Tolerance of near Infrared Calibrations to Temperature Variations; A Practical Evaluation

2000 ◽  
Vol 8 (2) ◽  
pp. 125-132 ◽  
Author(s):  
W.G. Hansen ◽  
S.C.C. Wiedemann ◽  
M. Snieder ◽  
V.A.L. Wortel
Keyword(s):  
Near Infrared ◽  
Model Performance ◽  
Partial Least Square ◽  
Least Square ◽  
Temperature Variations ◽  
Precise Control ◽  
Practical Evaluation ◽  
Temperature Variable ◽  
Value Determination ◽  
Model Robustness

Process samples of esters have been analysed by transmittance near infrared (NIR) at temperatures between 60 and 70°C (±0.2). Apart from density changes, these small temperature variations affect molecular associations by H-bonding. Partial Least Square (PLS) models based on the first OH overtone (1350–1500nm) have been made for hydroxyl value determination, including implicitly the temperature variable. The sensitivity of these NIR calibrations to temperature has been evaluated by an analysis of variance and the “Taguchi principle”, using both average model performance and model variance. An accurate and precise control of the sample temperature prior to scanning leads to the lowest prediction error. When temperature fluctuations can not be avoided, introduction of temperature variance in the calibration set can improve the model robustness; this strategy is only beneficial if temperature range, temperature distribution and number of PLS factors have been carefully optimised.

scholarly journals Aplikasi Near Infrared Spectroscopy (NIRS) Untuk Mendeteksi Pencemaran Tanah

2019 ◽  
Vol 4 (2) ◽  
pp. 397-406
Author(s):  
Puji Meihani ◽  
Agus Arip Munawar ◽  
Devianti Devianti
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Model Performance ◽  
Correction Method ◽  
Partial Least Square ◽  
Least Square ◽  
Standard Normal Variate ◽  
Standard Normal ◽  
Spectrum Correction

Abstrak. Penelitian ini bertujuan untuk mendeteksi pencemaran tanah (zat Pb, Zn dan Cu) dengan menggunakan NIRS. Metode yang dilakukan ialah skala laboratorium dan hasil uji menggunakan NIRS. Pada pengujian menggunakan NIRS, metode koreksi spektrum yang digunakan ialah Standard Normal Variate (SNV) dan De-Trending (DT) sedangkan dalam membangun model prediksi, metode regresi yang digunakan yakni Partial Least Square (PLS). Keakuratan model prediksi dilihat berdasarkan parameter statistika seperti r, R2, RMSEC dan RPD. Hasil yang didapatkan pada pengujian menggunakan NIRS pada prediksi data mentah untuk ketiga parameter (Pb, Zn dan Cu) didapatkan nilai RPD masing-masing 2.69, 2.69, dan 2.68. Nilai tersebut termasuk ke dalam kategori good model performance. Untuk meningkatkan nilai RPD, dilakukan prediksi setelah dikoreksi menggunakan SNV. Nilai RPD yang didapatkan pada masing-masing parameter (Pb, Zn dan Cu) adalah 5.21, 4.56, dan 4.78. Nilai-nilai prediksi tersebut masuk ke dalam kategori very good performance. Sedangkan nilai RPD untuk prediksi menggunakan SNV untuk ketiga parameter (Pb, Zn dan Cu) masing-masing 4.31, 4.39 dan 4.08 yang dikategorikan sebagai very good performance. Berdasarkan nilai RPD yang didapatkan dari ketiga prediksi, prediksi dengan menggunakan SNV yang paling baik karena memiliki nilai RPD yang paling tinggi.The Application of Near Infrared Spectroscopy (NIRS) to Soil Contamination DetectionAbstract. This study aims to soil pollution detection (Pb, Zn and Cu substances) by using NIRS. The method used are the laboratory scale and using NIRS. In using NIRS method, the spectrum correction method used is Standard Normal Variate (SNV) and De-Trending (DT). Prediction model using Partial Least Square (PLS). The accuracy of the prediction model is based on the statistical parameters such as r, R2, RMSEC and RPD. The results based on the NIRS method obtained the values of RPD are 2.69, 2.69, and 2.68 in prediction of raw data for parameters (Pb, Zn and Cu). These values belong to good model performance category. To increase the RPD score, prediction were made by using SNV spectrum correction method. RPD values obtained in each parameter (Pb, Zn and Cu) were 5.21, 4.56, and 4.78. These predictive values can be categorized as very good performance. The values of RPD for prediction used DT for the three parameters (Pb, Zn and Cu) 4.31, 4.39 and 4.08 which are categorized as very good performance. Based on RPD values obtained from the three predictions, predictions using SNV are the best because it has the highest RPD value.

