Line-scan Hyperspectral Imaging System Research Articles

Fertility detection of unincubated chicken eggs by hyperspectral transmission imaging in the Vis-SWNIR region

Detection of infertile eggs prior to incubation can lead to an increase in the hatchability rate and prevent the wastage of billions of non-fertile eggs ended up by failed incubation. In this study, the feasibility of a line-scan hyperspectral imaging system in the visible and short-wavelength near-infrared region was assessed for early detection of non-fertile eggs on day 0 before incubation. A total of 227 white-shell eggs including 131 fertile and 96 infertile eggs were collected from a flock with similar conditions in terms of hen age, feeding, and management. Hyperspectral images of eggs were captured on day 0 before incubation in a transmittance mode of illumination and then the eggs were incubated in a commercial incubator. The edge detection method was used to segment the egg, including both the white and yolk, from the background, and the image spectral information was extracted from the egg region. After applying various pretreatment methods, different classifiers including soft independent modeling of class analogy (SIMCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and artificial neural networks (ANN) classifiers were utilized to extract the predictive models. Following the acceptable results of SIMCA analysis accomplished by 1st derivative pretreatment (accuracy of 86.67%), the discrimination power plot was used to select the most informative wavebands. The results showed that by using fewer variables in effective wavebands better performance (precision and accuracy of 92.59% and 93.33%, respectively) could be obtained in comparison with the ANN classifier based on the whole spectral data (precision and accuracy of 89.29% and 91.11%, respectively). This study revealed the potential application of hyperspectral transmittance imaging in the Vis-SWNIR region to discern the fertile and infertile eggs before starting the incubation process.

Non-destructive estimation of needle leaf chlorophyll and water contents in Chinese fir seedlings based on hyperspectral reflectance spectra.

Chinese fir is the most important native softwood tree in China and has significant economic and ecological value. Accurate assessment of the growth status is critical for both seedling cultivation and germplasm evaluation of this commercially significant tree. Needle leaf chlorophyll content (LCC) and needle leaf water content (LWC), which are determinants of plant health and photosynthetic efficiency, are important indicators of the growth status in plants. In this study, for the first time, the LCC and LWC of Chinese fir seedlings were estimated based on hyperspectral reflectance spectra and machine learning algorithms. A line-scan hyperspectral imaging system with a spectral range of 870 to 1,720 nm was used to capture hyperspectral images of seedlings with varying LCC and LWC. The spectral data of the canopy area of the seedlings were extracted and preprocessed using the Savitzky-Golay smoothing (SG) algorithm. Subsequently, the Successive Projection Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) methods were employed to extract the most informative wavelengths. Moreover, SVM, PLSR and ANNs were utilized to construct models that predict LCC and LWC based on effective wavelengths. The results indicated that the CARS-ANNs were the best for predicting LCC, with R²C = 0.932, RSMEC = 0.224, and R²P = 0.969, RSMEP = 0.157. Similarly, the SPA-ANNs model exhibited the best prediction performance for LWC, with R²C = 0.952, RSMEC = 0.049, and R²P = 0.948, RSMEP = 0.051. In conclusion, the present study highlights the significant potential of combining hyperspectral imaging (HSI) with machine learning algorithms as a rapid, non-destructive, and highly accurate method for estimating LCC and LWC in Chinese fir.

Towards the non-invasive assessment of staling in bovine hides with hyperspectral imaging

