Mobile Camera Research Articles

HyperMRI: hyperspectral and magnetic resonance fusion methodology for neurosurgery applications

PurposeMagnetic resonance imaging (MRI) is a common technique in image-guided neurosurgery (IGN). Recent research explores the integration of methods like ultrasound and tomography, among others, with hyperspectral (HS) imaging gaining attention due to its non-invasive real-time tissue classification capabilities. The main challenge is the registration process, often requiring manual intervention. This work introduces an automatic, markerless method for aligning HS images with MRI.MethodsThis work presents a multimodal system that combines RGB-Depth (RGBD) and HS cameras. The RGBD camera captures the patient’s facial geometry, which is used for registration with the preoperative MR through ICP. Once MR-depth registration is complete, the integration of HS data is achieved using a calibrated homography transformation. The incorporation of external tracking with a novel calibration method allows camera mobility from the registration position to the craniotomy area. This methodology streamlines the fusion of RGBD, HS and MR images within the craniotomy area.ResultsUsing the described system and an anthropomorphic phantom head, the system has been characterised by registering the patient’s face in 25 positions and 5 positions resulted in a fiducial registration error of 1.88 ± 0.19 mm and a target registration error of 4.07 ± 1.28 mm, respectively.ConclusionsThis work proposes a new methodology to automatically register MR and HS information with a sufficient accuracy. It can support the neurosurgeons to guide the diagnosis using multimodal data over an augmented reality representation. However, in its preliminary prototype stage, this system exhibits significant promise, driven by its cost-effectiveness and user-friendly design.

Web-based diagnostic platform for microorganism-induced deterioration on paper-based cultural relics with iterative training from human feedback

PurposePaper-based artifacts hold significant cultural and social values. However, paper is intrinsically fragile to microorganisms, such as mold, due to its cellulose composition, which can serve as a microorganisms’ nutrient source. Mold not only can damage papers’ structural integrity and pose significant challenges to conservation works but also may subject individuals attending the contaminated artifacts to health risks. Current approaches for strain identification usually require extensive training, prolonged time for analysis, expensive operation costs, and higher risks of secondary damage due to sampling. Thus, in current conservation practices with mold-contaminated artifacts, little pre-screening or strain identification was performed before mold removal, and the cleaning techniques are usually broad-spectrum rather than strain-specific. With deep learning showing promising applications across various domains, this study investigated the feasibility of using a convolutional neural network (CNN) for fast in-situ recognition and classification of mold on paper.MethodsMolds were first non-invasively sampled from ancient Xuan Paper-based Chinese books from the Qing and Ming dynasties. Strains were identified using molecular biology methods and the four most prevalent strains were inoculated on Xuan paper to create mockups for image collection. Microscopic images of the molds as well as their stains situated on paper were collected using a compound microscope and commercial microscope lens for cell phone cameras, which were then used for training CNN models with a transfer learning scheme to perform the classification of mold. To enable involvement and contribution from the research community, a web interface that actuates the process while providing interactive features for users to learn about the information of the classified strain was constructed. Moreover, a feedback functionality in the web interface was embedded for catching potential classification errors, adding additional training images, or introducing new strains, all to refine the generalizability and robustness of the model.Results & ConclusionIn the study, we have constructed a suite of high-confidence classification CNN models for the diagnostic process for mold contamination in conservation. At the same time, a web interface was constructed that allows recurrently refining the model with human feedback through engaging the research community. Overall, the proposed framework opens new avenues for effective and timely identification of mold, thus enabling proactive and targeted mold remediation strategies in conservation.

MoodCapture: Depression Detection Using In-the-Wild Smartphone Images.

MoodCapture presents a novel approach that assesses depression based on images automatically captured from the front-facing camera of smartphones as people go about their daily lives. We collect over 125,000 photos in the wild from N=177 participants diagnosed with major depressive disorder for 90 days. Images are captured naturalistically while participants respond to the PHQ-8 depression survey question: "I have felt down, depressed, or hopeless". Our analysis explores important image attributes, such as angle, dominant colors, location, objects, and lighting. We show that a random forest trained with face landmarks can classify samples as depressed or non-depressed and predict raw PHQ-8 scores effectively. Our post-hoc analysis provides several insights through an ablation study, feature importance analysis, and bias assessment. Importantly, we evaluate user concerns about using MoodCapture to detect depression based on sharing photos, providing critical insights into privacy concerns that inform the future design of in-the-wild image-based mental health assessment tools.