scholarly journals Modeling and Prediction of Soil Organic Matter Content Based on Visible-Near-Infrared Spectroscopy

Forests ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1809
Author(s):  
Chunxu Li ◽  
Jinghan Zhao ◽  
Yaoxiang Li ◽  
Yongbin Meng ◽  
Zheyu Zhang
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Near Infrared ◽  
Scatter Correction ◽  
Organic Matter Content ◽  
Model Performance ◽  
Matter Content ◽  
Partial Least Square ◽  
Least Square ◽  
Successive Projections Algorithm

In order to explore the ever-changing law of soil organic matter (SOM) content in the forest of the Greater Khingan Mountains, a prediction model of the SOM content with a high accuracy and stability has been developed based on visible near-infrared (VIS-NIR) technology and multiple regression analysis. A total of 105 soil samples were collected from Cuifeng forest farm in Jagdaqi City, Greater Khingan Mountains region, Heilongjiang Province, China. Five classical preprocessing algorithms, including Savitzky−Golay convolution smoothing (S-G smoothing), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), first derivative, second derivative, and the combinations of the above five methods were applied to the raw spectra. Wavelengths were optimized with five methods of competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), uninformative variable elimination (UVE), synergy interval partial least square (SiPLS), and their combinations, and PLS models were developed accordingly. The results showed that when S-G smoothing is combined with SNV or MSC, both preprocessing strategies can improve the performance of the model. The prediction accuracy of SiPLS-PLS model and SiPLS-UVE-PLS model for the SOM content is higher than for other models, withan Rc2 of 0.9663 and 0.9221, RMSEC of 0.0645 and 0.0981, Rv2 of 0.9408 and 0.9270, and RMSEV of 0.0615 and 0.0683, respectively. The pretreatment strategies and characteristic variable selection methods used in this study could significantly improve the model performance and predicting efficiency.

Rapid Determining Contents of the Rhubarb Anthraquinones Compounds by Support Vector Machines Modeling based on Near Infrared Spectra

2020 ◽  
Vol 16 ◽  
Author(s):  
Linqi Liu ◽  
JInhua Luo ◽  
Chenxi Zhao ◽  
Bingxue Zhang ◽  
Wei Fan ◽  
...  
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Rapid Determination ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Near Infrared Spectra ◽  
Aloe Emodin ◽  
Relative Differences

BACKGROUND: Measuring medicinal compounds to evaluate their quality and efficacy has been recognized as a useful approach in treatment. Rhubarb anthraquinones compounds (mainly including aloe-emodin, rhein, emodin, chrysophanol and physcion) are its main effective components as purgating drug. In the current Chinese Pharmacopoeia, the total anthraquinones content is designated as its quantitative quality and control index while the content of each compound has not been specified. METHODS: On the basis of forty rhubarb samples, the correlation models between the near infrared spectra and UPLC analysis data were constructed using support vector machine (SVM) and partial least square (PLS) methods according to Kennard and Stone algorithm for dividing the calibration/prediction datasets. Good models mean they have high correlation coefficients (R2) and low root mean squared error of prediction (RMSEP) values. RESULTS: The models constructed by SVM have much better performance than those by PLS methods. The SVM models have high R2 of 0.8951, 0.9738, 0.9849, 0.9779, 0.9411 and 0.9862 that correspond to aloe-emodin, rhein, emodin, chrysophanol, physcion and total anthraquinones contents, respectively. The corresponding RMSEPs are 0.3592, 0.4182, 0.4508, 0.7121, 0.8365 and 1.7910, respectively. 75% of the predicted results have relative differences being lower than 10%. As for rhein and total anthraquinones, all of the predicted results have relative differences being lower than 10%. CONCLUSION: The nonlinear models constructed by SVM showed good performances with predicted values close to the experimental values. This can perform the rapid determination of the main medicinal ingredients in rhubarb medicinal materials.

