scholarly journals Fusion of Spectroscopy and Cobalt Electrochemistry Data for Estimating Phosphate Concentration in Hydroponic Solution

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2596 ◽  
Author(s):  
Dae-Hyun Jung ◽  
Hak-Jin Kim ◽  
Hyoung Kim ◽  
Jaeyoung Choi ◽  
Jeong Kim ◽  
...  
Keyword(s):  
Data Fusion ◽  
Spectral Data ◽  
Near Infrared ◽  
Accurate Determination ◽  
Model Performance ◽  
Phosphate Concentration ◽  
Least Squares Regression ◽  
Estimation Model ◽  
Network Estimation

Phosphate is a key element affecting plant growth. Therefore, the accurate determination of phosphate concentration in hydroponic nutrient solutions is essential for providing a balanced set of nutrients to plants within a suitable range. This study aimed to develop a data fusion approach for determining phosphate concentrations in a paprika nutrient solution. As a conventional multivariate analysis approach using spectral data, partial least squares regression (PLSR) and principal components regression (PCR) models were developed using 56 samples for calibration and 24 samples for evaluation. The R2 values of estimation models using PCR and PLSR ranged from 0.44 to 0.64. Furthermore, an estimation model using raw electromotive force (EMF) data from cobalt electrodes gave R2 values of 0.58–0.71. To improve the model performance, a data fusion method was developed to estimate phosphate concentration using near infrared (NIR) spectral and cobalt electrochemical data. Raw EMF data from cobalt electrodes and principle component values from the spectral data were combined. Results of calibration and evaluation tests using an artificial neural network estimation model showed that R2 = 0.90 and 0.89 and root mean square error (RMSE) = 96.70 and 119.50 mg/L, respectively. These values are sufficiently high for application to measuring phosphate concentration in hydroponic solutions.

Estimation of soil organic matter using proximal and satellite sensors after a wildfire in Mediterranean Croatia

2021 ◽  
Author(s):  
Iva Hrelja ◽  
Ivana Šestak ◽  
Igor Bogunović
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Data ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Nir Spectroscopy ◽  
Soil Samples ◽  
Least Squares Regression ◽  
Macro Scale ◽  
Sentinel 2

<p>Spectral data obtained from optical spaceborne sensors are being recognized as a valuable source of data that show promising results in assessing soil properties on medium and macro scale. Combining this technique with laboratory Visible-Near Infrared (VIS-NIR) spectroscopy methods can be an effective approach to perform robust research on plot scale to determine wildfire impact on soil organic matter (SOM) immediately after the fire. Therefore, the objective of this study was to assess the ability of Sentinel-2 superspectral data in estimating post-fire SOM content and comparison with the results acquired with laboratory VIS-NIR spectroscopy.</p><p>The study is performed in Mediterranean Croatia (44° 05’ N; 15° 22’ E; 72 m a.s.l.), on approximately 15 ha of fire affected mixed <em>Quercus ssp.</em> and <em>Juniperus ssp.</em> forest on Cambisols. A total of 80 soil samples (0-5 cm depth) were collected and geolocated on August 22<sup>nd</sup> 2019, two days after a medium to high severity wildfire. The samples were taken to the laboratory where soil organic carbon (SOC) content was determined via dry combustion method with a CHNS analyzer. SOM was subsequently calculated by using a conversion factor of 1.724. Laboratory soil spectral measurements were carried out using a portable spectroradiometer (350-1050 nm) on all collected soil samples. Two Sentinel-2 images were downloaded from ESAs Scientific Open Access Hub according to the closest dates of field sampling, namely August 31<sup>st</sup> and September 5<sup>th </sup>2019, each containing eight VIS-NIR and two SWIR (Short-Wave Infrared) bands which were extracted from bare soil pixels using SNAP software. Partial least squares regression (PLSR) model based on the pre-processed spectral data was used for SOM estimation on both datasets. Spectral reflectance data were used as predictors and SOM content was used as a response variable. The accuracy of the models was determined via Root Mean Squared Error of Prediction (RMSE<sub>p</sub>) and Ratio of Performance to Deviation (RPD) after full cross-validation of the calibration datasets.</p><p>The average post-fire SOM content was 9.63%, ranging from 5.46% minimum to 23.89% maximum. Models obtained from both datasets showed low RMSE<sub>p </sub>(Spectroscopy dataset RMSE<sub>p</sub> = 1.91; Sentinel-2 dataset RMSE<sub>p</sub> = 0.99). RPD values indicated very good predictions for both datasets (Spectrospcopy dataset RPD = 2.72; Sentinel-2 dataset RPD = 2.22). Laboratory spectroscopy method with higher spectral resolution provided more accurate results. Nonetheless, spaceborne method also showed promising results in the analysis and monitoring of SOM in post-burn period.</p><p><strong>Keywords:</strong> remote sensing, soil spectroscopy, wildfires, soil organic matter</p><p><strong>Acknowledgment: </strong>This work was supported by the Croatian Science Foundation through the project "Soil erosion and degradation in Croatia" (UIP-2017-05-7834) (SEDCRO). Aleksandra Perčin is acknowledged for her cooperation during the laboratory work.</p>

