Near-infrared Spectral Imaging Research Articles

Retraction Note: Application of near-infrared spectral imaging and artificial intelligence classification in basketball motion image recognition

Retraction Note: Application of near-infrared spectral imaging and artificial intelligence classification in basketball motion image recognition

Nutritional Quality Analysis and Classification Detection of Buckwheat in Different Harvest Periods.

For buckwheat, the optimal harvest period is difficult to determine-too early or too late a harvest affects the nutritional quality of buckwheat. In this paper, physical and chemical tests are combined with a method using near-infrared spectroscopy nondestructive testing technology to study buckwheat harvest and determine the optimal harvest period. Physical and chemical tests to determine the growth cycle were performed at 83 days, 90 days, 93 days, 96 days, 99 days, and 102 days, in which the buckwheat grain starch, fat, protein, total flavonoid, and total phenol contents were assessed. Spectral images of buckwheat in six different harvest periods were collected using a near-infrared spectral imaging system. Four preprocessing methods (SNV, S-G, DWT, and the normaliz function) and three dimensionality reduction algorithms (IVSO, VCPA, VISSA) were used to process the raw buckwheat spectral data, and the full and eigen spectra were established as a random forest (RF). Random forest (RF) and Least Squares Support Vector Machine (LS-SVM) classification models were used to determine the full and eigen spectra, respectively, and the optimal model for the buckwheat single harvest period was determined and validated. Through physical and chemical tests, it was concluded that the 90-day harvest buckwheat grain protein, fat, and starch contents were the highest, and that the total flavonoid and total phenolic contents were also high. The SNV preprocessing method was the most effective, and the feature bands extracted using the IVSO algorithm were more representative. The IVSO-RF model was the best discriminative model for the classification of buckwheat in different harvest periods, with the correct rates of the training and prediction sets reaching 100% and 96.67%, respectively. When applying the IVSO-RF model to the buckwheat single harvest period to verify the classification, the correct rate of the training set for each harvest period reached 96%, and that of the prediction set reached 100%. Near-infrared spectroscopy combined with the IVSO-RF modeling method for buckwheat harvest period detection is a rapid, nondestructive classification method. When this was combined with physical and chemical analyses, it was determined that a growth cycle of 90 days is the best harvest period for buckwheat. The results of this study can not only improve the quality of buckwheat crops but also be applied to other crops to determine their optimal harvest period.

Geographical and seasonal variations of gravity wave activities in the upper mesosphere measured by space-borne imaging of molecular oxygen nightglow

Geographical and seasonal variations of gravity wave events in the upper mesosphere were investigated using the nightglow imaging data obtained by the Visible and near-Infrared Spectral Imager (VISI) on the Ionosphere, Mesosphere, upper Atmosphere and Plasmasphere (IMAP) onboard the International Space Station (ISS). The nadir-imaging data of the O2(0–0) atmospheric band (762 nm) with the typical emission peak around 95 km altitude was used to investigate small-scale waves (horizontal wavelengths less than ~ 200 km) on a global scale. To detect gravity wave events, the variance of high-pass filtered nightglow images within a local 100 km radius was evaluated, with a threshold set at three times the standard deviation from the average variance of the background level. A data screening algorithm that evaluates the variance of upwelling contamination light emission was also introduced to remove contaminated data. Applying the variance filter and data screening algorithm to a nearly 3-year data set, from November 2012 to August 2015, occurrence maps of wave events for four seasons were derived. The occurrence maps show a higher frequency of wave events in winter high latitudes (> 40° N/S), considerably attributed to gravity wave activity associated with the polar night jet. Hot spots were observed near orographic sources in winter high latitudes, including the eastern part of North America, Europe, and the southern Andes. In the summer hemisphere, hot spots were detected at mid-to-high latitudes such as North America, Europe, and the eastern side of the Eurasian continent, and at equatorial latitudes just above the intertropical convection zone (ITCZ). They are likely gravity waves from deep convection that arise from mid-latitude summertime thunderstorms and the ITCZ, respectively. During the equinox seasons, hot spots were detected near convective sources such as the Amazon Rainforest, Congo Rainforest, and the Indochina peninsula.Graphical

Meta-Attention Network Based Spectral Reconstruction with Snapshot Near-Infrared Metasurface.