scholarly journals Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 885
Author(s):  
Sergio Ghidini ◽  
Luca Maria Chiesa ◽  
Sara Panseri ◽  
Maria Olga Varrà ◽  
Adriana Ianieri ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Tuna Fish ◽  
Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

scholarly journals The heritable landscape of near-infrared and Raman spectroscopic measurements to improve lipid content in Atlantic salmon fillets

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Gareth F. Difford ◽  
Siri S. Horn ◽  
Katinka R. Dankel ◽  
Bente Ruyter ◽  
Binyam S. Dagnachew ◽  
...  
Keyword(s):  
Atlantic Salmon ◽  
Lipid Content ◽  
Production Efficiency ◽  
Near Infrared ◽  
Reference Method ◽  
Partial Least Square ◽  
Least Square ◽  
Genetic Management ◽  
Genetic Progress ◽  
Breeding Programmes

Abstract Background Product quality and production efficiency of Atlantic salmon are, to a large extent, influenced by the deposition and depletion of lipid reserves. Fillet lipid content is a heritable trait and is unfavourably correlated with growth, thus genetic management of fillet lipid content is needed for sustained genetic progress in these two traits. The laboratory-based reference method for recording fillet lipid content is highly accurate and precise but, at the same time, expensive, time-consuming, and destructive. Here, we test the use of rapid and cheaper vibrational spectroscopy methods, namely near-infrared (NIR) and Raman spectroscopy both as individual phenotypes and phenotypic predictors of lipid content in Atlantic salmon. Results Remarkably, 827 of the 1500 individual Raman variables (i.e. Raman shifts) of the Raman spectrum were significantly heritable (heritability (h2) ranging from 0.15 to 0.65). Similarly, 407 of the 2696 NIR spectral landscape variables (i.e. wavelengths) were significantly heritable (h2 = 0.27–0.40). Both Raman and NIR spectral landscapes had significantly heritable regions, which are also informative in spectroscopic predictions of lipid content. Partial least square predicted lipid content using Raman and NIR spectra were highly concordant and highly genetically correlated with the lipid content values ($${r}_{\text{g}}$$ r g = 0.91–0.98) obtained with the reference method using Lin’s concordance correlation coefficient (CCC = 0.63–0.90), and were significantly heritable ($${h}^{2}$$ h 2 = 0.52–0.67). Conclusions Both NIR and Raman spectral landscapes show substantial additive genetic variation and are highly genetically correlated with the reference method. These findings lay down the foundation for rapid spectroscopic measurement of lipid content in salmonid breeding programmes.

scholarly journals Theoretical Simulation of Near-Infrared Spectrum of Piperine: Insight into Band Origins and the Features of Regression Models

2021 ◽  
pp. 000370282110279
Author(s):  
Justyna Grabska ◽  
Krzysztof B. Beć ◽  
Sophia Mayr ◽  
Christian W. Huck
Keyword(s):  
Infrared Spectrum ◽  
Regression Models ◽  
Near Infrared ◽  
Computing Time ◽  
Black Pepper ◽  
Partial Least Square ◽  
Least Square ◽  
Complex Nature ◽  
Chemical Information ◽  
Near Infrared Spectrum

We investigated the near-infrared spectrum of piperine using quantum mechanical calculations. We evaluated two efficient approaches, DVPT2//PM6 and DVPT2//ONIOM [PM6:B3LYP/6-311++G(2df, 2pd)] that yielded a simulated spectrum with varying accuracy versus computing time factor. We performed vibrational assignments and unveiled complex nature of the near-infrared spectrum of piperine, resulting from a high level of band convolution. The most meaningful contribution to the near-infrared absorption of piperine results from binary combination bands. With the available detailed near-infrared assignment of piperine, we interpreted the properties of partial least square regression models constructed in our earlier study to describe the piperine content in black pepper samples. Two models were compared with spectral data sets obtained with a benchtop and a miniaturized spectrometer. The two spectrometers implement distinct technology which leads to a profound instrumental difference and discrepancy in the predictive performance when analyzing piperine content. We concluded that the sensitivity of the two instruments to certain types of piperine vibrations is different and that the benchtop spectrometer unveiled higher selectivity. Such difference in obtaining chemical information from a sample can be one of the reasons why the benchtop spectrometer performs better in analyzing the piperine content of black pepper. This evidenced direct correspondence between the features critical for applied near-infrared spectroscopic routine and the underlying vibrational properties of the analyzed constituent in a complex sample.

