Multispectral Imaging Approach Research Articles

Detecting Starch-Adulterated Turmeric Using Vis-NIR Spectroscopy and Multispectral Imaging with Machine Learning

Chemicals are often added to turmeric to increase profits, posing significant health risks to consumers. At the same time, traditional methods for detecting contaminants in turmeric are complicated and time-consuming. This study aimed to develop a more practical approach using visible-near infrared (Vis-NIR) and multispectral imaging (MSI) techniques to detect starch adulteration in turmeric. The turmeric powder was mixed with starch (0.1, 0.5, 1, 2.5, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, and 100% (w/w)) to create spectral and MSI datasets within the 400 to 1050nm wavelength range. Spectra were corrected using spectral preprocessing techniques such as Savitzky-Golay (SG), Multiplicative Scatter Correction (MSC), and Standard Normal Variate (SNV). Principal Component Analysis (PCA) to reduce their dimensions and various machine learning (ML) models for prediction. The Random Forest Regressor (RFR) achieved a coefficient of determination (R²) of 0.999, a root mean squared error (RMSE) of 0.391mg (w/w), and a residual predictive deviation (RPD) of 92.3% in regression analysis. For classification, the Random Forest Classifiers (RFC) achieved an F1 score of 96.0% and a Matthews Correlation Coefficient (MCC) of 94.6%. In MSI analysis, the DenseNet201 model obtained an F1 score of 92.9% and an MCC of 91.9%. Moreover, the robustness of these models was cross-validated using leave-one-out cross-validation (LOOCV) and K-fold methods. The significance of the study lies in several critical areas, such as public health, advancement in technology, etc.The study's findings reveal that Vis-NIR and MSI approaches are excellent in detecting starch adulteration in turmeric with reliability. It has important implications for public health and food safety by offering a reliable tool for verifying the purity of turmeric and other food items.

Deep learning-assisted multispectral imaging for early screening of skin diseases

IntroductionMelanocytic nevi (MN), warts, seborrheic keratoses (SK), and psoriasis are four common types of skin surface lesions that typically require dermatoscopic examination for definitive diagnosis in clinical dermatology settings. This process is labor-intensive and resource-consuming. Traditional methods for diagnosing skin lesions rely heavily on the subjective judgment of dermatologists, leading to issues in diagnostic accuracy and prolonged detection times. ObjectivesThis study aims to introduce a multispectral imaging (MSI)-based method for the early screening and detection of skin surface lesions. By capturing image data at multiple wavelengths, MSI can detect subtle spectral variations in tissues, significantly enhancing the differentiation of various skin conditions. MethodsThe proposed method utilizes a pixel-level mosaic imaging spectrometer to capture multispectral images of lesions, followed by reflectance calibration and standardization. Regions of interest were manually extracted, and the spectral data were subsequently exported for analysis. An improved one-dimensional convolutional neural network is then employed to train and classify the data. ResultsThe new method achieves an accuracy of 96.82 % on the test set, demonstrating its efficacy. ConclusionThis multispectral imaging approach provides a non-contact and non-invasive method for early screening, effectively addressing the subjective identification of lesions by dermatologists and the prolonged detection times associated with conventional methods. It offers enhanced diagnostic accuracy for a variety of skin lesions, suggesting new avenues for dermatological diagnostics.

Functional multi-organelle units control inflammatory lipid metabolism of macrophages.

Eukaryotic cells contain several membrane-separated organelles to compartmentalize distinct metabolic reactions. However, it has remained unclear how these organelle systems are coordinated when cells adapt metabolic pathways to support their development, survival or effector functions. Here we present OrgaPlexing, a multi-spectral organelle imaging approach for the comprehensive mapping of six key metabolic organelles and their interactions. We use this analysis on macrophages, immune cells that undergo rapid metabolic switches upon sensing bacterial and inflammatory stimuli. Our results identify lipid droplets (LDs) as primary inflammatory responder organelle, which forms three- and four-way interactions with other organelles. While clusters with endoplasmic reticulum (ER) and mitochondria (mitochondria-ER-LD unit) help supply fatty acids for LD growth, the additional recruitment of peroxisomes (mitochondria-ER-peroxisome-LD unit) supports fatty acid efflux from LDs. Interference with individual components of these units has direct functional consequences for inflammatory lipid mediator synthesis. Together, we show that macrophages form functional multi-organellar units to support metabolic adaptation and provide an experimental strategy to identify organelle-metabolic signalling hubs.