Near-infrared (NIR) spectral information is important for detecting and analyzing material compositions. However, snapshot NIR spectral imaging systems still pose significant challenges owing to the lack of high-performance NIR filters and bulky setups, preventing effective encoding and integration with mobile devices. This study introduces a snapshot spectral imaging system that employs a compact NIR metasurface featuring 25 distinct C4 symmetry structures. Benefitting from the sufficient spectral variety and low correlation coefficient among these structures, center-wavelength accuracy of 0.05nm and full width at half maximum accuracy of 0.13nm are realized. The system maintains good performance within an incident angle of 1°. A novel meta-attention network prior iterative denoising reconstruction (MAN-IDR) algorithm is developed to achieve high-quality NIR spectral imaging. By leveraging the designed metasurface and MAN-IDR, the NIR spectral images, exhibiting precise textures, minimal artifacts in the spatial dimension, and little crosstalk between spectral channels, are reconstructed from a single grayscale recording image. The proposed NIR metasurface and MAN-IDR hold great promise for further integration with smartphones and drones, guaranteeing the adoption of NIR spectral imaging in real-world scenarios such as aerospace, health diagnostics, and machine vision.

Utilizing Near-Infrared Spectral Imaging for Rock Hardness Assessment

Utilizing Near-Infrared Spectral Imaging for Rock Hardness Assessment

Application of near-infrared spectral imaging and artificial intelligence classification in basketball motion image recognition

Application of near-infrared spectral imaging and artificial intelligence classification in basketball motion image recognition

An innovative fusion feature method of spectrum and visual image for diagnosing ‘Akizuki’ pear cork spot disorder

Cork spot disorder is a common physiological disease that severely affects the quality and yield of the ‘Akizuki’ pear. In this study, we propose a novel method, Near-infrared spectra and Visual images with Neural Architecture Search (NV-NAS), to achieve a highly accurate diagnosis of cork spot disorder. This method automatically generates fusion features by combining near-infrared spectral and visual image features and then searches for the best-performing model structure. The best result was obtained with near-infrared spectral features preprocessed by first derivatives (FD) and the visual image features extracted by DenseNet121 in the NV-NAS method. On the validation set, the accuracy was 95.5%, and on the test set, the accuracy was 88.2%. Compared to the modeling results of a single feature, the accuracy improved by 26.3% over spectra and by 11.2% over images on the test set. Additionally, in experiments with models of different depths, deep image features were more conducive to feature fusion, with accuracy improvements of 21.7% and 30.1% compared to features from the middle or bottom layers, respectively. The results indicate that the model built on the fusion feature shows high generalization performance and holds significant reference value for the more accurate diagnosis of the ‘Akizuki’ pear cork spot disorder.

Combining Vis-NIR and NIR Spectral Imaging Techniques with Data Fusion for Rapid and Nondestructive Multi-Quality Detection of Cherry Tomatoes.

Firmness, soluble solid content (SSC) and titratable acidity (TA) are characteristic substances for evaluating the quality of cherry tomatoes. In this paper, a hyper spectral imaging (HSI) system using visible/near-infrared (Vis-NIR) and near-infrared (NIR) was proposed to detect the key qualities of cherry tomatoes. The effects of individual spectral information and fused spectral information in the detection of different qualities were compared for firmness, SSC and TA of cherry tomatoes. Data layer fusion combined with multiple machine learning methods including principal component regression (PCR), partial least squares regression (PLSR), support vector regression (SVR) and back propagation neural network (BP) is used for model training. The results show that for firmness, SSC and TA, the determination coefficient R2 of the multi-quality prediction model established by Vis-NIR spectra is higher than that of NIR spectra. The R2 of the best model obtained by SSC and TA fusion band is greater than 0.9, and that of the best model obtained by the firmness fusion band is greater than 0.85. It is better to use the spectral bands after information fusion for nondestructive quality detection of cherry tomatoes. This study shows that hyperspectral imaging technology can be used for the nondestructive detection of multiple qualities of cherry tomatoes, and the method based on the fusion of two spectra has a better prediction effect for the rapid detection of multiple qualities of cherry tomatoes compared with a single spectrum. This study can provide certain technical support for the rapid nondestructive detection of multiple qualities in other melons and fruits.

