scholarly journals Prediction Model of the Key Components for Lodging Resistance in Rapeseed Stalk Using Near-Infrared Reflectance Spectroscopy (NIRS)

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Jie Kuai ◽  
Shengyong Xu ◽  
Cheng Guo ◽  
Kun Lu ◽  
Yaoze Feng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Spectral Data ◽  
Near Infrared ◽  
Soluble Sugar ◽  
Computing Time ◽  
Colorimetric Method ◽  
Mature Stage ◽  
Near Infrared Reflectance ◽  
Lodging Resistance ◽  
Physiological Basis

The chemical composition of rape stalk is the physiological basis for its lodging resistance. By taking the advantage of NIRS, we developed a rapid method to determine the content of six key composition without crushing the stalk. Rapeseed stalks in the mature stage of growth were collected from three cultivation modes over the course of 2 years. First, we used the near-infrared spectroscope to scan seven positions on the stalk samples and took their average to form the spectral data. The stalks were then crushed and sieved; then the ratio of carbon and nitrogen, ratio of acid-insoluble lignin and lignin, and the content of soluble sugar and cellulose were determined using the combustion method, weighing method, and colorimetric method, respectively. The partial least squares regression (PLSR) method was used to establish a prediction model between the spectral data and the chemical measurements, and all models were evaluated by an internal interaction verification and an external independent test set sample. To improve the accuracy of the model and reduce the computing time, some optimization methods have been applied. Some outliers were removed, and then the data were preprocessed to determine the best spectral information band and the optimal principal component number. The results showed that elimination of outliers effectively improved the precision of the prediction model and that no spectral pretreatment method exhibited the highest prediction accuracy. In summary, the NIRS-based prediction model could facilitate the rapid nondestructive detection in the key components of rapeseed stalk.

Prediction of neutral detergent fiber content in corn stover using near-infrared spectroscopy technique

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Xuyang Pan ◽  
Laijun Sun ◽  
Guobing Sun ◽  
Panxiang Rong ◽  
Yuncai Lu ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Prediction Model ◽  
Spectral Data ◽  
Corn Stover ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Fiber Content ◽  
Partial Least Square ◽  
Neutral Detergent Fiber

AbstractNeutral detergent fiber (NDF) content was the critical indicator of fiber in corn stover. This study aimed to develop a prediction model to precisely measure NDF content in corn stover using near-infrared spectroscopy (NIRS) technique. Here, spectral data ranging from 400 to 2500 nm were obtained by scanning 530 samples, and Monte Carlo Cross Validation and the pretreatment were used to preprocess the original spectra. Moreover, the interval partial least square (iPLS) was employed to extract feature wavebands to reduce data computation. The PLSR model was built using two spectral regions, and it was evaluated with the coefficient of determination (R2) and root mean square error of cross validation (RMSECV) obtaining 0.97 and 0.65%, respectively. The overall results proved that the developed prediction model coupled with spectral data analysis provides a set of theoretical foundations for NIRS techniques application on measuring fiber content in corn stover.

scholarly journals COMPARISON OF NEAR INFRARED CALIBRATIONS FOR ESTIMATING IN VITRO DIGESTIBILITY IN WHOLE-PLANT CORN HYBRIDS

1987 ◽  
Vol 67 (2) ◽  
pp. 557-562 ◽  
Author(s):  
E. V. VALDES ◽  
R. B. HUNTER ◽  
G. E. JONES
Keyword(s):  
Dry Matter ◽  
Near Infrared ◽  
Mature Stage ◽  
In Vitro Digestibility ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Corn Hybrids ◽  
Dry Matter Digestibility ◽  
Whole Plant

