Pushbroom Hyperspectral Imaging System Research Articles

Pushbroom hyperspectral imaging system with selectable region of interest for medical imaging.

A spatial-scanning pushbroom hyperspectral imaging (HSI) system incorporating a video camera (VC) which is not only used for direct video imaging but also for the selection of the region of interest within the VC’s full field-of-view is presented. Using a VC for these two applications brings many benefits to a pushbroom HSI system, such as a minimized data acquisition time and smaller data storage requirement. A detailed description of the system followed by the methods and formulas used for calibration and electronic hardware interfacing were discussed and analyzed using United States Air Force resolution chart, chicken breast tissue, and fluorescent targets as test samples. The proposed concepts and developed system can find potential biomedical imaging applications and can be extended to endoscopic imaging applications as well.

Use of radiofrequency power to enable glow discharge optical emission spectroscopy ultrafast elemental mapping of combinatorial libraries with nonconductive components: nitrogen-based materials.

Combinatorial chemistry and high-throughput techniques are an efficient way of exploring optimal values of elemental composition. Optimal composition can result in high performance in a sequence of material synthesis and characterization. Materials combinatorial libraries are typically encountered in the form of a thin film composition gradient which is produced by simultaneous material deposition on a substrate from two or more sources that are spatially separated and chemically different. Fast spatially resolved techniques are needed to characterize structure, composition, and relevant properties of these combinatorial screening samples. In this work, the capability of a glow discharge optical emission spectroscopy (GD-OES) elemental mapping system is extended to nitrogen-based combinatorial libraries with nonconductive components through the use of pulsed radiofrequency power. The effects of operating parameters of the glow discharge and detection system on the achievable spatial resolution were investigated as it is the first time that an rf source is coupled to a setup featuring a push-broom hyperspectral imaging system and a restrictive anode tube GD source. Spatial-resolution optimized conditions were then used to characterize an aluminum nitride/chromium nitride thin-film composition spread. Qualitative elemental maps could be obtained within 16.8 s, orders of magnitude faster than typical techniques. The use of certified reference materials allowed quantitative elemental analysis maps to be extracted from the emission intensity images. Moreover, the quantitative procedure allowed correcting for the inherent emission intensity inhomogeneity in GD-OES. The results are compared to quantitative depth profiles obtained with a commercial GD-OES instrument.

Modeling and compensating non-uniformity in push-broom NIR hyperspectral imaging system

In this paper, a novel pixel-based model for the response of push-broom near infrared (NIR) hyperspectral cameras based on focal-plane array (FPA) photo-detectors is proposed. The model focuses on practical issues affecting the system response, such as the spatial and spectral non-uniformity (NU) observed in NIR hyperspectral imaging systems, and its novelty relies on considering the operating temperature of the FPA as a signal perturbing the response of the entire system. These effects have been estimated by experimental procedures. The applicability and accuracy of the proposed model has been assessed by mitigating for the spatial and spectral non-uniform responses of a real NIR hyperspectral imaging sequence.

Application of hyperspectral imaging technology to discriminate different geographical origins of Jatropha curcas L. seeds

A rapid and non-invasive method was investigated to identify the geographical origin of Jatropha curcas L. seeds in China by using near-infrared hyperspectral imaging technique on the wavelength between 874 and 1734nm. Two hundred and forty J. curcas L. seed samples from four different geographical origins (Jiangsu, Sichuan, Hainan and Taiwan) in China were studied and all of them were scanned by a pushbroom hyperspectral imaging system. Then the obtained data sets were analyzed by spectral and image processing technique respectively. Successive projections algorithm (SPA) was used for selecting effective wavelengths. Dimension reduction was carried out on the region of interest (ROI) image by principal component analysis (PCA). The first principal component (PC) explained over 92% of variances of all spectral bands. Gray-level co-occurrence matrix (GLCM) analysis was implemented on the principal component (PC) image to extract 5 textural feature variables in total. Moreover, 7 morphological features of samples were computed additionally. Then least squares-support vector machine (LS-SVM) classification models were built based on the extracted spectral, textural, morphological, combined spectral and textural, combined spectral and morphological, combined textural and morphological, combined spectral, textural and morphological features, respectively. The satisfactory results show the correct discrimination rate of 93.75% for the prediction samples based on spectral and morphological features. The study demonstrated that hyperspectral image technique can be a reliable tool for discriminating different geographical origins of J. curcas L. seeds. The above results indicated that this objective and non-destructive method can be utilized for quality control purposes and seed breeding in future.