scholarly journals Feasibility of Determination of Foodborne Microbe Contamination of Fresh-Cut Shredded Cabbage Using SW-NIR

2019 ◽  
Vol 1 (2) ◽  
pp. 246-256
Author(s):  
Benjamaporn Matulaprungsan ◽  
Chalermchai Wongs-Aree ◽  
Pathompong Penchaiya ◽  
Phonkrit Maniwara ◽  
Sirichai Kanlayanarat ◽  
...  
Keyword(s):  
Quantitative Measurement ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Sample Type ◽  
Least Squares Regression ◽  
Calibration Equation ◽  
E Coli ◽  
Total Bacteria

Shredded cabbage is widely used in much ready-to-eat food. Therefore, rapid methods for detecting and monitoring the contamination of foodborne microbes is essential. Short wavelength near infrared (SW-NIR) spectroscopy was applied on two types of solutions, a drained solution from the outer surface of the shredded cabbage (SC) and a ground solution of shredded cabbage (GC) which were inoculated with a mixture of two bacterial suspensions, Escherichia coli and Salmonella typhimurium. NIR spectra of around 700 to 1100 nm were collected from the samples after 0, 4, and 8 h at 37 °C incubation, along with the growth of total bacteria, E. coli and S. typhimurium. The raw spectra were obtained from both sample types, clearly separated with the increase of incubation time. The first derivative, a Savitzky–Golay pretreatment, was applied on the GC spectra, while the second derivative was applied on the SC spectra before developing the calibration equation, using partial least squares regression (PLS). The obtained correlation (r) of the SC spectra was higher than the GC spectra, while the standard error of cross-validation (SECV) was lower. The ratio of prediction of deviation (RPD) of the SC spectra was higher than the GC spectra, especially in total bacteria, quite normal for the E. coli but relatively low for the S. typhimurium. The prediction results of microbial spoilage were more reliable on the SC than on the GC spectra. Total bacterial detection was best for quantitative measurement, as E. coli contamination could only be distinguished between high and low values. Conversely, S. typhimurium predictions were not optimal for either sample type. The SW-NIR shows the feasibility for detecting the existence of microbes in the solution obtained from SC, but for a more specific application for discrimination or quantitation is needed, proving further research in still required.

