Spectral Sensors Research Articles

Multi critical quality attributes monitoring of Chinese oral liquid extraction process with a spectral sensor fusion strategy

The traditional Chinese medicine (TCM) extraction process is a complicated dynamic system with many variables and disturbance. Therefore, multi critical quality attributes (CQAs) monitoring is of great significance to understand the whole process. Spectroscopy is a powerful process analytical tool used for process understanding. However, single senor sometimes could not provide comprehensive information. Sensor fusion is a very practical method to overcome this deficiency. In this study, the extraction process of Xiao’er Xiaoji Zhike Oral Liquid (XXZOL) was carried out in pilot scale, where near infrared (NIR) spectroscopy and mid infrared (MIR) spectroscopy were collected to determine the concentrations of seven CQAs (synephrine, arecoline, chlorogenic acid, forsythoside A, naringin, hesperidin and neohesperidin) during extraction process. Based on fused data blocks, fusion partial least squares (PLS) models were established. Two fusion data blocks are obtained from the concatenation of original spectra (low-level data fusion) and the concatenation of characteristic variables based on band selection (mid-level data fusion) respectively. The results indicated that for all seven analytes, the mid-level data fusion models were superior to the single spectral models, with the prediction performance significantly improved. Specifically, the coefficients of determination (Rp2 and Rt2) of NIR, MIR and fusion quantitative models were all higher than 0.95. The relative standard errors of prediction (RSEP) values were all within 10%, except for models of neohesperidin, which were 10.76%, 12.39%, 12.05%, 10.03% for NIR, MIR, low-level and mid-level models respectively. These results demonstrate that it is feasible to monitor the extraction process of Xiao’er Xiaoji Zhike Oral Liquid more accurately and rapidly by fusing NIR and MIR spectroscopy, and the proposed approach also has vital and valuable reference value for the rapid monitoring of the mixed decoction process of other TCM.

An Optoelectronic Spectral Sensor for Monitoring the Elimination of Uremic Markers with Low and Middle Molecular Weight during Hemodialysis Therapy

An Optoelectronic Spectral Sensor for Monitoring the Elimination of Uremic Markers with Low and Middle Molecular Weight during Hemodialysis Therapy

Using spectral sensors to determine photosynthetic photon flux density in daylight – A theoretical approach

Precise determination of the photosynthetic photon flux density (PPFD) is important for illumination in modern horticultural systems. This paper presents two methods to calculate the PPFD of daylight spectra using low cost optic sensors. The first method uses the spectral sensitivity functions of spectral sensors to recreate the quantum sensitivity curve of quantum sensors. Two sets of spectral sensitivity functions are compared. The second method calculates the PPFD based on the calculated correlated colour temperature and a spectral reconstruction using the CIE daylight model. It is demonstrated that all methods offer a useful estimation of the PPFD with correspondingly similar daylight spectra, but the supposedly simpler method, which is based on weighting of the individual channels, is more stable against deviations from the CIE daylight model.

On-site illicit-drug detection with an integrated near-infrared spectral sensor: A proof of concept

Illicit-drug production, trafficking and seizures are on an all-time high. This consequently raises pressure on investigative authorities to provide rapid forensic results to assist law enforcement and legal processes in drug-related cases. Ideally, every police officer is equipped with a detector to reliably perform drug testing directly at the incident scene. Such a detector should preferably be small, portable, inexpensive and shock-resistant but should also provide sufficient selectivity to prevent erroneous identifications. This study explores the concept of on-site drugs-of-abuse detection using a 1.8 × 2.2 mm2 multipixel near-infrared (NIR) spectral sensor that potentially can be integrated into a smartphone. This integrated sensor, based on an InGaAs-on-silicon technology, exploits an array of resonant-cavity enhanced photodetectors without any moving parts. A 100% correct classification of 11 common illicit drugs, pharmaceuticals and adulterants was achieved by chemometric modelling of the response of 15 wavelength-specific pixels. The performance on actual forensic casework was investigated on 246 cocaine-suspected powders and 39 MDMA-suspected ecstasy tablets yielding an over 90% correct classification in both cases. These findings show that presumptive drug testing by miniaturized spectral sensors is a promising development ultimately paving the way for a fully integrated drug-sensor in mobile communication devices used by law enforcement.

