scholarly journals Predicting ambient aerosol thermal–optical reflectance (TOR) measurements from infrared spectra: extending the predictions to different years and different sites

2016 ◽  
Vol 9 (2) ◽  
pp. 441-454 ◽  
Author(s):  
Matteo Reggente ◽  
Ann M. Dillner ◽  
Satoshi Takahama
Keyword(s):  
Mahalanobis Distance ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Optical Methods ◽  
Calibration Model ◽  
Optical Reflectance ◽  
Wide Range ◽  
Ft Ir ◽  
Absorbance Spectra

Abstract. Organic carbon (OC) and elemental carbon (EC) are major components of atmospheric particulate matter (PM), which has been associated with increased morbidity and mortality, climate change, and reduced visibility. Typically OC and EC concentrations are measured using thermal–optical methods such as thermal–optical reflectance (TOR) from samples collected on quartz filters. In this work, we estimate TOR OC and EC using Fourier transform infrared (FT-IR) absorbance spectra from polytetrafluoroethylene (PTFE Teflon) filters using partial least square regression (PLSR) calibrated to TOR OC and EC measurements for a wide range of samples. The proposed method can be integrated with analysis of routinely collected PTFE filter samples that, in addition to OC and EC concentrations, can concurrently provide information regarding the functional group composition of the organic aerosol. We have used the FT-IR absorbance spectra and TOR OC and EC concentrations collected in the Interagency Monitoring of PROtected Visual Environments (IMPROVE) network (USA). We used 526 samples collected in 2011 at seven sites to calibrate the models, and more than 2000 samples collected in 2013 at 17 sites to test the models. Samples from six sites are present both in the calibration and test sets. The calibrations produce accurate predictions both for samples collected at the same six sites present in the calibration set (R2 = 0.97 and R2 = 0.95 for OC and EC respectively), and for samples from 9 of the 11 sites not included in the calibration set (R2 = 0.96 and R2 = 0.91 for OC and EC respectively). Samples collected at the other two sites require a different calibration model to achieve accurate predictions. We also propose a method to anticipate the prediction error; we calculate the squared Mahalanobis distance in the feature space (scores determined by PLSR) between new spectra and spectra in the calibration set. The squared Mahalanobis distance provides a crude method for assessing the magnitude of mean error when applying a calibration model to a new set of samples.

scholarly journals Predicting ambient aerosol thermal-optical reflectance (TOR) measurements from infrared spectra: extending the predictions to different years and different sites

2015 ◽  
Vol 8 (11) ◽  
pp. 12433-12474 ◽  
Author(s):  
M. Reggente ◽  
A. M. Dillner ◽  
S. Takahama
Keyword(s):  
Mahalanobis Distance ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Optical Methods ◽  
Calibration Model ◽  
Optical Reflectance ◽  
Wide Range ◽  
Ft Ir ◽  
Absorbance Spectra

Abstract. Organic carbon (OC) and elemental carbon (EC) are major components of atmospheric particulate matter (PM), which has been associated with increased morbidity and mortality, climate change and reduced visibility. Typically OC and EC concentrations are measured using thermal optical methods such as thermal optical reflectance (TOR) from samples collected on quartz filters. In this work, we estimate TOR OC and EC using Fourier transform infrared (FT-IR) absorbance spectra from polytetrafluoroethylene (PTFE or Teflon) filters using partial least square regression (PLSR) calibrated to TOR OC and EC measurements for a wide range of samples. The proposed method can be integrated with analysis of routinely collected PTFE filter samples that, in addition to OC and EC concentrations, can concurrently provide information regarding the composition of the organic aerosol. We have used the FT-IR absorbance spectra and TOR OC and EC concentrations collected in the Interagency Monitoring of PROtected Visual Environment (IMPROVE) network (USA). We used 526 samples collected in 2011 at seven sites to calibrate the models, and more than 2000 samples collected in 2013 at 17 sites to test the models. Samples from six sites are present both in the calibration and test sets. The calibrations produce accurate predictions both for samples collected at the same six sites present in the calibration set (R2=0.97 and R2=0.95 for OC and EC respectively), and for samples from nine of the 11 sites not included in the calibration set (R2=0.96 and R2=0.91 for OC and EC respectively). Samples collected at the other two sites require a different calibration model to achieve accurate predictions. We also propose a method to anticipate the prediction error: we calculate the squared Mahalanobis distance in the feature space (scores determined by PLSR) between new spectra and spectra in the calibration set. The squared Mahalanobis distance provides a crude method for assessing the magnitude of mean error when applying a calibration model to a new set of samples.

