Near Infrared Technology for Precision Environmental Measurements: Part 2. Determination of Carbon in Green Grass Tissue

2003 ◽  
Vol 11 (4) ◽  
pp. 257-267 ◽  
Author(s):  
Susumu Morimoto ◽  
W.F. McClure ◽  
Benett Crowell ◽  
Donald L. Stanfield
Keyword(s):  
Near Infrared ◽  
Wavelength Region ◽  
Visible Spectrum ◽  
Simple Fact ◽  
Short Wavelength Region ◽  
Carbon And Nitrogen ◽  
Standard Normal Variate ◽  
Average Spectrum ◽  
Standard Normal ◽  
Pls Analysis

Composting is one of the most desirable techniques for reducing waste volume. To make good compost, the correct proportions of the elements carbon and nitrogen (30: 1 ratio) are important. In this paper, carbon quantification of green grass tissue using near infrared (NIR) technology was studied. Separate studies were conducted for the short-wavelength region (SWR = 700–1100 nm, a range that includes part of the visible spectrum) and long-wavelength region (LWR = 1100–2500 nm). Several spectral pretreatments (such as SNV, derivatives etc.) were implemented to optimise the stepwise multiple linear regression (SMLR) and partial least squares (PLS) calibrations. PLS analysis was conducted for all pretreatments. Results showed that the 2nd derivative of standard normal variate (SNV) pretreatment for the LWR and the SNV pretreatment for the SWR gave the best predictions. To simplify the PLS models, a weight index (WI), was defined as the absolute value of product between the regression vector from PLS analysis and the average spectrum. A simple PLS calibration was developed using selected peak wavelengths of regression vector with a minimum WI. The simple PLS models gave better results than the full PLS calibrations. According to this analysis, the C–H stretching of the first overtone at 1860 nm and the C–H stretching of the third overtone at 874 nm were the key bands for the SWR and LWR, respectively. SMLR analysis was performed on the same spectral data used in the PLS analysis. SMLR calibrations were developed using the key band chosen in PLS analysis. Although the performance of the calibrations were not as good as the PLS calibrations, the SMLR model produced acceptable calibrations for both the SWR and LWR. The simple fact that NIR technology can be used to determine both carbon and nitrogen very quickly makes it an ideal technology for monitoring material going into a composting operation.

scholarly journals Application of calibrations to hyperspectral images of food grains: example for wheat falling number

2017 ◽  
Author(s):  
Nicola Caporaso ◽  
Martin Whitworth ◽  
Ian Fisk
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Classification Model ◽  
Least Squares Regression ◽  
Falling Number ◽  
Linear Discriminant ◽  
Standard Normal Variate ◽  
Average Spectrum ◽  
Standard Normal ◽  
Improved Performance

The presence of a few kernels with sprouting problems in a batch of wheat can result in enzymatic activity sufficient to compromise flour functionality and bread quality. This is commonly assessed using the Hagberg Falling Number (HFN) method, which is a batch analysis. Hyperspectral imaging (HSI) can provide analysis at the single grain level with potential for improved performance. The present paper deals with the development and application of calibrations obtained using an HSI system working in the near infrared (NIR) region (~900–2500 nm) and reference measurements of HFN. A partial least squares regression calibration has been built using 425 wheat samples with a HFN range of 62–318 s, including field and laboratory pre-germinated samples placed under wet conditions. Two different approaches were tested to apply calibrations: i) application of the calibration to each pixel, followed by calculation of the average of the resulting values for each object (kernel); ii) calculation of the average spectrum for each object, followed by application of the calibration to the mean spectrum. The calibration performance achieved for HFN (R2 = 0.6; RMSEC ~ 50 sRMSEP ~ 63 s) compares favourably with other studies using NIR spectroscopy. Linear spectral pre-treatments lead to similar results when applying the two methods, while non-linear treatments such as standard normal variate showed obvious differences between these approaches. A classification model based on linear discriminant analysis (LDA) was also applied to segregate wheat kernels into low (<250 s) and high (>250 s) HFN groups. LDA correctly classified 86.4% of the samples, with a classification accuracy of 97.9% when using an HFN threshold of 150 s. These results are promising in terms of wheat quality assessment using a rapid and non-destructive technique which is able to analyse wheat properties on a single-kernel basis, and to classify samples as acceptable or unacceptable for flour production.