Smartphone microscopic method for imaging and quantification of microplastics in drinking water.

Analysis of microplastics in drinking water is often challenging due to smaller particle size and low particle count. In this study, we used a low cost and an easy to assemble smartphone microscopic system for imaging and quantitating microplastic particles as small as 20 μm. The system consisted of a spherical sapphire ball lens of 4 mm diameter attached to a smartphone camera as a major imaging component. It also involved pre-concentration of the sample using ZnCl2 solution. The spike recovery and limit of detection of the method in filtered distilled and deionized water samples (n = 9) were 55.6% ± 9.7% and 34 particles/L, respectively. Imaging performance of the microscopic system was similar to a commercial bright field microscopic system. The method was further implemented to examine microplastic particles in commercial bottled and jar water samples (n = 20). The particles count in bottled and jar water samples ranged from 0-91 particles/L to 0-130 particles/L, respectively. In both sample types, particles of diverse shape and size were observed. The particles collected from water samples were further confirmed by FTIR spectra (n = 36), which found 97% of the particles tested were made of plastic material. These findings suggested that the smartphone microscopic system can be implemented as a low-cost alternative for preliminary screening of microplastic in drinking water samples. RESEARCH HIGHLIGHTS: Ball lens based smartphone microscopic method was used for microplastic analysis. Particles of diverse shape and size were found in bottle and jar water samples.

Modeling Positions and Orientations of Cantaloupe Flowers for Automatic Pollination

An automatic system for cantaloupe flower pollination in greenhouses is proposed to meet the requirements of automatic pollination. The system consists of a mobile platform, robotic manipulator, and camera that reaches the flowers to detect and recognise their external features. The main task of the vision system is to detect the position and orientation of the flower in Cartesian coordinates, allowing the manipulator to reach the pose and perform pollination. A comprehensive method to ensure the accuracy of the pollination process is proposed that accurately determines the position and orientation of cantaloupe flowers in real environments. The vision system is used to capture images, detect the flower, and recognise its state according to its external features, such as size, colour, and shape, thereby providing appropriate nozzle access during pollination. The proposed approach begins with a segmentation method designed to precisely locate and segment the target cantaloupe flowers. Subsequently, a mathematical model is used to determine the key points that are important for establishing the growth orientation of each flower. Finally, an inverse-projection method is employed to convert the position of the flower from a two-dimensional (2D) image into a three-dimensional (3D) space, providing the necessary position for the pollination robot. The experimental process is conducted in a laboratory and proves the efficacy of the cantaloupe flower segmentation method, yielding precision, recall, and F1 scores of 87.91%, 90.76%, and 89.31%, respectively. Furthermore, the accuracy of the growth-orientation prediction method reaches approximately 86.7%. Notably, positional errors in 3D space predominantly fall within the allowable range, resulting in a successful pollination rate of up to 83.1%.

A Taxonomic Checklist of Insect Biodiversity in Loyola College Campus, Chennai, Tamil Nadu, India

A Taxonomic Checklist of Insect Biodiversity in Loyola College Campus, Chennai, Tamil Nadu, India

Smartphone Camera for Angiographic Computer Vision in Vascular Medicine

Aim: This study aimed to develop a TensorFlow Lite algorithm for angiography classification and to deploy it on a basic mobile smartphone device, thereby verifying the proof of concept for creating a comprehensive end-to-end mobile computer vision application for vascular medicine. Materials and Methods: After ethical approval by the local ethics committee, we collected institutional and open source peripheral angiograms of lower limbs. The angiograms were labeled by a researcher with more than 10 years of experience in vascular surgery. The labeling included dividing the angiograms according to their anatomical pattern into the Global Limb Anatomic Staging System (GLASS). The model was developed using the open-source TensorFlow framework for general image classification and deployed as an Android application. Results: The model utilized 700 angiograms, distributed as follows within the femoropoliteal GLASS disease (fp) categories: fp0 – 187 images, fp1 – 136 images, fp2 – 128 images, fp3 – 97 images, fp4 – 152 images. The reference dataset included 372 non-angiographic images (not_angio). Consequently, the entire model included 1,072 images. After training and deployment, the model demonstrated the following performance: a mean accuracy of 0.72. The best self-reported accuracy per class was for fp0 0.72, fp4 0.83 and not_angio 1.0 classes. Conclusion: We discovered that a smartphone camera could be utilized for angiographic computer vision through end-to-end applications accessible to every healthcare professional. However, the predictive abilities of the model are limited and require improvement. The development of a robust angiographic computer vision smartphone application should incorporate an upload function, undergo validation through head-to-head human-machine comparisons, potentially include segmentation, and feature a prospective design with explicit consent for using collected data in the development of AI models.

