Edible oil is an important source of macronutrients and a good vehicle for micronutrients such as Vitamin A. Quality of edible oils is determined by conventional laboratory methods that are often expensive, relying on chemicals, and slow turnaround. If quality tests are more readily available in low-resource settings, we can more effectively address human malnutrition, especially for vulnerable populations. However, there is a lack of incentive and efforts to bridge gap between efficient tests and available technology, though, so here we show initial outcomes from our edible oil quality analysis using optical approaches including ultraviolet and near-infrared spectroscopy.
Some medical diagnostic tools, such as urinalysis dipsticks, rely on reading colors accurately for making clinical decisions. Reading color visually can be subject to 1) perceptual differences (inter-observer), 2) environmental factors such as illumination, and 3) target coloring (metamerism), especially among users with limited training and experience. Mobile phone cameras and compact camera modules offer potential low-cost platforms for automated, objective color readouts. However, image colors are a function of camera sensor and illumination characteristics. To restore color fidelity, color correction techniques must be applied to account for systematic deviation. This work aims to provide a quantitative assessment of color correction techniques and reduce variability in color interpretation for urinalysis dipstick results using a low-cost imager. Three color correction methods – linear, polynomial, and root-polynomial regression – were compared for performance in color difference reduction. A standard color checker card was used as reference to compute color correction matrices. A custom imaging system with a low-cost camera module was developed to capture images under controlled illumination. Reference values of the color checker card were obtained with a CM-26d handheld spectrophotometer. The CIE2000 ∆E was used to quantify the color difference between the camera image and the spectrometer to evaluate 3 color correction algorithms. The derived color correction matrices were applied to urinalysis dipstick images and compared to the spectrometer readings. Results indicated that polynomial fitting showed the lowest ∆E during calibration but failed to properly correct urine dipstick colors. Root polynomial offered the best performance in reducing color differences to be below 3 to 4 ∆E. Utilizing L*a*b values for classifying a given dipstick result according to reference concentration levels, it was found that quadratic discriminant analysis (QDA) and k Nearest Neighbor (kNN) classifiers achieved an 82.9% and 97.1% accuracy, respectively.
Lateral flow assays (LFA’s) are a common diagnostic test form, particularly in low-to-middle income countries (LMIC’s). Visual interpretation of LFA’s can be subjective and inconsistent, especially with faint positive results, and commercial readers are expensive and challenging to implement in LMIC’s. We report a phone-agnostic Android app to acquire images and interpret results of a variety of LFA’s with no additional hardware. Starting from the open-source “rdt-scan” codebase, we integrated new features and revamped the peak detection method. This included improved perspective corrections, phone level check to eliminate shadows, high resolution still-image capture besides existing video frame capture, and new peak detection method. This peak detection incorporated smoothing and baseline removal from the one-dimensional profiles of a given color channel’s intensity averaged across the read window’s width, with location and relative size constraints to correctly report locations and peak heights of control and test lines. The app was tested in a real-world setting in conjunction with an open-access LFA for SARS-CoV-2 antigen developed by GH Labs. The app acquired 155 images of LFA cassettes, and results were compared against both visual interpretation by trained clinical staff and PCR results from the same patients. With an appropriate setting for test line intensity threshold, the app matched visual read for all cases but one missed visual positive. From ROC analyses against PCR, the app outperformed visual read by 1-3% across sensitivity, specificity, and AUC. The app thus demonstrated promise for accurate, consistent interpretation of LFA’s while generating digital records that could also be useful for health surveillance.
There have been numerous attempts to use mobile phone images to interpret results of lateral flow assays (LFA’s). Many initial efforts created attachments to position test strips by the camera or added external light sources. To see widespread use, especially in low-resource settings, for aiding test interpretation or performing some level of quantification, a mobile phone LFA reader should not require materials beyond the test and would be phone-agnostic. To assess the feasibility of this approach, twelve CareStart malaria LFA cassettes were run using spiked whole blood. About 880 images of these cassettes were acquired using three brands of phones and under various lighting conditions, imaging distances, and viewing angles. Test strip regions were converted to 1-dimensional (1D) intensity profiles along the direction of flow. Corrections for color accuracy, gamma, and white balance were implemented, and features such as peak height and area under the curve of control and test lines were used in linear regression. Both a fully connected and a 1D convolutional neural network were trained on the 1D profiles of test strips without feature extraction as well. The best regression models achieved R2 of 0.77 and prediction error of 102 ng/ml. A multi-class support vector machine provided 84% accuracy for a semi-quantitative approach of negative or weak, medium, and high positives. For all analyses, corrections to color, white balance, etc. did not provide meaningful improvements, and limiting analysis to a single phone was not substantially better. Thus, there is promise for a device-agnostic mobile phone LFA reader.