DWT-PLS Regression on Near-Infrared Spectra for Moisture and Volatile Determination of Coal

2013 ◽  
Vol 848 ◽  
pp. 313-316
Author(s):  
Xiao Li Yang ◽  
Fan Wang ◽  
Ji Shu Chen ◽  
Gong Zhe Ma
Keyword(s):  
Partial Least Squares Regression ◽  
Bituminous Coal ◽  
Near Infrared ◽  
Parameters Optimization ◽  
Discrete Wavelet ◽  
Pls Regression ◽  
Least Squares Regression ◽  
Satisfactory Performance ◽  
Near Infrared Spectra

We studied moisture and volatile determination in bituminous coal samples using near-infrared (NIR) spectra. This research was developted by applying partial least squares regression (PLS) and discrete wavelet transform (DWT). Firstly, NIR spectra were pre-processed by DWT for fitting and compression. Then, DWT coefficients were used to build regression model with PLS. We used NIR spectra to determination moisture and volatile determination in coal samples seperately and simultaneously. Through parameters optimization, the results show that DWT-PLS can obtain satisfactory performance for separate and simultanous determination.

Online determination of coffee roast degree toward controlling acidity

2020 ◽  
Vol 28 (4) ◽  
pp. 175-185
Author(s):  
Nathan Yergenson ◽  
D Eric Aston
Keyword(s):  
Near Infrared ◽  
Color Space ◽  
Visible Spectrum ◽  
Titratable Acidity ◽  
Least Squares Regression ◽  
L Value ◽  
Linear Effects ◽  
The Common ◽  
Roasting Temperature

Three methods of measuring coffee roast degree were compared using titratable acidity as an indicator of roast-dependent flavor change. The first roast degree method was based on prediction of the cracks with online near infrared spectroscopy and partial least squares regression, the second was based on changes in online near infrared absorbance, and the third was the common L* value from the CIELAB color space in the visible spectrum. Roasting trials utilized arabica coffee from eight origins in an air roaster, and results demonstrated the superiority of an online near infrared sensor for real-time roast degree measurement. A second dataset with constant temperature roasts showed how acidity can be controlled by changing both the roasting temperature and roast degree, finding the linear effects of roast time and roast degree on acidity.

scholarly journals Quantitatively estimating main soil water-soluble salt ions content based on Visible-near infrared wavelength selected using GC, SR and VIP

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6310 ◽  
Author(s):  
Haifeng Wang ◽  
Yinwen Chen ◽  
Zhitao Zhang ◽  
Haorui Chen ◽  
Xianwen Li ◽  
...  
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
Soil Salinization ◽  
Water Soluble ◽  
Soluble Salt ◽  
Support Vector ◽  
Least Squares Regression ◽  
Model Accuracy ◽  
Salt Ions ◽  
Spectral Models

Soil salinization is the primary obstacle to the sustainable development of agriculture and eco-environment in arid regions. The accurate inversion of the major water-soluble salt ions in the soil using visible-near infrared (VIS-NIR) spectroscopy technique can enhance the effectiveness of saline soil management. However, the accuracy of spectral models of soil salt ions turns out to be affected by high dimensionality and noise information of spectral data. This study aims to improve the model accuracy by optimizing the spectral models based on the exploration of the sensitive spectral intervals of different salt ions. To this end, 120 soil samples were collected from Shahaoqu Irrigation Area in Inner Mongolia, China. After determining the raw reflectance spectrum and content of salt ions in the lab, the spectral data were pre-treated by standard normal variable (SNV). Subsequently the sensitive spectral intervals of each ion were selected using methods of gray correlation (GC), stepwise regression (SR) and variable importance in projection (VIP). Finally, the performance of both models of partial least squares regression (PLSR) and support vector regression (SVR) was investigated on the basis of the sensitive spectral intervals. The results indicated that the model accuracy based on the sensitive spectral intervals selected using different analytical methods turned out to be different: VIP was the highest, SR came next and GC was the lowest. The optimal inversion models of different ions were different. In general, both PLSR and SVR had achieved satisfactory model accuracy, but PLSR outperformed SVR in the forecasting effects. Great difference existed among the optimal inversion accuracy of different ions: the predicative accuracy of Ca2+, Na+, Cl−, Mg2+ and SO42− was very high, that of CO32− was high and K+ was relatively lower, but HCO3− failed to have any predicative power. These findings provide a new approach for the optimization of the spectral model of water-soluble salt ions and improvement of its predicative precision.