A Preliminary Evaluation of the Diagnostic Performance of a Smartphone-Based Machine Learning-Assisted System for Evaluation of Clinical Activity Score in Digital Images of Thyroid-Associated Orbitopathy.

Background: We previously developed a machine learning (ML)-assisted system for predicting the clinical activity score (CAS) in thyroid-associated orbitopathy (TAO) using digital facial images taken by a digital single-lens reflex camera in a studio setting. In this study, we aimed to apply this system to smartphones and detect active TAO (CAS ≥3) using facial images captured by smartphone cameras. We evaluated the performance of our system on various smartphone models and compared it with the performance of ophthalmologists with varying clinical experience. Methods: We applied the preexisting ML architecture to classify photos taken with smartphones (Galaxy S21 Ultra, iPhone 12 pro, iPhone 11, iPhone SE 2020, Galaxy M20, and Galaxy A21S). The performance was evaluated with smartphone-captured images from 100 patients with TAO. Three ophthalmology residents, three general ophthalmologists with <5 years of clinical experience, and three oculoplastic specialists independently interpreted the same set of images taken under a studio environment and compared their results with those generated by the smartphone-based ML-assisted system. Reference CAS was determined by a consensus of three oculoplastic specialists. Results: Active TAO (CAS ≥3) was identified in 28 patients. Smartphone model used in capturing facial images influenced active TAO detection performance (F1 score 0.59-0.72). The smartphone-based system showed 74.5% sensitivity, 84.8% specificity, and F1 score 0.70 on top three smartphones. On images from all six smartphones, average sensitivity, specificity, and F1 score were 71.4%, 81.6%, and 0.66, respectively. Ophthalmology residents' values were 69.1%, 55.1%, and 0.46. General ophthalmologists' values were 61.9%, 79.6%, and 0.55. Oculoplastic specialists' values were 73.8%, 90.7%, and 0.75. This smartphone-based ML-assisted system predicted CAS within 1 point of reference CAS in 90.7% using facial images from smartphones. Conclusions: Our smartphone-based ML-assisted system shows reasonable accuracy in detecting active TAO, comparable with oculoplastic specialists and outperforming residents and general ophthalmologists. It may enable reliable self-monitoring for disease activity, but confirmatory research is needed for clinical application.

Visualization of the rolling motion of spheres connected with a rubber band using an image processing technique

The traditional physics laboratory has adopted a photogate and polarized light to measure rotation motion in a fixed system, but they are not applicable to rolling objects. Computer vision has played an increasingly important role in image analysis. The popularization of mobile phones, cameras and computers makes it possible to measure the rotation of an object in a simple but nonintrusive way in a physics laboratory. A ‘tracker’ has been used to analyze the rotation motion of large objects, such as vehicle wheels, by recognizing the position of a marker. However, it is still challenging to precisely recognize the marker in a small object via simple algorithms in a physics laboratory. In this paper, we introduce a facile image processing technique to visualize the rotation and spin motion of spheres by marking it with two red and green semi-spheres. A basic formula of projection was derived to analyze the expression of spin angles with the projection area of the semi-sphere. The precision and suitability of the image processing technique was proven to be effective through comparison with the photogate measurement technique. The image processing technique has also been used to analyze the rotation and spin motion of two spheres bonded with a rubber band. It provides not only the trajectory of sphere centers, but the evolution of spin angles, orbit diameter and orbit angles of spheres. By analyzing the relationship between spin angles and orbit diameter, it is clear that the rubber band experienced typical transformations between three different phases (twist, coil and supercoil) until the energy was exhausted. The method offers valuable insights into rotational dynamics, showcasing its potential for practical applications in the physics laboratory and in educational contexts.

