scholarly journals Field Detection of Rhizoctonia Root Rot in Sugar Beet by Near Infrared Spectrometry

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8068
Author(s):  
Leilane C. Barreto ◽  
Rosa Martínez-Arias ◽  
Axel Schechert
Keyword(s):  
Sugar Beet ◽  
Correlation Coefficient ◽  
Near Infrared ◽  
Pearson Correlation ◽  
Crown Rot ◽  
Infrared Spectrometry ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Beet Root ◽  
Rhizoctonia Root Rot

Rhizoctonia root and crown rot (RRCR) is an important disease in sugar beet production areas, whose assessment and control are still challenging. Therefore, breeding for resistance is the most practical way to manage it. Although the use of spectroscopy methods has proven to be a useful tool to detect soil-borne pathogens through leaves reflectance, no study has been carried out so far applying near-infrared spectroscopy (NIRS) directly in the beets. We aimed to use NIRS on sugar beet root pulp to detect and quantify RRCR in the field, in parallel to the harvest process. For the construction of the calibration model, mainly beets from the field with natural RRCR infestation were used. To enrich the model, artificially inoculated beets were added. The model was developed based on Partial Least Squares Regression. The optimized model reached a Pearson correlation coefficient (R) of 0.972 and a Ratio of Prediction to Deviation (RPD) of 4.131. The prediction of the independent validation set showed a high correlation coefficient (R = 0.963) and a root mean square error of prediction (RMSEP) of 0.494. These results indicate that NIRS could be a helpful tool in the assessment of Rhizoctonia disease in the field.

scholarly journals Quantification of Oil Content in Intact Sugar Beet Seed by Near-Infrared Spectroscopy

Agronomy ◽  
2018 ◽  
Vol 8 (11) ◽  
pp. 254 ◽  
Author(s):  
Rosa Martínez-Arias ◽  
María Ronquillo-López ◽  
Axel Schechert
Keyword(s):  
Sugar Beet ◽  
Oil Content ◽  
Near Infrared ◽  
Seed Oil ◽  
Predictive Accuracy ◽  
Pearson Correlation ◽  
Accurate Determination ◽  
Calibration Model ◽  
Oil Reserves

Sugar beet seed oil reserves play an important role in successful germination and seedling development. The purpose of this study was to establish a non-destructive near-infrared (NIR) methodology with good predictive accuracy to quantify stored seed oil in sugar beet seed. Reflectance NIR spectra were acquired from viable monogerm seeds. Calibration equations were developed using partial least squares. The optimized calibration model reached a Pearson correlation of 0.946; an independent prediction test reached a correlation of 0.919 and a Root Mean Square Error of Prediction of 0.388. The possible role of the outer pericarp in the prediction of oil content was additionally considered. The results indicate that the model is suitable for a rapid and accurate determination of the oil content in both polished and unpolished sugar beet seeds. This NIR application might help to understand the role of seed energy reservoirs in sugar beet germination and further plant growth.

scholarly journals Prediction of the Leaf Primordia of Potato Tubers Using Sensor Fusion and Wavelength Selection

2019 ◽  
Vol 5 (1) ◽  
pp. 10 ◽  
Author(s):  
Ahmed Rady ◽  
Daniel Guyer ◽  
William Kirk ◽  
Irwin R Donis-González
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Prediction Models ◽  
Calibration Model ◽  
Data Sets ◽  
Least Squares Regression ◽  
Potato Tubers ◽  
Russet Norkotah ◽  
Systems Models

The sprouting of potato tubers during storage is a significant problem that suppresses obtaining high quality seeds or fried products. In this study, the potential of fusing data obtained from visible (VIS)/near-infrared (NIR) spectroscopic and hyperspectral imaging systems was investigated, to improve the prediction of primordial leaf count as a significant sign for tubers sprouting. Electronic and lab measurements were conducted on whole tubers of Frito Lay 1879 (FL1879) and Russet Norkotah (R.Norkotah) potato cultivars. The interval partial least squares (IPLS) technique was adopted to extract the most effective wavelengths for both systems. Linear regression was utilized using partial least squares regression (PLSR), and the best calibration model was chosen using four-fold cross-validation. Then the prediction models were obtained using separate test data sets. Prediction results were enhanced compared with those obtained from individual systems’ models. The values of the correlation coefficient (the ratio between performance to deviation, or r(RPD)) were 0.95(3.01) and 0.9s6(3.55) for FL1879 and R.Norkotah, respectively, which represented a feasible improvement by 6.7%(35.6%) and 24.7%(136.7%) for FL1879 and R.Norkotah, respectively. The proposed study shows the possibility of building a rapid, noninvasive, and accurate system or device that requires minimal or no sample preparation to track the sprouting activity of stored potato tubers.

scholarly journals The application of NIR spectroscopy in moisture determining of vegetable seeds

2020 ◽  
Vol 38 (No. 2) ◽  
pp. 131-136
Author(s):  
Wojciech Poćwiardowski ◽  
Joanna Szulc ◽  
Grażyna Gozdecka
Keyword(s):  
Experimental Data ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Moisture Analysis ◽  
Non Destructive