scholarly journals Machine Learning-Based Prediction of Selected Parameters of Commercial Biomass Pellets Using Line Scan Near Infrared-Hyperspectral Image

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 316
Author(s):  
Lakkana Pitak ◽  
Kittipong Laloon ◽  
Seree Wongpichet ◽  
Panmanas Sirisomboon ◽  
Jetsada Posom
Keyword(s):  
Near Infrared ◽  
Hyperspectral Image ◽  
Thermal Conversion ◽  
High Energy ◽  
Volatile Matter ◽  
Successive Projections Algorithm ◽  
Color Distribution ◽  
Standard Normal Variate ◽  
Distribution Mapping ◽  
Standard Normal

Biomass pellets are required as a source of energy because of their abundant and high energy. The rapid measurement of pellets is used to control the biomass quality during the production process. The objective of this work was to use near infrared (NIR) hyperspectral images for predicting the properties, i.e., fuel ratio (FR), volatile matter (VM), fixed carbon (FC), and ash content (A), of commercial biomass pellets. Models were developed using either full spectra or different spatial wavelengths, i.e., interval successive projections algorithm (iSPA) and interval genetic algorithm (iGA), wavelengths and different spectral preprocessing techniques. Their performances were then compared. The optimal model for predicting FR could be created with second derivative (D2) spectra with iSPA-100 wavelengths, while VM, FC, and A could be predicted using standard normal variate (SNV) spectra with iSPA-100 wavelengths. The models for predicting FR, VM, FC, and A provided R2 values of 0.75, 0.81, 0.82, and 0.87, respectively. Finally, the prediction of the biomass pellets’ properties under color distribution mapping was able to track pellet quality to control and monitor quality during the operation of the thermal conversion process and can be intuitively used for applications with screening.

scholarly journals Люминесцентные свойства нестехиометрических кристаллов ниобата лития различного состава и генезиса (обзор)

2022 ◽  
Vol 130 (1) ◽  
pp. 171
Author(s):  
М.В. Смирнов ◽  
Н.В. Сидоров ◽  
М.Н. Палатников
Keyword(s):  
Wave Spectrum ◽  
Visible Region ◽  
Wavelength Region ◽  
Visible Spectrum ◽  
Luminescent Properties ◽  
Short Wave ◽  
Octahedral Cluster ◽  
Short Wavelength Region ◽  
Luminescence Centers ◽  
Near Ir

A brief review of the features of the defect structure and studies of the luminescent properties of nonlinear optical lithium niobate crystals of various compositions and genesis was given. It was established that the electron-hole pair NbNb4+-O- in the oxygen-octahedral cluster NbO6 emitted in the short-wavelength region of the visible spectrum (400-500 nm), while point defects (VLi and NbNb4+-NbLi4+ bipolarons) - in the long-wavelength region (500-620 nm). At the ratio of Li/Nb≈1 the luminescence was extinguished in the visible region of the spectrum due to decreasing the intrinsic luminescence centers. It was shown that the presence of polaron luminescence in the near-IR region (700-1050 nm) was due to the small polarons NbLi4+ and impurity ions Cr3+ localized in lithium and niobium octahedra. The energy transfer between the luminescence centers in the visible and near-IR spectral regions was detected. Moreover, luminescence in near-IR regions was dominant. Doping of LiNbO3 crystals with zinc and magnesium at ZnO<4.46 mol.% and MgO<5.29 mol.% led to decreasing luminescence of intrinsic defects (VLi, NbNb4+-NbLi4+). However, there was an increase of the contribution of the short-wave spectrum component at higher dopant concentrations because of the introduction of Zn and Mg into the origin positions of Nb ions.

On the Scales Associated with Near-Infrared Reflectance Difference Spectra

1995 ◽  
Vol 49 (6) ◽  
pp. 765-772 ◽  
Author(s):  
M. S. Dhanoa ◽  
S. J. Lister ◽  
R. J. Barnes
Keyword(s):  
Near Infrared ◽  
Scatter Correction ◽  
The Other ◽  
Difference Spectra ◽  
Near Infrared Reflectance ◽  
Standard Normal Variate ◽  
Near Infrared Spectra ◽  
Standard Normal ◽  
Relationship Of ◽  
The Relationship

Scale differences of individual near-infrared spectra are identified when set-independent standard normal variate (SNV) and de-trend (DT) transformations are applied in either SNV followed by DT or DT then SNV order. The relationship of set-dependent multiplicative scatter correction (MSC) to SNV is also referred to. A simple correction factor is proposed to convert derived spectra from one order to the other. It is suggested that the suitable order for the study of changes using difference spectra (when removing baselines) should be DT followed by SNV, which leads to all derived spectra on the scale of mean zero and variance equal to one. If baselines are identical, then SNV scale spectra can be used to calculate differences.

