: the improved BP neural network, traditional BP neural network, and support vector machine (SVM). Results suggested that the improved BP neural network could promote performance of the model, especially for the spectral pre‐processed data. Mixed‐feature models did better than individual feature models, with 100% accuracy of the predictive set. This study shows that hyperspectral imaging technology can be a potential rapid and nondestructive approach to identify different varieties of Longjing fresh teas.Practical applicationsThis article introduced application of hyperspectral imaging technology to identify Longjing fresh tea of different origins and varieties. Samples were analyzed using spectral and image characteristics. We provided a basis for full utilization of tea characteristics. At the same time, an improved BP neural network, with less calculation complexity and workload than the traditional BP neural network, was proposed. In summary, we outlined a convenient and reliable method for differentiation of Longjing fresh teas. Furthermore, we established a theoretical foundation for development of portable instruments to be used in similar studies.

Rapid determination of pit mud moisture content using hyperspectral imaging.

Lack of moisture can lead to the aging of pit mud, excessive moisture will make it difficult to maintain its shape or even collapse. Therefore, a rapid and nondestructive detection technology for moisture in pit mud using hyperspectral imaging was firstly investigated. Modeling efficiency of various processing was compared in visible (400–1,000 nm) and near‐infrared (900–1,700 nm) regions, and the optimal model was SNV‐SPA‐SVM in near‐infrared spectroscopy; the Rpre2 and RMSEP of model were .9953 and 0.0029, respectively. Furthermore, the distribution map showed that the moisture in the new cellar was generally lower than that of old, and the moisture distribution of the old pit mud was more even. Moreover, the moisture content of different layers in the same cellar increased from top to bottom. This work provides strong technical support for liquor brewing enterprises to effectively implement online monitoring of pit mud changes and open a new era for the application of hyperspectral imaging technology in the field of liquor solid‐state fermentation.

In-Field Detection of Yellow Rust in Wheat on the Ground Canopy and UAV Scale

The application of hyperspectral imaging technology for plant disease detection in the field is still challenging. Existing equipment and analysis algorithms are adapted to highly controlled environmental conditions in the laboratory. However, only real time information from the field scale is able to guide plant protection measures and to optimize the use of resources. At the field scale, many parameters such as the optimal measurement distance, informative feature sets, and suitable algorithms have not been investigated. In this study, the hyperspectral detection and quantification of yellow rust in wheat was evaluated using two measurement platforms: a ground-based vehicle and an unmanned aerial vehicle (UAV). Different disease development stages and disease severities were provided in a plot-based field experiment. Measurements were performed weekly during the vegetation period. Data analysis was performed by three prediction algorithms with a focus on the selection of optimal feature sets. In this context, the across-scale application of optimized feature sets, an approach of information transfer between scales, was also evaluated. Relevant aspects for an on-line disease assessment in the field integrating affordable sensor technology, sensor spatial resolution, compact analysis models, and fast evaluation have been outlined and reflected upon. For the first time, a hyperspectral imaging observation experiment of a plant disease was comparatively performed at two scales, ground canopy and UAV.

Structure Tensor and Guided Filtering-Based Algorithm for Hyperspectral Anomaly Detection

Anomaly detection is one of the most important applications of hyperspectral imaging technology. It is a challenging task due to the high dimensionality of hyperspectral images (HSIs), redundant information, noisy bands, and the limited capability of utilizing spatial information. In this paper, we address these problems and propose a novel anomaly detection method in HSIs. Our approach, called structure tensor and guided filter (STGF)-based strategy for anomaly detection, is based on the characteristics of HSIs. First, a novel band selection algorithm is proposed to reduce dimension, remove noisy bands, and select bands with effective information. Second, the selected bands are decomposed into two parts according to the characteristics of anomalies that are usually in a small area. Followed by this step, the backgrounds are removed through a simple differential operation for each selected band. Considering that not all of the bands provide the same contributions to anomaly detection, we then fuse the differential maps by a novel adaptive weighting method to obtain an initial detection map. Finally, GF is conducted to rectify the previous map under the condition that the neighboring pixels usually have quite strong correlations with each other. Experiments have been conducted on real-scene remote sensing HSI. Comparative analyses validate that the proposed STGF method presents superior performance in terms of detection accuracy and computational time.

Early Detection of Tomato Spotted Wilt Virus by Hyperspectral Imaging and Outlier Removal Auxiliary Classifier Generative Adversarial Nets (OR-AC-GAN)

Tomato spotted wilt virus is a wide-spread plant disease in the world. It can threaten thousands of plants with a persistent and propagative manner. Early disease detection is expected to be able to control the disease spread, to facilitate management practice, and further to guarantee accompanying economic benefits. Hyperspectral imaging, a powerful remote sensing tool, has been widely applied in different science fields, especially in plant science domain. Rich spectral information makes disease detection possible before visible disease symptoms showing up. In the paper, a new hyperspectral analysis proximal sensing method based on generative adversarial nets (GAN) is proposed, named as outlier removal auxiliary classifier generative adversarial nets (OR-AC-GAN). It is an all-in-one method, which integrates the tasks of plant segmentation, spectrum classification and image classification. The model focuses on image pixels, which can effectively visualize potential plant disease positions, and keep experts’ attention on these diseased pixels. Meanwhile, this new model can improve the performances of classic spectrum band selection methods, including the maximum variance principle component analysis (MVPCA), fast density-peak-based clustering, and similarity-based unsupervised band selection. Selecting spectrum wavebands reasonably is an important preprocessing step in spectroscopy/hyperspectral analysis applications, which can reduce the computation time for potential in-field applications, affect the prediction results and make the hyperspectral analysis results explainable. In the experiment, the hyperspectral reflectance imaging system covers the spectral range from 395 nm to 1005 nm. The proprosed model makes use of 83 bands to do the analysis. The plant level classification accuracy gets 96.25% before visible symptoms shows up. The pixel prediction false positive rate in healthy plants gets as low as 1.47%. Combining the OR-AC-GAN with three existing band selection algorithms, the performance of these band selection models can be significantly improved. Among them, MVPCA can leverage only 8 spectrum bands to get the same plant level classification accuracy as OR-AC-GAN, and the pixel prediction false positive rate in healthy plants is 1.57%, which is also comparable to OR-AC-GAN. This new model can be potentially transferred to other plant diseases detection applications. Its property to boost the performance of existing band selection methods can also accelerate the in-field applications of hyperspectral imaging technology.