Microbial spoilage or staling of bovine hides during storage leads to poor leather quality and increased chemical consumption during processing. Conventional microbiological examinations of hide samples which require time-consuming microbe culture cannot be employed as a practical staling detection approach for leather production. Hyperspectral imaging (HSI), featuring fast data acquisition and implementation flexibility has been considered ideal for in-line detection of microbial contamination in Agri- food products. In this study, a linescan hyperspectral imaging system working in a spectral range of 550 nm to 1700 nm was utilized as a rapid and non-destructive technique for predicting the aerobic plate counts (APC) on raw hide samples during storage. Fresh bovine hide samples were stored at 4 °C and 20 °C for 3 days. Every day, hyperspectral images were acquired on both sides for each sample. The APCs were determined simultaneously by conventional microbiological plating method. Leather quality was evaluated by microscopic inspection of grain surfaces, which indicate the acceptable threshold of microbe load on hide samples for leather processing. Partial least squares regression (PLSR) was applied to fit the spectral information extracted from the samples to the logarithmic values of APC to develop microbe load prediction models. All models showed good prediction accuracy, yielding a Rcv2 in the range of 0.74–0.92 and standard error of cross validation (SECV) in the range of 0.61–0.76 %. The prediction capability of the HSI was explored using the model developed with SNV + smoothened pre-processing to spatially predict plate count in the samples. Models established in this study successfully predicted the staling states characterised by bacterial loads on hide samples with low prediction errors. Models, visually, showed the differences in microbial load across the storage time and temperatures. Results illustrate that HSI can be potentially implemented as a non-invasive tool to predict microbe loads in bovine hides before leather processing, so that real-time grading of hides based on staling states can be achieved. This will reduce the cost of leather production and waste management and pave the way for allocating material supply for different production purposes.

Toward realization of high-throughput hyperspectral imaging technique for semiconductor device metrology

Background: High-throughput three-dimensional metrology techniques for monitoring in-wafer uniformity (IWU) and in-cell uniformity (ICU) are critical for enhancing the yield of modern semiconductor manufacturing processes. However, owing to physical limitations, current metrology methods are not capable of enabling such measurements. For example, the optical critical dimension technique is not suitable for ICU measurement, because of its large spot size. In addition, it is excessively slow for IWU measurement. Aim: To overcome the aforementioned limitation, we demonstrate a line-scan hyperspectral imaging (LHSI) system, which combines spectroscopy and imaging techniques to provide sufficient information for spectral and spatial resolution, as well as high throughput. Approach: The proposed LHSI system has a 5-μm spatial resolution together with 0.25-nm spectral resolution in the broad-wavelength region covering 350 to 1100 nm. Results: The system enables the simultaneous collection of massive amounts of spectral and spatial information with an extremely large field of view of 13 × 0.6 mm2. Additionally, throughput improvement by a factor of 103 to 104 can be achieved when compared with standard ellipsometry and reflectometry tools. Conclusions: Owing to its high throughput and high spatial and spectral resolutions, the proposed LHSI system has considerable potential to be adopted for high-throughput ICU and IWU measurements of various semiconductor devices used in high-volume manufacturing.

Blueberry bud freeze damage detection using optical sensors: Identification of spectral features through hyperspectral imagery

BACKGROUND: Highbush blueberry (Vaccinium corymbosum), the species primarily grown in the state of Washington, U.S., is relatively cold hardy. However, low temperatures in winter and early spring can still cause freeze damage to the buds. OBJECTIVE: This study explored hyperspectral imaging (HSI) for detecting freeze induced bud damage. Blueberry buds (c.v. Duke) were collected over two seasons and tested in the laboratory to detect damage at four typical phenological stages. METHODS: The HSI data was acquired via line scan HSI system with spectral wavelength ranging from 517 to 1729 nm for buds grouped into either normal or injured mortalities. The successive projection algorithm was employed for pertinent feature wavelength selection. Analysis of variance and linear regression were then applied for evaluating sensitivity of feature wavelengths. RESULTS: Overall, five salient wavelengths (706, 723, 872, 1384, and 1591 nm) were selected to detect bud freeze injury. A quadratic discriminant analysis method-based analysis verified reliability of these five wavelengths in bud damage detection with overall accuracy in the ranges of 64 to 82%for the test datasets of each stage in two seasons. CONCLUSIONS: This study indicated potential of optical sensing to identify the injured buds using five salient wavelengths.

Simulated altitude exposure assessment by hyperspectral imaging.

Testing the human body’s reaction to hypoxia (including the one generated by high altitude) is important in aeronautic medicine. This paper presents a method of monitoring blood oxygenation during experimental hypoxia using hyperspectral imaging (HSI) and a spectral unmixing model based on a modified Beer–Lambert law. A total of 20 healthy volunteers (males) aged 25 to 60 years were included in this study. A line-scan HSI system was used to acquire images of the faces of the subjects. The method generated oxyhemoglobin and deoxyhemoglobin distribution maps from the foreheads of the subjects at 5 and 10 min of hypoxia and after recovery in a high oxygen breathing mixture. The method also generated oxygen saturation maps that were validated using pulse oximetry. An interesting pattern of desaturation on the forehead was discovered during the study, showing one of the advantages of using HSI for skin oxygenation monitoring in hypoxic conditions. This could bring new insight into the physiological response to high altitude and may become a step forward in air crew testing.

