A Cost-Effective and Portable Optical Sensor System to Estimate Leaf Nitrogen and Water Contents in Crops

Non-invasive determination of leaf nitrogen (N) and water contents is essential for ensuring the healthy growth of the plants. However, most of the existing methods to measure them are expensive. In this paper, a low-cost, portable multispectral sensor system is proposed to determine N and water contents in the leaves, non-invasively. Four different species of plants—canola, corn, soybean, and wheat—are used as test plants to investigate the utility of the proposed device. The sensor system comprises two multispectral sensors, visible (VIS) and near-infrared (NIR), detecting reflectance at 12 wavelengths (six from each sensor). Two separate experiments were performed in a controlled greenhouse environment, including N and water experiments. Spectral data were collected from 307 leaves (121 for N and 186 for water experiment), and the rational quadratic Gaussian process regression (GPR) algorithm was applied to correlate the reflectance data with actual N and water content. By performing five-fold cross-validation, the N estimation showed a coefficient of determination ( R 2 ) of 63.91% for canola, 80.05% for corn, 82.29% for soybean, and 63.21% for wheat. For water content estimation, canola showed an R 2 of 18.02%, corn showed an R 2 of 68.41%, soybean showed an R 2 of 46.38%, and wheat showed an R 2 of 64.58%. The result reveals that the proposed low-cost sensor with an appropriate regression model can be used to determine N content. However, further investigation is needed to improve the water estimation results using the proposed device.

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Prediction of water content in biscuits using near infrared hyperspectral imaging spectroscopy and chemometrics

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033520902538 ◽

2020 ◽

Vol 28 (3) ◽

pp. 140-147

Author(s):

Eloïse Lancelot ◽

Philippe Courcoux ◽

Sylvie Chevallier ◽

Alain Le-Bail ◽

Benoît Jaillais

Keyword(s):

Linear Regression ◽

Multiple Linear Regression ◽

Water Content ◽

Hyperspectral Imaging ◽

Analysis Of Variance ◽

Near Infrared ◽

Imaging Spectroscopy ◽

Principal Component ◽

Coefficient Of Determination ◽

Water Contents

The possibility of using near infrared hyperspectral imaging spectroscopy to quantify the water content in commercial biscuits was investigated. Principal component analysis was successfully applied to hyperspectral images of commercial biscuits to monitor their water contents. Variables were selected and water contents quantified using analysis of variance, followed by multiple linear regression, and the results were compared with those obtained with variable importance in projection partial least squares. Initially equal to 212, the number of variables after application of analysis of variance was equal to 10. Analysis of variance–multiple linear regression gave better results: the coefficient of determination (R2) was higher than 0.92 and the root mean square error of validation was less than 0.015. The “prediction images” obtained were very relevant and can be used to study biscuit defects. The methodology developed could be implemented at the industrial level for biscuit quality control and for online monitoring of the uniform distribution of water in the superficial layer of biscuits.

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Prediction of water content and soluble solids content of ‘manalagi’ apples using near infrared spectroscopy

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/922/1/012062 ◽

2021 ◽

Vol 922 (1) ◽

pp. 012062

Author(s):

K Kusumiyati ◽

Y Hadiwijaya ◽

D Suhandy ◽

A A Munawar

Keyword(s):

Infrared Spectroscopy ◽

Water Content ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Quality Attributes ◽

Soluble Solids ◽

Coefficient Of Determination ◽

Soluble Solids Content ◽

Orthogonal Signal Correction ◽

Solids Content

Abstract The purpose of the research was to predict quality attributes of ‘manalagi’ apples using near infrared spectroscopy (NIRS). The desired quality attributes were water content and soluble solids content. Spectra data collection was performed at wavelength of 702 to 1065 nm using a Nirvana AG410 spectrometer. The original spectra were enhanced using orthogonal signal correction (OSC). The regression approaches used in the study were partial least squares regression (PLSR) and principal component regression (PCR). The results showed that water content prediction acquired coefficient of determination in calibration set (R2cal) of 0.81, coefficient of determination in prediction set (R2pred) of 0.61, root mean squares error of calibration set (RMSEC) of 0.009, root mean squares of prediction set (RMSEP) of 0.020, and ratio performance to deviation (RPD) of 1.62, while soluble solids content prediction displayed R2cal, R2pred, RMSEC, RMSEP, and RPD of 0.79, 0.85, 0.474, 0.420, and 2.69, respectively. These findings indicated that near infrared spectroscopy could be used as an alternative technique to predict water content and soluble solids content of ‘manalagi’ apples.

