scholarly journals Multi-Sensor Approach for Tropical Soil Fertility Analysis: Comparison of Individual and Combined Performance of VNIR, XRF, and LIBS Spectroscopies

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1028
Author(s):  
Tiago Rodrigues Tavares ◽  
José Paulo Molin ◽  
Lidiane Cristina Nunes ◽  
Marcelo Chan Fu Wei ◽  
Francisco José Krug ◽  
...  
Keyword(s):  
Data Fusion ◽  
Soil Fertility ◽  
Sensor Fusion ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Cost Effective ◽  
Tropical Soils ◽  
Least Squares Regression ◽  
Fertility Analysis ◽  
Fusion Approach

Rapid, cost-effective, and environmentally friendly analysis of key soil fertility attributes requires an ideal combination of sensors. The individual and combined performance of visible and near infrared (VNIR) diffuse reflectance spectroscopy, X-ray fluorescence spectroscopy (XRF), and laser-induced breakdown spectroscopy (LIBS) was assessed for predicting clay, organic matter (OM), cation exchange capacity (CEC), pH, base saturation (V), and extractable (ex-) nutrients in tropical soils. A set of 102 samples, collected from two agricultural fields, with broad ranges of fertility attributes were selected. Two contrasting data fusion approaches have been applied for modeling: (i) merging spectral data of different sensors followed by partial least squares regression (PLS), known as fusion before prediction; and (ii) applying the Granger and Ramanathan (GR) averaging approach, known as fusion after prediction. Results showed VNIR as individual technique to be the best for the prediction of clay and OM content (2.61 ≤ residual prediction deviation (RPD) ≤ 3.37), while the chemical attributes CEC, V, ex-P, ex-K, ex-Ca, and ex-Mg were better predicted (1.82 ≤ RPD ≤ 4.82) by elemental analysis techniques (i.e., XRF and LIBS). Only pH cannot be predicted regardless the technique. The attributes OM, V, and ex-P were best predicted using single-sensor approaches, while the attributes clay, CEC, pH, ex-K, ex-Ca, and ex-Mg were overall best predicted using multi-sensor approaches. Regarding the performance of the multi-sensor approaches, ex-K, ex-Ca, and ex-Mg, were best predicted (RPD of 4.98, 5.30, and 4.11 for ex-K, ex-Ca and ex-Mg, respectively) using two-sensor fusion approach (VNIR + XRF for ex-K and XRF + LIBS for ex-Ca and ex-Mg), while clay, CEC and pH were best predicted (RPD of 4.02, 2.63, and 1.32 for clay, CEC, and pH, respectively) with the three-sensor fusion approach (VNIR + XRF + LIBS). Therefore, the best combination of sensors for predicting key fertility attributes proved to be attribute-specific, which is a drawback of the data fusion approach. The present work is pioneering in highlighting benefits and limitations of the in tandem application of VNIR, XRF, and LIBS spectroscopies for fertility analysis in tropical soils.

scholarly journals Rapid Diagnosis of HIV-1 virus by Near Infrared Spectroscopy: based on Partial least squares regression

2021 ◽  
Vol 271 ◽  
pp. 03067
Author(s):  
Xiaohong He ◽  
Zhihong Song ◽  
Haifei Shang ◽  
Silang Yang ◽  
Lujing Wu ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
False Negative ◽  
Cost Effective ◽  
Rapid Diagnosis ◽  
Enzyme Linked Immunosorbent Assay ◽  
Least Squares Regression ◽  
Early Screening ◽  
Hiv 1

Currently, the laboratory diagnostic tests available for HIV-1 viral infection are mainly based on serological testing which relies on enzyme-linked immunosorbent assay (ELISA) for blood HIV antigen detection and reverse transcription polymerase chain reaction (RT-PCR) for HIV specific RNA sequence identification. However, these methods are expensive and time-consuming, and suffer from false positive and/or false negative results. Thus, there is an urgent need for developing a cost effective, rapid and accurate diagnostic method for HIV-1 infection. In order to reduce the barriers for effective diagnosis, a near-infrared spectroscopy (NIR) method was used to detect the HIV-1 virus in human serum, specifically, three absorption peaks with dose-dependent at 1582nm, 1810nm and 2363nm were found by multiple FBiPLSR test analysis for HIV-nano and HIV-EGFP, but not for MLV. Therefore, we recommend the use of 1582nm, 1810nm and 2363nm as the characteristic spectrum peak, for early screening and rapid diagnosis of serum HIV.

