scholarly journals Repaid Identification and Prediction of Cadmium–Lead Cross-Stress of Different Stress Levels in Rice Canopy Based on Visible and Near-Infrared Spectroscopy

2020 ◽  
Vol 12 (3) ◽  
pp. 469
Author(s):  
Shuangyin Zhang ◽  
Jun Li ◽  
Siying Wang ◽  
Yingjing Huang ◽  
Yizhuo Li ◽  
...  
Keyword(s):  
Prediction Model ◽  
Near Infrared ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Diagnostic Model ◽  
Infrared Reflectance ◽  
Cd Stress ◽  
Near Infrared Reflectance ◽  
Pb Stress ◽  
Foliar Concentration

Accurate detection of cadmium (Cd) and lead (Pb)-induced cross-stress on crops is essential for agricultural, ecological environment, and food security. The feasibility to diagnose and predict Cd–Pb cross-stress in agricultural soil was explored by measuring the visible and near-infrared reflectance of rice leaves. In this study, two models were developed—namely a diagnostic model and a prediction model. The diagnostic model was established based on visible and near-infrared reflectance spectroscopy (VNIRS) datasets with Support Vector Machine (SVM), followed by leave-one-out cross-validation (LOOCV). A partial least-squares (PLS) regression, as the prediction model was employed to predict the foliar concentration of Cd and Pb contents. To accurately calibrate the two models, a rigorous greenhouse experiment was designed and implemented, with 4 levels of treatments on each of the Cd and Pb stress on rice. Results show that with the appropriate pre-processing, the diagnostic model can identify 79% of Cd and 85% of Pb stress of any levels. The significant bands that have been used mainly distributed between 681–776 nm and 1224–1349 nm for Cd stress and 712–784 nm for Pb stress. The prediction model can estimate Cd with coefficient of determination of 0.7, but failed to predict Pb accurately. The results illustrated the feasibility to diagnose Cd stress accurately by measuring the visible and near-infrared reflectance of rice canopy in a cross-contamination soil environment. This study serves as one step forward to heavy metal pollutant detection in a farmland environment.

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

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.

Predicting intramuscular fat, moisture and Warner-Bratzler shear force in pork muscle using near infrared reflectance spectroscopy

2006 ◽  
Vol 82 (1) ◽  
pp. 111-116 ◽  
Author(s):  
N. Barlocco ◽  
A. Vadell ◽  
F. Ballesteros ◽  
G. Galietta ◽  
D. Cozzolino
Keyword(s):  
Shear Force ◽  
Near Infrared ◽  
Cross Validation ◽  
Nir Spectroscopy ◽  
Intramuscular Fat ◽  
Coefficient Of Determination ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Calibration Models ◽  
Intact Muscle

AbstractPartial least-squares (PLS) models based on visible (Vis) and near infrared reflectance (NIR) spectroscopy data were explored to predict intramuscular fat (IMF), moisture and Warner Bratzler shear force (WBSF) in pork muscles (m. longissimus thoracis) using two sample presentations, namely intact and homogenized. Samples were scanned using a NIR monochromator instrument (NIRSystems 6500, 400 to 2500 nm). Due to the limited number of samples available, calibration models were developed and evaluated using full cross validation. The PLS calibration models developed using homogenized samples and raw spectra yielded a coefficient of determination in calibration (R2) and standard error of cross validation (SECV) for IMF (R2=0·87; SECV=1·8 g/kg), for moisture (R2=0·90; SECV=1·1 g/kg) and for WBSF (R2=0·38; SECV=9·0 N/cm). Intact muscle presentation gave poorer PLS calibration models for IMF and moisture (R2<0·70), however moderate good correlation was found for WBSF (R2=0·64; SECV=8·5 N/cm). Although few samples were used, the results showed the potential of Vis-NIR to predict moisture and IMF using homogenized pork muscles and WBSF in intact samples.

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.

