The use of hyperspectral imaging in the VNIR (400-1000nm) and SWIR range (1000-2500nm) for detecting counterfeit drugs with identical API composition

Spectral enhancement of PlanetScope using Sentinal-2 images to estimate soybean yield and seed composition

Soybean is an essential crop to fight global food insecurity and is of great economic importance around the world. Along with genetic improvements aimed at boosting yield, soybean seed composition also changed. Since conditions during crop growth and development influences nutrient accumulation in soybean seeds, remote sensing offers a unique opportunity to estimate seed traits from the standing crops. Capturing phenological developments that influence seed composition requires frequent satellite observations at higher spatial and spectral resolutions. This study introduces a novel spectral fusion technique called multiheaded kernel-based spectral fusion (MKSF) that combines the higher spatial resolution of PlanetScope (PS) and spectral bands from Sentinel 2 (S2) satellites. The study also focuses on using the additional spectral bands and different statistical machine learning models to estimate seed traits, e.g., protein, oil, sucrose, starch, ash, fiber, and yield. The MKSF was trained using PS and S2 image pairs from different growth stages and predicted the potential VNIR1 (705 nm), VNIR2 (740 nm), VNIR3 (783 nm), SWIR1 (1610 nm), and SWIR2 (2190 nm) bands from the PS images. Our results indicate that VNIR3 prediction performance was the highest followed by VNIR2, VNIR1, SWIR1, and SWIR2. Among the seed traits, sucrose yielded the highest predictive performance with RFR model. Finally, the feature importance analysis revealed the importance of MKSF-generated vegetation indices from fused images.

Design of non-Gaussian multispectral shortwave infrared filters assessed by surface spectral reflectances on the ECOSTRESS library

This paper addresses the multispectral filter design problem for spectral ranges where a viewing subspace is not defined. The methodology of color filter design is extended to this case, which allows the optimization of custom filter transmittance that meets the physical constraints of available fabrication methods. Multispectral shortwave infrared filters are then designed for two scenarios: spectral reconstruction and false-color representation. The Monte Carlo method is used to verify the filter performance degradation due to deviations in fabrication. The results obtained indicate that the proposed method is useful for designing multispectral filters to be fabricated using generic processes without any additional constraints.

In vitro hyperspectral analysis of tattoo dyes

BACKGROUND

There is no method that can guarantee effective, quick, and noninvasive removal of tattoo dyes. Laser methods are considered to be the method of choice. In this study, an attempt was made to determine the in vitro spectral characteristics of selected dyes used in permanent makeup and tattoos and to analyze the obtained parameters in terms of laser treatments optimization.

MATERIALS AND METHODS

Hyperspectral analysis was performed to determine the spectral characteristics of the dye on the entire surface of the slide. Seven dyes used in permanent makeup and tattoos were analyzed in vitro. The maximum reflectance and the wavelength for a given dye were determined for the maximum reflectance in the studied wavelength range: 400-1000 nm. The optical properties of the dyes were determined based on visible light imaging using camera.

RESULTS

The maximum radiation reflectance ranges from 634 to 732 nm for the tested dyes. Visually very similar colors may differ significantly in the wavelength for which the maximum absorption of the radiation occurs. White and yellow dyes are characterized by the highest reflectance value. The black dye is characterized by the lowest reflectance coefficient. Low reflectance of black dye results in more safe and effective removal treatments.

CONCLUSION

The homogeneity of radiation absorption can be identified using methods of analysis and processing of images in visible light. Optimization of the wavelength of which the maximum absorption/reflectance of radiation occurs may allow us to increase the effectiveness of laser treatments for removing permanent makeup and tattoos.

Research progress on the application of spectral imaging technology in pharmaceutical tablet analysis

Tablet as a traditional dosage form in pharmacy has the advantages of accurate dosage, ideal dissolution and bioavailability, convenient to carry and transport. The most concerned tablet quality attributes include active pharmaceutical ingredient (API) contents and polymorphic forms, components distribution, hardness, density, coating state, dissolution behavior, etc., which greatly affect the bioavailability and consistency of tablet final products. In the pharmaceutical industry, there are usually industry standard methods to analyze the tablet quality attributes. However, these methods are generally time-consuming and laborious, and lack a comprehensive understanding of the properties of tablets, such as spatial information. In recent years, spectral imaging technology makes up for the shortcomings of traditional tablet analysis methods because it provides non-contact and rich information in time and space. As a promising technology to replace the traditional tablet analysis methods, it has attracted more and more attention. The present paper briefly describes a series of spectral imaging techniques and their applications in tablet analysis. Finally, the possible application prospect of this technology and the deficiencies that need to be improved were also prospected.

