Hand-held Sensor Research Articles

Colorimetric hand-held sensors and biosensors with a small digital camera as signal recorder, a review

Abstract Sensors, biosensors, lateral flow immunoassays, portable thin-layer chromatography and similar devices for hand-held assay are tools suitable for field or out of laboratories assays of various analytes. The assays frequently exert a limit of detection and sensitivity close to more expensive and elaborative analytical methods. In recent years, huge progress has been made in the field of optical instruments where digital cameras or light sensitive chips serve for the measurement of color density. General availability of cameras, a decrease of prices and their integration into wide spectrum phones, tablets and computers give the promise of easy application of analytical methods where such cameras will be employed. This review summarizes research on hand-held assays where small cameras like the ones integrated into smartphones are used. Discussion about such assays, their practical applicability and relevant specifications are also written here.

Sensor based calibration study for in-season nitrogen management of winter wheat in Turkey

The aims of this study were to compare the responses of four winter wheat cultivars to nitrogen fertilization with vegetation indices calculated using spectral reflection (GreenSeeker hand-held sensor) and to estimate in-season yield (INSEY) using the vegetation indices. The field experiment was conducted at Transitional Zone Agricultural Research Institute of Eskisehir province, Turkey in 2007-2008, 2008-2009 and 2009-2010 growing seasons. The experimental layout was a 2factor factorial in the randomized complete block design. Nitrogen rates were 0, 40, 80, 120, 160 and 200 kg N ha-1. Vegetation Index (NDVI) was obtained at growth stages of Zadoks 24 (tillering stage), Zadoks stage 30 (stem elongation), Zadoks stage 31 (the first node is visible) and Zadoks stage 32 (the second node is visible). The results revealed that Zadoks stage 30 was the most realistic reading time. NDVI had the advantage of providing information on biomass, in addition to nitrogen nutrition status of crops, enabling in-season yield estimation possible. Therefore, NDVI based calibration equations were preferred for testing in the fields of actual farmers for the last year of study. A comparison of the system with traditional farmer applications indicated that yield estimation obtained by the new system was quite similar yields with 13.2 kg ha-1 less N in the spring (ZD 3.0), showing its economically promising value.
 Asian J. Med. Biol. Res. June 2020, 6(2): 204-211

Development of a hand-held magnetic resonance sensor for the nondestructive quantification of fat and lean meat of fresh tuna

For the in-situ nondestructive fat quantification of fresh tuna meat, an original lightweight (5.7 kg) hand-held sensor that consists of a planar radio-frequency coil and a single-sided magnetic circuit was developed as a subunit of a time-domain proton magnetic resonance (MR) scanner system. The investigation depth of the sensor unit is 12 mm, which is sufficient to probe the meat section beneath thick skin with scales and the underlying subcutaneous fat layer of large fish such as tuna. The scanner was successfully applied in a laboratory to a fillet of a bluefin tuna (Thunnus thynnus) to measure meat sections 12 mm beneath the skin. The required measurement time was 100 s for each section. The results of MR scan at 11 locations on the fillet were compared with those of conventional destructive food analysis. Reasonable agreement with an error (root-mean-square residual) of as small as 1.8 wt% was obtained for fat quantification. The time-domain MR relaxometry for the same tuna fillet also allowed lean meat quantification with a small root-mean-square residual of 6.7 wt%.

Silicon photonics-based laser Doppler vibrometer array for carotid-femoral pulse wave velocity (PWV) measurement.

Pulse wave velocity (PWV) is a reference measure for aortic stiffness, itself an important biomarker of cardiovascular risk. To enable low-cost and easy-to-use PWV measurement devices that can be used in routine clinical practice, we have designed several handheld PWV sensors using miniaturized laser Doppler vibrometer (LDV) arrays in a silicon photonics platform. The LDV-based PWV sensor design and the signal processing protocol to obtain pulse transit time (PTT) and carotid-femoral PWV in a feasibility study in humans, are described in this paper. Compared with a commercial reference PWV measurement system, measuring arterial pressure waveforms by applanation tonometry, LDV-based displacement signals resulted in more complex signals. However, we have shown that it is possible to identify reliable fiducial points for PTT calculation using the maximum of the 2nd derivative algorithm in LDV-based signals, comparable to those obtained by the reference technique, applanation tonometry.