Perspectives on Novel Technologies of Processing and Monitoring the Safety and Quality of Prepared Food Products.

With the changes of lifestyles and rapid growth of prepared food industry, prepared fried rice that meets the consumption patterns of contemporary young people has become popular in China. Although prepared fried rice is convenient and nutritious, it has the following concerns in the supply chain: (1) susceptible to contamination by microorganisms; (2) rich in starch and prone to stall; and (3) vegetables in the ingredients have the issues of water loss and discoloration, and meat substances are vulnerable to oxidation and deterioration. As different ingredients are used in prepared fried rice, their food processing and quality monitoring techniques are also different. This paper reviews the key factors that cause changes in the quality of prepared fried rice, and the advantages and limitations of technologies in the processing and monitoring processes. The processing technologies for prepared fried rice include irradiation, high-voltage electric field, microwave, radio frequency, and ohmic heating, while the quality monitoring technologies include Raman spectral imaging, near-infrared spectral imaging, and low-field nuclear magnetic resonance technology. These technologies will serve as the foundation for enhancing the quality and safety of prepared fried rice and are essential to the further development of prepared fried rice in the emerging market.

Portable near-infrared spectral imaging combining deep learning and chemometrics for dry matter and soluble solids prediction in intact kiwifruit

A novel case of developing a portable spectral imaging device for kiwifruit analysis is presented. Furthermore, a new complementary spectral image processing strategy combining deep learning and advanced chemometric is proposed for processing the spectral images. The deep learning was used for detection and localisation of harvested fruit in the spectral image while the chemometric modelling was used to predict multiple fruit quality related properties i.e., dry matter and soluble solids content. The developed models were independently validated on fruit harvested from a different orchard as well as on a different variety. The one touch spectral imaging presented in this paper can allow widespread usage of spectral imaging for fresh fruit analysis, particularly benefitting non-experts in spectral imaging and chemometrics to routinely use the spectral imaging for fresh fruit analysis.

Honey quality detection based on near-infrared spectroscopy

As a natural agricultural product, honey is favored by consumers, and its variety and adulteration have a huge impact on the quality. Acacia honey, red jujube honey and rape honey were used as experimental objects, and their spectral reflectance curves were obtained through a near-infrared spectral image acquisition system. Spectral features were extracted from the preprocessed spectral reflectance curves, and a honey variety classification model based on near-infrared spectral features was established by machine learning. After statistical analysis, Principal Component Analysis Support Vector Machine after processing data through Successive Projections Algorithm (SPA-SVM) is the optimal classification model for three varieties of acacia honey, red jujube honey and rape honey, and the correct rate of honey variety classification reaches 95.83%. The spectral reflectance curve was used to establish a honey adulteration identification model based on the partial least squares-discriiminate analysis (PLS-DA), and the classification accuracy was 97.92% in the test set.

Effects of Baduanjin imagery and exercise on cognitive function in the elderly: A functional near-infrared spectroscopy study.