A Transformative Approach for Breast Cancer Detection Using Physics-Informed Neural Network and Surface Temperature Data

Abstract Early detection is the most effective defense against breast cancer. Mammography is a well-established X-ray based technique that is used for annual or biennial screening of women above age of 40. Since the dense breast tissue sometimes obscures the cancer in an x-ray image, about 10% of screened women are recalled and undergo additional adjunctive modalities, such as ultrasound, digital breast tomosynthesis or magnetic resonance imaging (MRI). These modalities have drawbacks such as additional radiation dosage, overdiagnosis and high cost. A new concurrent multi-spectral imaging approach is recently presented to eliminate the high recall rates by utilizing the breast surface temperature data with an inverse physics-informed neural network algorithm. The multi-spectral imaging does not use any harmful radiations, such as x-rays, is contact-less and does not require breast compression. It has been validated in 23 patients and offers a cost-effective solution to provide improved detection capability of cancerous cells. It is estimated to reduce the recall rates significantly from the current 10%, with a corresponding reduction in the biopsies. This adjunctive approach builds on the strength of mammography and offers a safe adjunct by relying on the higher metabolic rates of cancer cells.

Comparison between Different Optical Configurations of Active-FRAME Setup in Multispectral Imaging of Flames

Snapshot multispectral imaging of chemical species in the flame is essential for improved understanding of the combustion process. In this article, we investigate the different configurations of a structured laser sheet-based multispectral imaging approach called the Frequency Recognition Algorithm for Multiple Exposures (FRAME). Using FRAME, a snapshot of Laser-Induced Fluorescence (LIF) of Polycyclic Aromatic Hydrocarbons (PAH) excited by 283.5 nm laser and Laser-Induced Incandescence (LII) of soot particles excited by 532 nm laser are acquired simultaneously on a single FRAME image. A laminar diffusion flame of acetylene produced by a Gülder burner is used for the experiments. The standard FRAME approach is based on creating two spatially modulated laser sheets and arranging them in a cross-patterned configuration (X). However, the effect of using different configurations (angles) of the two laser sheets on the multispectral planar imaging of the flame has not yet been studied. Therefore, we have compared the FRAME approach in four different configurations while keeping the same flame conditions. First, we have compared the relation between laser fluence and LII signals with and without spatial modulation of the 532 nm laser sheet and found that both detections follow the same curve. When comparing the maps of flame species reconstructed from the standard FRAME configuration and other configurations, there are some dissimilarities. These differences are attributed to minor changes in the imaging plane, optical alignment, laser path length, different modulation frequencies of the laser sheet, laser extinction, laser fluence, etc.

Prediction of Stroke Onset Time With Combined Fast High-Resolution Magnetic Resonance Spectroscopic and Quantitative T2 Mapping.

The purpose of this work is to develop a multispectral imaging approach that combines fast high-resolution 3D magnetic resonance spectroscopic imaging (MRSI) and fast quantitative T2 mapping to capture the multifactorial biochemical changes within stroke lesions and evaluate its potentials for stroke onset time prediction. Special imaging sequences combining fast trajectories and sparse sampling were used to obtain whole-brain maps of both neurometabolites (2.0 × 3.0 × 3.0 mm3) and quantitative T2 values (1.9 × 1.9 × 3.0 mm3) within a 9-minute scan. Participants with ischemic stroke at hyperacute (0-24 h, n = 23) or acute (24 h-7d, n = 33) phase were recruited in this study. Lesion N-acetylaspartate (NAA), lactate, choline, creatine, and T2 signals were compared between groups and correlated with patient symptomatic duration. Bayesian regression analyses were employed to compare the predictive models of symptomatic duration using multispectral signals. In both groups, increased T2 and lactate levels, as well as decreased NAA and choline levels were detected within the lesion (all p < 0.001). Changes in T2, NAA, choline, and creatine signals were correlated with symptomatic duration for all patients (all p < 0.005). Predictive models of stroke onset time combining signals from MRSI and T2 mapping achieved the best performance (hyperacute: R2 = 0.438; all: R2 = 0.548). The proposed multispectral imaging approach provides a combination of biomarkers that index early pathological changes after stroke in a clinical-feasible time and improves the assessment of the duration of cerebral infarction. Developing accurate and efficient neuroimaging techniques to provide sensitive biomarkers for prediction of stroke onset time is of great importance for maximizing the proportion of patients eligible for therapeutic intervention. The proposed method provides a clinically feasible tool for the assessment of symptom onset time post ischemic stroke, which will help guide time-sensitive clinical management.