Use of Difference near Infrared Reflectance Spectra to Extract Relevant Information from the Spectra of Agro–Food Products

1998 ◽  
Vol 6 (1) ◽  
pp. 89-95 ◽  
Author(s):  
Ana Garrido-Varo ◽  
Ronald Carrete ◽  
Víctor Fernández-Cabanás
Keyword(s):  
Near Infrared ◽  
Food Products ◽  
Relevant Information ◽  
Near Infrared Reflectance ◽  
Standard Normal Variate ◽  
Corn Gluten ◽  
Corn Gluten Feed ◽  
Standard Normal ◽  
Gluten Feed ◽  
By Products

This paper compares the use of log 1/ R versus standard normal variate (SNV) and Detrending (DT) transformations calculated either of two forms, SNV followed by DT (SNV+DT) or DT then SNV (DT+SNV) for their abilities to enhance interpretation of spectra and to detect areas of maximum differences in composition of two agro–food products (sunflower seed and corn) and their corresponding by-products (sunflower meal and corn gluten feed). The results obtained show that the SNV+DT and the DT+SNV transformations of the raw data make the existing chemical differences between scattering agro–food products more easily interpretable.

Identification Performance of Different Types of Handheld Near-Infrared (NIR) Spectrometers for the Recycling of Polymer Commodities

2018 ◽  
Vol 72 (9) ◽  
pp. 1362-1370 ◽  
Author(s):  
Hui Yan ◽  
Heinz W. Siesler
Keyword(s):  
Near Infrared ◽  
Raw Materials ◽  
Discrimination Performance ◽  
Principal Component ◽  
Identification Performance ◽  
Score Plot ◽  
Discrimination Capability ◽  
Standard Normal Variate ◽  
Correct Assignment ◽  
Standard Normal

For sustainable utilization of raw materials and environmental protection, the recycling of the most common polymers—polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET), polyvinyl chloride (PVC), and polystyrene (PS)—is an extremely important issue. In the present communication, the discrimination performance of the above polymer commodities based on their near-infrared (NIR) spectra measured with four real handheld (<200 g) spectrometers based on different monochromator principles were investigated. From a total of 43 polymer samples, the diffuse reflection spectra were measured with the handheld instruments. After the original spectra were pretreated by second derivative and standard normal variate (SNV), principal component analysis (PCA) was applied and unknown samples were tested by soft independent modeling of class analogies (SIMCA). The results show that the five polymer commodities cluster in the score plots of their first three principal components (PCs) and, furthermore, samples in calibration and test sets can be correctly identified by SICMA. Thus, it was concluded that on the basis of the NIR spectra measured with the handheld spectrometers the SIMCA analysis provides a suitable analytical tool for the correct assignment of the type of polymer. Because the mean distance between clusters in the score plot reflects the discrimination capability for each polymer pair the variation of this parameter for the spectra measured with the different handheld spectrometers was used to rank the identification performance of the five polymer commodities.

scholarly journals Feasibility Study on Use of Near Infrared Spectroscopy for Rapid and Non-Destructive Determination of Gossypol Content in Intact Cottonseeds

2020 ◽  
Author(s):  
Cheng Li ◽  
Bangsong Su ◽  
Tianlun Zhao ◽  
Cong Li ◽  
Jinhong Chen ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Gossypol Content ◽  
Mean Square ◽  
Standard Normal Variate ◽  
Standard Normal