Field Nitrogen Dioxide and Ozone Monitoring Using Electrochemical Sensors with Partial Least Squares Regression

2021 ◽  
Vol 5 (1) ◽  
pp. 61
Author(s):  
Rachid Laref ◽  
Etienne Losson ◽  
Alexandre Sava ◽  
Maryam Siadat
Keyword(s):  
Nitrogen Dioxide ◽  
Electrochemical Sensors ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Monitoring Networks ◽  
Concentration Estimation ◽  
The Impact

Low-cost gas sensors detect pollutants gas at the parts-per-billion level and may be installed in small devices to densify air quality monitoring networks for the spread analysis of pollutants around an emissive source. However, these sensors suffer from several issues such as the impact of environmental factors and cross-interfering gases. For instance, the ozone (O3) electrochemical sensor senses nitrogen dioxide (NO2) and O3 simultaneously without discrimination. Alphasense proposes the use of a pair of sensors; the first one, NO2-B43F, is equipped with a filter dedicated to measure NO2. The second one, OX-B431, is sensitive to both NO2 and O3. Thus, O3 concentration can be obtained by subtracting the concentration of NO2 from the sum of the two concentrations. This technique is not practical and requires calibrating each sensor individually, leading to biased concentration estimation. In this paper, we propose Partial Least Square regression (PLS) to build a calibration model including both sensors’ responses and also temperature and humidity variations. The results obtained from data collected in the field for two months show that PLS regression provides better gas concentration estimation in terms of accuracy than calibrating each sensor individually.

scholarly journals Spectrophotometric determination of phenol in the presence of congeners by multivariated calibration

2001 ◽  
Vol 73 (4) ◽  
pp. 519-524 ◽  
Author(s):  
KELY VIVIANE DE SOUZA ◽  
PATRICIO PERALTA-ZAMORA
Keyword(s):  
Spectrophotometric Determination ◽  
Multivariate Calibration ◽  
Close Correlation ◽  
Degradation Process ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Calibration Model ◽  
Transient Species

The generation of poly-hydroxilated transient species during the photochemical treatment of phenol usually impedes the spectrophotmetric monitoring of its degradation process. Frequently, the appearance of compounds such as pyrocatechol, hydroquinone and benzoquinone produces serious spectral interference, which hinder the use of the classical univariate calibration process. In this work, the use of multivariate calibration is proposed to permit the spectrophotometric determination of phenol in the presence of these intermediates. Using 20 synthetic mixtures containing phenol and the interferents, a calibration model was developed by using a partial least square regression process (PLSR) and processing the absorbance signal between 180 and 300 nm. The model was validated by using 3 synthetic mixtures. In this operation, typical errors lower than 3% were observed. Close correlation between the results obtained by liquid chromatography and the proposed method was also observed.

scholarly journals Detection of Additives and Chemical Contaminants in Turmeric Powder Using FT-IR Spectroscopy

Foods ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 143 ◽  
Author(s):  
Sagar Dhakal ◽  
Walter F. Schmidt ◽  
Moon Kim ◽  
Xiuying Tang ◽  
Yankun Peng ◽  
...  
Keyword(s):  
Ir Spectroscopy ◽  
Curcuma Longa ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Pure Compounds ◽  
Ft Ir ◽  
Mixed Samples ◽  
Ft Ir Spectroscopy

Yellow turmeric (Curcuma longa) is widely used for culinary and medicinal purposes, and as a dietary supplement. Due to the commercial popularity of C. longa, economic adulteration and contamination with botanical additives and chemical substances has increased. This study used FT-IR spectroscopy for identifying and estimating white turmeric (Curcuma zedoaria), and Sudan Red G dye mixed with yellow turmeric powder. Fifty replicates of yellow turmeric—Sudan Red mixed samples (1%, 5%, 10%, 15%, 20%, 25% Sudan Red, w/w) and fifty replicates of yellow turmeric—white turmeric mixed samples (10%, 20%, 30%, 40%, 50% white turmeric, w/w) were prepared. The IR spectra of the pure compounds and mixtures were analyzed. The 748 cm−1 Sudan Red peak and the 1078 cm−1 white turmeric peak were used as spectral fingerprints. A partial least square regression (PLSR) model was developed for each mixture type to estimate adulteration concentrations. The coefficient of determination (R2v) for the Sudan Red mixture model was 0.97 with a root mean square error of prediction (RMSEP) equal to 1.3%. R2v and RMSEP for the white turmeric model were 0.95 and 3.0%, respectively. Our results indicate that the method developed in this study can be used to identify and quantify yellow turmeric powder adulteration.