A Review of Pharmaceutical Robot based on Hyperspectral Technology

The quality and safety of medicinal products are related to patients’ lives and health. Therefore, quality inspection takes a key role in the pharmaceutical industry. Most of the previous solutions are based on machine vision, however, their performance is limited by the RGB sensor. The pharmaceutical visual inspection robot combined with hyperspectral imaging technology is becoming a new trend in the high-end medical quality inspection process since the hyperspectral data can provide spectral information with spatial knowledge. Yet, there is no comprehensive review about hyperspectral imaging-based medicinal products inspection. This paper focuses on the pivotal pharmaceutical applications, including counterfeit drugs detection, active component analysis of tables, and quality testing of herbal medicines and other medical materials. We discuss the technology and hardware of Raman spectroscopy and hyperspectral imaging, firstly. Furthermore, we review these technologies in pharmaceutical scenarios. Finally, the development tendency and prospect of hyperspectral imaging technology-based robots in the field of pharmaceutical quality inspection is summarized.

Polysaccharide determination and habitat classification for fresh Dendrobiums with hyperspectral imagery and modified RBFNN

This research aimed to study the visual and nondestructive detection of mannose (MN) and Dendrobium polysaccharides (DP) in Dendrobiums by using hyperspectral imaging technology. In order to determine the MN and DP concentrations nondestructively, we built radial basis function neural network (RBFNN) models based on NIR spectra (874-1734 nm) with a novel chemometric method to calculate the radial bases. And excellent results with the R _P ² coefficients of 0.906 and 0.913 were obtained by the MN and DP detection models, respectively. In order to simplify the detection models based on full-range spectra, we designed an innovative genetic algorithm-successive projections algorithm (GA-SPA) strategy to extract the feature bands efficiently in two stages. Based on the feature bands selected by GA-SPA, we established the simplified detection models with the same high performance as those based on full-range spectra. By importing the feature bands of every pixel in the hyperspectral image into the simplified detection models, we successfully generated the distribution maps of MN and DP. Moreover, we also built an RBFNN classifier to categorize the habitats of Dendrobium. And the total classification accuracy reached 0.887. This research makes progress in Dendrobium quality evaluation and spectral detection technology.

Convolutional neural network for hyperspectral data analysis and effective wavelengths selection

Fusion of spectral and spatial information has been proved to be an effective approach to improve model performance in near-infrared hyperspectral data analysis. Regardless, most of the existing spectral-spatial classification methods require fairly complex pipelines and exact selection of parameters, which mainly depend on the investigator's experience and the object under test. Convolutional neural network (CNN) is a powerful tool for representing complicated data and usually works with few "hand-engineering", making it an appropriate candidate for developing a general and automatic approach. In this paper, a two-branch convolutional neural network (2B-CNN) was developed for spectral-spatial classification and effective wavelengths (EWs) selection. The proposed network was evaluated by three classification data sets, including herbal medicine, coffee bean and strawberry. The results showed that the 2B-CNN obtained the best classification accuracies (96.72% in average) when compared with support vector machine (92.60% in average), one dimensional CNN (92.58% in average), and grey level co-occurrence matrix based support vector machine (93.83% in average). Furthermore, the learned weights of the two-dimensional branch in 2B-CNN were adopted as the indicator of EWs and compared with the successive projections algorithm. The 2B-CNN models built with wavelengths selected by the weight indicator achieved the best accuracies (96.02% in average) among all the examined EWs models. Different from the conventional EWs selection method, the proposed algorithm works without any additional retraining and has the ability to comprehensively consider the discriminative power in spectral domain and spatial domain.

Hyperspectral near infrared imaging quantifies the heterogeneity of carbon materials

For many applications heterogeneity is a direct indicator of material quality. Reliable determination of chemical heterogeneity is however not a trivial task. Spectral imaging can be used for determining the spatial distribution of an analyte in a sample, thus transforming each pixel of an image into a sampling cell. With a large amount of image pixels, the results can be evaluated using large population statistics. This enables robust determination of heterogeneity in biological samples. We show that hyperspectral imaging in the near infrared (NIR) region can be used to reliably determine the heterogeneity of renewable carbon materials, which are promising replacements for current fossil alternatives in energy and environmental applications. This method allows quantifying the variation in renewable carbon and other biological materials that absorb in the NIR region. Reliable determination of heterogeneity is also a valuable tool for a wide range of other chemical imaging applications.