Early detection of disease infection in chilli crops using sensors

Control of diseases is a key aspect of profitable chilli (Capsicum annuum L.) crop production, and early detection of disease incidence is therefore an important aspect of crop management. Visual crop assessment is the most commonly used approach, but it is expensive where labour costs are high and tends to be unreliable, especially at low levels of infection. Alternative cost-effective approaches for detection of diseases and pests at an early stage are therefore desirable. This trial focused on the potential of sensor technologies to detect diseases in a chilli crop earlier than is currently possible with visual assessment. Experiments were conducted to determine whether multispectral data could be used to detect disease infection at the individual leaf and whole plant level. A multispectral camera mounted on an unmanned aerial vehicle (UAV) and a hand-held NDVI sensor were used to collect weekly data on plants in a fungicide field trial. Bacterial spot incidence in all treatments was low (<20%) but was detectable using the sensors. The hand-held NDVI sensor was able to detect diseased plants between 5 and 20 days before significant disease symptoms were visually recorded. The hand-held sensor was found to be much more sensitive in detecting diseased plants than the UAV mounted sensor.

Development of portable, non-destructive freshness indicative sensor for Indian Mackerel (Rastrelliger kanagurta) stored under ice

A handheld, portable and non-destructive instrumental sensor was developed to assess the freshness of Indian Mackerel (Rastrelliger kanagurta) stored under ice. The freshness sensor consists of a webcam, raspberry-Pi (small single-board computer), LCD display and a power bank. The color changes in fish eye as a result of spoilage during iced storage was measured as pixel count using image processing technique. Simultaneously, destructive fish quality and freshness results were obtained by estimating K-value and Psychrophilic count during the storage. Multiple linear regression analyses were performed to assess the relationship between pixel count and quality indicators. The analysis of results of K-value and Psychrophilic count revealed that fish quality and freshness limits can be established in storage days as extremely fresh up to third day, fresh till thirteenth day and then spoiled. Further, these quality limits were correlated with pixel count against storage days to establish three different ranges in pixel count. The obtained pixel count ranges were provided as input to the sensor for classification of fish samples into three indicative freshness levels i.e. Extremely fresh, Fresh and Spoiled. The validation study of sensor was conducted by assessing fish samples collected from local markets and observed that sensor was accurately predicting the freshness of fish.

Handheld Sensor for Point-of-Care Methanol Detection in Breath and Liquor

Handheld Sensor for Point-of-Care Methanol Detection in Breath and Liquor

Simple regression models to estimate light interception in wheat crops with Sentinel‐2 and a handheld sensor

AbstractCapture of radiation by crop canopies drives growth rate, grain set, and yield. Since the fraction of photosynthetically active radiation absorbed by green area (fAPARg) correlates with normalized difference vegetation index (NDVI), remote sensors have been used to monitor vegetation. With a 10‐m spatial resolution and 5‐d revisiting time, the recently launched Sentinel‐2 satellite is a promising tool for fAPARg monitoring. However, the available algorithm to estimate fAPARg is based on simulations of canopy interception of several vegetation types and was never tested in field crops. Handheld sensors, such as GreenSeeker, are another alternative to estimate fAPARg. Our objectives were (a) to test the ability of indices derived from Sentinel‐2 and GreenSeeker NDVI to capture fAPARg of wheat (Triticum aestivum L.) crops, (b) to compare these sensors’ performance against the moderate resolution imaging spectroradiometer (MODIS), and (c) to compare our Sentinel‐2 model estimations with the available algorithm. In wheat fields in the southwest Argentinean Pampas, on several sampling dates, we measured fAPARg with a quantum light sensor and NDVI with a GreenSeeker. We regressed fAPARg measurements with vegetation indices from the different sources and selected the best models. Sentinel‐2 and GreenSeeker NDVI precisely estimated fAPARg, with a performance similar to MODIS (p &lt; .05; RMSD = 0.09, 0.11, and 0.08; R2 = .89, .88, and .95, respectively). The available algorithm to estimate fAPARg with Sentinel‐2 yielded biased estimations, mainly in the lower range of fAPARg. These results suggest that simple models may provide fAPARg estimations with Sentinel‐2 and GreenSeeker in wheat crops with an accuracy suitable for agricultural applications.