A comparison of two near infrared (NIRA) calibrations (C1 and C2) for the prediction of in vitro dry matter digestibility (IVDM) in whole-plant corn (WPC) was conducted. C1 consisted of 40 WPC samples collected from four locations across Ontario (Brucefield, London, Guelph and Elora). C2 consisted of 90 samples and included the above locations plus Pakenham and Winchester. Nine wavelengths were used in both equations but only three were common in C1 and C2 equations. These wavelengths were 2139 nm, 2100 nm, and 1445 nm, respectively. The predictions of IVDM utilizing both C1 and C2 were good. Coefficients of determination (r2) and standard error of the estimate (SEE) for calibration and prediction sets were 0.91, 1.7; 0.85, 1.7 for C1 and 0.88, 1.6; 0.77, 1.6 for C2 respectively. Regression analysis within location, however, showed low r2 values for the prediction of IVDM for Pakenham and Winchester in both calibrations. The more mature stage of harvest at these locations might be the cause of the poorer predictions. Key words: In vitro digestibility, whole-plant corn, near infrared reflectance

scholarly journals THE DEVELOPMENT OF PORTABLE DETECTOR FOR APPLE' S SOLUBLE SOLIDS CONTENT BASED ON VISIBLE AND NEAR INFRARED SPECTRUM

2020 ◽  
pp. 277-288
Author(s):  
Fa Peng ◽  
ShuangXi Liu ◽  
Hao Jiang ◽  
XueMei Liu ◽  
JunLin Mu ◽  
...  
Keyword(s):  
Infrared Spectrum ◽  
Prediction Model ◽  
Spectral Data ◽  
Near Infrared ◽  
Mobile App ◽  
Partial Least Square ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Solids Content ◽  
Near Infrared Spectrum

In order to detect the soluble solids content of apples quickly and accurately, a portable apple soluble solids content detector based on USB2000 + micro spectrometer was developed. The instrument can communicate with computer terminal and mobile app through network port, Bluetooth and other ways, which can realize the rapid acquisition of apple spectral information. Firstly, the visible / near-infrared spectrum data and soluble solids content information of 160 apple samples were collected; secondly, the spectral data preprocessing methods were compared, and the results showed that the prediction model of sugar content based on partial least square (PLS) method after average smoothing preprocessing was accurate. The correlation coefficient (RP) and root mean square error (RMSEP) of the prediction model were 0.902 and 0.589 ° Brix, respectively. Finally, on the basis of average smoothing preprocessing, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to optimize the wavelength of spectral data, and PLS model was constructed based on the selected 17 characteristic wavelengths, which can increase the accuracy of soluble solids content prediction model, increase the RP to 0.912, and reduce RMSEP to 0.511 ° Brix. The portable visible / near infrared spectrum soluble solids prediction model based on the instrument and method has high accuracy, and the detector can quickly and accurately measure the soluble solids content of apple.

scholarly journals Use of Visible and Near-Infrared Reflectance Spectroscopy Models to Determine Soil Erodibility Factor (K) in an Ecologically Restored Watershed

2020 ◽  
Vol 12 (18) ◽  
pp. 3103
Author(s):  
Qinghu Jiang ◽  
Yiyun Chen ◽  
Jialiang Hu ◽  
Feng Liu
Keyword(s):  
Land Use ◽  
Spectral Data ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Soil Erodibility ◽  
Infrared Reflectance ◽  
Cultivated Land ◽  
Near Infrared Reflectance ◽  
Natural Land ◽  
Soil Erodibility Factor

This study aimed to assess the ability of using visible and near-infrared reflectance (Vis–NIR) spectroscopy to quantify soil erodibility factor (K) rapidly in an ecologically restored watershed. To achieve this goal, we explored the performance and transferability of the developed spectral models in multiple land-use types: woodland, shrubland, terrace, and slope farmland (the first two types are natural land and the latter two are cultivated land). Subsequently, we developed an improved approach by combining spectral data with related topographic variables (i.e., elevation, watershed location, slope height, and normalized height) to estimate K. The results indicate that the calibrated spectral model using total samples could estimate K factor effectively (R2CV = 0.71, RMSECV = 0.0030 Mg h Mj−1 mm−1, and RPDCV = 1.84). When predicting K in the new samples, models performed well in natural land soils (R2P = 0.74, RPDP = 1.93) but failed in cultivated land soils (R2P = 0.24, RPDP = 0.99). Furthermore, the developed models showed low transferability between the natural and cultivated land datasets. The results also indicate that the combination of spectral data with topographic variables could slightly increase the accuracies of K estimation in total and natural land datasets but did not work for cultivated land samples. This study demonstrated that the Vis–NIR spectroscopy could be used as an effective method in predicting K. However, the predictability and transferability of the calibrated models were land-use type dependent. Our study also revealed that the coupling of spectrum and environmental variable is an effective improvement of K estimation in natural landscape region.