AnarMitra: Enhancing Pomegranate Farming with CNN Technology

Abstract: This study introduces AnarMitra, a mobile application designed to revolutionize pomegranate cultivation practices. AnarMitra employs machine learning algorithms to swiftly assess ripeness levels and detect prevalent diseases in pomegranates, such as bacterial blight and fungal infections. Utilizing smartphone cameras, farmers can effortlessly capture images for immediate feedback on fruit quality and health. The paper details AnarMitra's development process, including model training with diverse datasets and algorithmic methodologies for classification and disease detection. Field trials validate AnarMitra's efficacy in enhancing productivity and fostering sustainable agricultural practices, empowering farmers with timely insights for informed decision-making and crop management.

Dermoscopy can be safely and reliably used in ophthalmology

ObjectiveTo determine if dermoscopy, a technique widely utilized in dermatology for improved diagnosis of skin lesions, can be used comfortably for evaluating periorbital, eyelid, and conjunctival lesions. DesignProof-of-concept study in which a technique for performing dermoscopy near the eye was developed, related educational material was prepared, and a protocol for dermoscopic image capture was created. MethodsTechnicians used the developed materials to learn to take high-quality pictures with a 10x dermoscope attached to a standard cell phone camera. The images were assessed for diagnostic utility by an oculoplastic surgeon and two dermatologists. Participants115 patients recruited from ophthalmology clinics from July 2021 to April 2023 were photographed, yielding 129 lesions with high-quality dermoscopic images as assessed by an oculoplastic surgeon and two dermatologists. ResultsTechnicians reported a significant increase in confidence (measured on a 1–10 scale) with dermoscopy after training (pre-instruction mean = 1.72, median = 1, mode = 1, IQR = 1.25 vs mean = 7.69, median = 7.75, mode = 7 and 8, IQR = 1.5 post-instruction. Wilcoxon rank sum test with continuity correction, W = 0, p < 0.001, paired t = 13.95, p < 0.0001). Incorporating a contact plate with a 4 × 4mm reticule on the dermoscope aided in photographing ocular and periocular lesions. ConclusionMedical support staff in eye-care offices can be taught to use dermoscopes to capture high-quality images of periorbital, eyelid, and conjunctival lesions. Dermoscopy illuminates diagnostic features of lesions and thus offers a new avenue to improve decision-making in ophthalmology. Dermoscopy can be incorporated into telemedicine evaluations by ophthalmologists, oculoplastic surgeons, or affiliated dermatologists for triage of or rendering advice to patients and for planning of surgery if needed.

Sensitive assessment of bilirubin using various color space signals derived from captured images of microfluidic paper-based analytical devices

Many neonates’ manifest hyperbilirubinemia and jaundice during the first week following birth because of bilirubin accumulation in the blood. Newborns with severe hyperbilirubinemia develop neurotoxicity. Therefore, sensitive, accurate, cost-effective, and timely determination of bilirubin levels is highly desired. Herein, we present three simple systems of bilirubin assessment based on spectrophotometric, and digital imaging tools. Image-capturing devices pose a formidable challenge to sophisticated spectrophotometers and field-monitoring devices because of their simplicity and sensitivity. The first and second systems of bilirubin assessment, in solution, relied on spectrophotometric and conventional color responses of digital images collected with conventional or android smartphone camera, with LODs of 0.060–0.082 and LOQs of 0.200–0.273 ppm bilirubin. The third system utilized a microfluidic paper-based analytical device (μ-PAD) that is patterned with a wax printer on Whatman grade-1 chromatographic papers, with linear calibration graphs of up to 2.70,s and LODs of 0.035–0.040 and LOQs of 0.117–0.133 μg bilirubin/μ-PAD.