scholarly journals A fast and low-cost approach to quality control of alcohol-based hand sanitizer using portable near infrared spectrometer and chemometrics

2021 ◽  
pp. 096703352098731
Author(s):  
Adenilton C da Silva ◽  
Lívia PD Ribeiro ◽  
Ruth MB Vidal ◽  
Wladiana O Matos ◽  
Gisele S Lopes
Keyword(s):  
Quality Control ◽  
Discriminant Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Chemical Components ◽  
Linear Discriminant ◽  
Hand Sanitizer

The use of alcohol-based hand sanitizers is recommended as one of several strategies to minimize contamination and spread of the COVID-19 disease. Current reports suggest that the virucidal potential of ethanol occurs at concentrations close to 70%. Traditional methods of verifying the ethanol concentration in such products invite potential errors due to the viscosity of chemical components or may be prohibitively expensive to undertake in large demand. Near infrared (NIR) spectroscopy and chemometrics have already been used for the determination of ethanol in other matrices and present an alternative fast and reliable approach to quality control of alcohol-based hand sanitizers. In this study, a portable NIR spectrometer combined with classification chemometric tools, i.e., partial least square discriminant analysis (PLS–DA) and linear discriminant analysis with successive algorithm projection (SPA–LDA) were used to construct models to identify conforming and non-conforming commercial and laboratory synthesized hand sanitizer samples. Principal component analysis (PCA) was applied in an exploratory data study. Three principal components accounted for 99% of data variance and demonstrate clustering of conforming and non-conforming samples. The PLS–DA and SPA–LDA classification models presented 77 and 100% of accuracy in cross/internal validation respectively and 100% of accuracy in the classification of test samples. A total of 43% commercial samples evaluated using the PLS–DA and SPA–LDA presented ethanol content non-conforming for hand sanitizer gel. These results indicate that use of NIR spectroscopy and chemometrics is a promising strategy, yielding a method that is fast, portable, and reliable for discrimination of alcohol-based hand sanitizers with respect to conforming and non-conforming ethanol concentrations.

Is the introduction of benthic species necessary for open-water chemical reconstruction in diatom-based transfer functions?

2002 ◽  
Vol 59 (6) ◽  
pp. 938-951 ◽  
Author(s):  
Aline Philibert ◽  
Yves T Prairie
Keyword(s):  
Transfer Functions ◽  
Model Performance ◽  
Open Water ◽  
Partial Least Square ◽  
Least Square ◽  
Weighted Averaging ◽  
Benthic Species ◽  
Inference Models ◽  
Number Of Species ◽  
Chemical Conditions

Despite the overwhelming tendency in paleolimnology to use both planktonic and benthic diatoms when inferring open-water chemical conditions, it remains questionable whether all taxa are appropriate and necessary to construct useful inference models. We examined this question using a 75-lake training set from Quebec (Canada) to assess whether model performance is affected by the deletion of benthic species. Because benthic species are known to experience very different chemical conditions than their planktonic counterparts, we hypothesized that they would introduce undesirable noise in the calibration. Surprisingly, such important variables as pH, total phosphorus (TP), total nitrogen (TN), and dissolved organic carbon (DOC) were well predicted from weighted-averaging partial least square (WA-PLS) models based solely on benthic species. Similar results were obtained regardless of the depth of the lakes. Although the effective number of occurrence (N2) and the tolerance of species influenced the stability of the model residual error (jackknife), the number of species was the major factor responsible for the weaker inference models when based on planktonic diatoms alone. Indeed, when controlled for the number of species in WA-PLS models, individual planktonic diatom species showed superior predictive power over individual benthic species in inferring open-water chemical conditions.