A Simple Multispectral Imaging Algorithm for Detection of Defects on Red Delicious Apples

Purpose: A multispectral algorithm for detection and differentiation of defective (defects on apple skin) and normal Red Delicious apples was developed from analysis of a series of hyperspectral line-scan images. Methods: A fast line-scan hyperspectral imaging system mounted on a conventional apple sorting machine was used to capture hyperspectral images of apples moving approximately 4 apples per second on a conveyor belt. The detection algorithm included an apple segmentation method and a threshold function, and was developed using three wavebands at 676 nm, 714 nm and 779 nm. The algorithm was executed on line-by-line image analysis, simulating online real-time line-scan imaging inspection during fruit processing. Results: The rapid multispectral algorithm detected over 95% of defectiv e apples and 91% of normal apples investigated. Conclusions: The multispectral defect detection algorithm can potentially be used in commercial apple processing lines.

Quantitative Determination of Onion Internal Quality Using Reflectance, Interactance, and Transmittance Modes of Hyperspectral Imaging

<abstract><title><italic>Abstract.</italic></title> The internal quality of onions is important to both consumers and onion processors, but current methods for evaluating the internal quality are mostly destructive. The overall goal of this study was to investigate the feasibility of using hyperspectral imaging technology to quantitatively predict the amount of dry matter, the soluble solids content, and the firmness of onions. A total of 308 onions were scanned using a line-scan hyperspectral imaging system with three sensing modes (reflectance, interactance, and transmittance) in the spectral region of 400-1000 nm. An ellipsoidal model was developed to correct the uneven illumination caused by the curvature of the surface in the reflectance images. The spectra extracted from onion spectral images were used to develop partial least squares (PLS) regression models. Results showed that interactance achieved comparable or even better results than transmittance, and the two modes performed significantly better than diffuse reflectance. In interactance mode, soluble solids content [coefficient of determination (R²) = 0.93, standard error of prediction (SEP) = 1.46 °Brix] and dry matter (R² = 0.93, SEP = 1.61%) can be estimated better than firmness (R² = 0.59, SEP = 9.75 N). This study demonstrated for the first time that the interactance mode of the hyperspectral imaging technique can be used to quantitatively predict the internal quality properties of an onion. The lab-based hyperspectral imaging system has the potential to be used in an automated online quality inspection system for predicting the internal quality of onions.

Near-infrared hyperspectral imaging in tandem with partial least squares regression and genetic algorithm for non-destructive determination and visualization of Pseudomonas loads in chicken fillets

Hyperspectral imaging was exploited for its potential in direct and fast determination of Pseudomonas loads in raw chicken breast fillets. A line-scan hyperspectral imaging system (900–1700nm) was employed to obtain sample images, which were then further corrected, modified and processed. The prepared images were correlated with the true Pseudomonas counts of these samples using partial least squares (PLS) regression. To enhance model performance, different spectral extraction approaches, spectral preprocessing methods as well as wavelength selection schemes based on genetic algorithm were investigated. The results revealed that extraction of mean spectra is more efficient for representation of sample spectra than computation of median spectra. The best full wavelength model was attained based on spectral images preprocessed with standard normal variate, and the correlation coefficients (R) and root mean squared errors (RMSEs) for the model were above 0.81 and below 0.80log10CFUg−1, respectively. In development of simplified models, wavelengths were selected by using a proposed two-step method based on genetic algorithm. The best model utilized only 14 bands in five segments and produced R and RMSEs of 0.91 and 0.55log10CFUg−1, 0.87 and 0.65log10CFUg−1 as well as 0.88 and 0.64log10CFUg−1 for calibration, cross-validation and prediction, respectively. Moreover, the prediction maps offered a novel way for visualizing the gradient of Pseudomonas loads on meat surface. Hyperspectral imaging is demonstrated to be an effective tool for nondestructive measurement of Pseudomonas in raw chicken breast fillets.