Handheld UV fluorescence spectrophotometer device for the classification and analysis of petroleum oil samples

Oil spills can be environmentally devastating and result in unintended economic and social consequences. An important element of the concerted effort to respond to spills includes the ability to rapidly classify and characterize oil spill samples, preferably on-site. An easy-to-use, handheld sensor is developed and demonstrated in this work, capable of classifying oil spills rapidly on-site. Our device uses the computational power and affordability of a Raspberry Pi microcontroller and a Pi camera, coupled with three ultraviolet light emitting diodes (UV-LEDs), a diffraction grating, and collimation slit, in order to collect a large data set of UV fluorescence fingerprints from various oil samples. Based on a 160-sample (in 5x replicates each with slightly varied dilutions) database this platform is able to classify oil samples into four broad categories: crude oil, heavy fuel oil, light fuel oil, and lubricating oil. The device uses principal component analysis (PCA) to reduce spectral dimensionality (1203 features) and support vector machine (SVM) for classification with 95% accuracy. The device is also able to predict some physiochemical properties, specifically saturate, aromatic, resin, and asphaltene percentages (SARA) based off linear relationships between different principal components (PCs) and the percentages of these residues. Sample preparation for our device is also straightforward and appropriate for field deployment, requiring little more than a Pasteur pipette and not being affected by dilution factors. These properties make our device a valuable field-deployable tool for oil sample analysis.

Qualitative discrimination of Chinese dianhong black tea grades based on a handheld spectroscopy system coupled with chemometrics.

The evaluation of Chinese dianhong black tea (CDBT) grades was an important indicator to ensure its quality. A handheld spectroscopy system combined with chemometrics was utilized to assess CDBT from eight grades. Both variables selection methods, namely genetic algorithm (GA) and successive projections algorithm (SPA), were employed to acquire the feature variables of each sample spectrum. A partial least‐squares discriminant analysis (PLS‐DA) and support vector machine (SVM) algorithms were applied for the establishment of the grading discrimination models based on near‐infrared spectroscopy (NIRS). Comparisons of the portable and benchtop NIRS systems were implemented to obtain the optimal discriminant models. Experimental results showed that GA‐SVM models by the handheld sensors yielded the best predictive performance with the correct discriminant rate (CDR) of 98.75% and 100% in the training set and prediction set, respectively. This study demonstrated that the handheld system combined with a suitable chemometric and feature information selection method could successfully be used for the rapid and efficient discrimination of CDBT rankings. It was promising to establish a specific economical portable NIRS sensor for in situ quality assurance of CDBT grades.

Assessing Performance of Vegetation Indices to Estimate Nitrogen Nutrition Index in Pepper

Vegetation indices (VIs) can be useful tools to evaluate crop nitrogen (N) status. To be effective, VIs measurements must be related to crop N status. The nitrogen nutrition index (NNI) is a widely accepted parameter of crop N status. The present work evaluates the performance of several VIs to estimate NNI in sweet pepper (Capsicum annuum). The performance of VIs to estimate NNI was evaluated using parameters of linear regression analysis conducted for calibration and validation. Three different sweet pepper crops were grown with combined irrigation and fertigation, in Almería, Spain. In each crop, five different N concentrations in the nutrient solution were frequently applied by drip irrigation. Proximal crop reflectance was measured with Crop Circle ACS470 and GreenSeeker handheld sensors, approximately every ten days, throughout the crops. The relative performance of VIs differed between phenological stages. Relationships of VIs with NNI were strongest in the early fruit growth and flowering stages, and less strong in the vegetative and harvest stages. The green band-based VIs, GNDVI, and GVI, provided the best results for estimating crop NNI in sweet pepper, for individual phenological stages. GNDVI had the best performance in the vegetative, flowering, and harvest stages, and GVI had the best performance in the early fruit growth stage. Some of the VIs evaluated are promising tools to estimate crop N status in sweet pepper and have the potential to contribute to improving crop N management of sweet pepper crops.