Abstract Background Gossypol found in cottonseeds is toxic to human beings and monogastric animals and is a primary parameter for integrated utilization of cottonseed products. It is usually determined by the techniques relied on complex pretreatment procedures and the samples after determination cannot be used in breeding program, so it is of great importance to predict the gossypol content in cottonseeds rapidly and non-destructively to substitute the traditional analytical method. Results Gossypol content in cottonseeds was investigated by near-infrared spectroscopy (NIRS) and High-performance liquid chromatography (HPLC). Partial least squares regression, combined with spectral pretreatment methods including Savitzky-Golay smoothing, standard normal variate, multiplicative scatter correction, and first derivate, were tested for optimizing the calibration models. NIRS technique was efficient in predicting gossypol content in intact cottonseeds, as revealed by the root-mean-square error of cross-validation (RMSECV), root-mean-square error of prediction (RMSEP), coefficient for determination of prediction (Rp2), and residual predictive deviation (RPD) values for all models, being 0.05–0.07, 0.04–0.06, 0.82–0.92, and 2.3–3.4, respectively. The optimized model pretreated by Savitzky-Golay smoothing + standard normal variate + first derivate resulted in good determination of gossypol content in intact cottonseeds. Conclusions Near infrared spectroscopy coupled with different spectral pretreatments and PLS regression has exhibited the feasibility in predicting gossypol content in intact cottonseeds, rapidly and non-destructively. It could be used as an alternative method to substitute for traditional one to determine the gossypol content in intact cottonseeds.

Shortwave Near-Infrared Spectroscopy for Rapid Detection of Aflatoxin B1 Contamination in Polished Rice

2019 ◽  
Vol 82 (5) ◽  
pp. 796-803
Author(s):  
R. PUTTHANG ◽  
P. SIRISOMBOON ◽  
C. DACHOUPAKAN SIRISOMBOON
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Aflatoxin B1 ◽  
Rapid Detection ◽  
Near Infrared ◽  
Cross Validation ◽  
Polished Rice ◽  
Standard Normal Variate ◽  
Standard Normal ◽  
Rice Samples

ABSTRACT The objective of this research was to apply near-infrared spectroscopy, with a short-wavelength range of 950 to 1,650 nm, for the rapid detection of aflatoxin B1 (AFB1) contamination in polished rice samples. Spectra were obtained by reflection mode for 105 rice samples: 90 samples naturally contaminated with AFB1 and 15 samples artificially contaminated with AFB1. Quantitative calibration models to detect AFB1 were developed using the original and pretreated absorbance spectra in conjunction with partial least squares regression with prediction testing and full cross-validation. The statistical model from the external validation process developed from the treated spectra (standard normal variate and detrending) was most accurate for prediction, with a correlation coefficient (r) of 0.952, a standard error of prediction of 3.362 μg/kg, and a bias of −0.778 μg/kg. The most predictive models according to full cross-validation were developed from the multiplicative scatter correction pretreated spectra (r = 0.967, root mean square error in cross-validation [RMSECV] = 2.689 μg/kg, bias = 0.015 μg/kg) and standard normal variate pretreated spectra (r = 0.966, RMSECV = 2.691 μg/kg, bias = 0.008 μg/kg). A classification-based partial least squares discriminant analysis model of AFB1 contamination classified the samples with 90% accuracy. The results indicate that the near-infrared spectroscopy technique is potentially useful for screening polished rice samples for AFB1 contamination.

scholarly journals Prediction of Soil-Available Potassium Content with Visible Near-Infrared Ray Spectroscopy of Different Pretreatment Transformations by the Boosting Algorithms

2020 ◽  
Vol 10 (4) ◽  
pp. 1520
Author(s):  
Xiu Jin ◽  
Shaowen Li ◽  
Wu Zhang ◽  
Juanjuan Zhu ◽  
Jia Sun
Keyword(s):  
Regression Models ◽  
Near Infrared ◽  
Potassium Concentration ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Standard Normal Variate ◽  
Standard Normal ◽  
Boosting Algorithms ◽  
Near Infrared Ray

The application of visible near-infrared (VIS-NIR) analysis technology to quantify the nutrients in soil has been widely recognized. It is important to improve the performance of regression models that can predict the soil-available potassium concentration. This study collected soil samples from southern Anhui, China, and concentrated on the modelling methods by using 29 pretreatment methods. The results show that a combination of three methods, Savitzky–Golay, standard normal variate, and dislodge tendency, exhibited better stability than others because it was the most capable of achieving levels A and B of the ratio of performance of deviation. The boosting algorithms that form an ensemble of multiple weak predictors exhibited better performance than partial least square (PLS) regression and support vector regression (SVR) for the prediction of soil-available potassium. These regression models could be employed to precisely predict the soil-available potassium concentration.

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