scholarly journals A Near Standard Soil Samples Spectra Enhanced Modeling Strategy for Cd Concentration Prediction

2021 ◽  
Vol 13 (14) ◽  
pp. 2657
Author(s):  
Yulong Tu ◽  
Bin Zou ◽  
Huihui Feng ◽  
Mo Zhou ◽  
Zhihui Yang ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Spectral Response ◽  
Soil Samples ◽  
Coefficient Of Determination ◽  
Response Characteristics ◽  
Spectral Bands ◽  
Standard Soil ◽  
Modeling Strategy ◽  
Concentration Prediction

Visible and near-infrared (VNIR) spectroscopy technology for soil heavy metal (HM) concentration prediction has been widely studied. However, its spectral response characteristics are still uncertain. In this study, a near standard soil Cd samples (NSSCd) spectra enhanced modeling strategy was developed in order to to reveal the soil cadmium (Cd) spectral response characteristics and predict its concentration. NSSCd were produced by adding the quantitative Cd solution into background soil. Then, prior spectral bands (i.e., the bands with higher variable importance in projection (VIP) score in NSSCd spectra) were used for predicting Cd concentration in soil samples collected from the Hengyang mining area and Baoding agriculture area. The partial least squares (PLS) and competitive adaptive reweighted sampling-partial least squares (CARS-PLS) were used for validation. Compared to using entire VNIR spectral ranges, the new modeling strategy performed very well, with the coefficient of determination (R2) and the ratio of prediction to deviation (RPD) showing an improvement from 0.63 and 1.72 to 0.71 and 1.95 in Hengyang and from 0.54 and 1.57 to 0.76 and 2.19 in Baoding. These results suggest that NSS prior spectral bands are critical for soil HM prediction. Our results represent an exciting finding for the future design of remote sensing sensors for soil HM detection.

scholarly journals Rapid tannin profiling of tree fodders using untargeted mid-infrared spectroscopy and partial least squares regression

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Jordi Ortuño ◽  
Sokratis Stergiadis ◽  
Anastasios Koidis ◽  
Jo Smith ◽  
Chris Humphrey ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Multivariate Calibration ◽  
Plant Ecology ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy

Abstract Background The presence of condensed tannins (CT) in tree fodders entails a series of productive, health and ecological benefits for ruminant nutrition. Current wet analytical methods employed for full CT characterisation are time and resource-consuming, thus limiting its applicability for silvopastoral systems. The development of quick, safe and robust analytical techniques to monitor CT’s full profile is crucial to suitably understand CT variability and biological activity, which would help to develop efficient evidence-based decision-making to maximise CT-derived benefits. The present study investigates the suitability of Fourier-transformed mid-infrared spectroscopy (MIR: 4000–550 cm−1) combined with multivariate analysis to determine CT concentration and structure (mean degree of polymerization—mDP, procyanidins:prodelphidins ratio—PC:PD and cis:trans ratio) in oak, field maple and goat willow foliage, using HCl:Butanol:Acetone:Iron (HBAI) and thiolysis-HPLC as reference methods. Results The MIR spectra obtained were explored firstly using Principal Component Analysis, whereas multivariate calibration models were developed based on partial least-squares regression. MIR showed an excellent prediction capacity for the determination of PC:PD [coefficient of determination for prediction (R2P) = 0.96; ratio of prediction to deviation (RPD) = 5.26, range error ratio (RER) = 14.1] and cis:trans ratio (R2P = 0.95; RPD = 4.24; RER = 13.3); modest for CT quantification (HBAI: R2P = 0.92; RPD = 3.71; RER = 13.1; Thiolysis: R2P = 0.88; RPD = 2.80; RER = 11.5); and weak for mDP (R2P = 0.66; RPD = 1.86; RER = 7.16). Conclusions MIR combined with chemometrics allowed to characterize the full CT profile of tree foliage rapidly, which would help to assess better plant ecology variability and to improve the nutritional management of ruminant livestock.

scholarly journals Prediction of Grape Sap Flow in a Greenhouse Based on Random Forest and Partial Least Squares Models