Potential of hyperspectral imaging for rapid identification of true and false honeysuckle tea leaves

Honeysuckle (Lonicerae japonicae Flos, LJF) tea is a favorite cool tea in China and Southeast Asia. However, some unscrupulous traders usually use Lonicera Flos (LF) as LJF to sell for earning high profit. In order to identify true and false honeysuckle tea leaves rapidly and precisely, hyperspectral imaging technology was applied to develop a nondestructive identification model for LJF and LF. Firstly, the original spectral data were analyzed by three pretreatment methods including Savitzky–Golay (SG) convolution smoothing, multiple scatter correct and standard normal variate transformation (SNV). Then, a full-band analysis model was established by using the partial least squares-discriminant analysis method. And after the selection of characteristic wavelengths by regression coefficients algorithm, the identification analysis models based on the back-propagation neural network and extreme learning machine (ELM) discriminant were established. The results showed that the BP neural network and ELM discriminant analysis model based on SNV denoising at 9 characteristic wavelengths could achieve the best identification results. The recognition rates of both modeling sets and forecasting sets could reach 100%. Therefore, the application of hyperspectral imaging technology can identify LJF and LF effectively and nondestructively, and has potential in the identification of true and false honeysuckle tea leaves.

Outdoor Applications of Hyperspectral Imaging Technology for Monitoring Agricultural Crops: A Review

Background: Although hyperspectral imaging was originally introduced for military, remote sensing, and astrophysics applications, the use of analytical hyperspectral imaging techniques has been expanded to include monitoring of agricultural crops and commodities due to the broad range and highly specific and sensitive spectral information that can be acquired. Combining hyperspectral imaging with remote sensing expands the range of targets that can be analyzed. Results: Hyperspectral imaging technology can rapidly provide data suitable for monitoring a wide range of plant conditions such as plant stress, nitrogen status, infections, maturity index, and weed discrimination very rapidly, and its use in remote sensing allows for fast spatial coverage. Conclusions: This paper reviews current research on and potential applications of hyperspectral imaging and remote sensing for outdoor field monitoring of agricultural crops. The instrumentation and the fundamental concepts and approaches of hyperspectral imaging and remote sensing for agriculture are presented, along with more recent developments in agricultural monitoring applications. Also discussed are the challenges and limitations of outdoor applications of hyperspectral imaging technology such as illumination conditions and variations due to leaf and plant orientation.

Recent Advances for Rapid Identification of Chemical Information of Muscle Foods by Hyperspectral Imaging Analysis

Muscle foods play an important role in providing a vital source of high-quality protein, amino acids and vitamin for human health. Chemical composition is one of the most vital information of muscle foods, which directly relates to the quality of pork, beef, chicken, fish and other meats. Therefore, it is significant to identify the chemical information of muscle foods for the purpose of controlling the quality and safety of meat. Hyperspectral imaging can obtain spectral and spatial information of targets simultaneously and has been developed for rapid and nondestructive determination and identification of chemical information of muscle foods. This review focuses on recent applications of hyperspectral imaging technology for the measurement and analysis of chemical composition of muscle foods, including moisture content, fat and fatty acid, pH, protein content, pigment, salt content and freshness attributes. The fundamentals of hyperspectral imaging as well as future development trends are also presented and discussed.

Comparison of Spectral and Image Morphological Analysis for Egg Early Hatching Property Detection Based on Hyperspectral Imaging

The use of non-destructive methods to detect egg hatching properties could increase efficiency in commercial hatcheries by saving space, reducing costs, and ensuring hatching quality. For this purpose, a hyperspectral imaging system was built to detect embryo development and vitality using spectral and morphological information of hatching eggs. A total of 150 green shell eggs were used, and hyperspectral images were collected for every egg on day 0, 1, 2, 3 and 4 of incubation. After imaging, two analysis methods were developed to extract egg hatching characteristic. Firstly, hyperspectral images of samples were evaluated using Principal Component Analysis (PCA) and only one optimal band with 822 nm was selected for extracting spectral characteristics of hatching egg. Secondly, an image segmentation algorithm was applied to isolate the image morphologic characteristics of hatching egg. To investigate the applicability of spectral and image morphological analysis for detecting egg early hatching properties, Learning Vector Quantization neural network (LVQNN) was employed. The experimental results demonstrated that model using image morphological characteristics could achieve better accuracy and generalization than using spectral characteristic parameters, and the discrimination accuracy for eggs with embryo development were 97% at day 3, 100% at day 4. In addition, the recognition results for eggs with weak embryo development reached 81% at day 3, and 92% at day 4. This study suggested that image morphological analysis was a novel application of hyperspectral imaging technology to detect egg early hatching properties.