Near-infrared, mid-infrared or combined diffuse reflectance spectroscopy for assessing soil fertility in rice fields in sub-Saharan Africa

Geoderma ◽  
2019 ◽  
Vol 354 ◽  
pp. 113840 ◽  
Author(s):  
Jean-Martial Johnson ◽  
Elke Vandamme ◽  
Kalimuthu Senthilkumar ◽  
Andrew Sila ◽  
Keith D. Shepherd ◽  
...  
Keyword(s):  
Soil Fertility ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Rice Fields ◽  
Sub Saharan Africa ◽  
Mid Infrared ◽  
Sub Saharan

scholarly journals Estimating Soil Properties and Nutrients by Visible and Infrared Diffuse Reflectance Spectroscopy to Characterize Vineyards

Agronomy ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1895
Author(s):  
José Ramón Rodríguez-Pérez ◽  
Víctor Marcelo ◽  
Dimas Pereira-Obaya ◽  
Marta García-Fernández ◽  
Enoc Sanz-Ablanedo
Keyword(s):  
Soil Properties ◽  
Diffuse Reflectance Spectroscopy ◽  
Full Range ◽  
Cost Effective ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Standard Normal Variate ◽  
Standard Normal ◽  
Western Spain ◽  
Shortwave Infrared

Visible, near, and shortwave infrared (VIS-NIR-SWIR) reflectance spectroscopy, a cost-effective and rapid means of characterizing soils, was used to predict soil sample properties for four vineyards (central and north-western Spain). Sieved and air-dried samples were measured using a portable spectroradiometer (350–2500 nm) and compared for pistol grip (PG) versus contact probe (CP) setups. Raw data processed using standard normal variate (SVN) and detrending transformation (DT) were grouped into four subsets (VIS: 350–700 nm; NIR: 701–1000 nm; SWIR: 1001–2500 nm; and full range: 350–2500 nm) in order to identify the most suitable range for determining soil characteristics. The performance of partial least squares regression (PLSR) models in predicting soil properties from reflectance spectra was evaluated by cross-validation. The four spectral subsets and transformed reflectances for each setup were used as PLSR predictor variables. The best performing PLSR models were obtained for pH, electrical conductivity, and phosphorous (R2 values above 0.92), while models for sand, nitrogen, and potassium showed moderately good performances (R2 values between 0.69 and 0.77). The SWIR subset and SVN + DT processing yielded the best PLSR models for both the PG and CP setups. VIS-NIR-SWIR reflectance spectroscopy shows promise as a technique for characterizing vineyard soils for precision viticulture purposes. Further studies will be carried out to corroborate our findings.

scholarly journals Ancient human bones studied and compared by near infrared spectroscopy, thermogravimetry and chemometrics

2018 ◽  
Vol 27 (1) ◽  
pp. 6-14 ◽  
Author(s):  
Alessandra Biancolillo ◽  
Mauro Tomassetti ◽  
Remo Bucci ◽  
Simona Izzo ◽  
Francesca Candilio ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Data Fusion ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Human Bones ◽  
Fusion Approach ◽  
Ancient Human Bones

Near infrared spectroscopy and thermogravimetry have been coupled with chemometric exploratory methods in order to investigate ancient (pre-Roman/Roman) human bones from two different necropolises in Central-South Italy (Cavo degli Zucchi and Elea Velia). These findings have been investigated by principal component analysis and they have also been compared with ancient human bones from two Sudanese necropolises (Saggai and Geili). Samples coming from African and European necropolises, mainly differ in two aspects: the burial procedures and their historical period. The ritual applied in the European region involved cremation, while the one applied in the African necropolises did not. Bones from Italian sites (Cavo degli Zucchi and Elea Velia) are Pre-Roman/Roman while the others (from middle Nile) come from the Prehistoric, Meroitic, and Christian Sudanese age. Near infrared spectroscopy and thermogravimetric measures have been analysed either individually or by a mid-level data-fusion approach. Principal component analysis of the near infrared spectroscopy data allowed differentiation between burnt and unburnt samples, while from the scores plots extracted from the principal component analysis model based on the entire derived thermograms, it was possible to recognize the different clusters related to the various dating of samples. The data-fusion analysis led to considerations similar to those obtained from the model based on thermogravimetry data. Finally, instead of inspecting the entire thermogravimetry curves, principal component analysis was carried out on carbonates, total collagen and water losses only. In this case, the data-fusion approach has led to extremely interesting results; in fact, this model clearly shows that samples group in separate clusters in agreement with their age and the different burial rituals.