THE SUPER HUMAN EYE

How can new colour and spectral sensors see what is invisible to people?

Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning

BackgroundHigh-throughput plant phenotyping (HTPP) methods have the potential to speed up the crop breeding process through the development of cost-effective, rapid and scalable phenotyping methods amenable to automation. Crop disease resistance breeding stands to benefit from successful implementation of HTPP methods, as bypassing the bottleneck posed by traditional visual phenotyping of disease, enables the screening of larger and more diverse populations for novel sources of resistance. The aim of this study was to use HTPP data obtained through proximal phenotyping to predict yellow rust scores in a large winter wheat field trial.ResultsThe results show that 40–42 spectral vegetation indices (SVIs) derived from spectroradiometer data are sufficient to predict yellow rust scores using Random Forest (RF) modelling. The SVIs were selected through RF-based recursive feature elimination (RFE), and the predicted scores in the resulting models had a prediction accuracy of rs = 0.50–0.61 when measuring the correlation between predicted and observed scores. Some of the most important spectral features for prediction were the Plant Senescence Reflectance Index (PSRI), Photochemical Reflectance Index (PRI), Red-Green Pigment Index (RGI), and Greenness Index (GI).ConclusionsThe proposed HTPP method of combining SVI data from spectral sensors in RF models, has the potential to be deployed in wheat breeding trials to score yellow rust.

Integrated near-infrared spectral sensing

Spectral sensing is increasingly used in applications ranging from industrial process monitoring to agriculture. Sensing is usually performed by measuring reflected or transmitted light with a spectrometer and processing the resulting spectra. However, realizing compact and mass-manufacturable spectrometers is a major challenge, particularly in the infrared spectral region where chemical information is most prominent. Here we propose a different approach to spectral sensing which dramatically simplifies the requirements on the hardware and allows the monolithic integration of the sensors. We use an array of resonant-cavity-enhanced photodetectors, each featuring a distinct spectral response in the 850-1700 nm wavelength range. We show that prediction models can be built directly using the responses of the photodetectors, despite the presence of multiple broad peaks, releasing the need for spectral reconstruction. The large etendue and responsivity allow us to demonstrate the application of an integrated near-infrared spectral sensor in relevant problems, namely milk and plastic sensing. Our results open the way to spectral sensors with minimal size, cost and complexity for industrial and consumer applications.

Analysis of Protein and Fat in Milk Using Multiwavelength Gradient-Boosted Regression Tree

Traditional chemical measurement methods for the milk composition are not only time-consuming and laborious but also highly polluting. This has necessitated the development of a new method to facilitate fast, easy, and real-time determination of milk composition. This article presents the use of a multichannel infrared spectral sensor and broadband infrared (IR) light source to obtain multiwavelength feature data simultaneously. Furthermore, the gradient-boosted regression tree (GBRT) algorithm was used to develop a method for accurate milk content determination under different conditions. To this end, we developed a near-infrared (NIR) light-strength-acquisition device and accompanying software, compared the effectiveness of different machine learning algorithms, and established an optimal prediction model. Subsequently, the optimal prediction network was selected depending on the milk composition, thereby realizing the highest prediction accuracy. The results obtained in this study revealed that the milk protein and fat contents could be determined from the NIR absorption multispectra based on machine learning of the corresponding samples with coefficients of determination ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> ) values of 0.949 and 0.996, respectively. The corresponding root-mean-squared estimation errors of the prediction were 0.058 and 0.085, respectively. These experimental results indicate that the proposed milk quality evaluation system can be used to obtain real-time results. Moreover, it is simple, fast, affordable, and environmentally friendly.