Water ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 3078
Author(s):  
Xuelian Peng ◽  
Xiaotao Hu ◽  
Dianyu Chen ◽  
Zhenjiang Zhou ◽  
Yinyin Guo ◽  
...  
Keyword(s):  
Random Forest ◽  
Least Squares ◽  
Partial Least Squares ◽  
Flow Rate ◽  
Sap Flow ◽  
Deficit Irrigation ◽  
Coefficient Of Determination ◽  
Growth Stages ◽  
Irrigation Amount ◽  
Flow Rates

Understanding variations in sap flow rates and the environmental factors that influence sap flow is important for exploring grape water consumption patterns and developing reasonable greenhouse irrigation schedules. Three irrigation levels were established in this study: adequate irrigation (W1), moderate deficit irrigation (W2) and deficit irrigation (W3). Grape sap flow estimation models were constructed using partial least squares (PLS) and random forest (RF) algorithms, and the simulation accuracy and stability of these models were evaluated. The results showed that the daily mean sap flow rates in the W2 and W3 treatments were 14.65 and 46.94% lower, respectively, than those in the W1 treatment, indicating that the average daily sap flow rate increased gradually with an increase in the irrigation amount within a certain range. Based on model error and uncertainty analyses, the RF model had better simulation results in the different grape growth stages than the PLS model did. The coefficient of determination and Willmott’s index of agreement for RF model exceeded 0.78 and 0.90, respectively, and this model had smaller root mean square error and d-factor (evaluation index of model uncertainty) values than the PLS model did, indicating that the RF model had higher prediction accuracy and was more stable. The relative importance of the model predictors was determined. Moreover, the RF model more comprehensively reflected the influence of meteorological factors and the moisture content in different soil layers on the sap flow rate than the PLS model did. In summary, the RF model accurately simulated sap flow rates, which is important for greenhouse grape irrigation.

Partial least squares regression (PLSR) associated with spectral response to predict soil attributes in transitional lithologies

2017 ◽  
Vol 64 (5) ◽  
pp. 682-695 ◽  
Author(s):  
Marcos Rafael Nanni ◽  
Everson Cezar ◽  
Carlos Antonio da Silva Junior ◽  
Guilherme Fernando Capristo Silva ◽  
Anderson Antonio da Silva Gualberto
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Spectral Response ◽  
Least Squares Regression ◽  
Soil Attributes

Developing near infrared spectroscopy models for predicting chemistry and responses to stress in Pinus radiata (D. Don)

2021 ◽  
pp. 096703352110065
Author(s):  
Judith S Nantongo ◽  
BM Potts ◽  
T Rodemann ◽  
H Fitzgerald ◽  
NW Davies ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Pinus Radiata ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Individual Plant ◽  
Least Squares Regression ◽  
Plant Parts ◽  
Quantitative Genetic

Incorporating chemical traits in breeding requires the estimation of quantitative genetic parameters, especially the levels of additive genetic variation. This requires large numbers of samples from pedigreed populations. Conventional wet chemistry procedures for chemotyping are slow, expensive and not a practical option. This study focuses on the chemical variation in Pinus radiata, where the near infrared (NIR) spectral properties of the needles, bark and roots before and after exposure to methyl jasmonate (MJ) and artificial bark stripping (strip) treatments were investigated as an alternative approach. The aim was to test the capability of NIR spectroscopy to (i) discriminate samples exposed to MJ and strip assessed 7, 14, 21 and 28 days after treatment from untreated samples, and (ii) quantitatively predict individual chemical compounds in the three plant parts. Using principal components analysis (PCA) on the spectral data, we differentiated between treated and untreated samples for the individual plant parts. Based on partial least squares–discriminant analysis (PLS-DA) models, the best discrimination of treated from non-treated samples with the smallest root mean square error cross-validation (RMSECV) and highest coefficient of determination (r2) was achieved in the fresh needles (r2 = 0.81, RMSECV= 0.24) and fresh inner bark (r2 = 0.79, RMSECV = 0.25) for MJ-treated samples 14 days and 21 days after treatment, respectively. Using partial least squares regression, models for individual compounds gave high (r2), residual predictive deviation (RPD), lab to NIR error (PRL) or range error ratio (RER) for fructose (r2 = 0.84, RPD = 1.5, PRL = 0.71, RER = 7.25) and glucose (r2 = 0.83, RPD = 1.9, PRL = 1.14, RER = 8.50) and several diterpenoids. This provides an optimistic outlook for the use of NIR spectroscopy-based models for the larger-scale prediction of the P. radiata chemistry needed for quantitative genetic studies.