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Near Infrared Reflectance Spectroscopy Coupled to Chemometrics as a Cost-Effective, Rapid and Non-Destructive Tool for Fish Fraud Control: Monitoring Source, Condition and Nutritional Value of Five Common Whitefish Species

Journal of AOAC International ◽

10.1093/jaoacint/qsaa114 ◽

2020 ◽

Author(s):

Diogo B Gonçalves ◽

Carla S P Santos ◽

Teresa Pinho ◽

Rafael Queirós ◽

Pedro D Vaz ◽

...

Keyword(s):

Near Infrared ◽

Atlantic Cod ◽

Cost Effective ◽

Reflectance Spectroscopy ◽

Machine Learning Algorithms ◽

Coefficient Of Determination ◽

Infrared Reflectance ◽

Near Infrared Reflectance Spectroscopy ◽

Near Infrared Reflectance ◽

Infrared Reflectance Spectroscopy

Abstract Fish fraud is a problematic issue for the industry that to be properly addressed requires the use of accurate, rapid and cost-effective tools. In this work, near infrared reflectance spectroscopy (NIRS) was used to predict nutritional values (protein, lipids and moisture) as well as to discriminate between source (farmed vs. wild fish) and condition (fresh, defrosted or frozen fish). Five whitefish species consisting of Alaskan pollock (Gadus chalcogrammu), Atlantic cod (Gadus morhua), European plaice (Pleuronectes platessa), Common sole (Solea solea) and Turbot (Psetta maxima), including farmed, wild, fresh and frozen ones, were scanned by a low-cost handheld near infrared reflectance spectrometer with a spectral range between 900 nm and 1700 nm. Several machine learning algorithms were explored for both regression and classification tasks, achieving precisions and coefficient of determination higher than 88% and 0.78, respectively. Principal component analysis (PCA) was used to cluster samples according to classes where good linear discriminations were denoted. Loadings from PCA reveal bands at 1150, 1200 and 1400 nm as the most discriminative spectral regions regarding classification of both source and condition, suggesting the absorbance of OH, CH, CH2 and CH3 groups as the most important ones. This study shows the use of NIRS and both linear and non-linear learners as a suitable strategy to address the fish fraud problematic and fish quality control.

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Review of the Performance of Low-Cost Sensors for Air Quality Monitoring

Atmosphere ◽

10.3390/atmos10090506 ◽

2019 ◽

Vol 10 (9) ◽

pp. 506 ◽

Cited By ~ 39

Author(s):

Federico Karagulian ◽

Maurizio Barbiere ◽

Alexander Kotsev ◽

Laurent Spinelle ◽

Michel Gerboles ◽

...

Keyword(s):

Air Quality ◽

Low Cost ◽

Cost Effective ◽

Coefficient Of Determination ◽

Atmospheric Conditions ◽

Site Location ◽

Independent Evaluation ◽

Spatial Coverage ◽

Other Information ◽

Reference Measurements

A growing number of companies have started commercializing low-cost sensors (LCS) that are said to be able to monitor air pollution in outdoor air. The benefit of the use of LCS is the increased spatial coverage when monitoring air quality in cities and remote locations. Today, there are hundreds of LCS commercially available on the market with costs ranging from several hundred to several thousand euro. At the same time, the scientific literature currently reports independent evaluation of the performance of LCS against reference measurements for about 110 LCS. These studies report that LCS are unstable and often affected by atmospheric conditions—cross-sensitivities from interfering compounds that may change LCS performance depending on site location. In this work, quantitative data regarding the performance of LCS against reference measurement are presented. This information was gathered from published reports and relevant testing laboratories. Other information was drawn from peer-reviewed journals that tested different types of LCS in research studies. Relevant metrics about the comparison of LCS systems against reference systems highlighted the most cost-effective LCS that could be used to monitor air quality pollutants with a good level of agreement represented by a coefficient of determination R2 > 0.75 and slope close to 1.0. This review highlights the possibility to have versatile LCS able to operate with multiple pollutants and preferably with transparent LCS data treatment.