Turbidity measurement based on a multi-wavelength spectral sensor

Turbidity measurement based on a multi-wavelength spectral sensor

Implementation of a mobile spectrometer using a near infrared MEMS Fabry–Pérot interferometer sensor

Abstract Miniaturised MEMS-based Fabry-Pérot interferometer (FPI) spectral sensors allow the design of compact spectrometers in the near infrared (NIR) range. These small-size instruments can be used for quality control of alimentation products, sorting of plastics and fabrics in respect to the material composition or defining genuineness of goods. This article describes design details and achieved results in development of an inexpensive user friendly hand-held NIR spectrometer incorporating a MEMS-FPI sensor with the spectral range of 1550–1850 nm. Implemented electronic circuitry as well as the optical configuration of the device are discussed, used electronic components and the background for the choice of the light source are presented. Furthermore, the associated software for device operation and data visualisation is described. Achieved technical parameters of the device are discussed and illustrated by examples of acquired spectra. Shared experience in operating a MEMS-FPI sensor could be especially useful for designers targeting low-cost instruments for use by general public.

Ultra-Compact Fourier Transform Near-Infrared MEMS Spectral Sensor for Smart Industry and IoT

Miniaturized spectrometers are being developed in the recent few years leveraging the rapidly-progressing technology and triggering new markets in the field of on-site and mobile spectroscopic analysis. Although some devices were commercialized for the near-infrared spectroscopy (NIRS), their size and cost still don't meet the requirements for the ubiquitous deployment of the spectrometer as a spectral sensor needed for the smart industry and IoT applications. In this work we report an integrated microelectromechanical system (MEMS)-based Fourier Transform Infrared (FT-IR) spectral sensor. A monolithic scanning Michelson interferometer MEMS chip and an extended InGaAs photodetector chip are integrated with a micro-optical system in a tiny package with a footprint of 18 mm, presenting the smallest reported FT-IR solution to date. A compact light source head is designed and implemented targeting diffuse-reflection applications. The reported sensor's spectral resolution is about 66.6 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> across the spectral range of 1350-2550 nm. The signal-to-noise ratio (SNR) achieved is 8000:1 at 2400 nm in 2 seconds, with an order of magnitude improvement with respect to previously reported fiber-coupled solution. The presented solution provides a low cost, low power, wide wavelength range NIR spectral sensor that can be manufactured with high volumes leveraging economies of scale.

Ultra-portable, smartphone-based spectrometer for heavy metal concentration measurement in drinking water samples

Heavy metals are very toxic and hazardous for human health. Onsite screening of heavy metal contaminated samples along with location-based automation data collection is a tedious job. Traditionally high-end equipment’s such as gas chromatography mass spectrometer (GC–MS) and atomic absorption spectrometers have been used to measure the concentration of different heavy metals in water samples but most of them are costly, bulky, and time consuming, and requires expert human intervention. This manuscript reports an ultra-portable, rapid, cost-effective, and easy-to-use solution for onsite heavy metal concentration measurement in drinking water samples. Presented solution combines off-the-shelf available chemical kits for heavy metal detection and developed spectrometer-based readout for concentration prediction, quality judgment, and automatic data collection. Two chemical kits for copper and iron detection have been imported form Merck and have been used for overall training and testing. The developed spectrometer has capability to work with smartphone-based android app and also can work in standalone mode. The developed spectrometer uses white light-emitting diode as a source and commercially imported spectral sensor (AS7262) for visible radiation reception. A low-power sub-GHZ-based wireless embedded platform has been developed and interfaced with source and detector. A power management module also has been designed to monitor the battery status and also to generate low battery indication. Overall modules has been packaged in custom designed enclosure to avoid external light interference. The developed system has been trained using standard buffer samples with known heavy metal concentrations and further tested for water samples collected from institute colony and nearby villages. The obtained results have been validated with commercially imported system from HANNA instruments, and it has been observed that developed system has shown excellent accuracy to predict heavy metal concentration (tested for Fe and Cu) in water samples.