A smartphone-enabled colorimetric sensing platform based on Au-decorated Fe3O4 nanoparticles as nanozyme for dual-mode visual monitoring of dopamine hydrochloride

The potential risks associated with the use of dopamine hydrochloride (DH) on the quality of meat and poultry products, as well as human health, are examined in this study. A novel approach for the synthesis of Au-decorated magnetic spherical nanoparticles (Fe3O4@Au) is proposed, aiming to develop colorimetric analysis sensors for mobile devices. Notably, this newly developed nanocomposite exhibits inherent peroxidase-like activity, enabling efficient catalysis of the oxidation reaction between the substrate 2,2′-azino-bis-(3-ethylbenzthiazoline-6-sulphonate) (ABTS) and H2O2. The results of the study led to the development of a colorimetric smartphone sensor, known as Color Image Capture, which demonstrated high sensitivity and visual capability in detecting DH. The methodology involved the utilization of a smartphone and hydrogen peroxide in conjunction with a Fe3O4@Au nanocomposite for the quantification of DH. The alteration in color exhibited by the platform was captured using a mobile camera and subsequently analyzed using a smartphone application. The smartphone colorimetric sensor exhibited a limit of detection (LOD) of 0.0722 μmol/L, enabling the quantitative detection of DH within the concentration range of 0.005–0.125 mmol/L. The colorimetric measurement technique demonstrated efficacy in quantifying DH levels in pork samples. The development of innovative catalysts and the implementation of novel methodologies have contributed novel insights into the detection of DH in intricate food and biological matrices.

Open-source, 3D printable IOL holder for detailed, smartphone-based anterior segment photography.

Smartphones are increasingly relevant resources in medical practice as they are ubiquitous and reasonably cheap. Among the advantages of using smartphones in medical practise, there is the possibility of obtaining reproducible photographic documentation of various conditions. This is particularly true in the ophthalmic field, where anterior segment color photography plays a significant role in the diagnosis and the management of ocular surface diseases. Here we propose an original design for an open-source smartphone accessory for taking and sharing high-definition photographs of the anterior segment. It can be easily reproduced via 3D printing, and it only needs to be integrated with an intraocular lens (IOL), widely available to the majority of ophthalmologists. Compared to other solutions described previously, it allows a precise and reproducible placement of the IOL on the smartphone camera, avoiding manual positioning that could result tricky and time-consuming. The IOL holder is cheap, scalable, portable and it can be quickly assembled and disassembled, without permanently modifying the smartphone camera.

Wearable, disposable and non-enzymatic fluorescence nanosensor for monitoring sweat glucose through smartphone

The promise of non-invasive health monitoring has fuelled the development of wearable biosensors for in situ detection of biological analytes in sweat. This paper presents a new fluorescent nanosensor probe based on boric acid functionalized and heteroatom doped carbon quantum dots (CQDs) embedded in paper-based analytical devices and integrated with a hydrophilic cotton thread-based microfluidic channel for the development of a flexible, wearable, biocompatible, and biodegradable sweat sensor for real-time measurement of sweat glucose concentration via smartphone readout. The morphological, physiochemical, compositional, crystallinity, and optical properties of nanosensors were extensively investigated. CQD particle sizes were found to be widely dispersed, ranging from 2.6 to 7.1 nm, with an average diameter of 4.38 nm. A dual response via fluorescence and colorimetric change was demonstrated for on-site sweat glucose monitoring using a smartphone camera to capture images in RGB (red, green, and blue) format and analyse them simultaneously by a customized android app, with quantitative results displayed on the smartphone. The limit of detection value for in situ measurement of sweat glucose concentration was 1.40 to 2.00 μM (25.25 to 36.13 μg/dL) and the limit of quantification value was 4.67 to 6.69 μM (84.16–120.44 μg/dL) in the pH range of 4.5 to 7.5. The developed sensor's performance metrics were validated using a standard clinical technique, which indicates strong correlation between two methods with Pearson correlation coefficient values of 0.83 and 0.80 for non-diabetic and type- II diabetes patients, respectively. Besides, interference, spike, and recovery studies were carried out with recovery rates ranging from 96.5 to 105.8 %, demonstrating good repeatability and reproducibility. Furthermore, feasible fluorescence quenching processes have been explored. In addition, the disposable and biodegradable nature of the nanosensor probe was studied by throwing it into the soil in the natural environment, where it dissolves almost spontaneously after three weeks.