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Low-Cost Multispectral Sensor Array for Determining Leaf Nitrogen Status

Nitrogen ◽

10.3390/nitrogen1010007 ◽

2020 ◽

Vol 1 (1) ◽

pp. 67-80

Author(s):

Mohammad Habibullah ◽

Mohammad Reza Mohebian ◽

Raju Soolanayakanahally ◽

Ali Newaz Bahar ◽

Sally Vail ◽

...

Keyword(s):

Field Experiment ◽

Sensor Array ◽

Near Infrared ◽

Low Cost ◽

Leaf Nitrogen ◽

Greenhouse Experiment ◽

Nitrogen Status ◽

N Level ◽

Sensor Module ◽

Reflectance Data

A crop’s health can be determined by its leaf nutrient status; more precisely, leaf nitrogen (N) level, is a critical indicator that carries a lot of worthwhile nutrient information for classifying the plant’s health. However, the existing non-invasive techniques are expensive and bulky. The aim of this study is to develop a low-cost, quick-read multi-spectral sensor array to predict N level in leaves non-invasively. The proposed sensor module has been developed using two reflectance-based multi-spectral sensors (visible and near-infrared (NIR)). In addition, the proposed device can capture the reflectance data at 12 different wavelengths (six for each sensor). We conducted the experiment on canola leaves in a controlled greenhouse environment as well as in the field. In the greenhouse experiment, spectral data were collected from 87 leaves of 24 canola plants, subjected to varying levels of N fertilization. Later, 42 canola cultivars were subjected to low and high nitrogen levels in the field experiment. The k-nearest neighbors (KNN) algorithm was employed to model the reflectance data. The trained model shows an average accuracy of 88.4% on the test set for the greenhouse experiment and 79.2% for the field experiment. Overall, the result concludes that the proposed cost-effective sensing system can be viable in determining leaf nitrogen status.

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Determination of Water Content and Resistivity of Perfluorosulfonic Acid Fuel Cell Membranes

MRS Proceedings ◽

10.1557/proc-393-157 ◽

1995 ◽

Vol 393 ◽

Cited By ~ 2

Author(s):

Ashish Sen. ◽

Kevin E.Leach ◽

Richard D.Varjian

Keyword(s):

Water Content ◽

Water Uptake ◽

Room Temperature ◽

Near Infrared ◽

Nitrogen Atmosphere ◽

Fuel Cell Membranes ◽

Water Saturated ◽

Water Contents ◽

Lower Resistivity

ABSTRACTWater uptake and resistivity have been determined for Dupont's Nafion-115®and Dow membrane 800 EW while in contact with a water-saturated nitrogen atmosphere using Fourier transform near-infrared (NIR), and AC impedance four point techniques in the temperature range of 23°C to 100°C. Results show that at room temperature there is a significant increase in water content and a corresponding decrease in the electrical resistivity as the relative humidity increases from 0% to 100%. Results also indicate that there is a substantial decrease in water uptake from water vapor at 100°C relative to that at 23°C. The water content of Dow membrane is higher than Nafion-115 under all conditions tested. The water contents of Dow PFSA 800 EW and Nafion-115 membranes at about 92% R.H. and 23°C are approximately 25 wt% and 18 wt%, respectively. The corresponding water content values at 100°C are 10 wt% and 8 wt%, respectively. The resistivity of the membranes decreases sharply with the temperature up to 60°C, reaches a minimum near 80°C then increases up to 100°C. The Dow membrane has lower resistivity than Nafion-115 over the entire range.