Oil Content Estimation of Individual Kernels of Quercus Ilex Subsp. Rotundifolia [(Lam) O. Schwarz] Acorns by Fourier Transform near Infrared Spectroscopy and Partial Least Squares Regression

2007 ◽  
Vol 15 (4) ◽  
pp. 247-260 ◽  
Author(s):  
Cristina Sousa-Correia ◽  
Ana Alves ◽  
José C. Rodrigues ◽  
Suzana Ferreira-Dias ◽  
José M. Abreu ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Least Squares ◽  
Partial Least Squares ◽  
Oil Content ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Association Studies ◽  
Extraction Procedure ◽  
Coefficient Of Determination ◽  
Least Squares Regression

The aim of this work was to use Fourier transform near infrared (FT-NIR) spectroscopy and partial least squares regression (PLSR) to estimate the oil content of individual Holm oak ( Quercus sp.) acorn kernels from different trees, sites and years that should be used in the future for molecular marker association studies. Sampling of acorns in two consecutive years (2003 and 2004) and from different sites in Portugal provided independent sample sets. A total of 89 samples (acorn kernels) representative of the natural oil content range were extracted. The results of the analyses performed by three people revealed accuracy of the oil extraction procedure ( n-hexane) and the precision (repeatability) of this method, assessed during a four-day period, gave a standard deviation of 0.1%. Careful wavenumber selection and several steps of validation of the PLSR models led to a final robust model that allowed the precise prediction of the oil content of individual acorns. By using the wavenumber ranges from 5995 to 5323 cm−1 and from 4478 to 4177 cm−1 of the vector normalised spectra, a PLSR model with a coefficient of determination ( r 2) of 0.992 and a root mean square error of cross-validation ( RMSECV) of 0.37% was achieved. The RPD value of about 10 and a bias of almost zero showed that the developed models are good for process control, development, and applied research. Oil content estimation of individual Quercus sp. acorns by FT-NIR and PLSR was shown to be possible. The varying water content detected in the spectra of the milled kernels after drying in similar conditions, within and especially between years, could be handled.

Rapid detection of infrared inactive sodium chloride content in frozen tuna fish for determining commercial value using short wavelengths

2019 ◽  
Vol 27 (6) ◽  
pp. 424-431 ◽  
Author(s):  
Suttahatai Pochanagone ◽  
Ronnarit Rittiron
Keyword(s):  
Sodium Chloride ◽  
Least Squares ◽  
Partial Least Squares ◽  
Rapid Detection ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Chloride Content ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Tuna Fish

The sodium chloride content in the flesh of tuna fish is one of the factors for determining the price in the fishing industry. Titration is a standard method for the analysis of salt and this is time consuming. Near infrared spectroscopy is a potential alternative method for rapid detection without the need for wet chemical assay. Although sodium chloride is infrared inactive, this study investigated the influence of salt on the absorbance of near infrared energy and showed that the sodium chloride content can be determined using changes in the water band at 970 nm. Calibration equations were developed from frozen fish pieces and ground samples using multiple linear regression for the wavelength region of 700–1000 nm. The best result was achieved from frozen samples with a coefficient of determination for the calibration set ([Formula: see text]) = 0.71, standard error of calibration (SEC) = 0.20%, coefficient of determination for the validation set ([Formula: see text]) = 0.64, standard error of prediction (SEP) = 0.26% and bias = − 0.00%. In order to verify the significant variables used to determine infrared inactive sodium chloride, partial least squares regression was performed on frozen samples. The important variable in multiple linear regression and partial least squares regression was the absorbance band at 976 nm attributed to water molecules. The result from partial least squares calibration showed [Formula: see text] = 0.83, SEC = 0.20%, [Formula: see text] = 0.54, SEP = 0.25% and bias = 0.00%. The salt values predicted using the near infrared models were not significantly different from the reference values obtained by the standard titration method at the 95% confidence interval.