Influence of abattoir, carcass suspension method, ageing time and anatomical position of the measurement on visual and near infrared spectrum characteristics of the longissimus dorsi of lambs

2009 ◽  
Vol 2009 ◽  
pp. 144-144
Author(s):  
O Oltra ◽  
L Farmer ◽  
B W Moss ◽  
J Birnie
Keyword(s):  
Prediction Model ◽  
Near Infrared ◽  
Ageing Time ◽  
Eating Quality ◽  
Longissimus Dorsi ◽  
Near Infrared Reflectance ◽  
Anatomical Position ◽  
Optimal Protocol ◽  
On Line ◽  
Accuracy And Repeatability

Visual and near infrared reflectance (VisNIR) has been identified as a possible on-line method that could discriminate some eating quality attributes of lamb (Andres et al. 2007). However, the accuracy and repeatability of this method for predicting eating quality depends on the development of an optimal sampling protocol (Shackelford et al., 2004) and of an optimal formula for the prediction model. The aims of this experiment were to identify if factors such as abattoir, carcass suspension method, ageing time and anatomical position along the Longissimus dorsi muscle can affect the characteristic of the VisNIR spectra. The information obtained from this study will allow the development an optimal protocol and prediction model.

Geographic Classification of Extra Virgin Olive Oils from the Eastern Mediterranean by Chemometric Analysis of Visible and Near-Infrared Spectroscopic Data

2003 ◽  
Vol 57 (2) ◽  
pp. 158-163 ◽  
Author(s):  
Gerard Downey ◽  
Peter McIntyre ◽  
Antony N. Davies
Keyword(s):  
Discriminant Analysis ◽  
Spectral Data ◽  
Near Infrared ◽  
Eastern Mediterranean ◽  
Geographic Origin ◽  
Near Infrared Reflectance ◽  
Virgin Olive Oils ◽  
Olive Oils ◽  
Sample Set ◽  
Factorial Discriminant Analysis

Visible and near-infrared reflectance spectra have been examined for their ability to classify extra virgin olive oils from the eastern Mediterranean on the basis of their geographic origin. Classification strategies investigated were partial least-squares regression, factorial discriminant analysis, and k-nearest neighbors analysis. Discriminant models were developed and evaluated using spectral data in the visible (400–750 nm), near-infrared (1100–2498 nm), and combined (400–2498 nm) wavelength ranges. A variety of data pretreatments was applied. Best results were obtained using factorial discriminant analysis on raw spectral data over the combined wavelength range; a correct classification rate of 93.9% was obtained on a prediction sample set. Though the overall sample set was limited in numbers, these results demonstrate the potential of near-infrared spectroscopy to classify extra virgin olive oils on the basis of their geographic origin.

scholarly journals Dry Matter Estimation of Standing Corn with Near-Infrared Reflectance Spectroscopy

2021 ◽  
Vol 37 (5) ◽  
pp. 775-781
Author(s):  
Matthew F. Digman ◽  
Jerry H. Cherney ◽  
Debbie J. Cherney
Keyword(s):  
Moisture Content ◽  
Prediction Model ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Whole Plant ◽  
Plant Moisture

