scholarly journals Evaluation of Optimized Preprocessing and Modeling Algorithms for Prediction of Soil Properties Using VIS-NIR Spectroscopy

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6745
Author(s):  
Rebecca-Jo Vestergaard ◽  
Hiteshkumar Bhogilal Vasava ◽  
Doug Aspinall ◽  
Songchao Chen ◽  
Adam Gillespie ◽  
...  
Keyword(s):  
Soil Properties ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Fine Sand ◽  
Detailed Comparison ◽  
Coarse Sand ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Modeling Approaches

The absorbance spectra for air-dried and ground soil samples from Ontario, Canada were collected in the visible and near-infrared (VIS-NIR) region from 343 to 2200 nm. The study examined thirteen combination of six preprocessing (1st derivative, 2nd derivative, Savitzky-Golay, Gap, SNV and Detrend) method included in ‘prospectr’ R package along with four modeling approaches: partial least square regression (PLSR), cubist, random forest (RF), and extreme learning machine (ELM) for prediction of the soil organic matter (SOM). The 1st derivative + gap, 2nd derivative + gap and standard normal variance (SNV) were the best preprocessing algorithms. Thus, only these three preprocessing algorithms along with four modeling approaches were used for prediction of soil pH, electrical conductively (EC), %sand, %silt, %clay, %very coarse sand (VCS), %coarse sand (CS), %medium sand (ms) and %fine sand (fs). The results showed that OM, pH, %sand, %silt and %CS were all predicted with confidence (R2 > 0.60) and the combination of 1st derivative + gap and RF gained the best performance. A detailed comparison of the preprocessing and modeling algorithms for various soil properties in this study demonstrate that for better prediction of soil properties using VIS-NIR spectroscopy requires different preprocessing and modeling algorithms. However, in general RF and 1st derivative + gap can be labeled at the best combination of preprocessing and modelling algorithms.

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.

Determination of Fat, Protein, and Total Solids in Cheese by Near-Infrared Reflectance Spectroscopy

1995 ◽  
Vol 78 (3) ◽  
pp. 802-806 ◽  
Author(s):  
José Louis Rodriguez-Otero ◽  
Maria Hermida ◽  
Alberto Cepeda
Keyword(s):  
Near Infrared ◽  
Independent Set ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Sample Treatment ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Total Solids

Abstract Near-infrared reflectance (NIR) spectroscopy was used to analyze fat, protein, and total solids in cheese without any sample treatment. A set of 92 samples of cow’s milk cheese was used for instrument calibration by principal components analysis and modified partial least-square regression. The following statistical values were obtained: standard error of calibration (SEC) = 0.388 and squared correlation coefficient (R2) = 0.99 for fat, SEC = 0.397 and R2 = 0.98 for protein, and SEC = 0.412 and R2 = 0.99 for total solids. To validate the calibration, an independent set of 25 cheese samples of the same type was used. Standard errors of validation were 0.47,0.50, and 0.61 for fat, protein, and total solids, respectively, and hf for the regression of measurements by reference methods versus measurements by NIR spectroscopy was 0.98 for the 3 components.

scholarly journals Rapid monitoring the water extraction process of Radix Paeoniae Alba using near infrared spectroscopy

2017 ◽  
Vol 10 (03) ◽  
pp. 1750002 ◽  
Author(s):  
Tian Hu ◽  
Tongtong Li ◽  
Lei Nie ◽  
Lixuan Zang ◽  
Hengchang Zang ◽  
...  
Keyword(s):  
Gallic Acid ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Extraction Process ◽  
Partial Least Square ◽  
Least Square ◽  
Water Extraction ◽  
Partial Least Square Regression ◽  
Wide Field ◽  
Radix Paeoniae Alba

Near infrared (NIR) spectroscopy has been developed into one of the most important process analytical techniques (PAT) in a wide field of applications. The feasibility of NIR spectroscopy with partial least square regression (PLSR) to monitor the concentration of paeoniflorin, albiflorin, gallic acid, and benzoyl paeoniflorin during the water extraction process of Radix Paeoniae Alba was demonstrated and verified in this work. NIR spectra were collected in transmission mode and pretreated with smoothing and/or derivative, and then quantitative models were built up using PLSR. Interval partial least squares (iPLS) method was used for the selection of spectral variables. Determination coefficients ([Formula: see text] and [Formula: see text]), root mean squares error of prediction (RMSEP), root mean squares error of calibration (RMSEC), and residual predictive deviation (RPD) were applied to verify the performance of the models, and the corresponding values were 0.9873 and 0.9855, 0.0487[Formula: see text]mg/mL, 0.0545[Formula: see text]mg/mL and 8.4 for paeoniflorin; 0.9879, 0.9888, 0.0303[Formula: see text]mg/mL, 0.0321[Formula: see text]mg/mL and 9.1 for albiflorin; 0.9696, 0.9644, 0.0140[Formula: see text]mg/mL, 0.0145[Formula: see text]mg/mL and 5.1 for gallic acid; 0.9794, 0.9781, 0.00169[Formula: see text]mg/mL, 0.00171[Formula: see text]mg/mL and 6.9 for benzoyl paeoniflorin, respectively. The results turned out that this approach was very efficient and environmentally friendly for the quantitative monitoring of the water extraction process of Radix Paeoniae Alba.

