Near-infrared Hyperspectral Data Research Articles

Noninvasive diagnostics supporting system for choroidal melanoma: a pilot study.

To examine the usefulness of a near-infrared hyperspectral imager (NIR-HSI) system in discriminating uveal melanoma from other intraocular tumors. The NIR-HSI, which had been developed as a screening system for age-related macular degeneration, was used to measure near-infrared hyperspectral data (NIR-HSD) of a lesion located at the ocular fundus of 17 Japanese patients, including 5 with choroidal melanoma and 12 with other intraocular tumors. The index was derived from each NIR-HSD. Non-parametric statistical analysis was performed. Diagnostic accuracy of 94.1% was achieved when the threshold value of the index was set to minimize the average value of false-positive and -negative fractions. The NIR-HSI system is useful as a noninvasive diagnostic supporting system for choroidal melanoma.

Detection of Callosobruchus maculatus (F.) infestation in soybean using soft X-ray and NIR hyperspectral imaging techniques

Soybean (Glycine max L.) is a major oilseed crop grown throughout the world and, total post-harvest losses of soybean are approximately 10%, and 3% of produced soybean is lost during storage. Cowpea weevil (Callosobruchus maculatus (F.)) is the major storage pest which causes extensive storage losses of legumes. Detection of early stages of cowpea weevil infestation could assist farmers and storage facility managers in implementing suitable control practices for insect disinfestations. Soft X-ray and near-infrared (NIR) hyperspectral imaging techniques were used to acquire images of soybeans infested by egg, larval, and pupal stages of C. maculatus along with uninfested and completely damaged (hollowed-out after emergence of adults) soybeans. From soft X-ray images, totally, 33 features (12 histogram and 21 textural features) were extracted and from hyperspectral data 48 features were extracted (30 histogram and 18 spectral features) for analysis. Linear and quadratic discriminant analysis (LDA and QDA) models were developed using these extracted features to classify different stages of infestation. The LDA classifier for soft X-ray images correctly identified more than 86% of uninfested soybeans and 83% of soybeans infested with all developmental stages of C. maculatus except the egg stage. Pair-wise LDA classification models developed from NIR hyperspectral data yielded more than 86 and 87% classification accuracy for uninfested and infested seeds, respectively. The QDA pair-wise classifiers positively differentiated more than 79% uninfested seeds from infested seeds. The principal component analysis of NIR hyperspectral data identified the wavelengths of 960 nm, 1030 nm and 1440 nm being responsible for more than 99% of spectral variability. Combining soft X-ray features with hyperspectral features increased the classification accuracies for egg and larvae compared to either imaging system used alone.

A hematite-bearing layer in Gale Crater, Mars: Mapping and implications for past aqueous conditions

Oversampled Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) visible and near-infrared hyperspectral data over Mount Sharp in Gale Crater, Mars, were used to generate spatially sharpened maps of the location of red crystalline hematite within the uppermost stratum of an ∼6.5-km-long ridge on the mound’s northern flank. Finely layered strata underlie the ridge to the north and have dips consistent with the nearby Mount Sharp sedimentary sequence. Fe-Mg smectites are exposed in a valley to the south of the ridge. Emplacement of the hematite is hypothesized to result either from exposure of anoxic Fe^(2+)-rich groundwater to an oxidizing environment, leading to precipitation of hematite or its precursors, or from in-place weathering of precursor silicate materials under oxidizing conditions. These hypotheses and implications for habitability will be testable with in situ measurements by the Mars rover Curiosity when it reaches Mount Sharp.