Multispectral Imaging Analysis of Skin Lesions in Patients with Neurofibromatosis Type 1.

Neurofibromatosis type 1 (NF1) is a rare disease, affecting around 1 in 3500 individuals in the general population. The rarity of the disease contributes to the scarcity of the available diagnostic and therapeutic approaches. Multispectral imaging is a non-invasive imaging method that shows promise in the diagnosis of various skin diseases. The device utilized for the present study consisted of four sets of narrow-band LEDs, including 526 nm, 663 nm, and 964 nm for diffuse reflectance imaging and 405 nm LEDs, filtered through a 515 nm long-pass filter, for autofluorescence imaging. RGB images were captured using a CMOS camera inside of the device. This paper presents the results of this multispectral skin imaging approach to distinguish the lesions in patients with NF1 from other more common benign skin lesions. The results show that the method provides a potential novel approach to distinguish NF1 lesions from other benign skin lesions.

In vivo evaluation of sunscreen application by multispectral imaging: A new tool for educating sunscreen users.

The sun protection factor (SPF) of sunscreens is evaluated using standardized protocols based on the application of 2mg/cm2 of product. However, the amount of product applied by sunscreen users in real life is likely to be much lower. To evaluate a new multispectral imaging approach for determining the actual quantity of sunscreen applied by users and to assess the benefits of an application guide for the use of an SPF 50+ sunscreen. Analyses of the reflectance spectra obtained from multispectral images were used to determine the actual dose of sunscreen that 26 healthy volunteers applied to their face following three application modalities: a single application, reapplication after 30min, and application according to an instruction guide. Without the application guide, volunteers applied an average of 1.04mg/cm2 of sunscreen during the single application and 1.23mg/cm2 during the repeated application. With the application guide, the amount of sunscreen applied was 1.45mg/cm2 : around 40% higher than during the single application. Spreading of the sunscreen was also less uniform with the unguided single application than with the other application modalities. This study showed that the multispectral imaging approach can be used to measure the amount of sunscreen applied in vivo. Our findings confirmed that the standard dose used for SPF measurements and other sunscreen tests is far higher than that applied by users in practice. Providing users with precise guidelines could increase the amount of sunscreen applied, resulting in more adequate photoprotection.

Collaborative representation of convolutional neural network features to detect artificial ripening of banana using multispectral imaging

With the increasing demand for fruits in the consumer market and to fulfill the needs of consumers, fruits are deliberately resorted to ripen artificially using industrial-grade Calcium Carbide (CaC2) which has carcinogenic properties. The preferences to employ imaging sensing technology across the electromagnetic spectrum have gained noteworthy attention in recent times. This work presents the study to distinguish between natural and artificially ripened bananas using a multispectral imaging approach. Specifically, we carry out the analysis using multispectral images collected for natural and artificially ripened bananas in eight narrow spectrum bands spanning from visible (VIS) to near-infrared (NIR) wavelength. Further, to present the contribution of each individual spectral band, we propose a scheme that extracts the relevant features from “convolution 5 (conv5)” and “fully connected 6 (fc6)” layers of convolutional neural network (CNN) based on AlexNet architecture and processes these features independently with collaborative representation classifier (CRC) in a robust manner. Essentially, we demonstrate the experimental classification accuracy based on multispectral images comprised of 5760 sample images using 10-fold cross-validation. Based on the proposed scheme, the highest average classification accuracy of about 88.82 ± 1.65% is obtained using our proposed approach presenting the significance of our work. Practical applications The practical application of our proposed approach is to distinguish between natural and artificially ripened banana fruits in a market chain. Further, the approach is non-invasive and non-destructive based on multispectral imaging which has the potential to carry out real-time quality analysis of bananas, especially to detect adulteration.

Snapshot multicolor fluorescence imaging using double multiplexing of excitation and emission on a single detector

Fluorescence-based multispectral imaging of rapidly moving or dynamic samples requires both fast two-dimensional data acquisition as well as sufficient spectral sensitivity for species separation. As the number of fluorophores in the experiment increases, meeting both these requirements becomes technically challenging. Although several solutions for fast imaging of multiple fluorophores exist, they all have one main restriction; they rely solely on spectrally resolving either the excitation- or the emission characteristics of the fluorophores. This inability directly limits how many fluorophores existing methods can simultaneously distinguish. Here we present a snapshot multispectral imaging approach that not only senses the excitation and emission characteristics of the probed fluorophores but also all cross term combinations of excitation and emission. To the best of the authors’ knowledge, this is the only snapshot multispectral imaging method that has this ability, allowing us to even sense and differentiate between light of equal wavelengths emitted from the same fluorescing species but where the signal components stem from different excitation sources. The current implementation of the technique allows us to simultaneously gather 24 different spectral images on a single detector, from which we demonstrate the ability to visualize and distinguish up to nine fluorophores within the visible wavelength range.