New approach to improve the calibration of main fatty acids by near-infrared reflectance spectroscopy in ruminant meat

2014 ◽  
Vol 54 (10) ◽  
pp. 1848 ◽  
Author(s):  
B. P. Mourot ◽  
D. Gruffat ◽  
D. Durand ◽  
G. Chesneau ◽  
S. Prache ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Linoleic Acid ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Coefficient Of Determination ◽  
Calibration Model ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy

This study aims to investigate alternative near-infrared reflectance spectroscopy (NIRS) strategies for predicting beef polyunsaturated fatty acids (PUFA) composition, which have a great nutritional interest, and are actually poorly predicted by NIRS. We compared the results of NIRS models for predicting fatty acids (FA) of beef meat by using two databases: a beef database including 143 beef samples, and a ruminant database including 76 lamb and 143 beef samples. For all the FA, particularly for PUFA, the coefficient of determination of cross-validation (R2CV) and the residual predictive deviation (RPD) of models increased when the ruminant muscle samples database was used instead of the beef muscle database. The R2CV values for the linoleic acid, total conjugated linoleic acid and total PUFA increased from 0.44, 0.79 and 0.59 to 0.68, 0.9, 0.8, respectively, and RPD values for these FA increased from 1.33, 2.14, 1.54 to 1.76, 3.11 and 2.24, respectively. RPD above 2.5 indicates calibration model is considered as acceptable for analytical purposes. The use of a universal equation for ruminant meats to predict FA composition seems to be an encouraging strategy.

scholarly journals Identifying Freshness of Spinach Leaves Stored at Different Temperatures Using Hyperspectral Imaging

Foods ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 356 ◽  
Author(s):  
Zhu ◽  
Feng ◽  
Zhang ◽  
Bao ◽  
He
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Principal Component ◽  
Imaging Systems ◽  
Support Vector ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Different Temperatures ◽  
Learning Machine ◽  
Spinach Leaves

Spinach is prone to spoilage in the course of preservation. Spinach leaves stored at different temperatures for different durations will have varying degrees of freshness. In order to monitor the freshness of spinach leaves during storage, a rapid and non-destructive method—hyperspectral imaging technology—was applied in this study. Visible near-infrared reflectance (Vis-NIR) (380–1030 nm) and near-infrared reflectance (NIR) (874–1734 nm) hyperspectral imaging systems were used. Spinach leaves preserved at different temperatures with different durations (0, 3, 6, 9 days at 4 °C and 0, 1, 2 days at 20 °C) were studied. Principal component analysis (PCA) was adopted as a qualitative analysis method. The second-order derivative spectra were utilized to select effective wavelengths. Partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and extreme learning machine (ELM) were used to build models based on full spectra and effective wavelengths. All three models achieved good results, with accuracies above 92% for both Vis-NIR spectra and NIR spectra. ELM obtained the best results, with all accuracies reaching 100%. The overall results indicate the possibility of the freshness identification of spinach preserved at different temperatures for different durations using two kinds of hyperspectral imaging systems.

scholarly journals Prediction of Composition of Broiler Chicken Excreta Using Near-infrared Reflectance Spectroscopy

2005 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
I. T. Kadim ◽  
W. Al-Marzooqi ◽  
O. Mahgoub ◽  
K. Annamalai
Keyword(s):  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Acid Detergent Fibre ◽  
Gross Energy ◽  
Calibration Equations

Near-infrared reflectance spectroscopic (NIRS) calibrations were developed for the prediction of the content of dry matter (DM); nitrogen (N), ether extract (EE), neutral detergent fibre (NDF), acid detergent fibre (ADF), gross energy (GE), calcium (Ca) and phosphate (P) in broiler excreta samples. The chemical composition of broiler excreta was determined by the conventional chemical analysis methods in the laboratory and compared with NIRS. Excreta samples (n = 72) were oven dried (60 oC) and analyzed for DM, N, EE, NDF, ADF, GE, Ca and P. The determined values (mean ± SD) were as follows: DM: 31.46 ± 7.65 (range:19.14 - 44.51), N: 5.85 ± 2.88 (range: 4.85 -7.00), EE: 1.37 ± 0.25 (range: 0.88-1.99), ADF: 16.71 ± 1.99 (range: 12.11-19.97), NDF: 26.26 ± 1.63 (range: 22.03-30.21), GE: 15.27 ± 0.33 (range: 14.52-16.11), Ca: 2.57 ± 0.22 (range: 2.16-3.01), P: 1.79 ± 0.15 (range: 1.41-2.11). The samples were then scanned in a NIRS model 5000 analyzer and the spectra obtained for each sample. Calibration equations and prediction values were developed for broiler excreta samples. The software used modified partial least square regression statistic, as it is most suitable for natural products. For broiler excreta samples, the coefficient of determination (R2) and the standard error of prediction (SEP) was DM = 0.97, 1.27, N = 0.95, 0.72, EE = 0.92, 0.07, ADF = 0.87, 0.78, NDF = 0.88, 0.72, GE = 0.89; 0.24, Ca = 0.96, 0.06, P = 0.93, 0.09, respectively. The results indicate that it is possible to calibrate NIRS to predict major constituents in broiler excreta samples.

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