ObjectiveCognitive function is essential in ensuring the quality of life of the elderly. This study aimed to investigate the effects of Baduanjin imagery and Baduanjin movement (a traditional Chinese health exercise, TCHE) on cognitive function in the elderly using functional near-infrared spectroscopy (fNIRS).Methods72 participants with a mean age of 66.92 years (SD = 4.77) were recruited for this study. The participants were randomly assigned to three groups: the Baduanjin imagery, the Baduanjin exercise, and the Control. Stroop task was used to record the accuracy and reaction times, and a near-infrared spectral brain imaging system was used to monitor the brain's oxy-hemoglobin concentration responses.Results(1) For the reaction times of Stroop incongruent tasks, the main effect of the test phase (F = 114.076, p < 0.001) and the interaction effect between test phase and group (F = 10.533, p < 0.001) were all significant. The simple effect analysis further demonstrated that the reaction times of the Baduanjin imagery group and Baduanjin exercise group in the post-test was faster than that in the pre-test (ps < 0.001); (2) Analysis of fNIRS data showed the significant interaction effect (F = 2.554, p = 0.013) between the test phase and group in the left dorsolateral prefrontal cortex. Further analysis showed that, during the post-test incongruent tasks, the oxy-Hb variations were significantly higher in participants of the Baduanjin imagery group (p = 0.005) and Baduanjin exercise group (p = 0.002) than in the control group; For the right inferior frontal gyrus, the interaction between the test phase and group was significant (F = 2.060, p = 0.044). Further analysis showed that, during the post-test incongruent tasks, the oxy-Hb variations were significantly higher in participants of the Baduanjin imagery group than in the control group (p = 0.001).ConclusionBaduanjin imagery and exercise positively affect cognitive performance; Baduanjin imagery and exercise activated the left dorsolateral prefrontal cortex; Baduanjin imagery activated the right inferior frontal gyrus, while Baduanjin exercise could not.

Monitoring of Nitrogen Transport in Pear Trees Based on Ground Hyperspectral Remote Sensing and Digital Image Information

To study nitrogen transport monitoring in pear trees based on ground-based hyperspectral remote sensing and digital image information. First, based on ground hyperspectral remote sensing and digital image information, combined with the characteristics of visible and near-infrared spectral data and digital image data, a beet nitrogen nutrition diagnosis model based on visible and near-infrared spectral and digital image information was established. The results showed that under water and nitrogen management conditions, the marked nitrogen use efficiency at soil profiles 15, 45, and 75 cm was 28.2%, 22.3%, and 16.3%, respectively. From the analysis of soil and plant nitrogen measured in this experiment, it can be seen that nitrogen nitrate fertilizer should be properly applied to pear trees in the future.

Invisible NIR Spectral Imaging and Laser‐Induced Thermal Imaging of Na(Nd/Y)F4@glass with Opposite Effect for Optical Security

AbstractCurrently, advanced optical security techniques are imminent in response to the increasingly rampant counterfeit and pirated goods on a global scale. Herein, hexagonal Na(NdxY1−x)F4 (x = 0–1) solid‐solution nanocrystals (NCs) embedded glasses are successfully fabricated via in situ glass crystallization. Benefited from the protection of inorganic glass, Na(NdxY1−x)F4 NCs exhibit superior long‐term stability and can endure high‐power 808 nm laser irradiation. Importantly, opposite variations between near‐infrared (NIR) luminescence and laser‐induced temperature can be detected for the nanocomposites, which are sensitive to Nd3+ doping concentration. As a proof‐of‐concept experiment, Nd3+ concentration dependent anti‐counterfeiting patterns with high‐concealment and high‐precision are designed and show bright emission and dimmish thermal images in Nd3+‐tenuous composites under the irradiation of 808 nm laser, while opposite displays can be achieved in Nd3+‐enriched one. This kind of information encryption triggered by a commercial single light source to achieve dual opposing displays in one material can effectively improve security in anti‐counterfeiting.

Effects of Acute Moderate- and High-Intensity Aerobic Exercise on Oxygenation in Prefrontal Cortex of Male Methamphetamine-Dependent Patients.

The aim of this study was to explore the influence of different intensities of acute aerobic exercise on brain activation in male methamphetamine (MA)-dependent patients during exercise. Twenty MA-dependent patients were divided randomly into two groups participating in 35 min of either moderate- or high-intensity aerobic exercise. Functional near-infrared spectral imaging (fNIRS) was used to detect hemodynamic changes in prefrontal cortex during the main 25-min exercise stage. The results revealed that high-intensity acute aerobic exercise aroused more cerebral oxygenation changes in the prefrontal cortex and left dorsolateral prefrontal cortex during exercise as compared with moderate-intensity exercise. Furthermore, there was a stronger positive connection observed between orbital frontal cortex and left dorsolateral prefrontal cortex in the high-intensity group than in the moderate-intensity group. Together these results suggest that for submaximal exercise intensities, high-intensity exercise may bring more benefits to male MA-dependent patients than moderate-intensity.