HIGHLIGHTSQuadratic relationships were established to relate ear moisture or stover moisture to whole plant moisture, and they explained 90% and 84% of whole plant moisture, respectively. Based on our observations, the moisture content of a corn field can be estimated within +1% w.b. in 19 out of 20 fields by sampling 5-10 plants. The calibration offered by SCiO was successful at predicting oven-dried moisture content based on traditional NIRS metrics of R2 = 0.92, RMSE = 3.6, RPD = 3.2, and RER = 15. However, the 95% prediction bands were +6.9% w.b., which would indicate little utility in estimating ear moisture content. Based on a prediction model that was developed using the data collected for this study, a significant instrument-to-instrument bias was observed, indicating the necessity of including multiple SCiO devices in calibration spectra collection. ABSTRACT. Determining the appropriate time to harvest whole-plant corn is an essential factor driving the successful preservation via anaerobic fermentation (ensiling). The current options for timely on-farm monitoring of corn moisture in the field include selecting a set of representative plants, chopping and drying a subsample, or harvesting a portion of the field using a harvester equipped with an on-board moisture sensing system. Both methods are time-consuming and expensive, limiting their practicality for harvest decision-making. This work’s objective was to develop a practical solution that utilizes the moisture content of the ear to estimate whole-plant moisture. An improvement of this method was also considered that utilized a hand-held near-infrared reflectance spectroscopy (NIRS) device to predict ear moisture in situ. Based on the data collected during this work, a quadratic relationship was developed where ear moisture explained 90% of the variability in whole-plant corn moisture. However, based on our observations, the hand-held NIRS evaluated would have little utility in predicting whole-plant corn moisture with either the calibration developed here or provided by the manufacturer. The manufacturer’s prediction model yielded the best result with an R2 of 0.92, and a ratio of performance to deviation of 3.19. However, the 95% prediction band was +6.85% w.b. Finally, we determined that for a corn field uniform in appearance, sampling five to ten plants is likely to provide a reasonable estimate of field moisture. Keywords: Corn silage, Forage analysis, Harvest timing, Moisture content, NIRS.

scholarly journals Prediksi Kadar Salinitas, pH dan C-Organik Tanah Menggunakan Near Infrared Kecamatan Baitussalam Kabupaten Aceh Besar

2020 ◽  
Vol 4 (4) ◽  
pp. 542-551
Author(s):  
Riska Nurul Saputri ◽  
Ichwana Ichwana ◽  
Agus Arip Munawar
Keyword(s):  
Prediction Model ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Least Square ◽  
Organic Soils ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Spectrum Correction

Abstrak. Akuisisi spektrum Near Infrared Reflectance Spectroscopy (NIRS) terkait kualitas dan kondisi tanah telah banyak dilakukan dalam berbagai penelitian. Pada penelitian ini menggunakan model prediksi Partileal Least Squares (PLS) dengan metode koreksi spektrum Mean Normalization (MN), Savitzky-Golay Smoothing, dan kombinasi Mean Normalization (MN) dan Savitzky-Golay Smoothing. Sampel tanah yang digunakan berasal dari Kecamatan Baitussalam Kabupaten Aceh Besar karena dianggap sesuai untuk prediksi kadar salinitas, pH dan C-Organik tanah. Hasil dari penelitian menunjukkan adanya korelasi antara prediksi Near Infrared Reflectance Spectroscopy (NIRS) dengan hasil aktual laboratorium setelah dilakukan pembangunan model prediksi Partileal Least Square (PLS) dan dievaluasi dengan parameter statistika; penggunaan pretreatment Mean Normalization (MN) merupakan metode terbaik atau pilihan, dimana dapat meningkatkan keakuratan hasil prediksi kadar salinitas, pH dan C-Organik tanah.Prediction of Salinity, pH and C-Organic Soils Level Using Near  in Baitussalam Regency, Aceh Besar RegencyAbstract. Near Infrared Reflectance Spectroscopy (NIRS) spectrum acquisition related to soil quality and condition has been carried out in various studies. This study used prediction model Partileal Least Squares (PLS) with the spectrum correction methods used are Mean Normalization (MN), Savitzky-Golay Smoothing, and Combination of Mean Normalization (MN) and Savitzky-Golay Smoothing. The soil samples used were from Baitussalam regency, Aceh Besar regency because they were considered suitable for the prediction of salinity, pH and C-Organic soils. The results of this study showed a correlation between the prediction of Near Infrared Reflectance Spectroscopy (NIRS) with the actual results of the laboratory after the construction of the prediction model Partileal Least Square (PLS) and and evaluated with statistical parameters; the use of pretreatment Mean Normalization (MN) is the best or preferred spectrum correction method, which can improve the accuracy of the predicted results of salinity, pH and C-Organic soil.