Development of a handheld dual-channel optical fiber fluorescence sensor based on a smartphone.

In order to meet the needs of on-site, accurate, fast, and remote detection, we design a smartphone-based handheld dual-channel optical fiber fluorescence sensor (DOFFS), which is composed of a semiconductor laser for exciting fluorescence signals, a smartphone with a dual-bandpass filter for collecting fluorescence signals, a fiber coupler for transmitting light, and batteries for laser power supply. All the components are integrated into a 3D printed shell, on the side of which there are two fiber flanges used for fiber probe connection. The fluorescence signals of green and red quantum dots modified on the fiber probes can be captured by the smartphone camera and calculated by a self-developed Android application. The comparisons of single-channel and dual-channel fluorescence signals with pH show that the performance of the sensor is good. The proposed sensor not only can simultaneously detect dual-channel signals for fast detection needs, but it also is handheld with a small size of 79×57×154mm3 and inner power supply, and the fiber probes can be easily replaced, supporting remote and on-site applications. It is a potential tool for many occasions in many fields.

Indoor Topological Localization Based on a Novel Deep Learning Technique

Millions of people in the world suffer from vision impairment or vision loss. Traditionally, they rely on guide sticks or dogs to move around and avoid potential obstacles. However, both guide sticks and dogs are passive. They are unable to provide conceptual knowledge or semantic contents of an environment. To address this issue, this paper presents a vision-based cognitive system to support the independence of visually impaired people. More specifically, a 3D indoor semantic map is firstly constructed with a hand-held RGB-D sensor. The constructed map is then deployed for indoor topological localization. Convolutional neural networks are used for both semantic information extraction and location inference. Semantic information is used to further verify localization results and eliminate errors. The topological localization performance can be effectively improved despite significant appearance changes within an environment. Experiments have been conducted to demonstrate that the proposed method can increase both localization accuracy and recall rates. The proposed system can be potentially deployed by visually impaired people to move around safely and have independent life.

The Effect of Crop Rotation, Tillage, Residue Management and N Fertilization Rate on Winter Wheat Growth and Development, Evaluated with an Optical Sensor

Crop growth and development, as well as yield, are the result of the efficiency of the chosen agricultural management system within the boundaries of the agro-ecological environment. End of season yield results do not permit the evaluation of within season management interactions with the production environment and do not allow for full understanding of the management practice applied. Crop growth and development were measured during the 2017, 2018 and 2019 crop cycles with an optical handheld NDVI sensor for all plots of the different management treatments of a long-term (since 2015) sustainability trial. Cropping systems varying in (1) crop rotation [winter wheat (Triticum aestivum L.) after maize (Zea mays L.), winter wheat (Triticum aestivum L.) after sunflower (Helianthus annuus L.) and winter wheat (Triticum aestivum L.) after winter pea (Pisum sativum L.)], (2) tillage (chisel tillage vs. no tillage), (3) residue management (chopped vs. anchored) and (4) N fertilization rate (N0, N50, N100 and N150) were compared. The NDVI handheld sensor was evaluated as a tool to monitor crop growth and development and was found to be an excellent tool for this purpose. The measurement with the handheld sensor was non-destructive and fast so that representative plot area could be measured easly and time efficiently. Crop rotation influnced early crop growth, with lower NDVI values for crops sown after maize and sunflower than crops after pea. The differences between the rotations diminished later in the growing season, and there was no advers effect on final yield. Fertilization rate significantly increased the NDVI values compared to non-fertilized plots. No tillage with residue retention, chopped or anchored, was characterized by a slower initial growth than chisel tillage practices, but this was compensated for by increased crop performance in the later stages. The results indicated that different rotation, tillage, residue management and N fertilization rate practices influence crop growth and development. It is important to monitor and understand crop growth under different management systems to select the right varieties and adjust timing and practice of input supply in each cropping system.