A novel proposal to investigate the interplay between the spatial and spectral domains in near-infrared spectral imaging data by means of Image Decomposition, Encoding and Localization (IDEL)

The emergence of new spectral imaging applications in many science fields and in industry has not come to be a surprise, considering the immense potential this technique has to map spectral information. In the case of near-infrared spectral imaging, a rapid evolution of the technology has made it more and more appealing in non-destructive analysis of food and materials as well as in process monitoring applications. However, despite its great diffusion, some challenges remain open from the data analysis point of view, with the aim to fully uncover patterns and unveil the interplay between both the spatial and spectral domains. Here we propose a new approach, called Image Decomposition, Encoding and Localization (IDEL), where a spatial perspective is taken for the analysis of spectral images, while maintaining the significant information within the spectral domain. The methodology benefits from wavelet transform to exploit spatial features, encoding the outcoming images into a set of descriptors and utilizing multivariate analysis to isolate and extract the significant spatial-spectral information. A forensic case study of near-infrared images of biological stains on cotton fabrics is used as a benchmark. The stain and fabric have hardly distinguishable spectral signatures due to strong scattering effects that originate from the rough surface of the fabric and the high spectral absorbance of cotton in the near-infrared range. There is no selective information that can isolate signals related to these two components in the spectral images under study, and the complex spatial structure is highly interconnected to the spectral signatures. IDEL was capable of isolating the stains, (spatial) scattering effects, and a possible drying effect from the stains. It was possible to recover, at the same time, specific spectral regions that mostly highlight these isolated spatial structures, which was previously unobtainable.

Chemometric approaches for calibrating high-throughput spectral imaging setups to support digital plant phenotyping by calibrating and transferring spectral models from a point spectrometer

Visible and near-infrared (Vis-NIR) spectral imaging is appearing as a potential tool to support high-throughput digital agricultural plant phenotyping. One of the uses of spectral imaging is to predict non-destructively the chemical constituents in the plants such as nitrogen content which can be related to the functional status of plants. However, before using high-throughput spectral imaging, it requires extensive calibration, just as needed for any other spectral sensor. Calibrating the high-throughput spectral imaging setup can be a challenging task as the resources needed to run experiments in high-throughput setups are far more than performing measurements with point spectrometers. Hence, to supply a resource-efficient approach to calibrate spectral cameras integrated with high-throughput plant phenotyping setups, this study proposes the use of chemometric calibration transfer (CT) and model update. The main idea was to use a point spectrometer to develop the primary model and transfer it to the spectral cameras integrated into the high-throughput setups. The potential of the approach was showed using a real Vis-NIR dataset related to nitrogen prediction in wheat plants measured with point spectrometer, tabletop spectral cameras and spectral cameras integrated with a high-throughput plant phenotyping setup. For CT and model update, direct standardization and parameter-free calibration enhancement approaches were explored. A key aim of this study was to only use and compare techniques that does not require any further optimization as they can be easily implemented by the plant biologist in future applications. The proposed approach based on the transfer of point spectroscopy models to spectral cameras in a high-throughput setup can allow spectral calibrations to be sharable and widely applicable, thus helping the global digital plant phenotyping community.

A generic workflow combining deep learning and chemometrics for processing close-range spectral images to detect drought stress in Arabidopsis thaliana to support digital phenotyping

Close-range spectral imaging (SI) of agricultural plants is widely performed for digital plant phenotyping. A key task in digital plant phenotyping is the non-destructive and rapid identification of drought stress in plants so as to allow plant breeders to select potential genotypes for breeding drought-resistant plant varieties. Visible and near-infrared SI is a key sensing technique that allows the capture of physicochemical changes occurring in the plant under drought stress. The main challenges are in processing the massive spectral images to extract information relevant for plant breeders to support genotype selection. Hence, this study presents a generic data processing workflow for analysing SI data generated in real-world digital phenotyping experiments to extract meaningful information for decision making by plant breeders. The workflow is a combination of chemometric approaches and deep learning. The usefulness of the proposed workflow is demonstrated on a real-life experiment related to drought stress detection and quantification in Arabidopsis thaliana plants grown in a semi-controlled environment. The results show that the proposed approach is able to detect the presence of drought just 3 days after its induction compared to the well-watered plants. Furthermore, the unsupervised clustering approach provides detailed time-series images where the drought-related changes in plants can be followed visually along the time course. The developed approach facilitates digital phenotyping and can thus accelerate breeding of drought-tolerant plant varieties.