Confocal Raman spectroscopy (CRS) is a noninvasive optical method capable of providing endogenous molecule fingerprinting information as well as allowing depth-resolved measurements into biological tissue. For precise data acquisition in highly scattering tissue in vivo, reflectance confocal microscopy (RCM) has been integrated as imaging guidance with confocal Raman spectroscopy system. However, building a CRS system for point of interest (POI) Raman measurement with simultaneous full field of view (FOV) RCM imaging using a single laser is a challenge. In this work, we addressed the challenge using an optical Faraday isolator to separate the returning reflectance confocal signal from the incident laser beam. A single laser source was used for both RCM and CRS measurements by utilizing two polarized beam splitters (PBS): one for splitting the beam into a RCM illumination beam and a CRS excitation beam; the other for merging the two beam together before entering the objective lens. The confocal setting minimizes the Raman signal contribution from the scanning RCM beam to as small as 0.18%. Furthermore, this small portion of Raman signal will not contaminate the CRS Raman spectrum because they are excited by the same laser wavelength. The co-registration of the sectioning plane of the RCM and CRS were confirmed to be within 0.2 micron. This new integrated confocal Raman system allowed us to acquire confocal Raman signals at specific POI under real-time full FOV RCM guidance and monitoring. Application examples for both ex vivo sample and in vivo skin measurements will be presented.
Optical spectroscopic devices have historically been too expensive or not portable enough to take full advantage of their abilities to offer real-time, on-site, objective results, especially in the developing world. Recent advancements toward smaller and cheaper hardware, especially in the visible and near infrared (NIR) ranges, could enable widespread use in low resource settings, down to a rural health clinic or at the individual farm level. We recently designed and tested a spectroscopic device with these goals in mind. It is based on an initial commercial version of a low cost MEMS spectral detection chip operating in the NIR, or more properly short wave infrared (SWIR) region. Custom optics, electronics, and mechanical designs were created to produce a complete handheld system capable of operation in the lab or in the field. Initial lab testing indicated excellent reproducibility both within and between five different devices. We have verified desired performance (e.g. acceptable signal to noise for target integration times, spectral features equivalent to lab-grade devices, etc.) for applications including pharmaceutical analysis and for analyzing multiple agricultural materials, including soils, plants, fertilizers, and manures. We have also developed a custom mobile app to accompany the devices in upcoming field testing, which will validate their performance in realistic settings in sub-Saharan Africa.
Adulteration of milk for economic gains is a widespread issue throughout the developing world that can have far-reaching health and nutritional impacts. Milk analysis technologies, such as infrared spectroscopy, can screen for adulteration, but the cost of these technologies has prohibited their use in low resource settings. Recent developments in infrared and Raman spectroscopy hardware have led to commercially available low-cost devices. In this work, we evaluated the performance of two such spectrometers in detecting and quantifying the presence of milk adulterants. Five common adulterants – ammonium sulfate, melamine, sodium bicarbonate, sucrose, and urea, were spiked into five different raw cow and goat milk samples at different concentrations. Collected MIR and Raman spectra were analyzed using partial least squares regression. The limit of detection (LOD) for each adulterant was determined to be in the range of 0.04 to 0.28% (400 to 2800 ppm) using MIR spectroscopy. Raman spectroscopy showed similar LOD’s for some of the adulterants, notably those with strong amine group signals, and slightly higher LOD’s (up to 1.0%) for other molecules. Overall, the LODs were comparable to other spectroscopic milk analyzers on the market, and they were within the economically relevant concentration range of 100 to 4000 ppm. These lower cost spectroscopic devices therefore appear to hold promise for use in low resource settings.
Colorectal cancer (CRC) is the third most common type of cancer and forth leading cause of cancer-related death. Early diagnosis is the key to long-term patient survival. Programmatic screening for the general population has shown to be cost-effective in reducing the incidence and mortality from CRC. Current CRC screening strategy relies on a broad range of test techniques such as fecal based tests and endoscopic exams. Occult blood tests like fecal immunochemical test is a cost effective way to detect CRC but have limited diagnostic values in detecting adenomatous polyp, the most treatable precursor to CRC. In the present work, we proposed the use of surface enhanced Raman spectroscopy (SERS) with silver nanoparticles as substrate to analyze blood plasma for detecting both CRC and adenomatous polyps. Blood plasma samples collected from healthy subjects and patients diagnosed with adenomas and CRC were prepared with nanoparticles and measured using a real-time fiber optic probe based Raman system. The collected SERS spectra are analyzed with partial least squares-discriminant analysis. Classification of normal versus CRC plus adenomatous polyps achieved diagnostic sensitivity of 86.4% and specificity of 80%. This exploratory study suggests that blood plasma SERS analysis has potential to become a screening test for detecting both CRC and adenomas.