scholarly journals Improving In-Situ Estimation of Soil Profile Properties Using a Multi-Sensor Probe

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1011 ◽  
Author(s):  
Xiaoshuai Pei ◽  
Kenneth Sudduth ◽  
Kristen Veum ◽  
Minzan Li
Keyword(s):  
Soil Properties ◽  
Soil Profile ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Chemical Properties ◽  
Estimation Accuracy ◽  
Soil Core ◽  
Least Squares Regression ◽  
Calibration Methods

Optical diffuse reflectance spectroscopy (DRS) has been used for estimating soil physical and chemical properties in the laboratory. In-situ DRS measurements offer the potential for rapid, reliable, non-destructive, and low cost measurement of soil properties in the field. In this study, conducted on two central Missouri fields in 2016, a commercial soil profile instrument, the Veris P4000, acquired visible and near-infrared (VNIR) spectra (343–2222 nm), apparent electrical conductivity (ECa), cone index (CI) penetrometer readings, and depth data, simultaneously to a 1 m depth using a vertical probe. Simultaneously, soil core samples were obtained and soil properties were measured in the laboratory. Soil properties were estimated using VNIR spectra alone and in combination with depth, ECa, and CI (DECS). Estimated soil properties included soil organic carbon (SOC), total nitrogen (TN), moisture, soil texture (clay, silt, and sand), cation exchange capacity (CEC), calcium (Ca), magnesium (Mg), potassium (K), and pH. Multiple preprocessing techniques and calibration methods were applied to the spectral data and evaluated. Calibration methods included partial least squares regression (PLSR), neural networks, regression trees, and random forests. For most soil properties, the best model performance was obtained with the combination of preprocessing with a Gaussian smoothing filter and analysis by PLSR. In addition, DECS improved estimation of silt, sand, CEC, Ca, and Mg over VNIR spectra alone; however, the improvement was more than 5% only for Ca. Finally, differences in estimation accuracy were observed between the two fields despite them having similar soils, with one field demonstrating better results for all soil properties except silt. Overall, this study demonstrates the potential for in-situ estimation of profile soil properties using a multi-sensor approach, and provides suggestions regarding the best combination of sensors, preprocessing, and modeling techniques for in-situ estimation of profile soil properties.

scholarly journals Direct Determination of Moisture in Powder Milk Using Near Infrared Spectroscopy

2006 ◽  
Vol 2006 ◽  
pp. 1-4 ◽  
Author(s):  
R. Nagarajan ◽  
Parul Singh ◽  
Ranjana Mehrotra
Keyword(s):  
Moisture Content ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Milk Powder ◽  
Direct Determination ◽  
Wavelength Region ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Karl Fischer

Moisture content in commercially available milk powder was investigated using near infrared (NIR) diffuse reflectance spectroscopy with an Indian low-cost dispersive NIR spectrophotometer. Different packets of milk powder of the same batch were procured from the market. Forty-five samples with moisture range 4–10% were prepared in the laboratory. Spectra of the samples were collected in the wavelength region 800–2500 nm. Moisture values of all the samples were simultaneously determined by Karl Fischer (KF) titration. These KF values were used as reference for developing calibration model using partial least squares regression (PLSR) method. The calibration and validation statistics areR cal2:0.9942,RMSEC:0.1040, andR val2:0.9822,RMSEV:0.1730. Five samples of unknown moisture contents were taken for NIR prediction using developed calibration model. The agreement between NIR predicted results and those of Karl Fischer values is appreciable. The result shows that the instrument can be successfully used for the determination of moisture content in milk powder.

scholarly journals Estimation of Glucosinolates and Anthocyanins in Kale Leaves Grown in a Plant Factory Using Spectral Reflectance

Horticulturae ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 56
Author(s):  
Milon Chowdhury ◽  
Viet-Duc Ngo ◽  
Md Nafiul Islam ◽  
Mohammod Ali ◽  
Sumaiya Islam ◽  
...  
Keyword(s):  
Spectral Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Principal Component Regression ◽  
Principal Component ◽  
Least Squares Regression ◽  
Regression Methods ◽  
Freeze Dried ◽  
Plant Factory ◽  
Functional Components