Chemometric approaches for calibrating high-throughput spectral imaging setups to support digital plant phenotyping by calibrating and transferring spectral models from a point spectrometer

Visible and near-infrared (Vis-NIR) spectral imaging is appearing as a potential tool to support high-throughput digital agricultural plant phenotyping. One of the uses of spectral imaging is to predict non-destructively the chemical constituents in the plants such as nitrogen content which can be related to the functional status of plants. However, before using high-throughput spectral imaging, it requires extensive calibration, just as needed for any other spectral sensor. Calibrating the high-throughput spectral imaging setup can be a challenging task as the resources needed to run experiments in high-throughput setups are far more than performing measurements with point spectrometers. Hence, to supply a resource-efficient approach to calibrate spectral cameras integrated with high-throughput plant phenotyping setups, this study proposes the use of chemometric calibration transfer (CT) and model update. The main idea was to use a point spectrometer to develop the primary model and transfer it to the spectral cameras integrated into the high-throughput setups. The potential of the approach was showed using a real Vis-NIR dataset related to nitrogen prediction in wheat plants measured with point spectrometer, tabletop spectral cameras and spectral cameras integrated with a high-throughput plant phenotyping setup. For CT and model update, direct standardization and parameter-free calibration enhancement approaches were explored. A key aim of this study was to only use and compare techniques that does not require any further optimization as they can be easily implemented by the plant biologist in future applications. The proposed approach based on the transfer of point spectroscopy models to spectral cameras in a high-throughput setup can allow spectral calibrations to be sharable and widely applicable, thus helping the global digital plant phenotyping community.

Facile and Sensitive Detection of Nitrogen-Containing Organic Bases with Near Infrared C-Dots Derived Assays.

In this article, we have designed both colorimetric (including solution and test paper type) and spectral sensors (including UV-vis and PL type) for the quick and sensitive detection of general nitrogen-containing organic bases (NCOBs); the limit of detection could reach as low as 0.50 nM. NCOBs included 11 examples, covering aliphatic and aromatic amines, five- and six-membered heterocyclics, fused-ring heterocyclics, amino acids, and antibiotics. Furthermore, the assays demonstrated high reliability in sensing NCOBs and excellent ability to distinguish NCOBs from oxygen and sulfur containing organics. The assays developed could find important applications for the detection of NCOBs in the fields of biomedicine, chemistry, and agriculture.

LIBS as a Spectral Sensor for Monitoring Metallic Molten Phase in Metallurgical Applications—A Review

This review article discusses the latest advances on molten phase monitoring in metallurgical processes by using Laser-Induced Breakdown Spectroscopy (LIBS). LIBS is an analytical laser-based technique, where a pulsed laser is focused on a sample to create a plasma. The optical emission from the plasma can be transferred through open-path optical configuration or via an optical fiber to a spectrometer to receive analytical information in the form of elemental composition. Thus, a relatively long-distance analysis can be performed using LIBS. Several modern experimental arrangements, patents and industrial notes are assessed, and the literature is reviewed. The review includes applications of LIBS to analyze steel, iron, aluminum, copper, slags, metal melts, and other materials. Temperature, pressure, and atmospheric composition are crucial parameters of any melting process. Hence, past studies on molten phases describing these parameters have been discussed. Finally, the review addresses the last technological advances for these types of applications. It also points out the need of development in some fields and some limitations to overcome. In addition, the review highlights the use of modern machine learning and data processing techniques to increase the effectiveness of calibration and quantification approaches. These developments are expected to improve the performance of LIBS systems already implemented at an industrial scale and ease the development of new applications in pyrometallurgical processes to address the stringent market and environmental regulations.