scholarly journals NIR assignment of isopsoralen by 2D-COS technology and model application in Yunkang Oral Liquid

2015 ◽  
Vol 08 (06) ◽  
pp. 1550023 ◽  
Author(s):  
Yanling Pei ◽  
Zhisheng Wu ◽  
Xinyuan Shi ◽  
Xiaoning Pan ◽  
Yanfang Peng ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Least Squares ◽  
Partial Least Squares ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Cross Validation ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Oral Liquid ◽  
Spectral Assignment

Near infrared (NIR) assignment of Isopsoralen was performed using deuterated chloroform solvent and two-dimensional correlation spectroscopy (2D-COS) technology. Yunkang Oral Liquid was applied to study Isopsoralen, the characteristic bands by spectral assignment as well as the bands by interval partial least squares (iPLS) and synergy interval partial least squares (siPLS) were used to establish partial least squares (PLS) model. The coefficient of determination in calibration [Formula: see text] were 0.9987, 0.9970 and 0.9982. The coefficient of determination in cross validation [Formula: see text] were 0.9985, 0.9921 and 0.9982. The coefficient of determination in prediction [Formula: see text] were 0.9987, 0.9955 and 0.9988. The root mean square error of calibration (RMSEC) were 0.27, 0.40 and 0.31 ppm. The root mean square error of cross validation (RMSECV) were 0.30, 0.67 and 0.32 ppm. The root mean square error of prediction (RMSEP) were 0.23, 0.43 and 0.22 ppm. The residual predictive deviation (RPD) were 31.00, 16.58 and 32.41. It turned out that the characteristic bands by spectral assignment had the same results with the chemometrics methods in PLS model. It provided guidance for NIR spectral assignment of chemical compositions in Chinese Materia Medica (CMM).

scholarly journals Development of UV-vis and FTIR Partial Least Squares models: comparison and combination of two spectroscopy techniques with chemometrics for polyphenols quantification in red wine

OENO One ◽  
2020 ◽  
Vol 54 (4) ◽  
pp. 779-792
Author(s):  
Clément Miramont ◽  
Michael Jourdes ◽  
Pierre-Louis Teissedre
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Condensed Tannin ◽  
Coefficient Of Determination ◽  
Anthocyanin Content ◽  
Vis Spectroscopy ◽  
Anthocyanin Concentration ◽  
Models Comparison ◽  
Tannin Concentration ◽  
Visible Wavelengths

Polyphenolic compounds are considered to have a major impact on the quality of red wines. Sensory perception, such as astringency and bitterness, are mainly related to condensed tannin, while colour intensity and evolution is due to anthocyanin composition. Therefore, the quick analytical measurement of phenolic compounds appears to be a real challenge for wine monitoring. Fourier transform infrared (FTIR) and ultraviolet-visible (UV-vis) spectroscopy with chemometrics are good candidates for predicting polyphenolic contents in wines, but they have not yet been compared in terms of efficiency of each wavelength area. Thus, the possibility of combining the two areas has not been investigated.This work sought to determine the tannin and anthocyanin content of ninety-two wines. The wine selection covered different vintages, varieties and regions. Tannin concentration was analysed by precipitation with protein and polysaccharide and by the Bate-Smith assay. Free anthocyanin concentration was analysed by bisulfite bleaching and the monomers/polymers ratio was analysed using the Adams-Harbertson method. Molecular anthocyanin concentration was also obtained by HPLC/UV-vis. Two spectra were collected using UV-vis and FTIR devices. The data collected were statistically analysed using the partial least squares (PLS) regression method.The correlations obtained were relevant to both of the spectrum areas studied, with a coefficient of determination for cross validation larger than 0.7 for most parameters studied. While the two spectroscopic methods gave almost identical results, FTIR indicated higher robustness for the prediction of tannin concentration. Conversely, UV-vis appeared to be more relevant when determining anthocyanin concentration and evolution. Finally, the models obtained when combining the two spectrum areas gave slightly better results. When a selection of different visible wavelengths were added to the FTIR spectrum, the results showed that the prediction of anthocyanin parameters improved considerably, thus highlighting the importance of the visible area when estimating these compounds.

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