Hormetic effects of low-dose gamma rays in soybean seeds and seedlings: A detection technique using optical sensors

The use of radiation technology in agriculture has increased remarkably in recent years. The stimulating effect of low-dose gamma rays, known as hormesis, improves the adaptive response of living organisms to more severe challenges. Different areas of agriculture such as plant physiology, crop breeding and seed biology demand accurate methods for real-time diagnosis of the ionizing radiation effect on biological systems. However, traditional measures are still calculated from slow, laborious and destructive techniques, which depend on human vision. Here, we present two robust optical sensors based on multispectral and fluorescence imaging approaches for rapid and non-destructive detection of the hormetic effects of low-dose gamma radiation in soybean seeds and seedings. Multispectral images were acquired from seeds treated with gamma radiation (0, 12, 16 and 20 Gy), and the reflectance, autofluorescence, texture and color features were extracted from images. Next, a chlorophyll fluorescence-based technique was applied to measure the minimal level of fluorescence (F0), maximum fluorescence (FM), average chlorophyll a fluorescence and quantum yield of photosystem II (FV/FM) in seedlings grown from irradiated and non-irradiated seeds. Using a reflectance technology, the seedlings were also evaluated for anthocyanin, chlorophyll a and normalized difference vegetation index (NDVI). Results showed that multispectral and fluorescence-based techniques provide useful information on the hormetic effects of gamma rays in soybean seeds and seedlings, which are invisible to the naked eye. Our findings provide a better understanding of the link between spectral and fluorescence parameters and the hormetic effect of low-dose gamma rays. From a practical point of view, such optical sensors can provide markers for fast, non-destructive and reliable identification of the best gamma-ray doses to stimulate soybean growth parameters. However, we strongly support in-depth studies of the dose–response relationship in seeds with different initial physiological potential from different soybean genotypes.

Discordance between eNOS phosphorylation and activation revealed by multispectral imaging and chemogenetic methods

Nitric oxide (NO) synthesized by the endothelial isoform of nitric oxide synthase (eNOS) is a critical determinant of vascular homeostasis. However, the real-time detection of intracellular NO-a free radical gas-has been difficult, and surrogate markers for eNOS activation are widely utilized. eNOS phosphorylation can be easily measured in cells by probing immunoblots with phosphospecific antibodies. Here, we pursued multispectral imaging approaches using biosensors to visualize intracellular NO and Ca2+ and exploited chemogenetic approaches to define the relationships between NO synthesis and eNOS phosphorylation in cultured endothelial cells. We found that the G protein-coupled receptor agonists adenosine triphosphate (ATP) and histamine promoted rapid increases in eNOS phosphorylation, as did the receptor tyrosine kinase agonists insulin and Vascular Endothelial Growth Factor (VEGF). Histamine and ATP also promoted robust NO formation and increased intracellular Ca2+ By contrast, neither insulin nor VEGF caused any increase whatsoever in intracellular NO or Ca2+-despite eliciting strong eNOS phosphorylation responses. Our findings demonstrate an unexpected and striking discordance between receptor-modulated eNOS phosphorylation and NO formation in endothelial cells. Previous reports in which phosphorylation of eNOS has been studied as a surrogate for enzyme activation may need to be reassessed.

A multispectral imaging approach integrated into the study of Late Antique textiles from Egypt.