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Variable selection for the determination of total polar materials in fried oils by near infrared spectroscopy

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033518812884 ◽

2018 ◽

Vol 27 (2) ◽

pp. 107-114 ◽

Cited By ~ 1

Author(s):

Mari M Cascant ◽

Salvador Garrigues ◽

Miguel de la Guardia

Keyword(s):

Variable Selection ◽

Near Infrared ◽

Low Cost ◽

Oil Quality ◽

Analytical Techniques ◽

Coefficient Of Determination ◽

Prediction Errors ◽

Polar Materials ◽

Selection Of

Total polar materials (TPM) content is considered as the best indicator and the most common parameter to check the quality of deep-frying oils. The development of simpler and quicker analytical techniques than the available methods to monitor oil quality in restaurants and fried food outlets is an important topic related to the human health. This paper reports a comparison of the variable selection of near infrared (NIR) spectra by multiple linear regression (MLR-NIR) with partial least squares (PLS-NIR) models for the quantification of TPM in fried vegetable oils. The use of PLS-NIR offers an alternative in laboratory bench equipment for the determination of TPM in oils employed for frying different kinds of foods with relative prediction errors of 6.5%, a coefficient of determination for prediction of 0.99 and a residual predictive deviation (RPD) of 9.2 when selected wavenumber intervals were employed. MLR-NIR allows the selection of a reduced number of wavenumber in order to develop low cost instruments to evaluate the frying oil quality. Based on the NIR signals at four wavenumbers, the relative prediction error was 12.1%, the coefficient of determination for prediction was 0.96 and the RPD was 5.0.

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Vein Pattern Locating Technology for Cannulation: A Review of the Low-Cost Vein Finder Prototypes Utilizing near Infrared (NIR) Light to Improve Peripheral Subcutaneous Vein Selection for Phlebotomy

Sensors ◽

10.3390/s19163573 ◽

2019 ◽

Vol 19 (16) ◽

pp. 3573 ◽

Cited By ~ 5

Author(s):

Cheng-Tang Pan ◽

Mark D. Francisco ◽

Chung-Kun Yen ◽

Shao-Yu Wang ◽

Yow-Ling Shiue

Keyword(s):

Near Infrared ◽

Infrared Imaging ◽

Low Cost ◽

Venous Blood ◽

Cost Effective ◽

Medical Laboratory ◽

Scientific Publications ◽

Medical Practitioners ◽

Nir Light ◽

Number Of Patients

One of the most common means for diagnosis is through medical laboratory testing, which primarily uses venous blood as a sample. This requires an invasive method by cannulation that needs proper vein selection. The use of a vein finder would help the phlebotomist to easily locate the vein, preventing possible pre-analytical error in the specimen collection and even more discomfort and pain to the patient. This paper is a review of the scientific publications on the different developed low-cost vein finder prototypes utilizing camera assisted near infrared (NIR) light technology. Methods: Electronic databases were searched online, these included PubMed (PMC), MEDLINE, Science Direct, ResearchGate, and Institute of Electrical and Electronics Engineers (IEEE) Xplore digital library. Specifically, publications with the terms vein finder prototype, NIR technology, vein detection, and infrared imaging were screened. In addition, reference lists were used to further review related publications. Results: Cannulation challenges medical practitioners because of the different factors that can be reduced by the utilization of a vein finder. A limited number of publications regarding the assessment of personnel performing cannulation were observed. Moreover, variations in methodology, number of patients, type of patients according to their demographics and materials used in the assessment of the developed prototypes were noted. Some studies were limited with regard to the actual human testing of the prototype. Conclusions: The development of a low-cost effective near infrared (NIR) vein finder remains in the phase of improvement. Since, it is being challenged by different human factors, increasing the number of parameters and participants/human for actual testing of the prototypes must also be taken into consideration for possible commercialization. Finally, it was noted that publications regarding the assessment of the performance of phlebotomists using vein finders were limited.