scholarly journals Comparison of Hyperspectral Imaging and Near-Infrared Spectroscopy to Determine Nitrogen and Carbon Concentrations in Wheat

2021 ◽  
Vol 13 (6) ◽  
pp. 1128
Author(s):  
Iman Tahmasbian ◽  
Natalie K Morgan ◽  
Shahla Hosseini Bai ◽  
Mark W Dunlop ◽  
Amy F Moss
Keyword(s):  
Infrared Spectroscopy ◽  
Hyperspectral Imaging ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Economic Benefits ◽  
Partial Least Square ◽  
Least Square ◽  
Statistical Population ◽  
Processing Plants ◽  
C And N

Hyperspectral imaging (HSI) is an emerging rapid and non-destructive technology that has promising application within feed mills and processing plants in poultry and other intensive animal industries. HSI may be advantageous over near infrared spectroscopy (NIRS) as it scans entire samples, which enables compositional gradients and sample heterogenicity to be visualised and analysed. This study was a preliminary investigation to compare the performance of HSI with that of NIRS for quality measurements of ground samples of Australian wheat and to identify the most important spectral regions for predicting carbon (C) and nitrogen (N) concentrations. In total, 69 samples were scanned using an NIRS (400–2500 nm), and two HSI cameras operated in 400–1000 nm (VNIR) and 1000–2500 nm (SWIR) spectral regions. Partial least square regression (PLSR) models were used to correlate C and N concentrations of 63 calibration samples with their spectral reflectance, with 6 additional samples used for testing the models. The accuracy of the HSI predictions (full spectra) were similar or slightly higher than those of NIRS (NIRS Rc2 for C = 0.90 and N = 0.96 vs. HSI Rc2 for C (VNIR) = 0.97 and N (SWIR) = 0.97). The most important spectral region for C prediction identified using HSI reflectance was 400–550 nm with R2 of 0.93 and RMSE of 0.17% in the calibration set and R2 of 0.86, RMSE of 0.21% and ratio of performance to deviation (RPD) of 2.03 in the test set. The most important spectral regions for predicting N concentrations in the feed samples included 1451–1600 nm, 1901–2050 nm and 2051–2200 nm, providing prediction with R2 ranging from 0.91 to 0.93, RMSE ranging from 0.06% to 0.07% in the calibration sets, R2 from 0.96 to 0.99, RMSE of 0.06% and RPD from 3.47 to 3.92 in the test sets. The prediction accuracy of HSI and NIRS were comparable possibly due to the larger statistical population (larger number of pixels) that HSI provided, despite the fact that HSI had smaller spectral range compared with that of NIRS. In addition, HSI enabled visualising the variability of C and N in the samples. Therefore, HSI is advantageous compared to NIRS as it is a multifunctional tool that poses many potential applications in data collection and quality assurance within feed mills and poultry processing plants. The ability to more accurately measure and visualise the properties of feed ingredients has potential economic benefits and therefore additional investigation and development of HSI in this application is warranted.

scholarly journals Discrimination and Content Analysis of Fritillaria Using Near Infrared Spectroscopy

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yu Meng ◽  
Shisheng Wang ◽  
Rui Cai ◽  
Bohai Jiang ◽  
Weijie Zhao
Keyword(s):  
Infrared Spectroscopy ◽  
Quantitative Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Traditional Chinese Herbal Medicine ◽  
Total Alkaloids ◽  
Rapid Discrimination ◽  
Different Origins

Fritillaria is a traditional Chinese herbal medicine which can be used to moisten the lungs. The objective of this study is to develop simple, accurate, and solvent-free methods to discriminate and quantify Fritillaria herbs from seven different origins. Near infrared spectroscopy (NIRS) methods are established for the rapid discrimination of seven different Fritillaria samples and quantitative analysis of their total alkaloids. The scaling to first range method and the partial least square (PLS) method are used for the establishment of qualitative and quantitative analysis models. As a result of evaluation for the qualitative NIR model, the selectivity values between groups are always above 2, and the mistaken judgment rate of fifteen samples in prediction sets was zero. This means that the NIR model can be used to distinguish different species of Fritillaria herbs. The established quantitative NIR model can accurately predict the content of total alkaloids from Fritillaria samples.

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