Learning Individual Models to Estimate the Walking Direction of Mobile Phone Users

Pedestrian Dead Reckoning algorithms are commonly used to assist pedestrian navigation with handheld sensors. The estimation of the walking direction remains an important source of positioning error of mobile phone users since this direction may be different from the device’s pointing direction. A better understanding of human walking gait has enabled to produce new algorithms to mitigate the impact of the way the device is held in hand. WAISS algorithm is one of them. It is based on the study of horizontal hand accelerations and their modeling using Gaussian Mixture Models (GMM). However, ongoing search for universal modeling of handheld device carrying mode defeats the varying nature of human gait. This paper investigates the impact of individual gait characteristics and their modeling to improve the estimation of the walking direction. Different models are learned for curved and straight lines and varying GMM are proposed to account for inter-individual gait variations. This results in a reduced walking direction error with a 8.1° mean error to the 90th percentile computed for 3 subjects over a 1.5 km indoor/outdoor walk.

Handheld four-dimensional optical sensor

Spectral and imaging systems enable retrieval of the molecular and surface profile information of objects of interest. Camera has become an indispensable device in everyday life to capture images for surface profile investigation. However up to now, spectral data are still neglected even though we receive this information most of the time. In this work, we developed a handheld four-dimensional optical sensor by combining a lightweight imaging spectrometer and a binocular camera. Based on this compact system, we can acquire spatial images, depth information and spectral data simultaneously. This technology can be applied to any kind of image capture equipment. It can be used to remotely and quickly collect spectral information and spatial images synchronously and has important value for object classification.

Smart Robotic System Tracks Buried Pipelines, Inspects for External Damage

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 192773, “A Smart Robotic System for Noncontact Condition Monitoring and Fault Detection in Buried Pipelines,” by Xiaoxiong Zhang and Amit Shukla, Khalifa University; Abdulla Al Ali, ADNOC; and Hamad Karki, Khalifa University, prepared for the 2018 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, 12–14 November. The paper has not been peer reviewed. The complete paper describes the development of a smart robotic inspection system for noncontact condition monitoring and fault detection in buried pipelines. Steered by a pipe locator, the smart robot, called an autonomous ground vehicle (AGV), can autonomously track the buried pipeline and simultaneously inspect it externally with a metal magnetic memory (MMM) sensor. The smart robotic system is designed to overcome the shortcomings of both manual external inspection and noninvasive magnetometric diagnosis (NIMD), making pipeline inspection safer, more efficient, and less expensive. Introduction Condition monitoring and defect inspection of buried pipelines has been a constant challenge for all oil and gas operations. Maintaining safety and prolonging the service life of ferrous metal pipelines that are exposed to harsh operating environments and damage from corrosion, erosion, and cracking requires regular inspection to diagnose existing or potential defects. Pipelines can be inspected in two ways: internally and externally. Internal, or inline, inspection primarily uses an intelligent pipeline inspection gauge equipped with sensors to measure the size, location, and orientation of defects inside the pipeline. In external inspection, which is the subject of the paper, workers drive a vehicle along the pipeline to visually inspect for detection of leakage or any other kind of visible damage. Such manual external inspection is highly inefficient, expensive, and hazardous. It is also difficult to obtain any important information about anomalies brewing in the buried pipes or cathodic protection layers using this method. Much work has been undertaken to develop nondestructive testing (NDT) technologies to inspect pipelines. However, most of these NDT sensors work only in close vicinity to the pipeline surface, so this method requires excavating the pipeline and exposing the structure. This shortcoming has instigated research toward other NDT techniques such as NIMD, which allows noncontact detection of anomalies from a distance in the core metal of pipelines buried deeply underground. NIMD sensors work on the principle of measuring distortions of residual magnetic fields caused by the variation in the pipeline’s metal magnetic permeability in a stress concentration zone (SCZ). The SCZ, and the potential changes in metal magnetic permeability, result from the combined influence of residual stress, vibration, bending and loading of pipelines, installation stress, temperature fluctuations, and other factors. These handheld magnetic sensors are used by field operators, making inspection of long pipelines in extreme environmental conditions unfeasible. Efforts to develop more-intelligent and -efficient methods of external inspection led to the design of various types of in-pipe inspection robots. Overall, all types of in-pipe robots are designed for solving specific problems relating to the pipeline’s interior environment, which is complex, invisible, and unpredictable. The technology presented in this paper resulted from the idea of using a robot that can simultaneously track and externally inspect the pipeline.

