Near-infrared Hyperspectral Reflectance Imaging Research Articles

Finite Element Analysis and Near-Infrared Hyperspectral Reflectance Imaging for the Determination of Blueberry Bruise Grading.

Bruising of the subcutaneous tissues of blueberries is an important form of mechanical damage. Different levels of bruising have a significant effect on the post-harvest marketing of blueberries. To distinguish different grades of blueberry bruises and explore the effects of different factors, explicit dynamic simulation and near-infrared hyperspectral reflectance imaging were employed without harming the blueberries in this study. Based on the results of the compression experiment, an explicit dynamic simulation of blueberries was performed to measure the potential locations of bruises and preliminarily divide the bruise stages. A near-infrared hyperspectral reflectance imaging system was used to detect the actual blueberry bruises. According to the blueberry photos taken by the near-infrared hyperspectral reflectance imaging system, the actual bruise rates of blueberries were obtained by using the Environment for Visualizing Images software for training and classification. Bruise grades of blueberries were divided accordingly. Response surface methodology was used to determine the effects of ripeness, loading speed and loading location on the blueberry bruising rate. Under the optimized parameters, the actual damage rate of blueberries was 1.1%. The results provide an important theoretical basis for the accurate and rapid identification and classification of blueberry bruise damage.

Study on the Relations between Hyperspectral Images of Bananas (Musa spp.) from Different Countries, Their Compositional Traits and Growing Conditions.

Bananas are some of the most popular fruits around the world. However, there is limited research that explores hyperspectral imaging of bananas and its relationship with the chemical composition and growing conditions. In the study, the relations that exist between the visible near-infrared hyperspectral reflectance imaging data in the 400–1000 nm range of the bananas collected from different countries, the compositional traits and local growing conditions (altitude, temperature and rainfall) and production management (organic/conventional) were explored. The main compositional traits included moisture, starch, dietary fibre, protein, carotene content and the CIE L*a*b* colour values were also determined. The principal component analysis showed the preliminary separation of bananas from different geographical origins and production systems. The compositional and spectral data revealed positively and negatively moderate correlations (r around ±0.50, p < 0.05) between the carotene, starch content, and colour values (a*, b*) on the one hand and the wavelength ranges 405–525 nm, 615–645 nm, 885–985 nm on the other hand. Since the variation in composition and colour values were related to rainfall and temperature, the spectral information is likely also influenced by the growing conditions. The results could be useful to the industry for the improvement of banana quality and traceability.

Rapid prototyping of biomimetic vascular phantoms for hyperspectral reflectance imaging.

The emerging technique of rapid prototyping with three-dimensional (3-D) printers provides a simple yet revolutionary method for fabricating objects with arbitrary geometry. The use of 3-D printing for generating morphologically biomimetic tissue phantoms based on medical images represents a potentially major advance over existing phantom approaches. Toward the goal of image-defined phantoms, we converted a segmented fundus image of the human retina into a matrix format and edited it to achieve a geometry suitable for printing. Phantoms with vessel-simulating channels were then printed using a photoreactive resin providing biologically relevant turbidity, as determined by spectrophotometry. The morphology of printed vessels was validated by x-ray microcomputed tomography. Channels were filled with hemoglobin (Hb) solutions undergoing desaturation, and phantoms were imaged with a near-infrared hyperspectral reflectance imaging system. Additionally, a phantom was printed incorporating two disjoint vascular networks at different depths, each filled with Hb solutions at different saturation levels. Light propagation effects noted during these measurements—including the influence of vessel density and depth on Hb concentration and saturation estimates, and the effect of wavelength on vessel visualization depth—were evaluated. Overall, our findings indicated that 3-D-printed biomimetic phantoms hold significant potential as realistic and practical tools for elucidating light–tissue interactions and characterizing biophotonic system performance.

