The Interaction between Standard Normal Variate and Derivatives

NIR news ◽  
2008 ◽  
Vol 19 (7) ◽  
pp. 16-17 ◽  
Author(s):  
Tom Fearn
Keyword(s):  
Standard Normal Variate ◽  
Standard Normal

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.

Remediation-related monitoring of heavy metal concentration of contaminated Technosol using hyperspectral measurements

2021 ◽  
Author(s):  
Friederike Kaestner ◽  
Magdalena Sut-Lohmann ◽  
Thomas Raab ◽  
Hannes Feilhauer ◽  
Sabine Chabrillat
Keyword(s):  
Heavy Metal ◽  
Random Forest ◽  
Metal Concentration ◽  
Strong Dependence ◽  
Scatter Correction ◽  
Heavy Metal Concentration ◽  
Standard Normal Variate ◽  
Multiplicative Scatter Correction ◽  
Second Derivatives ◽  
Standard Normal

<p>Across Europe there are 2.5 million potentially contaminated sites, approximately one third have already been identified and around 15% have been sanitized. Phytoremediation is a well-established technique to tackle this problem and to rehabilitate soil. However, remediation methods, such as biological treatments with microorganisms or phytoremediation with trees, are still relatively time consuming. A fast monitoring of changes in heavy metal content over time in contaminated soils with hyperspectral spectroscopy is one of the first key factors to improve and control existing bioremediation methods.</p><p>At former sewage farms near Ragow (Brandenburg, Germany), 110 soil samples with different contamination levels were taken at a depth between 15-20 cm. These samples were prepared for hyperspectral measurements using the HySpex system under laboratory conditions, combing a VNIR (400-1000 nm) and a SWIR (1000-2500 nm) line-scan detector. Different spectral pre-processing methods, including continuum removal, first and second derivatives, standard normal variate, normalisation and multiplicative scatter correction, with two established estimation models such as Partial Least Squares Regression (PLSR) and Random Forest Regression (RFR), were applied to predict the heavy metal concentration (Ba, Ni, Cr, Cu) of this specific Technosol. The coefficient of determination (R2) shows for Ba and Ni values between 0.50 (RMSE: 9%) and 0.61 (RMSE: 6%) for the PLSR and between 0.84 (RMSE: 0.03%) and 0.91 (RMSE: 0.02%) for the RFR model. The results for Cu and Cr show values between 0.57 (RMSE: 17.9%) and 0.69 (RMSE: 15%) for the PLSR and 0.86 (0.12%) and 0.93 (0.01%) for the RFR model. The pre-processing method, which improve the robustness and performance of both models best, is multiplicative scatter correction followed by the standard normal variate for the first and second derivatives. Random Forest in a first approach seems to deliver better modeling performances. Still, the pronounced differences between PLSR and RFR fits indicate a strong dependence of the results on the respective modelling technique. This effect is subject to further investigation and will be addressed in the upcoming analysis steps.</p>

scholarly journals PREDIKSI TOTAL PADATAN TERLARUT BUAH MELON GOLDEN (Cucumis melo L.) MENGGUNAKAN VIS-SWNIRS DAN ANALISIS MULTIVARIAT

2020 ◽  
Vol 25 (2) ◽  
Author(s):  
Yuda Hadiwijaya ◽  
Kusumiyati Kusumiyati ◽  
Agus Arip Munawar
Keyword(s):  
Cucumis Melo ◽  
Partial Least Squares Regression ◽  
Scatter Correction ◽  
Least Squares Regression ◽  
Orthogonal Signal ◽  
Standard Normal Variate ◽  
Orthogonal Signal Correction ◽  
Signal Correction ◽  
Standard Normal ◽  
Cucumis Melo L

Penelitian ini bertujuan memprediksi total padatan terlarut buah melon golden menggunakan Vis-SWNIRS dan analisis multivariat. 82 sampel buah melon golden dipanen untuk dianalisis di Laboratorium Hortikultura, Fakultas Pertanian, Universitas Padjadjaran. Nirvana AG410 spectrometer dengan rentang panjang gelombang 300 sampai 1050 nm digunakan untuk pengambilan data spektra pada sampel buah melon utuh. Metode koreksi spektra yang digunakan yaitu standard normal variate (SNV), multiplicative scatter correction (MSC), dan orthogonal signal correction (OSC). Pemodelan kalibrasi dilakukan menggunakan partial least squares regression (PLSR). Hasil penelitian menunjukkan bahwa penggunaan metode koreksi spektra OSC menampikan model kalibrasi terbaik dibandingkan spektra original dan 2 spektra lainnya yang telah dikoreksi. Koefisien determinasi pada spektra OSC memperlihatkan nilai R2 tertinggi yaitu 0.99, disamping itu nilai ratio performance to deviation (RPD) yang diperoleh sebesar 3.40. Hal ini membuktikan bahwa total padatan terlarut buah melon golden dapat diprediksi dengan akurasi yang tinggi menggunakan Vis-SWNIRS dan analisis multivariat.