In vivo endoscopic Raman spectroscopy of human tissue using a fiber optic probe has been previously demonstrated. However, there remain several technical challenges, such as a robust control over the laser radiation dose and measurement repeatability during endoscopy. A decrease in the signal to noise was also observed due to aging of Raman probe after repeated cycles of harsh reprocessing procedures. To address these issues, we designed and tested a disposable, biocompatible, and sterile sheath for use with a fiber optic endoscopic Raman probe. The sheath effectively controls contamination of Raman probes between procedures, greatly reduces turnaround time, and slows down the aging of the Raman probes. A small optical window fitted at the sheath cap maintained the measurement distance between Raman probe end and tissue surface. To ensure that the sheath caused a minimal amount of fluorescence and Raman interference, the optical properties of materials for the sheath, optical window, and bonding agent were studied. The easy-to-use sheath can be manufactured at a moderate cost. The sheath strictly enforced a maximum permissible exposure standard of the tissue by the laser and reduced the spectral variability by 1.5 to 8.5 times within the spectral measurement range.
To simplify imaging focusing and calibration tasks, a laser-scanning microscope needs to scan at a moderate frame rate. The inertia of a galvanometric scanner leads to time delays when following external commands, which subsequently introduces image distortions that deteriorate as scan frequency increases. Sinusoidal and triangular waveforms were examined as fast axis driving patterns. The interplay among driving pattern, frequency, sampling rate, phase shift, linear scanning range, and their effect on reconstructed images was discussed. Utilizing position feedback from the linear galvo scanners, the effect of response time could be automatically compensated in real time. Precompensated triangular driving waveform offered the least amount of image distortion.
Optical microangiography (OMAG) has been extensively utilized to study three-dimensional tissue vasculature in vivo. However, with the limited image resolution (∼10 μm) of the commonly used systems, some concerns were raised: (1) whether OMAG is capable of providing the imaging of capillary vessels that are of an average diameter of ∼6 μm; (2) if yes, whether OMAG can provide meaningful quantification of vascular density within the scanned tissue volume. Multiphoton microscopy (MPM) is capable of depth-resolved high-resolution (∼1 μm) imaging of biological tissue structures. With externally labeled plasma, the vascular network including single capillaries can be well visualized. We compare the vascular images of in vivo mouse brain acquired by both OMAG and MPM systems. We found that within the penetration depth range of the MPM system, OMAG is able to accurately visualize blood vessels including capillaries. Although the resolution of OMAG may not be able to 100% resolve two closely packed tiny capillaries in tissue, it is still capable of visualizing most of the capillaries because there are interstitial tissue spaces between them. We believe our validation results reinforce the application of OMAG in microvasculature-related studies.
With the outbreak of Bovine Spongiform Encephalopathy (BSE) (commonly known as mad cow disease) in 1987 in the United Kingdom and a recent case discovered in Alberta, more and more emphasis is placed on food and farm feed quality and safety issues internationally. The disease is believed to be spread through farm feed contamination by animal byproducts in the form of meat-and-bone-meal (MBM). The paper reviewed the available techniques necessary to the enforcement of legislation concerning the feed safety issues. The standard microscopy method, although highly sensitive, is laborious and costly. A method to routinely screen farm feed contamination certainly helps to reduce the complexity of safety inspection. A hyperspectral imaging system working in the near-infrared wavelength region of 1100-1600 nm was used to study the possibility of detection of ground broiler feed contamination by ground pork. Hyperspectral images of raw broiler feed, ground broiler feed, ground pork, and contaminated feed samples were acquired. Raw broiler feed samples were found to possess comparatively large spectral variations due to light scattering effect. Ground feed adulterated with 1%, 3%, 5%, and 10% of ground pork was tested to identify feed contamination. Discriminant analysis using Mahalanobis distance showed that the model trained using pure ground feed samples and pure ground pork samples resulted in 100% false negative errors for all test replicates of contaminated samples. A discriminant model trained with pure ground feed samples and 10% contamination level samples resulted in 12.5% false positive error and 0% false negative error.
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