Nondestructive Determination of Apple Internal Qualities Using Near-Infrared Hyperspectral Reflectance Imaging

Hyperspectral reflectance imaging technology in near-infrared regions (900–1,700 nm) was used to evaluate soluble solids content (SSC), firmness, moisture content (MC), and pH values of ‘Fuji’ apples during a 13-week storage period. Totally, 167 apples were divided into calibration set (125) and prediction set (42) based on the joint x-y distance sample set partitioning method. Mean spectrum of the regions of interest in the hyperspectral image of each apple was used for analysis. Two typical variable selection methods, i.e., successive projection algorithm (SPA) and uninformative variable elimination (UVE), were applied to extract the characteristic variables from full spectra (FS). The partial least squares (PLS) regression, least squares support vector machine (LSSVM), and backpropagation (BP) network modeling methods were used to establish models to predict SSC, firmness, MC, and pH of apples, respectively. The results showed that the SSC and MC could be predicted exactly by all developed models, and SPA-LSSVM and FS-BP could be used to predict pH value roughly. All models failed to predict firmness. The SPA-LSSVM model had better comprehensive ability in determining SSC, MC, and pH than others, with the correlation coefficient of prediction of 0.961, 0.984, and 0.882 and residual predictive deviation of 3.49, 5.51, and 2.06, respectively. The results demonstrated the feasibility of using near-infrared hyperspectral reflectance imaging technology as a non-invasive method for predicting SSC, MC, and pH of apples simultaneously.

Identification of Kiwifruits Treated with Exogenous Plant Growth Regulator Using Near-Infrared Hyperspectral Reflectance Imaging

The goal of this study was to explore the potential of using near-infrared (NIR) hyperspectral imaging in combination with multivariate analysis for the identification of kiwifruits treated with exogenous plant growth regulator (EPGR). One hundred twenty kiwifruits, variety ‘Xixuan,’ treated with EPGR and another 120 ‘Xixuan’ kiwifruits without EPGR (named as normal ones) were used in the study. Hyperspectral images were acquired using a near-infrared hyperspectral imaging system in the spectral range of 865.11–1,711.71 nm. Based on the Kennard-Stone method, the samples were divided into two sets: 180 samples in calibration set and 60 samples in validation set. Standard normal variate transformation was used to preprocess obtained spectra. Principal component analysis (PCA) and successive projections algorithm (SPA) were used to select principal components and characteristic wavelengths. Partial least square (PLS) regression and support vector machine (SVM) modeling methods were used to establish models for identifying EPGR-treated kiwifruits and normal ones. The results indicated that average correct identification rates of all models were higher than 98.9 and 96.7 % for the calibration set and validation set, respectively. The identification performance of PLS was better than that of SVM, and the best model was PLS-SPA, whose average accuracy rate reached 100 % for the calibration set and 98.4 % for the validation set. The results demonstrated that NIR hyperspectral imaging technique can be used as a noninvasive method for distinguishing kiwifruits treated with EPGR from normal ones.

Near-infrared hyperspectral reflectance imaging for detection of bruises on pickling cucumbers

Mechanical injury often causes hidden internal damage to pickling cucumbers, which lowers the quality of pickled products and can incur economic losses to the processor. A near-infrared hyperspectral imaging system was developed to capture hyperspectral images from pickling cucumbers in the spectral region of 900–1700 nm. The system consisted of an imaging spectrograph attached to an InGaAs camera with line-light fiber bundles as an illumination source. Hyperspectral images were taken from the pickling cucumbers at 0–3, and 6 days after they were subjected to dropping or rolling under load which simulated damage caused by mechanical harvesting and handling systems. Principal component analysis (PCA), band ratio, and band difference were applied in the image processing to segregate bruised cucumbers from normal cucumbers. Bruised tissue had consistently lower reflectance than normal tissue and the former increased over time. Best detection accuracies from the PCA were achieved when a bandwidth of 8.8 nm and the spectral region of 950–1350 nm were selected. The detection accuracies from the PCA decreased from 95 to 75% over the period of 6 days after bruising, which was attributed to the self-healing of the bruised tissue after mechanical injury. The best band ratio of 988 and 1085 nm had detection accuracies between 93 and 82%, whereas the best band difference of 1346 and 1425 nm had accuracies between 89 and 84%. The general classification performance analysis suggested that the band ratio and difference methods had similar performance, but they were better than the PCA.