Characterisation of non-viable whole barley, wheat and sorghum grains using near-infrared hyperspectral data and chemometrics

Undesired germination of cereal grains diminishes process utility and economic return. Pre-germination, the term used to describe untimely germination, leads to reduced viability of a grain sample. Accurate and rapid identification of non-viable grain is necessary to reduce losses associated with pre-germination. Viability of barley, wheat and sorghum grains was investigated with near-infrared hyperspectral imaging. Principal component analyses applied to cleaned hyperspectral images were able to differentiate between viable and non-viable classes in principal component (PC) five for barley and sorghum and in PC6 for wheat. An OH stretching and deformation combination mode (1,920-1,940 nm) featured in the loading line plots of these PCs; this water-based vibrational mode was a major contributor to the viable/non-viable differentiation. Viable and non-viable classes for partial least squares-discriminant analysis (PLS-DA) were assigned from PC scores that correlated with incubation time. The PLS-DA predictions of the viable proportion correlated well with the viable proportion observed using the tetrazolium test. Partial least squares regression analysis could not be used as a source of contrast in the hyperspectral images due to sampling issues.

Near-Infrared Hyperspectral Unmixing Based on a Minimum Volume Criterion for Fast and Accurate Chemometric Characterization of Counterfeit Tablets

A rapid detection of the nonauthenticity of suspect tablets is a key first step in the fight against pharmaceutical counterfeiting. The chemical characterization of these tablets is the logical next step to evaluate their impact on patient health and help authorities in tracking their source. Hyperspectral unmixing of near-infrared (NIR) image data is an emerging effective technology to infer the number of compounds, their spectral signatures, and the mixing fractions in a given tablet, with a resolution of a few tens of micrometers. In a linear mixing scenario, hyperspectral vectors belong to a simplex whose vertices correspond to the spectra of the compounds present in the sample. SISAL (simplex identification via split augmented Lagrangian), MVSA (minimum volume simplex analysis), and MVES (minimum-volume enclosing simplex) are recent algorithms designed to identify the vertices of the minimum volume simplex containing the spectral vectors and the mixing fractions at each pixel (vector). This work demonstrates the usefulness of these techniques, based on minimum volume criteria, for unmixing NIR hyperspectral data of tablets. The experiments herein reported show that SISAL/MVSA and MVES largely outperform MCR-ALS (multivariate curve resolution-alternating least-squares), which is considered the state-of-the-art in spectral unmixing for analytical chemistry. These experiments are based on synthetic data (studying the effect of noise and the presence/absence of pure pixels) and on a real data set composed of NIR images of counterfeit tablets.

Near Infrared Hyperspectral Imaging Applied to Mapping Chemical Composition in Wood Samples

This paper describes a method for the two-dimensional mapping of chemical composition on the transverse face of cross-sectional discs from trees. The method uses an imaging spectrograph coupled to a near infrared (NIR) camera (900–1700 nm) to obtain NIR hyperspectral data sets which are processed using partial least squares regression to visualise the distribution and variation of lignin, galactose and glucose in Pinus radiata discs with R2/standard error of performance values of 0.84/1.48 (lignin), 0.87/0.68 (galactose) and 0.87/0.95 (glucose). The hardware design and software control are described along with a method for calibration based on one dimension spatially resolved predictions of chemical composition from conventional NIR spectroscopy. The NIR imaging system was designed as a rapid and cost-effective means of mapping chemical composition over the entire disc at a spatial resolution of ∼4mm2/pixel. The resulting maps of chemical composition clearly indicate, at high spatial resolution, the extent of heterogeneity that occurs in logs.

Screening of compound feeds using NIR hyperspectral data

Recent developments in spectroscopy have led to the use of spectroscopic imaging instruments for the control and monitoring of food and feed products. This kind of instruments offers the possibility of collecting thousands of spectra of particles being the result of the grinding of compound feedstuffs. The major advantages are that the recognition of feed ingredients is independent on the expertise of the analyst and that it is possible to automate all procedures and to analyse more samples per unit of time than classical microscopy or NIR microscopy. The objective of this study is the development of a new method for a rapid, precise and reliable screening of compound feeds. For that, a classification tree was built by sorting the particles in a dichotomist way where each node constitutes a discriminating step. These steps are completed by discriminant equations created from the hyperspectral databases obtained with a near infrared (NIR) camera for each class of raw materials. Discriminant equations were constructed using Support Vector Machines (SVM). For a new sample the aim is to determine its composition by using the classification tree. As general conclusion, hyperspectral data in combination with SVM as classification technique is a promised methodology for the determination of open formulations.