Using Small Unmanned Aircraft Systems for Early Detection of Drought Stress in Turfgrass

ABSTRACTRecent advances in small unmanned aircraft systems (sUAS) and sensors may improve accuracy and efficiency in turfgrass research and management compared with conventional methods. We evaluated the ability of sUAS combined with ultra‐high spatial resolution remote sensing to detect early drought stress. Results were compared with ground‐based techniques in creeping bentgrass (Agrostis stolonifera L.) irrigated at different levels from well‐watered to severe deficit (100 to 15% evapotranspiration [ET] replacement). Small UAS‐based measurements with a modified digital camera included three reflectance bands (near infrared [NIR, 680–780 nm] and overlapping green [G] and blue [B] bands [400–580 nm]) and eight derived vegetation indices (VIs). Ground‐based measurements included soil volumetric water content (VWC), turfgrass quality (TQ), green cover (GC), soil temperature (Tsoil), and reflectance with handheld optical sensors. Declines in VWC in deficit‐irrigation treatments were detected with NIR and six of eight VIs from sUAS, and the normalized difference vegetation index (NDVI) and red band reflectance from a handheld sensor, before symptoms appeared in TQ and GC. The most consistently sensitive parameters of sUAS throughout the 3‐yr study were NIR and GreenBlue VI [(G − B)/(G + B)], which detected drought stress &gt;5 d before decreases in TQ. Results indicate that ultra‐high spatial resolution remote sensing with sUAS detected drought stress before it was visible to a human observer and could be valuable for improving irrigation management in turfgrass.

Enhancing the sensitivity of point-of-use electrochemical microfluidic sensors by ion concentration polarisation – A case study on arsenic

Point of use (POU) sensors are extremely relevant, being capable of providing fast and reliable analysis in remote and resource-limited settings. Of all the diverse techniques utilised for POU sensors, a combination of electrochemistry and microfluidics may have the greatest potential towards quantitative assessment of heavy metal ions. The major challenge in combining these for sensing applications lies in the complexity of fabricating integrated devices and the insufficient quantity of analytes in the sample volume. To address these issues, we have developed a radial microfluidic device capable of electrokinetic preconcentration by ion concentration polarization (ICP) and integrated it with electroactive surfaces. The proposed sensor is the first demonstration of concentration of heavy metal ions by ICP and its quantitative assessment by voltammetry. Utilising the integrated sensor, we have shown the sensing of As3+ down to 1 ppb by linear sweep voltammetry with ∼40 μL of sample. The sensor was also tested successfully for sensing As3+ in a field sample from an arsenic affected region of India. The sensor was also tested for the detection of other metal ions too. This work would facilitate the development of highly sensitive POU hand-held sensors for water quality monitoring in resource-limited areas.

Fast and robust optically pumped cesium magnetometer

Abstract We present a fast and robust optically pumped magnetometer that is based on a feedback-controlled spin ensemble of cesium atoms in spin-polarized vapor. The table-top system is intended for operation in unshielded environment, and its design allows conversion into a handheld sensor head. Under strongly disturbed environmental conditions in the laboratory, the sensor exhibits a speed of more than 56 kHz, while having a slew rate of 39 mT/s and a full dynamic range of 10 – 120 μT. Under these conditions a sensitivity of 33 pT / Hz $\text{pT}/\sqrt{\text{Hz}}$ is reached. By reducing the speed to 3.6 kHz the sensitivity can be improved to 6 pT / Hz $\text{pT}/\sqrt{\text{Hz}}$ , which is close to the shot noise limit. We describe the sensor design and its optimization and demonstrate the performance of the sensor under conditions appropriate for magnetic susceptometry measurements.