The spectral reflectance technique for the quantification of the functional components was applied in different studies for different crops, but related research on kale leaves is limited. This study was conducted to estimate the glucosinolate and anthocyanin components of kale leaves cultivated in a plant factory based on diffuse reflectance spectroscopy through regression methods. Kale was grown in a plant factory under different treatments. After specific periods of transplantation, leaf samples were collected, and reflectance spectra were measured immediately from nine different points on each leaf. The same leaf samples were freeze-dried and stored for analysis of the functional components. Regression procedures, such as principal component regression (PCR), partial least squares regression (PLSR), and stepwise multiple linear regression (SMLR), were applied to relate the functional components with the spectral data. In the laboratory analysis, progoitrin and glucobrassicin, as well as cyanidin and malvidin, were found to be dominating components in glucosinolates and anthocyanins, respectively. From the overall analysis, the SMLR model showed better performance, and the identified wavelengths for estimating the glucosinolates and anthocyanins were in the early near-infrared (NIR) region. Specifically, reflectance at 742, 761, 787, 796, 805, 833, 855, 932, 947, and 1000 nm showed a strong correlation.

scholarly journals Combined Use of Vis-NIR and XRF Sensors for Tropical Soil Fertility Analysis: Assessing Different Data Fusion Approaches

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 148
Author(s):  
Tiago Rodrigues Tavares ◽  
José Paulo Molin ◽  
S. Hamed Javadi ◽  
Hudson Wallace Pereira de Carvalho ◽  
Abdul Mounem Mouazen
Keyword(s):  
Data Fusion ◽  
Least Squares ◽  
Near Infrared ◽  
Model Averaging ◽  
Tropical Soils ◽  
Exchange Capacity ◽  
Support Vector ◽  
Combined Use ◽  
Fusion Methods ◽  
The Individual

Visible and near infrared (vis-NIR) diffuse reflectance and X-ray fluorescence (XRF) sensors are promising proximal soil sensing (PSS) tools for predicting soil key fertility attributes. This work aimed at assessing the performance of the individual and combined use of vis-NIR and XRF sensors to predict clay, organic matter (OM), cation exchange capacity (CEC), pH, base saturation (V), and extractable (ex-) nutrients (ex-P, ex-K, ex-Ca, and ex-Mg) in Brazilian tropical soils. Individual models using the data of each sensor alone were calibrated using multiple linear regressions (MLR) for the XRF data, and partial least squares (PLS) regressions for the vis-NIR data. Six data fusion approaches were evaluated and compared against individual models using relative improvement (RI). The data fusion approaches included (i) two spectra fusion approaches, which simply combined the data of both sensors in a merged dataset, followed by support vector machine (SF-SVM) and PLS (SF-PLS) regression analysis; (ii) two model averaging approaches using the Granger and Ramanathan (GR) method; and (iii) two data fusion methods based on least squares (LS) modeling. For the GR and LS approaches, two different combinations of inputs were used for MLR. The GR2 and LS2 used the prediction of individual sensors, whereas the GR3 and LS3 used the individual sensors prediction plus the SF-PLS prediction. The individual vis-NIR models showed the best results for clay and OM prediction (RPD ≥ 2.61), while the individual XRF models exhibited the best predictive models for CEC, V, ex-K, ex-Ca, and ex-Mg (RPD ≥ 2.57). For eight out of nine soil attributes studied (clay, CEC, pH, V, ex-P, ex-K, ex-Ca, and ex-Mg), the combined use of vis-NIR and XRF sensors using at least one of the six data fusion approaches improved the accuracy of the predictions (with RI ranging from 1 to 21%). In general, the LS3 model averaging approach stood out as the data fusion method with the greatest number of attributes with positive RI (six attributes; namely, clay, CEC, pH, ex-P, ex-K, and ex-Mg). Meanwhile, no single approach was capable of exploiting the synergism between sensors for all attributes of interest, suggesting that the selection of the best data fusion approach should be attribute-specific. The results presented in this work evidenced the complementarity of XRF and vis-NIR sensors to predict fertility attributes in tropical soils, and encourage further research to find a generalized method of data fusion of both sensors data.

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