Automated Model-Based Calibration of Short-Wavelength Infrared (SWIR) Imaging Spectrographs

Among the variety of available hyperspectral imaging systems, the line-scan technique stands out for its short acquisition time and good signal-to-noise ratio. However, due to imperfections in the camera lens and, in particular, optical components of the imaging spectrograph, the acquired images are spatially and spectrally distorted, which can significantly degrade the accuracy of the subsequent hyperspectral image analysis. In this work, we propose and evaluate an automated method for correction of spatial and spectral distortions introduced by a line-scan hyperspectral imaging system operating in the short wavelength infrared (SWIR) spectral range from 1000 nm to 2500 nm. The proposed method is based on non-rigid registration of the distorted and reference images corresponding to two passive calibration objects. The results of the validation show that the proposed method is accurate, efficient, and applicable for calibration of line-scan hyperspectral imaging systems. Moreover, the design of the method and of the calibration objects allows integration with systems operating in diffuse reflectance or transmittance modes.

Line-scan hyperspectral imaging system for real-time inspection of poultry carcasses with fecal material and ingesta

In poultry processing plants, fecal material and ingesta are the primary source of carcass contamination with microbial pathogens. The current practice of the poultry inspection in the United States is primarily human visual observations. Since the visual inspection is becoming more challenging in poultry processing plants adopting high-speed lines, a rapid sorting system could significantly improve the detection and monitoring of carcasses with surface fecal material and ingesta. As a result, we developed a prototype line-scan hyperspectral imaging system configured as a real-time multispectral imaging subsystem for online detection of surface fecal material and ingesta. Specifically, we integrated a commercially available off-the-shelf hyperspectral image camera into the system with two line lights and a custom software program for real-time multispectral imaging. The bottleneck of the imaging system was the data acquisition. For that reason, a multithreaded software architecture was designed and implemented not only to meet the application requirements such as speed and detection accuracy, but also to be customizable to different imaging applications such as systemic disease detection in the future. The image acquisition and processing speed tests confirmed the system could operate to scan poultry carcasses in commercial poultry processing plants. The fecal detection algorithm was based on the previous research using different hyperspectral imaging systems. A new carcass detection and image formation algorithm was developed to allow existing image processing and detection algorithms reusable without any modifications. Sixteen chicken carcasses and four different types of fecal and ingesta samples were used in a study to test the imaging system at two different speeds (140 birds per minute and 180 birds per minute) in a pilot-scale poultry processing facility. The study found that the system could grab and process three waveband images of carcasses moving up to 180 birds per minute (a line-scan rate 286 Hz) and detect fecal material and ingesta on their surfaces. The detection accuracy of the system varied between 89% and 98% with minimum false positive errors (less than 1%), depending on tested detection algorithms. Therefore, these findings provide the basis of not only a commercially viable imaging platform for fecal detection but also a single poultry inspection system for multiple tasks such as systemic disease detection and quality sorting.

Line-scan hyperspectral imaging for real-time in-line poultry fecal detection

The ARS multispectral imaging system with three-band common aperture camera was able to inspect fecal contaminants in real-time mode during poultry pro- cessing. Recent study has demonstrated several image processing methods including binning, cuticle removal filter, median filter, and morphological analysis in real-time mode could remove most false positive errors. The ARS research groups (Beltsville, MD and Athens, GA) and their industry partner are now merging the fecal detection and systemically disease detection systems onto a common platform using a line-scan hyperspectral imaging system. This system will aid in commercialization by creating one hyperspectral imaging system with user-defined wavelengths that can be installed at different locations of the processing line to solve significant food safety prob- lems of disease as well as contaminant detection. Thus, this research demonstrated the feasibility of line-scan hyper- spectral imaging system in terms of processing speed and detection accuracy for a real-time, in-line fecal detection at current processing speed (at least 140 birds per min) of commercial poultry plant. Our preliminary results showed the real-time hyperspectral imaging system could detect small amount (about 10 mg) of fecal (duodenum, cecum, colon) and ingesta contaminants. The system performance could be improved by optimizing line lighting system especially near-infrared bands for quality images and additional spectral images to minimize false positive detection errors. The newly developed line-scan hyper- spectral imaging system could improve Food Safety Inspection Service (FSIS)'s poultry safety inspection pro- gram significantly.