scholarly journals Rapid Analysis of Alcohol Content During the Green Jujube Wine Fermentation by FT-NIR

2020 ◽  
Vol 145 ◽  
pp. 01037
Author(s):  
Guifeng Li ◽  
Ni Yan ◽  
Lu Yuan ◽  
Jianhu Wu ◽  
Junjie Du ◽  
...  
Keyword(s):  
Latent Variables ◽  
Near Infrared ◽  
Scatter Correction ◽  
Nir Spectroscopy ◽  
Quantitative Model ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Alcohol Content ◽  
Mean Square Errors

The near-infrared (NIR) spectroscopy combined with partial least square regression (PLS) were applied for the prediction of the alcohol content of jujube wine. The NIR spectroscopy was used to collect the spectral data of the jujube wine samples during fermentation and the data were used to establish the quantitative model of alcohol content to achieve rapid on-line detection. The NIR spectroscopy in the range of 950 to 1650 nm from jujube wine were collected and pre-treated by MSC (Multiplicative Scatter Correction) and FD (First Derivative). The alcohol content was measured with alcohol meter. Spectral wavelength selection and latent variables were optimized for the lowest root mean square errors. The results show that the FD - PLS model, which yielded R2 of 0.9246 and RMSEC of 0.6572, is superior to the MSC- PLS model. Results confirmed that NIR spectroscopy is a promising technique for routine assessment of alcohol content of jujube wine and is a viable and advantageous alternative to the chemical procedures involving laborious extractions. The feasibility of the method was thus verified.

Determination of routine chemicals, physical indices and macromolecular substances in reconstituted tobacco using near infrared spectroscopy combined with sample set partitioning

2020 ◽  
Vol 28 (3) ◽  
pp. 153-162
Author(s):  
Lijun Wu ◽  
Baoxing Wang ◽  
Lei Zhang ◽  
Rumin Duan ◽  
Rui Gao ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Set Partitioning ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Least Square Regression ◽  
Sample Set

Near infrared spectroscopy coupled with sample set partitioning based on joint X-Y distances combined with partial least square regression was applied to the quantitative analysis of six routine chemicals, five physical indices and four macromolecular substances in reconstituted tobacco. The quantitative regression models of these indices were established by joint X-Y distances combined with partial least square regression. Results showed remarkable correlation between predicted and measured values of the 15 indices. The root mean square error of prediction of all the indices was low, and the correlation coefficients of these PLS models were all greater than 0.85. This was the first study in which NIR spectroscopy had been used to determine the macromolecular substances as well as certain physical indices in reconstituted tobacco. Results showed that this method could be feasibly applied for rapid detection of these properties of industrial products.

Non-Contact Quality Monitoring of Laid Fabric Carbon/Epoxy Resin Prepreg Using near Infrared Spectroscopy

2007 ◽  
Vol 15 (5) ◽  
pp. 299-305 ◽  
Author(s):  
Bo Jiang ◽  
Yu Dong Huang ◽  
Wei Li ◽  
Li Liu
Keyword(s):  
Epoxy Resin ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Processing Parameters ◽  
Resin Content ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Volatile Content ◽  
Resin Solution

Near infrared (NIR) spectroscopy has been applied for non-contact monitoring of the quality of laid fabric carbon/epoxy resin prepreg. Partial least square regression was used to develop the models with 94 samples in the calibration set and 30 samples in the prediction set, respectively. The NIR spectroscopy method was able to predict the resin content and the volatile content with root mean square error of prediction of 0.85% and 0.22%, respectively. The developed models of resin content and volatile content were put into process control software so that during manufacture the laid fabric carbon/epoxy resin prepreg could be monitored and the processing parameters could be adjusted according to the results given by the NIR analysis. The prepreg could be analysed once within 30 s without sample destruction. The change of the concentration of epoxy resin solution and the distance of nip rollers could be used to control the resin content and an increase or decrease in the concentration of the solvent and the production speed could control the volatile content. This study indicates that NIR analysis is sufficiently accurate and effective for quality control in laid fabric carbon/epoxy resin prepreg.