This work explores the use of multispectral imaging (MSI) techniques applied to the investigation of Late Antique (c. 250–800 AD) textiles found in Egypt. Although the use of these techniques is well-established in the study of polychrome surfaces, they have only been sparingly and often unsystematically applied to the investigation of textiles. The aim of this work is therefore to bridge this gap by showing how this non-invasive, relatively inexpensive and portable methodology can be used to map the photoluminescence and reflective characteristics of textiles under different wavelengths of light, and to provide qualitative and holistic insights into the chemical nature of the materials that compose them. Standardised acquisition and post-processing methods were applied to produce visible-reflected (VIS), ultraviolet-induced visible luminescence (UVL), infrared-reflected (IRR), infrared-reflected false colour (IRRFC), ultraviolet-reflected (UVR) and ultraviolet-reflected false colour (UVRFC) images that provided preliminary indications of the colourants used and their spatial distribution. This proved to be an important aid in planning more targeted and effective sampling strategies and facilitated comparisons between objects. Visible-induced visible luminescence (VIVL) and multiband-reflected (MBR) imaging were also explored for the first time with application to textiles, demonstrating their potential in mapping red and blue colourants respectively. The physical properties observed from all of these images were then related to the more detailed information provided by complementary non-invasive techniques, such as fibre optic reflectance spectroscopy (FORS), and micro-invasive approaches, such as high-performance liquid chromatography mass spectrometry (HPLC-MS). Guidelines towards the interpretation of complex MSI images and a discussion of the potential and limitations of relating multispectral data to chemical properties are presented. An important result of this work is the delineation of a protocol, which combines optical microscopy (OM), MSI, FORS and HPLC-MS and shows a high degree of potential, not only for the investigation of Late Antique textiles but for textiles in museum and historic collections generally.

Method using in vivo quantitative spectroscopy to guide design and optimization of low-cost, compact clinical imaging devices: emulation and evaluation of multispectral imaging systems.

.With recent proliferation in compact and/or low-cost clinical multispectral imaging approaches and commercially available components, questions remain whether they adequately capture the requisite spectral content of their applications. We present a method to emulate the spectral range and resolution of a variety of multispectral imagers, based on in-vivo data acquired from spatial frequency domain spectroscopy (SFDS). This approach simulates spectral responses over 400 to 1100 nm. Comparing emulated data with full SFDS spectra of in-vivo tissue affords the opportunity to evaluate whether the sparse spectral content of these imagers can (1) account for all sources of optical contrast present (completeness) and (2) robustly separate and quantify sources of optical contrast (crosstalk). We validate the approach over a range of tissue-simulating phantoms, comparing the SFDS-based emulated spectra against measurements from an independently characterized multispectral imager. Emulated results match the imager across all phantoms ( absorption, reduced scattering). In-vivo test cases (burn wounds and photoaging) illustrate how SFDS can be used to evaluate different multispectral imagers. This approach provides an in-vivo measurement method to evaluate the performance of multispectral imagers specific to their targeted clinical applications and can assist in the design and optimization of new spectral imaging devices.

Simple multispectral imaging approach for determining the transfer of explosive residues in consecutive fingerprints

This novel investigation focused on studying the transfer of explosive residues (TNT, HMTD, PETN, ANFO, dynamite, black powder, NH4NO3, KNO3, NaClO3) in ten consecutive fingerprints to two different surfaces – cotton fabric and polycarbonate plastic – by using multispectral imaging (MSI). Imaging was performed employing a reflex camera in a purpose-built photo studio. Images were processed in MATLAB to select the most discriminating frame – the one that provided the sharpest contrast between the explosive and the material in the red-green-blue (RGB) visible region. The amount of explosive residues transferred in each fingerprint was determined as the number of pixels containing explosive particles. First, the pattern of PETN transfer by ten different persons in successive fingerprints was studied. No significant differences in the pattern of transfer of PETN between subjects were observed, which was also confirmed by multivariate analysis of variance (MANOVA). Then, the transfer of traces of the nine above explosives in ten consecutive fingerprints to cotton fabric and polycarbonate plastic was investigated. The obtained results demonstrated that the amount of explosive residues deposited on successive fingerprints tended to undergo a power or exponential decrease, with the exception of inorganic salts (NH4NO3, KNO3, NaClO3) and ANFO (consists of 90% NH4NO3).

2D multi-spectral imaging for fast MRI near metal.

To develop a fast 2D method for MRI near metal with reduced B0 in-plane and through-slice artifacts. Multi-spectral imaging (MSI) approaches reduce artifacts in MR images near metal, but require 3D imaging of multiple excited volumes regardless of imaging geometry or artifact severity. The proposed 2D MSI method rapidly excites a limited slice and spectral region using gradient reversal between excitation and refocusing pulses, then uses standard 2D imaging, with the process repeating to cover multiple spectral offsets that are combined as in other MSI techniques. 2D MSI was implemented in a spin-echo-train sequence and validated in phantoms and in vivo by comparing it with standard spin-echo imaging and existing MSI techniques. 2D MSI images for each spatial-spectral region follow isocontours of the dipole-like B0 field variation, and thus frequency variation, near metal devices. Artifact correction in phantoms and human subjects with metal is comparable to 3D MSI methods, and superior to standard spin-echo techniques. Scan times are reduced compared with 3D MSI methods in cases where a limited number of slices are needed, though signal-to-noise ratio is also reduced as expected. 2D MSI offers a fast and flexible alternative to 3D MSI for artifact reduction near metal. Magn Reson Med 79:968-973, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Rapid and Non-destructive Detection of Iron Porphyrin Content in Pork Using Multispectral Imaging Approach