Aptamer-conjugated gold nanoparticles for portable, ultrasensitive naked-eye detection of C-reactive protein based on the Tyndall effect

BackgroundC-reactive protein (CRP) represents an early clinical biomarker that indicates the presence of inflammatory or infectious conditions in the human body. Today's procedures approved by the Food and Drug Administration (FDA) imply expensive equipment and highly trained personnel to perform the test. Therefore, a new diagnostic method with high detection efficiency and less cost is urgently needed for delivering rapid and timely results in point-of-care (POC) service. ResultsHerein, we propose a new, equipment-free, and portable sensing method for the future POC detection of CRP based on the Tyndall effect (TE). In our study, aptamer-conjugated citrate-stabilized gold nanoparticles (apta-AuNPs) are exploited as the sensing platform. The apta-AuNPs’ interaction with CRP in a saline environment leads to their aggregation, thus enhancing the scattering of light when the solution is exposed to a 640 nm pointer laser line. Firstly, the enhancement of the scattering light as a function of increasing concentration of CRP in solution is measured spectroscopically using a typical 90-degree angle spectrofluorometer and then the measurements are compared to the classic colorimetric detection using an UV–Vis spectrophotometer. Finally, to achieve high portability and accessibility, we demonstrate that the measurement of CRP concentration can be performed with similar accuracy but in a more direct and inexpensive way by using a laser pointer pen as the excitation source and a camera of a low-budget smartphone as a quantitative reader instead of most expensive spectrofluorometer. SignificanceThe portable TE-based assay exhibits a wide linear dynamic range (1–60 μg/mL) for the detection of CRP with a limit of detection (LOD) of 92 ng/mL The proposed method is capable to integrate both standard and high-sensitivity CRP analysis in a single procedure with increased sensitivity and prompt delivery of analysis results. Moreover, the sensing procedure is significantly faster than the FDA approved ones with a detection time of only 10 min. Finally, as a proof-of-concept, our findings demonstrate excellent recovery for CRP detection in spiked and diluted urine samples, highlighting the strong potential of this sensing method for POC applications.

Evaluating smartphone-based optical readouts for immunoassays in human and veterinary healthcare: A comparative study

Recent advances have significantly enhanced the use of smartphone devices for medical diagnostics. This study uses high-resolution cameras in mobile devices to capture and process bioassay images, enabling the quantification of diverse biomarkers across a range of diagnostic tests conducted on 96-well microplates. The study evaluates the effectiveness of this technology through protein quantification techniques and immunoassays that generate colorimetric responses at specific wavelengths. It includes the assessment of bicinchoninic acid and Bradford protein quantification methods, alongside a conventional immunoassay for detecting mare antibodies in colostrum to monitor foal immunodeficiencies. Further application involves the readout of magneto-actuated immunoassays aimed at quantifying bacteria. The results obtained from benchtop spectrophotometry at 595, 562, and 450 nm are compared with those acquired using a smartphone, which identified color intensities in shades of blue, purple, and yellow. This comparison yields promising correlations for the samples tested, suggesting a high degree of accuracy in the smartphone capability to analyze bioassay outcomes. The analysis via smartphone is facilitated by a specific app, which processes the images captured by the phone camera to quantify color intensities corresponding to different biomarker concentrations. Detection limits of 12.3 and 22.8 μg mL−1 for the bicinchoninic acid assay and 36.7 and 45.4 μg mL−1 for the Bradford are obtained for protein quantification using the spectrophotometer and the smartphone app, respectively. For mare's antibodies in colostrum, the values are 1.14 and 1.72 ng mL−1, while the detection of E. coli is performed at 2.0 x 104 and 2.9 × 104 CFU mL−1, respectively. This approach offers further advantages, including wide availability, cost-effectiveness, portability, compared to traditional and expensive benchtop instruments.

Development of a sustainable procedure for smartphone-based colorimetric determination of benzalkonium chloride in pharmaceutical preparations