scholarly journals Rapid Determination of β-Glucan Content of Hulled and Naked Oats Using near Infrared Spectroscopy Combined with Chemometrics

Foods ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 43
Author(s):  
Maninder Meenu ◽  
Yaqian Zhang ◽  
Uma Kamboj ◽  
Shifeng Zhao ◽  
Lixia Cao ◽  
...  
Keyword(s):  
Near Infrared ◽  
Rapid Determination ◽  
Wavelength Region ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Naked Oats ◽  
Non Destructive ◽  
Selection Of

The quantification of β-glucan in oats is of immense importance for plant breeders and food scientists to develop plant varieties and food products with a high quantity of β-glucan. However, the chemical analysis of β-glucan is time consuming, destructive, and laborious. In this study, near-infrared (NIR) spectroscopy in conjunction with Chemometrics was employed for rapid and non-destructive prediction of β-glucan content in oats. The interval Partial Least Square (iPLS) along with correlation matrix plots were employed to analyze the NIR spectrum from 700–1300 nm, 1300–1900 nm, and 1900–2500 nm for the selection of important wavelengths for the prediction of β-glucan. The NIR spectral data were pre-treated using Savitzky Golay smoothening and normalization before employing partial least square regression (PLSR) analysis. The PLSR models were established based on the selection of wavelengths from PLS loading plots that present a high correlation with β-glucan content. It was observed that wavelength region 700–1300 nm is sufficient for the satisfactory prediction of β-glucan of hulled and naked oats with R2c of 0.789 and 0.677, respectively, and RMSE < 0.229.

scholarly journals Concentration monitoring with near infrared chemical imaging in a tableting press

2018 ◽  
Author(s):  
Himmat Dalvi ◽  
Clémence Fauteux-Lefebvre ◽  
Jean-Maxime Guay ◽  
Nicolas Abatzoglou ◽  
Ryan Gosselin
Keyword(s):  
Near Infrared ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Chemical Imaging ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Monitoring Methods ◽  
Comparative Results ◽  
Analytical Tools

Monitoring powder potency and homogeneity is important in achieving real-time release testing in a continuous tablet manufacturing operation. If quality related issues are encountered, monitoring powder potency inside a feed frame offers a last opportunity to intervene in the process before tablet compression. Feed frame monitoring methods based on near infrared (NIR) spectroscopy have been increasingly reported in recent years. New process analytical tools with the potential of being deployed alone or in combination with NIR spectroscopy for feed frame monitoring are now available commercially. The present study evaluated the potential of near infrared chemical imaging (NIR CI) for in-line monitoring of a prototype pharmaceutical composition containing ascorbic acid (AA), microcrystalline cellulose and dicalcium phosphate. NIR spectroscopy was the reference method. In-line calibration models based on partial least square regression were developed and validated with a range of AA concentrations. The ability of NIR spectroscopy and NIR CI to predict concentrations in test runs was ascertained both independently and in combination. NIR CI, with a single bandpass filter, predicted AA concentrations—present at commercially relevant concentrations—with acceptable accuracy. Comparative results showed that NIR CI has the potential for in-line monitoring of blend concentrations inside feed frames. In addition to the advantage of increased sample size, it also has the potential to detect segregation inside feed frames.

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.

Exploring the Vis-NIR wavelength importance in SOC models under field and lab conditions in a long-term field experiment

2021 ◽  
Author(s):  
Javier Reyes ◽  
Mareike Ließ
Keyword(s):  
Field Experiment ◽  
Spectral Response ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Field Conditions ◽  
Partial Least Square Regression ◽  
Preprocessing Technique ◽  
Term Field

&lt;p&gt;Soil organic carbon (SOC) is of particular interest in the study of agricultural systems as an indicator of soil quality and soil fertility. In the use of Vis-NIR spectroscopy for SOC detection, the interpretation of the spectral response with regards to the importance of individual wavelengths is challenging due to the soil&amp;#8217;s composition of multiple organic and minerals compounds. Under field conditions, additional aspects affect the spectral data compared to lab conditions. This study compared the spectral wavelength importance in partial least square regression (PLSR) models for SOC between field and lab conditions. Surface soil samples were obtained from a long-term field experiment (LTE) with high SOC variability located in the state of Saxony-Anhalt, Germany. Data sets of Vis-NIR spectra were acquired in the lab and field using two spectrometers, respectively. Four different preprocessing methods were applied before building the models. Wavelength importance was observed using variable importance in projection. Differences in wavelength importance were observed depending on the measurement device, measurement condition, and preprocessing technique, although pattern matches were identifiable, especially in the NIR range. It is these pattern matches that aid model interpretation to effectively determine SOC under field conditions.&lt;/p&gt;

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