The feasibility of using multispectral imaging (MSI) technique (405–970 nm) for predicting iron porphyrin (IP) content in pork was assessed. Based on nine feature wavelengths identified by successive projections algorithm (SPA), a partial least squares regression (PLSR) calibration model was established, leading to a determination coefficient (R2) of 0.996 for the prediction set. Visualization of IP content in each pixel of the multispectral images using the prediction model offers an effective way to monitor the distribution and real-time evolution of IP content during the manufacturing process. The results revealed the potentiality of MSI technique combined with PLSR calibration analysis as a rapid and non-destructive evaluation method of IP content for the agri-food industry.

A multispectral imaging approach to colour measurement and colour matching of single yarns without winding

This paper investigates a multispectral imaging approach to colour measurement and colour matching of single yarns. The small size of a single yarn makes it impossible for spectrophotometers directly to acquire its spectral reflectance. Multispectral imaging systems, on the other hand, have the potential to measure the reflectance of single yarns as they can record both the spectral and the spatial information of a sample. A multispectral imaging system, namely imaging colour measurement, has been developed to conduct colour measurement of single yarns. A single yarn is first detected from backgrounds by a modified K‐means clustering method. The reflectance of the single yarn is then specified by an averaging method. Comparative experiments based on 100 pairs of single yarns and corresponding yarn windings show that the reflectance magnitude of a single yarn acquired by imaging colour measurement is smaller than that of corresponding yarn winding measured by a Datacolor 650 spectrophotometer. Experiments on 16 single yarns show that the repeatability and spatial reproducibility of the imaging colour measurement system in measuring a single yarn colour are 0.1185 and 0.2827 CMC(2:1) units. A colour matching comparison experiment (pass or fail), using 24 pairs of single yarns and corresponding pairs of solid‐colour yarn dyed fabrics, shows that single yarns measured by imaging colour measurement can achieve similar colour matching results to solid‐colour yarn dyed fabrics measured by the Datacolor 650 spectrophotometer, with degrees of similarity of 87.5 and 83.3% when the CMC(2:1) and CIE2000(2:1:1) colour difference formulas are employed.

Classification of peacock feather reflectance using principal component analysis similarity factors from multispectral imaging data.

Iridescent structural colors in biology exhibit sophisticated spatially-varying reflectance properties that depend on both the illumination and viewing angles. The classification of such spectral and spatial information in iridescent structurally colored surfaces is important to elucidate the functional role of irregularity and to improve understanding of color pattern formation at different length scales. In this study, we propose a non-invasive method for the spectral classification of spatial reflectance patterns at the micron scale based on the multispectral imaging technique and the principal component analysis similarity factor (PCASF). We demonstrate the effectiveness of this approach and its component methods by detailing its use in the study of the angle-dependent reflectance properties of Pavo cristatus (the common peacock) feathers, a species of peafowl very well known to exhibit bright and saturated iridescent colors. We show that multispectral reflectance imaging and PCASF approaches can be used as effective tools for spectral recognition of iridescent patterns in the visible spectrum and provide meaningful information for spectral classification of the irregularity of the microstructure in iridescent plumage.

Discrimination of Kernel Quality Characteristics for Sunflower Seeds Based on Multispectral Imaging Approach

Multispectral imaging in the visible and near-infrared (405–970 nm) regions was tested for nondestructive discrimination of insect-infested, moldy, heterochromatic, and rancidity in sunflower seeds. An excellent classification (accuracy >97 %) for intact sunflower seeds could be achieved using Fisher’s linear discriminant function based on 10 feature wavelengths that were selected from the original 19 wavelengths by Wilks’ lambda stepwise method. Intact sunflower seeds with different degree of rancidity could be precisely clustered by multispectral imaging technology combined with principal component analysis-cluster analysis (PCA-CA). Our results demonstrate the capability of multispectral imaging technology as a tool for rapid and nondestructive analysis of seed quality attributes, which enables many applications in the agriculture and food industry.