GAN meets chemometrics: Segmenting spectral images with pixel2pixel image translation with conditional generative adversarial networks

In analytical chemistry, spectral imaging of complex analytical systems is commonly performed. A major task in spectral imaging analysis is to extract signals related to important analytes present in the imaged scene. Hence, the first task of spectral image analysis is to perform an image segmentation to extract the relevant signals to analyze. However, in the chemometric domain, the traditional image segmentation methods are limited either to threshold-based or pixel-wise classification, therefore, no approache uses the contextual information present in the imaging scene. This study presents for the first time a pixel2pixel (p2p) image translation using conditional generative adversarial networks (cGAN) for the segmentation of spectral images. The p2p cGAN trains two neural models simultaneously where one model (generator) learns to segment and the other model learns to detect if the segmentation performed by the generator model is correct. During the process of generation and detection, the model automatically learns to segment the spectral images accurately. As an application of the p2p cGAN, a case of segmenting visible and near-infrared spectral images of plants was presented. Furthermore, as a comparison, threshold-based and pixel-wise image classification based on partial least-square discriminant analysis were also presented. The results showed that the p2p cGAN based image translation performed the best segmentation task with an intersection over union score of 0.95 ​± ​0.04. The advanced new DL based image processing approaches can complement spectral image processing.

Complementary chemometrics and deep learning for semantic segmentation of tall and wide visible and near-infrared spectral images of plants

Close range spectra imaging of agricultural plants is widely performed to support digital plant phenotyping, a task where physicochemical changes in plants are monitored in a non-destructive way. A major step before analyzing the spectral images of plants is to distinguish the plant from the background. Usually, this is an easy task and can be performed using mathematical operations on the combinations of selected spectral bands, such as estimating the normalized difference vegetative index (NDVI). However, when the background of plants contains objects with similar spectral properties as plant then the segmentation based on the threshold of NDVI images can suffer. Another common approach is to train pixel classifiers on spectra extracted from selected locations in the spectral image, but such an approach does not take the spatial information about the plant structure into account. From a technical perspective, plant spectral imaging for digital phenotyping applications usually involves imaging several plants together for a comparative purpose, hence, the imaging scene is relatively big in terms of memory. To solve the challenge of plant segmentation and handling the memory challenge, this study proposes a novel approach, which combines chemometrics with advanced deep learning (DL) based semantic segmentation. The approach has four key steps. As a first step, the spectral image is pre-processed to reduce illumination effects present in the close-range spectral images of plants resulting from the interaction of light with complex plant geometry. Different chemometric pre-processing methods were explored to find possible improvements in the segmentation performance of the DL model. The second step was to perform a principal components analysis (PCA) to reduce the dimensionality of the images, thus drastically reducing their size so that they can be handled more easily using the available computer memory during the training of the DL model. As the third step, small random images (128 × 128) were subsampled from the tall and wide image matrices to generate the training and validation sets for training the DL models. In the last step, a U-net based deep semantic segmentation model was trained and validated on the sub-sampled spectral images. The results showed that the proposed approach allowed efficient handling and training of the DL segmentation model. The intersection over union (IoU) scores for the segmentation was 0.96 for the independent test set image. The segmentation based on variable sorting for normalization and standard normal variate pre-processed data achieved the highest IoU scores. A combination of chemometrics and DL led to an efficient segmentation of tall and wide spectral images which otherwise would have given out-of-memory errors. The developed method can facilitate digital phenotyping tasks where close-range spectral imaging is used to estimate the physicochemical properties of plants.