scholarly journals Environmental assessment of soil quality indices using near infrared reflectance spectroscopy

2021 ◽  
Vol 922 (1) ◽  
pp. 012009
Author(s):  
D Devianti ◽  
Sufardi ◽  
S Syafriandi ◽  
A A Munawar
Keyword(s):  
Spectral Data ◽  
Soil Quality ◽  
Environmental Assessment ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Soil Samples ◽  
Infrared Reflectance ◽  
Quality Indices ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance

Abstract The main purpose of this preset study is to assess soil quality indices in form of potassium (K) and phosphorus (P) contents using a non-invasive and environmental friendly approach namely near infrared reflectance spectroscopy. Soil samples were obtained from Aceh Besar district in rice field land-use. Near infrared spectral data of soil samples were acquired and recorded as absorbance in wavelength range from 1000 to 2500 nm. On the other hand, actual P and K were measured using standard laboratory procedures by means of Kjeldahl methods. Spectral data were corrected and pre-treated using mean centering approach and applied to all dataset. Prediction models were developed using principal component regression and validated using leverage cross validation. The results showed that both soil quality indices can be predicted with maximum correlation coefficient (r) of 0.98 and ratio prediction to deviation (RPD) index of 3.47 for P, and r of 0.91, RPD of 2.68 for K respectively. It may conclude that environmental assessment, particularly for soil quality determination can be conducted rapidly and non-invasively using near infrared spectroscopy approach.

Application of Partial Least-Squares Regression to Near-Infrared Reflectance Spectroscopic Determination of Shive Content in Flax

2003 ◽  
Vol 57 (5) ◽  
pp. 551-556 ◽  
Author(s):  
Miryeong Sohn ◽  
Franklin E. Barton ◽  
Wiley H. Morrison ◽  
Douglas D. Archibald
Keyword(s):  
Least Squares ◽  
Spectral Data ◽  
Partial Least Squares ◽  
Standard Error ◽  
Near Infrared ◽  
Single Sample ◽  
Calibration Model ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Sample Set

Shive, the nonfiberous core portion of the stem, in flax fiber after retting is related to fiber quality. The objective of this study is to develop a standard calibration model for determining shive content in retted flax by using near-infrared reflectance spectroscopy. Calibration samples were prepared by manually mixing pure, ground shive and pure, ground fiber from flax retted by three different methods (water, dew, and enzyme retting) to provide a wide range of shive content from 0 to 100%. Partial least-squares (PLS) regression was used to generate a calibration model, and spectral data were processed using various pretreatments such as a multiplicative scatter correction (MSC), normalization, derivatives, and Martens' Uncertainty option to improve the calibration model. The calibration model developed with a single sample set resulted in a standard error of 1.8% with one factor. The best algorithm was produced from first-derivative processing of the spectral data. MSC was not effective processing for this model. However, a big bias was observed when independent sample sets were applied to this calibration model to predict shive content in flax fiber. The calibration model developed using a combination sample set showed a slightly higher standard error and number of factors compared to the model for a single sample set, but this model was sufficiently accurate to apply to each sample set. The best algorithm for the combination sample set was generated from second derivatives followed by MSC processing of spectral data and from Martens' Uncertainty option; it resulted in a standard error of 2.3% with 2 factors. The value of the digital second derivative centered at 1674 nm for these spectral data was highly correlated to shive content of flax and could form the basis for a simple, low-cost sensor for the shive or fiber content in retted flax.

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