Non-invasive measurement of frog skin reflectivity in high spatial resolution using a dual hyperspectral approach.

BackgroundMost spectral data for the amphibian integument are limited to the visible spectrum of light and have been collected using point measurements with low spatial resolution. In the present study a dual camera setup consisting of two push broom hyperspectral imaging systems was employed, which produces reflectance images between 400 and 2500 nm with high spectral and spatial resolution and a high dynamic range.Methodology/Principal FindingsWe briefly introduce the system and document the high efficiency of this technique analyzing exemplarily the spectral reflectivity of the integument of three arboreal anuran species (Litoria caerulea, Agalychnis callidryas and Hyla arborea), all of which appear green to the human eye. The imaging setup generates a high number of spectral bands within seconds and allows non-invasive characterization of spectral characteristics with relatively high working distance. Despite the comparatively uniform coloration, spectral reflectivity between 700 and 1100 nm differed markedly among the species. In contrast to H. arborea, L. caerulea and A. callidryas showed reflection in this range. For all three species, reflectivity above 1100 nm is primarily defined by water absorption. Furthermore, the high resolution allowed examining even small structures such as fingers and toes, which in A. callidryas showed an increased reflectivity in the near infrared part of the spectrum.Conclusion/SignificanceHyperspectral imaging was found to be a very useful alternative technique combining the spectral resolution of spectrometric measurements with a higher spatial resolution. In addition, we used Digital Infrared/Red-Edge Photography as new simple method to roughly determine the near infrared reflectivity of frog specimens in field, where hyperspectral imaging is typically difficult.

Chemical-free assessment and mapping of major constituents in beef using hyperspectral imaging

Developing a rapid and non-destructive method for food safety and quality monitoring has become a crucial request from the meat industry. Hyperspectral imaging technique provides extraordinary advantages over the traditional imaging and spectroscopy techniques in food quality evaluation due to the spatial and spectral information that it can offer. In this study, a laboratory-based pushbroom hyperspectral imaging system in reflectance mode was developed in the near infrared (NIR) range (900–1700nm) for non-invasive determination of the major chemical compositions of beef. Beef samples collected from different breeds were scanned by the system followed by traditional assessment of their chemical composition by using the ordinary wet-chemical methods. The extracted spectral data and the measured quality parameters were modeled by partial least squares regression (PLSR) for predicting water, fat and protein contents yielding a reasonable accuracy with determination coefficients (RP2) of 0.89, 0.84 and 0.86 concomitant with standard error of prediction (SEP) of 0.46%, 0.65% and 0.29%, respectively. Some image processing algorithms were developed and the most relevant wavelengths were selected to visualize the predicted chemical constituents in each pixel of the hyperspectral image yielding the spatially distributed visualizations of the sample contents. The results were promising and implied that hyperspectral imaging technique associated with appropriate chemometric multivariate analyses has a great potential for simultaneous assessment of various chemical constituents without using hazardous chemical reagents.

NIR hyperspectral imaging as non-destructive evaluation tool for the recognition of fresh and frozen–thawed porcine longissimus dorsi muscles

Abstract Identification of fresh and frozen–thawed meat is an important authenticity issue, although a challenging task. The potential application of a pushbroom hyperspectral imaging system in the near-infrared (NIR) range (900–1700 nm) was explored as a rapid and non-destructive technique for the investigation of meat freshness. The effect of standard freeze–thaw routines was studied in pork samples from the longissimus dorsi muscle. Partial least squares discriminant analysis (PLS-DA) models were used to distinguish between fresh and frozen–thawed samples. Optimal wavelengths were selected and used for sample discrimination with reduced spectral data and image processing. Classification models with reduced spectral data achieved an overall correct classification of 100% for an independent set of samples. An image processing algorithm was also developed for visualizing the classification results. The best classification model obtained was successfully applied to the images to produce classification maps with high overall accuracy. Results confirmed the possibility of developing a fast and reliable system for discrimination between fresh and frozen–thawed pork based on reflectance in the NIR wavelength range. Industrial relevance The application of NIR hyperspectral analysis could enable the development of a rapid, reliable and non-destructive method for authentication of fresh meat samples for the benefit of the retail sector and the consumer.