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Luminescent κ-Carrageenan-Based Electrolytes Containing Neodymium Triflate

Molecules ◽

10.3390/molecules24061020 ◽

2019 ◽

Vol 24 (6) ◽

pp. 1020 ◽

Cited By ~ 2

Author(s):

S. Nunes ◽

S. Saraiva ◽

R. Pereira ◽

M. Silva ◽

L. Carlos ◽

...

Keyword(s):

Ionic Conductivity ◽

Alkaline Earth ◽

Near Infrared ◽

Low Cost ◽

Earth Metal ◽

Cost Effective ◽

Red Seaweeds ◽

Multi Wavelength ◽

Electrochemical Devices ◽

Uv Visible

In recent years, the synthesis of polymer electrolyte systems derived from biopolymers for the development of sustainable green electrochemical devices has attracted great attention. Here electrolytes based on the red seaweeds-derived polysaccharide κ-carrageenan (κ-Cg) doped with neodymium triflate (NdTrif3) and glycerol (Gly) were obtained by means of a simple, clean, fast, and low-cost procedure. The aim was to produce near-infrared (NIR)-emitting materials with improved thermal and mechanical properties, and enhanced ionic conductivity. Cg has a particular interest, due to the fact that it is a renewable, cost-effective natural polymer and has the ability of gelling in the presence of certain alkali- and alkaline-earth metal cations, being good candidates as host matrices for accommodating guest cations. The as-synthesised κ-Cg-based membranes are semi-crystalline, reveal essentially a homogeneous texture, and exhibit ionic conductivity values 1–2 orders of magnitude higher than those of the κ-Cg matrix. A maximum ionic conductivity was achieved for 50 wt.% Gly/κ-Cg and 20 wt.% NdTrif3/κ-Cg (1.03 × 10−4, 3.03 × 10−4, and 1.69 × 10−4 S cm−1 at 30, 60, and 97 °C, respectively). The NdTrif-based κ-Cg membranes are multi-wavelength emitters from the ultraviolet (UV)/visible to the NIR regions, due to the κ-Cg intrinsic emission and to Nd3+, 4F3/2→4I11/2-9/2.

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Energy Efficient Rapid Removal of Arsenic in an Electrocoagulation Reactor with Hybrid Fe/Al Electrodes: Process Optimization Using CCD and Kinetic Modeling

Water ◽

10.3390/w12102876 ◽

2020 ◽

Vol 12 (10) ◽

pp. 2876 ◽

Cited By ~ 1

Author(s):

Saif Ullah Khan ◽

Izharul Haq Farooqi ◽

Muhammad Usman ◽

Farrukh Basheer

Keyword(s):

Energy Consumption ◽

Arsenic Removal ◽

Low Cost ◽

Minimum Energy ◽

Ferric Hydroxide ◽

Cost Effective ◽

Complete Removal ◽

Coefficient Of Determination ◽

Time Duration ◽

Removal Of Arsenic

Threats due to insufficient, inadequate and costlier methods of treating contaminants such as arsenic have emphasized the significance of optimizing and managing the processes adopted. This study was aimed at the complete elimination of arsenic from an aqueous medium with minimum energy consumption using the electrocoagulation process. Arsenic removal around 95% was rapidly attained for optimized conditions having a pH of 7, 0.46 A current intensity, 10 mg/L initial concentration and only 2 min of applied time duration using the energy of 3.1 watt-hour per gram of arsenic removed. Low values of applied current for longer durations resulted in the complete removal of arsenic with low energy consumption. Various hydroxide complexes including ferrous hydroxide and ferric hydroxide assisted in the removal of arsenic by adsorption along with co-precipitation. Surface models obtained were checked and found with a reasonably good fit having high values of coefficient of determination of 0.933 and 0.980 for removal efficiency and energy consumption, respectively. Adsorption was found to follow pseudo-first-order kinetics. Multivariate optimization proved it as a low-cost effective technology having an operational cost of 0.0974 Indian rupees (equivalent to USD 0.0013) per gram removal of arsenic. Overall, the process was well optimized using CCD based on response surface methodology.

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