Методичні підходи щодо використання знімків бпла для оцінки технологічних стресових станів посівів

The article is devoted to the applied aspects of UAV use, namely the monitoring of winter wheat crops in relation to the stresses caused by technological stresses. First of all, this applies to the prolonged action of herbicides left over from the predecessor crop and which cause stress in new crops. The issue has local specifics related to the inconsistency of plant cultivation technologies and to the insufficient study of the impact of the latest plant protection products in domestic soil conditions. Restoration of crop yields is possible with timely identification of the causes of stress, but decision-making time is limited, which requires the introduction of the latest monitoring technologies suitable for industrial scale. In laboratory studies using phytochambers, the presence of both spectral indicators of healthy and affected plants and the difference in their dimensions were recorded. However, such differences can be explained by other stressors, so it was not possible to establish clear criteria for spectral or spectral-spatial monitoring methods that clearly indicated the stress caused by the after-effects of herbicides. In field studies using the Slantrange complex mounted on a DJI Matrice 600 UAV as an object of study, the distribution of stress areas in the field was analyzed. It was found that, in purely spectral and spectral-spatial monitoring of winter wheat, it was not possible to reliably identify the stressful nature caused by the aftereffects of herbicides, ie ground platforms for spectral sensor equipment are ineffective. It is proved that the maps of stress indices obtained on the basis of high-resolution data from UAVs can be considered as a separate object of research on the interpretation of the causes of stress of complex biotechnical objects such as crops. Improving the reliability and reliability of monitoring data can be achieved by implementing systems of machine data processing and computer training to find correlations between the distribution of stress in plants in the field and the implementation of technological operations, terrain. Key words: UAV, stress, prolonged action of herbicides, Slantrange

Broadacre Mapping of Wheat Biomass Using Ground-Based LiDAR Technology

Crop biomass is an important attribute to consider in relation to site-specific nitrogen (N) management as critical N levels in plants vary depending on crop biomass. Whilst LiDAR technology has been used extensively in small plot-based phenomics studies, large-scale crop scanning has not yet been reported for cereal crops. A LiDAR sensing system was implemented to map a commercial 64-ha wheat paddock to assess the spatial variability of crop biomass. A proximal active reflectance sensor providing spectral indices and estimates of crop height was used as a comparison for the LiDAR system. Plant samples were collected at targeted locations across the field for the assessment of relationships between sensed and measured crop parameters. The correlation between crop biomass and LiDAR-derived crop height was 0.79, which is similar to results reported for plot scanning studies and greatly superior to results obtained for the spectral sensor tested. The LiDAR mapping showed significant crop biomass variability across the field, with estimated values ranging between 460 and 1900 kg ha−1. The results are encouraging for the use of LiDAR technology for large-scale operations to support site-specific management. To promote such an approach, we encourage the development of an automated, on-the-go data processing capability and dedicated commercial LiDAR systems for field operation.

Wearable light spectral sensor optimized for measuring daily α-opic light exposure.

Light has many non-visual effects on human physiology, including alterations in sleep, mood, and alertness. These effects are mainly mediated by photoreceptors containing the photopigment melanopsin, which has a peak sensitivity to short wavelength ('blue') light. Commercially available light sensors are commonly wrist-worn and report photopic illuminance and are calibrated to perceive visual brightness and hence cannot be used to investigate the non-visual impacts of light. In this paper, we report the development of a wearable spectrophotometer designed to be worn as a pendant or affixed to clothing to capture spectral power density data close to eye level in the visible wavelength range 380-780 nm. From this, the relative impact of a given light stimulus can be determined for each photoreceptive input in the human eye by calculating effective illuminances. This device showed high accuracy for all effective illuminances while measuring a range of commonly encountered light sources by calibrating for directional response, dark noise, sensor saturation, non-linearity, stray-light and spectral response. Features of the device include IoT-integration, onboard data storage and processing, Bluetooth Low Energy (BLE) enabled data transfer, and cloud storage in one cohesive unit.