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.

scholarly journals Hyperspectral Imaging to Assess the Presence of Powdery Mildew (Erysiphe necator) in cv. Carignan Noir Grapevine Bunches

Agronomy ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 88 ◽  
Author(s):  
Claudia Pérez-Roncal ◽  
Ainara López-Maestresalas ◽  
Carlos Lopez-Molina ◽  
Carmen Jarén ◽  
Jorge Urrestarazu ◽  
...  
Keyword(s):  
Powdery Mildew ◽  
Hyperspectral Imaging ◽  
External Validation ◽  
Spatial Location ◽  
Image Features ◽  
Range Image ◽  
Spatial Image ◽  
Standard Normal Variate ◽  
Mean Centering ◽  
Standard Normal

Powdery mildew is a worldwide major fungal disease for grapevine, which adversely affects both crop yield and produce quality. Disease identification is based on visible signs of a pathogen once the plant has already been infected; therefore, techniques that allow objective diagnosis of the disease are currently needed. In this study, the potential of hyperspectral imaging (HSI) technology to assess the presence of powdery mildew in grapevine bunches was evaluated. Thirty Carignan Noir grape bunches, 15 healthy and 15 infected, were analyzed using a lab-scale HSI system (900–1700 nm spectral range). Image processing was performed to extract spectral and spatial image features and then, classification models by means of Partial Least Squares Discriminant Analysis (PLS-DA) were carried out for healthy and infected pixels distinction within grape bunches. The best discrimination was achieved for the PLS-DA model with smoothing (SM), Standard Normal Variate (SNV) and mean centering (MC) pre-processing combination, reaching an accuracy of 85.33% in the cross-validation model and a satisfactory classification and spatial location of either healthy or infected pixels in the external validation. The obtained results suggested that HSI technology combined with chemometrics could be used for the detection of powdery mildew in black grapevine bunches.

scholarly journals Low-Content Quantitation in Entecavir Tablets Using 1064 nm Raman Spectroscopy

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yanlei Kang ◽  
Yushan Zhou ◽  
Qiaoyu Wu ◽  
Ning Wang ◽  
Jianguang Zhou
Keyword(s):  
Raman Spectroscopy ◽  
Limit Of Detection ◽  
Pharmaceutical Formulations ◽  
Active Pharmaceutical Ingredient ◽  
Difficult Problem ◽  
Excitation Wavelength ◽  
Standard Normal Variate ◽  
First Choice ◽  
Standard Normal ◽  
Calibration Samples

The nondestructive and high sensitive analysis of a low content of an active pharmaceutical ingredient (API) was a difficult problem, especially in a complex system of pharmaceutical formulations. In this paper, a rapid and no sample preparation method was developed, which used a 1064 nm Raman spectrometer to detect entecavir monohydrate (ETV-H) in Baraclude tablets. Entecavir was a drug approved by FDA for the treatment of chronic hepatitis B and became the first choice in the market. The wavelength selection results displayed that the signal-to-background ratio of the Raman spectrum with 1064 nm excitation wavelength was 14 times that of the commonly used 785 nm wavelength. The partial least squares (PLS) method was used to calibrate concentration models containing 0.1% to 1.0% w/w% ETV-H in calibration set samples. Different preprocessing methods were used to eliminate the background interference and extract more spectral information. Calibration samples were used to choose the best performing model. Then, all the calibration samples combined with the best performing models’ parameters successfully predicted the content of ETV-H in Baraclude tablets. Combining baseline processing and standard normal variate (SNV) with PLS, the model showed a good result with an R2 of 0.973, RMSEC of 0.05%, and RMSEP of 0.03% on the spectral region of 1350–1700 cm−1. The limit of detection of this model was 0.17%. These results showed that 1064 nm Raman spectroscopy technology could be an alternative analytical procedure to quantify low-content API in intact tablets.

scholarly journals Determination of Total Flavonoids for Paris Polyphylla Var. Yunnanensis in Different Geographical Origins Using UV and FT-IR Spectroscopy

2019 ◽  
Vol 102 (2) ◽  
pp. 457-464
Author(s):  
Yuangui Yang ◽  
Yanli Zhao ◽  
Zhitian Zuo ◽  
Yuanzhong Wang
Keyword(s):  
Uv Spectrophotometry ◽  
Total Flavonoids ◽  
Least Squares Regression ◽  
Standard Normal Variate ◽  
Paris Polyphylla ◽  
Orthogonal Signal Correction ◽  
Relevant Variables ◽  
Standard Normal ◽  
Ft Ir

Abstract Background: Paris polyphylla var. Yunnanensis (PPY) is used in the clinical treatment of tumors, hemorrhages, and anthelmintic. Objective: The aim of this study is to determine total flavonoids of PPY in the Yunnan and Guizhou Provinces, China. Methods: In this study, total flavonoids were determined by UV spectrophotometry at first. Then, Fourier transform mid-infrared (FT-IR) based on various pretreatments include standard normal variate (SNV), first derivative (FD), second derivative (SD), Savitzky-Golay (SG), and orthogonal signal correction (OSC) were investigated. In addition, several relevant variables were screened by competitive adaptive reweighted sampling (CARS). The contentof total flavonoids and selected variables of FT-IRwere used to establish a partial least squares regression for PPY in different regions. Results: The results indicated that CARS was an effective method for decreasing the variable of thedatabase and improving the prediction of the model.FT-IR with pretreatment SNV + OSC + FD + SG had thebest performance, with R2 > 0.9 and residual predictive deviation = 3.3515, which could be used forthe predictive model of total flavonoids. Conclusions: Those results would provide a fast and robust strategy for the determination of total flavonoids of PPY in different geographical origin. Highlights: Various pretreatments, including SNV, FD, SD, SG, and OSC, were compared; several relevant variables were selected by CARS; and the content of total flavonoids and selected variable were used to establish a partial leastsquares regression for PPY in different regions.

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.

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