The aim of the study was to elaborate a universal calibration for the near infrared (NIR) spectrophotometer to determine the moisture of various kinds of vegetable seeds. The research was conducted on the seeds of 5 types of vegetables – carrot, parsley, lettuce, radish and beetroot. For the spectra correlation with moisture values, the method of partial least squares regression (PLS) was used. The resulting qualitative indicators of a calibration model (R = 0.9968, Q = 0.8904) confirmed an excellent fit of the obtained calibration to the experimental data. As a result of the study, the possibilities of creating a calibration model for NIR spectrophotometer for non-destructive moisture analysis of various kinds of vegetable seeds was confirmed.<br /><br />

scholarly journals Monitoring of CO2 Absorption Solvent in Natural Gas Process Using Fourier Transform Near-Infrared Spectrometry

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Mohd Yusop Nurida ◽  
Dolmat Norfadilah ◽  
Mohd Rozaiddin Siti Aishah ◽  
Chan Zhe Phak ◽  
Syafiqa M. Saleh
Keyword(s):  
Fourier Transform ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Infrared Spectrometry ◽  
Calibration Model ◽  
Co2 Absorption ◽  
Processing Unit

The analytical methods for the determination of the amine solvent properties do not provide input data for real-time process control and optimization and are labor-intensive, time-consuming, and impractical for studies of dynamic changes in a process. In this study, the potential of nondestructive determination of amine concentration, CO2 loading, and water content in CO2 absorption solvent in the gas processing unit was investigated through Fourier transform near-infrared (FT-NIR) spectroscopy that has the ability to readily carry out multicomponent analysis in association with multivariate analysis methods. The FT-NIR spectra for the solvent were captured and interpreted by using suitable spectra wavenumber regions through multivariate statistical techniques such as partial least square (PLS). The calibration model developed for amine determination had the highest coefficient of determination (R2) of 0.9955 and RMSECV of 0.75%. CO2 calibration model achieved R2 of 0.9902 with RMSECV of 0.25% whereas the water calibration model had R2 of 0.9915 with RMSECV of 1.02%. The statistical evaluation of the validation samples also confirmed that the difference between the actual value and the predicted value from the calibration model was not significantly different and acceptable. Therefore, the amine, CO2, and water models have given a satisfactory result for the concentration determination using the FT-NIR technique. The results of this study indicated that FT-NIR spectroscopy with chemometrics and multivariate technique can be used for the CO2 solvent monitoring to replace the time-consuming and labor-intensive conventional methods.

scholarly journals Rapid Estimation of Potato Quality Parameters by a Portable Near-Infrared Spectroscopy Device

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8222
Author(s):  
Olga Escuredo ◽  
Laura Meno ◽  
María Shantal Rodríguez-Flores ◽  
Maria Carmen Seijo
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Dry Matter ◽  
Near Infrared ◽  
Reducing Sugars ◽  
Quality Parameters ◽  
Portable Devices ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Potato Quality

The aim of the present work was to determine the main quality parameters on tuber potato using a portable near-infrared spectroscopy device (MicroNIR). Potato tubers protected by the Protected Geographical Indication (PGI “Patata de Galicia”, Spain) were analyzed both using chemical methods of reference and also using the NIR methodology for the determination of important parameters for tuber commercialization, such as dry matter and reducing sugars. MicroNIR technology allows for the attainment/estimation of dry matter and reducing sugars in the warehouses by directly measuring the tubers without a chemical treatment and destruction of samples. The principal component analysis and modified partial least squares regression method were used to develop the NIR calibration model. The best determination coefficients obtained for dry matter and reducing sugars were of 0.72 and 0.55, respectively, and with acceptable standard errors of cross-validation. Near-infrared spectroscopy was established as an effective tool to obtain prediction equations of these potato quality parameters. At the same time, the efficiency of portable devices for taking instantaneous measurements of crucial quality parameters is useful for potato processors.

Optimization of Wheat Protein Near-Infrared Calibration Model Based on SPXY

2013 ◽  
Vol 803 ◽  
pp. 122-126 ◽  
Author(s):  
Xiao Dong Mao ◽  
Lai Jun Sun ◽  
Gang Hao ◽  
Lu Lu Xu ◽  
Guang Yan Hui
Keyword(s):  
Spectral Data ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Calibration Model ◽  
Spectral Features ◽  
Mean Square ◽  
Wheat Protein ◽  
Least Squares Regression ◽  
Training Set ◽  
Real Samples

It is very crucial that arepresentative training set can be extracted from a pool of real samples. Inthis paper, a representative set of correction samples of wheat protein contentis selected by using SPXY algorithm firstly; Secondly, the spectral data ispretreated to enhance spectral features; Thirdly, the model of wheat grainprotein is established by using partial least squares regression. The resultsshow that the model established by the calibration set selected by SPXY isbetter than the model established by the calibration set selected randomly.Root mean square error of prediction(RMSEP) and prediction correlationcoefficient(R) are 0.41094 and 0.97705 respectively, which are similar to themodel established by the initial calibration set.

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.

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