A Self-Established "Machining-Measurement-Evaluation" Integrated Platform for Taper Cutting Experiments and Applications.

Taper-cutting experiments are important means of exploring the nano-cutting mechanisms of hard and brittle materials. Under current cutting conditions, the brittle-ductile transition depth (BDTD) of a material can be obtained through a taper-cutting experiment. However, taper-cutting experiments mostly rely on ultra-precision machining tools, which have a low efficiency and high cost, and it is thus difficult to realize in situ measurements. For taper-cut surfaces, three-dimensional microscopy and two-dimensional image calculation methods are generally used to obtain the BDTDs of materials, which have a great degree of subjectivity, leading to low accuracy. In this paper, an integrated system-processing platform is designed and established in order to realize the processing, measurement, and evaluation of taper-cutting experiments on hard and brittle materials. A spectral confocal sensor is introduced to assist in the assembly and adjustment of the workpiece. This system can directly perform taper-cutting experiments rather than using ultra-precision machining tools, and a small white light interference sensor is integrated for in situ measurement of the three-dimensional topography of the cutting surface. A method for the calculation of BDTD is proposed in order to accurately obtain the BDTDs of materials based on three-dimensional data that are supplemented by two-dimensional images. The results show that the cutting effects of the integrated platform on taper cutting have a strong agreement with the effects of ultra-precision machining tools, thus proving the stability and reliability of the integrated platform. The two-dimensional image measurement results show that the proposed measurement method is accurate and feasible. Finally, microstructure arrays were fabricated on the integrated platform as a typical case of a high-precision application.

PRISMA sensor evaluation: a case study of mineral mapping performance over Makhtesh Ramon, Israel

ABSTRACT The Italian Space Agency (ASI)’s PRecursore IperSpettrale della Missione Applicativa (PRISMA) hyperspectral remote sensing (HSR) satellite was launched on 22 March 2019. This spectral imaging sensor, with 238 bands across the 400–2500 nm range and 30-m spatial resolution, can advance remote-sensing studies for vast applications worldwide. The present study evaluates the capability of the sensor’s L1 (Top-of the- atmosphere TOA radiance) and L2D (reflectance) products for mapping minerals and geology in Makhtesh Ramon (MR), a national park in southern Israel that covers approximately 200 km2. The exceptional geological features in this area, with hardly any vegetation and mostly clear skies year-round, make it an ideal site for remote-sensing studies in general and HSR studies in particular. The quality of the PRISMA sensor’s technical and thematic performance was assessed by examining its radiometry and spectral products against a highly calibrated airborne HSR sensor (AisaFENIX 1K; 420 spectral bands, range 375–2500 nm, 1.5-m spatial resolution). Airborne and field data acquisition captured the entire MR area, followed by comprehensive fieldwork with a portable ASD FieldSpec spectrometer (400–2500 nm spectral range). The PRISMA sensor’s radiance performance was evaluated by examining the top of the atmosphere radiance product against simulated radiance using the MODTRAN® radiative transfer code. The simulated radiance used in-situ ground reflectance measurements in several locations across MR. The L1 and L2D products presented fair results with some outliers for PRISMA’s L2D SWIR 2 long-wavelength region. The L2D product was further studied to map mineral occurrences and demonstrated promising results compared to MR’s well-known geology and mineral distribution, and it was highly correlated to the high-spatial-resolution spectral mapping generated by the AisaFENIX 1K airborne sensor. Based on these results, we successfully mapped different types of minerals, such as iron oxides in the VNIR, gypsum in SWIR 1, and clay in SWIR 2. The PRISMA data’s reflectance interference across the longer wavelengths of SWIR 2 did not permit fine mapping for carbonates, probably because of the L2D’s poor performance in this spectral range. The quantitative performance of PRISMA’s mineral mapping was judged relative to the quantitative products of the AisaFENIX 1K sensor for these same minerals, revealing about 80% accuracy for PRISMA’s products.