Determination of total viable count (TVC) in chicken breast fillets by near-infrared hyperspectral imaging and spectroscopic transforms

Near infrared (NIR) hyperspectral imaging (HSI) and different spectroscopic transforms were investigated for their potential in detecting total viable counts in raw chicken fillets. A laboratory-based pushbroom hyperspectral imaging system was utilized to acquire images of raw chicken breast fillets and the resulting reflectance images were corrected and transformed into hypercubes in absorbance and Kubelka–Munck (K–M) units. Full wavelength partial least regression models were established to correlate the three spectral profiles with measured bacterial counts, and the best calibration model was based on absorbance spectra, where the correlation coefficients (R) were 0.97 and 0.93, and the root mean squared errors (RMSEs) were 0.37 and 0.57log10 colony forming units (CFU) per gram for calibration and cross validation, respectively. To simplify the models, several wavelengths were selected by stepwise regression. More robustness was found in the resulting simplified models and the model based on K–M spectra was found to be excellent with an indicative high ratio of performance to deviation (RPD) value of 3.02. The correlation coefficients and RMSEs for this model were 0.96 and 0.40log10 CFU per gram as well as 0.94 and 0.50log10 CFU per gram for calibration and cross validation, respectively. Visualization maps produced by applying the developed models to the images could be an alternative to test the adaptability of a calibration model. Moreover, multi-spectral imaging systems were suggested to be developed for online applications.

Non-destructive assessment of microbial contamination in porcine meat using NIR hyperspectral imaging

Abstract Temperature fluctuation during cold storage of meat products usually leads to undesirable microbial growths, which affect the overall product quality. In this study, a pushbroom hyperspectral imaging system in the near-infrared (NIR) range (900–1700 nm) as a rapid and non-destructive technique was exploited for determining the total viable count (TVC) and psychrotrophic plate count (PPC) in chilled pork during storage. Fresh pork samples from the longissimus dorsi muscle were obtained directly from a commercial slaughtering plant, and stored in the refrigerated temperatures at 0 °C and 4 °C for 21 days. Every 48 h, a NIR hyperspectral image in the reflectance mode was acquired directly for each sample. The TVC and PPC were determined simultaneously by classical microbiological plating methods and multivariate statistical models for predicting contamination and spoilage conditions in the samples were then developed. Partial least squares regression (PLS) was applied to fit the spectral information extracted from the samples to the logarithmic values of TVC and PPC. The best regressions were obtained with R2 of 0.86 and 0.89 for log (TVC) and log (PPC), respectively. The most important wavelengths were then selected for regression and for spatial visualization of contamination. Results are encouraging and show the promising potential of hyperspectral technology for detecting bacterial spoilage in pork and tracking the increase of microbial growth of chilled pork during storage at different temperatures. Industrial relevance: A novel method based on hyperspectral imaging technique has been successfully developed for determining the total viable count (TVC) and psychrotrophic plate count (PPC) in chilled pork during storage non-destructively for the meat industry.

Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging

Currently, blueberries are inspected and sorted by color, size and/or firmness (or softness) in packing houses, using different inspection techniques like machine vision and mechanical vibration or impact. A new inspection technique is needed for effectively assessing both external features and internal quality attributes of individual blueberries. This paper reports on the use of hyperspectral imaging technique for predicting the firmness and soluble solids content (SSC) of blueberries. A pushbroom hyperspectral imaging system was used to acquire hyperspectral reflectance images from 302 blueberries in two fruit orientations (i.e., stem and calyx ends) for the spectral region of 500–1000nm. Mean spectra were extracted from the regions of interest for the hyperspectral images of each blueberry. Prediction models were developed based on partial least squares method using cross validation and were externally tested with 25% of the samples. Better firmness predictions (R=0.87) were obtained, compared to SSC predictions (R=0.79). Fruit orientation had no or insignificant effect on the firmness and SSC predictions. Further analysis showed that blueberries could be sorted into two classes of firmness. This research has demonstrated the feasibility of implementing hyperspectral imaging technique for sorting blueberries for firmness and possibly SSC to enhance the product quality and marketability.

Hyperspectral imaging for the age estimation of blood stains at the crime scene

The age estimation of blood stains can provide important information on the temporal aspects of a crime. As previously shown, visible spectroscopy of blood stains can successfully be used for their age estimation. In the present study we evaluated the feasibility to use hyperspectral imaging for this purpose. Visible reflectance spectra of blood stains were recorded using a pushbroom hyperspectral imaging system. From these spectra, the relative amounts of oxyhemoglobin, methemoglobin and hemichrome within the blood stains were derived. By comparison of the hemoglobin derivative fractions with a reference dataset, the age of blood stains up to 200 days old was estimated. The absolute error of the age estimation task increased with age, with a median relative error of 13.4% of the actual age. To test the practical applicability of this method, a simulated crime scene was analyzed, in which blood stains of several ages were deposited. Hyperspectral imaging combined with the proposed analysis provided insight in the absolute age of the blood stains. Additionally, the blood stains were clustered based on their hemoglobin derivative fractions, without the use of a reference dataset. Results demonstrated that the order of formation of blood stains can be determined, even under unknown environmental circumstances, when no proficient reference dataset is available. These findings are an important step toward the practical implementation of blood stain age estimation in forensic casework.