A sustainable procedure offering green, simple, and rapid analysis was developed to determine benzalkonium chloride (BKC) in pharmaceutical preparations. The determination using smartphones was based on the ion pair colorimetric reaction with bromothymol blue (BTB), which produces a yellow color. The intensity of the product color, which is proportional to the concentration of BKC, was detected and evaluated using a smartphone camera and an image processing application. The procedure was performed in a microliter and was rapidly detected within 1 min after incubation. This offered high throughput at 28 samples per well plate in duplicate. Linear calibration, which was a plot of BKC concentrations and relative red intensities, was in the range of 2.0–24.0 μg/mL with an R2 of 0.997. The limits of detection (LOD) and quantitation (LOQ) were 1.0 and 3.2 μg/mL, respectively. This work was successful in applying it to pharmaceutical materials, disinfectant products, and pharmaceutical products containing BKC. It was discovered that the concentrations of BKC as an active ingredient in pharmaceutical materials were 82% w/v, whereas those in disinfectant products ranged from 0.4 to 2.1% w/v. In pharmaceutical products, ophthalmic drops and nasal sprays contain BKC as preservatives in the 0.01–0.02, and the 0.02% w/v, respectively. The results obtained by the proposed procedure compared with a reference titration method showed no significant differences at a 95% confidence level with 1.2–3.4% RSDs. This promotes the efficiency of pharmaceutical preparations regarding infection prevention and control by ensuring that available disinfectants contain a sufficient concentration of BKC. Additionally, this improves the efficiency of pharmaceutical preparations for quality control of pharmaceutical products by ensuring that the available preservatives maintain a sufficient concentration throughout the lifespan of the products.

Crop Leaf Disease Prediction Using Machine Learning

In India, crop area is largest in the world and produces major crops like wheat, pulses, fruits, rice and vegetables Despite of using modem taming techniques along with traditional, infectious plant diseases is major problem which can be caused by different viruses, fungus and bacteria. This mainly affects crop production as well as crop quality. It is very important to identify diseases at early stage Nowadays, automatic crop de detection has become a important research domain. It helps in detecting the symptoms of the disease when they are found on the e In this paper we will focus on finding the diseases in order to increase crop quality and production effectively. Here, we will focus on r diseases by observing leaves of plants at initial stage using machine learning. · In this paper, we designed a Deep Convolutional Neural Network based on LeNet to perform soybean leaf spot disease recognition and classification using affected areas of disease spots. The affected areas of disease spots were segmented from the leaves images using the Unsupervised fuzzy clustering algorithm. The proposed Deep Convolutional Neural Network model achieved a testing accuracy of 89.84%, and poor per class recognition results in 1378 images misclassified, and 1271 images correct classified. TheVGG16 achieved the best performance reaching a 93.54% success rate, and better per class recognition results in 1245 images misclassified, and 1404 images correct classified · In order to address the challenges related to the classification and recognition of soybean disease and healthy leaf identification, it is essential to have access to high-quality images. A meticulously curated dataset named “SoyNet” has been created to provide a clean and comprehensive dataset for research purposes. The dataset comprises over 9000 highquality soybean images, encompassing healthy and diseased leaves. These images have been captured from various angles and directly sourced from soybean agriculture fields; The soybean leaves images are organized into two sub-folders: SoyNet Raw Data and SoyNet Pre-processing Data.The SoyNet Pre-processing Data folder comprises resized images of 256∗256 pixels and the grayscale versions of disease and healthy images, following a similar organizational structure. We captured the images using the Nikon digital camera and the Motorola mobile phone camera, utilizing different angles, lighting conditions, and backgrounds. They were taken in different lighting conditions and backgrounds at soybean cultivation fields to represent the real-world scenario accurately. The proposed dataset is valuable for testing, training, and validating soybean leaf disease classification

Classification and Identification of Perfume Scents by an Enantioselective Colorimetric Sensor Array of Chiral Metal-Organic-Framework-Based Fabry-Pérot Films.

Many odors, like perfumes, are complex mixtures of chiral and achiral molecules where the cost-efficient (enantio-)selective sensing represents a major technical challenge. Here, we present a colorimetric sensor array of surface-mounted metal-organic-framework (SURMOF) films in Fabry-Pérot (FP) cavities. The optical properties of the FP-SURMOF films with different chiral and achiral structures are affected by the (enantio-)selective adsorption of the analytes in the SURMOF pores, resulting in different responses to the analyte molecules. The read-out of the sensor array is performed by the digital camera of a common smartphone, where the RGB values are determined. By analyzing the sensor array data with simple machine learning algorithms, the analytes are discriminated. After demonstrating the enantioselective response for a pair of pure chiral odor molecules, we apply the sensor array to detect and discriminate a large number (16) of common commercial perfumes and eau de toilettes. While our untrained human nose is not able to discriminate all perfumes, the presented colorimetric sensor array can classify all perfumes with great classification accuracy. Moreover, the sensor array was used to identify unlabeled samples correctly. We foresee such an FP-chiral-SURMOF-based sensor array as a powerful approach toward inexpensive selective odors sensing applications.