Determination of cellulose, hemicellulose and lignin content using near-infrared spectroscopy in flax fiber

2019 ◽  
Vol 89 (23-24) ◽  
pp. 4875-4883 ◽  
Author(s):  
Jing Huang ◽  
Chongwen Yu
Keyword(s):  
Spectral Range ◽  
Near Infrared ◽  
Lignin Content ◽  
Full Range ◽  
Accurate Determination ◽  
Principal Component ◽  
Flax Fiber ◽  
Fiber Composition ◽  
Wet Chemical

The rapid and accurate determination of flax fiber composition is necessary for its application, but until now it has mainly been tested by the wet chemical method, which is time-consuming and not environmentally friendly. In this paper, near-infrared (NIR) spectroscopy was studied to determinate the main composition of flax, in which 43 flax samples were tested according to the traditional Chinese wet chemical component test standard. Five sets of spectra were generated to show the characteristic of each sample; in total 215 spectra sets were collected using a Fourier transform near-infrared spectrometer. The methods of partial least squares (PLS) and principal component regression (PCR) were used to establish the relationships between the data from the chemical and NIR methods. PLS proved to be a better quantitative method than PCR, based on the value of the coefficient of multiple determination for calibration ( Rc2) and prediction ( Rp2), the ratio of performance to standard deviate (RPD) and the root mean square error of prediction (RMSEP). With the best pretreatment method, the spectral range of 10,000–4000 cm–1yielded a better predictive result than the full range, with Rc2of 0.968, Rp2of 0.955, RMSEP of 1.060%, RPD of 4.641 for cellulose and Rc2of 0.958, Rp2of 0.906, RMSEP of 0.678%, RPD of 3.305 for hemicellulose, while the spectral range 6900–5600 cm–1yielded a better predictive result with Rc2of 0.936, Rp2of 0.769, RMSEP of 0.455%, and RPD of 2.366 for lignin. The study shows that NIR models can provide a simple and fast way to analyze flax fiber composition, which is also beneficial to evaluate its quality.

scholarly journals Direct Determination of Lycopene Content in Tomatoes (Lycopersicon esculentum) by Attenuated Total Reflectance Infrared Spectroscopy and Multivariate Analysis

2006 ◽  
Vol 89 (5) ◽  
pp. 1257-1262 ◽  
Author(s):  
Yuwana Halim ◽  
Steven J Schwartz ◽  
David Francis ◽  
Nathan A Baldauf ◽  
Luis E Rodriguez-Saona
Keyword(s):  
Ir Spectroscopy ◽  
Accurate Determination ◽  
Direct Determination ◽  
Reference Method ◽  
Attenuated Total Reflectance ◽  
Least Squares Regression ◽  
Total Reflectance ◽  
Minimal Sample ◽  
Lycopene Content

Abstract Lycopene is a potent antioxidant that has been shown to play critical roles in disease prevention. Efficient assays for detection and quantification of lycopene are desirable as alternatives to time- and labor-intensive methods. Attenuated total reflectance infrared (ATR-IR) spectroscopy was used for quantification of lycopene in tomato varieties. Calibration models were developed by partial least-squares regression (PLSR) using quantitative measures of lycopene concentration from liquid chromatography as reference method. IR spectra showed a distinct marker band at 957 cm1 for trans Carbon-Hydrogen (CH) deformation vibration of lycopene. PLSR models predicted the lycopene content accurately and reproducibly with a correlation coefficient (σ) of 0.96 and standard error of cross-validation <0.80 mg/100 g. ATR-IR spectroscopy allowed for rapid, simple, and accurate determination of lycopene in tomatoes with minimal sample preparation. Results suggest that the ATR-IR method is applicable for high-throughput quantitative analysis and screening for lycopene in tomatoes.

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