Application of Visible and Near Infrared Hyperspectral Imaging to Differentiate Between Fresh and Frozen–Thawed Fish Fillets

The potential of visible and near infrared (VIS/NIR) hyperspectral imaging was investigated as a rapid and nondestructive technique to determine whether fish has been frozen–thawed. A total of 108 halibut (Psetta maxima) fillets were studied, including 48 fresh and 60 frozen–thawed (F-T) samples. Regarding the F-T samples, two speeds of freezing (fast and slow) were tested. The hyperspectral images of fillets were captured using a pushbroom hyperspectral imaging system in the spectral region of 380 to 1,030 nm. All images were calibrated for reflectance, followed by the minimum noise fraction rotation to reduce the noise. A region-of-interest (ROI) at the image center was selected, and the average spectral data were generated from the ROI image. Dimension reduction was carried out on the ROI image by principal component analysis. The first three principal components (PCs) explained over 98 % of variances of all spectral bands. Gray-level co-occurrence matrix analysis was implemented on the three PC images to extract 36 textural feature variables in total. Least squares-support vector machine classification models were developed to differentiate between fresh and F-T fish based on (1) spectral variables; (2) textural variables; (3) combined spectral and textural variables, respectively. Satisfactory average correct classification rate of 97.22 % for the prediction samples based on (3) was achieved, which was superior to the results based on (1) or (2). The results turned worse when different freezing rates were taken into consideration to classify three groups of fish. The overall results indicate that VIS/NIR hyperspectral imaging technique is promising for the reliable differentiation between fresh and F-T fish.

Application of NIR hyperspectral imaging for discrimination of lamb muscles

The potential of near-infrared (NIR) hyperspectral imaging system coupled with multivariate analysis was evaluated for discriminating three types of lamb muscles. Samples from semitendinosus (ST), Longissimus dorsi (LD) and Psoas Major (PM) of Charollais breed were imaged by a pushbroom hyperspectral imaging system with a spectral range of 900–1700 nm. Principal component analysis (PCA) was used for dimensionality reduction, wavelength selection and visualizing hyperspectral data. Six optimal wavelengths (934, 974, 1074, 1141, 1211 and 1308 nm) were selected from the eigenvector plot of PCA and then used for discrimination purpose. The results showed that it was possible to discriminate lamb muscles with overall accuracy of 100% using NIR hyperspectral reflectance spectra. An image processing algorithm was also developed for visualizing classification results in a pixel-wise scale with a high overall accuracy.

Visible/near-infrared hyperspectral imaging for beef tenderness prediction

Beef tenderness is an important quality attribute for consumer satisfaction. The current beef quality grading system does not incorporate a direct measure of tenderness because there is currently no accurate, rapid, nondestructive method for predicting tenderness available to the beef industry. The objective of this study was to develop and test a visible/near-infrared hyperspectral imaging system to predict tenderness of 14-day aged, cooked beef from hyperspectral images of fresh ribeye steaks acquired at 14-day post-mortem. A pushbroom hyperspectral imaging system (wavelength range: 400–1000 nm) with a diffuse-flood lighting system was developed and calibrated. Hyperspectral images of beef-steak ( n = 111) at 14-day post-mortem were acquired. After imaging, steaks were cooked and slice shear force (SSF) values were collected as a tenderness reference. All images were corrected for reflectance. After reflectance calibration, a region-of-interest (ROI) of 200 × 600 pixels at the center was selected and principal component analysis was carried out on the ROI images to reduce the dimension along the spectral axis. The first five principal components explained over 90% of the variance of all spectral bands in the image. Gray-level textural co-occurrence matrix analysis was conducted to extract second-order statistical textural features from the principal component images. These features were then used in a canonical discriminant model to predict three beef tenderness categories, namely tender (SSF ≤ 205.80 N), intermediate (205.80 N < SSF < 254.80 N), and tough (SSF ≥ 254.80 N). With a leave-one-out cross-validation procedure, the model predicted the three tenderness categories with a 96.4% accuracy. All of the tough samples were correctly identified. Our results indicate that hyperspectral imaging has considerable promise for predicting beef tenderness.

Partial least squares analysis of near-infrared hyperspectral images for beef tenderness prediction

Tenderness is a primary determinant of consumer satisfaction of beef steaks. The objective of this study was to implement and test near-infrared (NIR) hyperspectral imaging to forecast 14-day aged, cooked beef tenderness from the hyperspectral images of fresh ribeye steaks (n = 319) acquired at 3–5 day post-mortem. A pushbroom hyperspectral imaging system (wavelength range: 900–1700 nm) with a diffuse-flood lighting system was developed. After imaging, steaks were vacuum-packaged and aged until 14 days postmortem. After aging, the samples were cooked and slice shear force (SSF) values were collected as a tenderness reference. After reflectance calibration, a Region-of-Interest (ROI) of 150 × 300 pixels at the center of longissimus muscle was selected. Partial least squares regression (PLSR) was carried out on each ROI image to reduce the dimension along the spectral axis. Gray-level textural co-occurrence matrix analysis with two quantization levels (64 and 256) was conducted on the PLSR bands to extract second-order statistical textural features. These features were then used in a canonical discriminant model to predict three beef tenderness categories, namely tender (SSF ≤ 205.80 N), intermediate (205.80 N < SSF < 254.80 N), and tough (SSF ≥ 254.80 N). The model with a quantization level of 256 performed better than the one with a quantization level of 64. This model correctly classified 242 out of 314 samples with an overall accuracy of 77.0%. Fat, protein, and water absorption bands were identified between 900 and 1700 nm. Our results show that NIR hyperspectral imaging holds promise as an instrument for forecasting beef tenderness.

Study on microscope hyperspectral medical imaging method for biomedical quantitative analysis

A microscopic pushbroom hyperspectral imaging system was developed based on the microscopic technology and spectral imaging technology according to the principle of spectral imager in remote sensing. The basic principle and key technologies of this system were presented and the system performance was also analyzed. Some methods and algorithms were proposed to preprocess and normalize the microscopic hyperspectral data and retrieve the transmittance spectrum of samples. As a case study, the microscopic hyperspectral imaging system was used to image the retina sections of different rats and get some significant results. Experiment results show that the system can be used for the quantitative assessment and evaluating the effect of medication in biomedical research.

New microscopic pushbroom hyperspectral imaging system for application in diabetic retinopathy research

To aid ophthalmologists in determining the pathogenesis of diabetic retinopathy and in evaluating the effects of medication, a microscopic pushbroom hyperspectral imaging system is developed. 40 healthy Wistar rats of half gender are selected in this study. They are divided into three groups (six rats failed to be models). 10 normal rats as the normal control group, 12 diabetic rats without any treatment as the model control group, and another 12 diabetic rats treated with LCVS1001 as the LCVS1001 group. The microscopic hyperspectral image of each retina section is collected and processed. Some typical spectrum curves between 400 and 800 nm of the outer nuclear layer are extracted, and images at various wavelengths are analyzed. The results show that a small trough appears near 522.2 nm in the typical spectrum curve of the model control group, and the transmittance of it is higher than that of the normal control group. In addition, the spectrum of the LCVS1001 group changes gradually to the normal spectrum after treatment with LCVS1001. Our findings indicate that LCVS1001 has some therapeutic effect on the diabetic retinopathy of rats, and the microscopic pushbroom hyperspectral imaging system can be used to study the pathogenesis of diabetic retinopathy.

CALIBRATION OF A PUSHBROOM HYPERSPECTRAL IMAGING SYSTEM FOR AGRICULTURAL INSPECTION

A method to calibrate a pushbroom hyperspectral imaging system has been demonstrated for use in agriculturalinspection where the imaged object is close to the imaging system. The method consists of a modified geometric control pointcorrection to remove smile and keystone effect from the system, and both wavelength and distance calibrations to reduce thewavelength and distance errors to less than 0.5 nm and 0.01 mm (across entrance slitwidth), respectively. Next, apixelbypixel percent reflectance calibration was performed at all wavelengths with dark current and 99% reflectancecalibrationpanel measurements, and results were verified with measurements on a certified gradient Spectralon panel withnominal values ranging from 12% to 99%. Results indicate that the method is capable of calibrating the hyperspectral systemacross the entire spectral range of the detector, but errors increase below 420 nm and above 840 nm. Further research shouldbe performed to evaluate the stability of the calibration over time, and techniques must be developed to implement thecalibration for realtime analysis.