scholarly journals Non-Destructive Quality Assessment of Tomato Paste by Using Portable Mid-Infrared Spectroscopy and Multivariate Analysis

Foods ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1300
Author(s):  
Didem Peren Aykas ◽  
Karla Rodrigues Borba ◽  
Luis E. Rodriguez-Saona
Keyword(s):  
External Validation ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Soluble Solids ◽  
Quality Traits ◽  
Sampling System ◽  
Mid Infrared ◽  
Tomato Paste ◽  
Ft Ir

This research aims to provide simultaneous predictions of tomato paste’s multiple quality traits without any sample preparation by using a field-deployable portable infrared spectrometer. A total of 1843 tomato paste samples were supplied by four different leading tomato processors in California, USA, over the tomato seasons of 2015, 2016, 2017, and 2019. The reference levels of quality traits including, natural tomato soluble solids (NTSS), pH, Bostwick consistency, titratable acidity (TA), serum viscosity, lycopene, glucose, fructose, ascorbic acid, and citric acid were determined by official methods. A portable FT-IR spectrometer with a triple-reflection diamond ATR sampling system was used to directly collect mid-infrared spectra. The calibration and external validation models were developed by using partial least square regression (PLSR). The evaluation of models was conducted on a randomly selected external validation set. A high correlation (RCV = 0.85–0.99) between the reference values and FT-IR predicted values was observed from PLSR models. The standard errors of prediction were low (SEP = 0.04–35.11), and good predictive performances (RPD = 1.8–7.3) were achieved. Proposed FT-IR technology can be ideal for routine in-plant assessment of the tomato paste quality that would provide the tomato processors with accurate results in shorter time and lower cost.

scholarly journals Nondestructive Prediction of Isoflavones and Oligosaccharides in Intact Soybean Seed Using Fourier Transform Near-Infrared (FT-NIR) and Fourier Transform Infrared (FT-IR) Spectroscopic Techniques

Foods ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 232
Author(s):  
Hanim Z. Amanah ◽  
Salma Sultana Tunny ◽  
Rudiati Evi Masithoh ◽  
Myoung-Gun Choung ◽  
Kyung-Hwan Kim ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Near Infrared ◽  
Prediction Models ◽  
Soybean Seed ◽  
Fourier Transform Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Calibration Model ◽  
Spectroscopic Techniques ◽  
Ft Ir

The demand for rapid and nondestructive methods to determine chemical components in food and agricultural products is proliferating due to being beneficial for screening food quality. This research investigates the feasibility of Fourier transform near-infrared (FT-NIR) and Fourier transform infrared spectroscopy (FT-IR) to predict total as well as an individual type of isoflavones and oligosaccharides using intact soybean samples. A partial least square regression method was performed to develop models based on the spectral data of 310 soybean samples, which were synchronized to the reference values evaluated using a conventional assay. Furthermore, the obtained models were tested using soybean varieties not initially involved in the model construction. As a result, the best prediction models of FT-NIR were allowed to predict total isoflavones and oligosaccharides using intact seeds with acceptable performance (R2p: 0.80 and 0.72), which were slightly better than the model obtained based on FT-IR data (R2p: 0.73 and 0.70). The results also demonstrate the possibility of using FT-NIR to predict individual types of evaluated components, denoted by acceptable performance values of prediction model (R2p) of over 0.70. In addition, the result of the testing model proved the model’s performance by obtaining a similar R2 and error to the calibration model.

scholarly journals A Rapid Method to Measure Serum Retinol Concentrations in Japanese Black Cattle Using Multidimensional Fluorescence

2020 ◽  
Author(s):  
Yoshio Tamura ◽  
Hiroki Inoue ◽  
Satoshi Takemoto ◽  
Kazuo Hirano ◽  
Kazutoshi Miyaura
Keyword(s):  
Mean Squared Error ◽  
Fluorescence Analysis ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Retinol Binding Protein ◽  
Calibration Model ◽  
Serum Retinol ◽  
Japanese Black Cattle ◽  
Multidimensional Fluorescence

Abstract Vitamin A levels in fattening Japanese Black cattle affect meat quality; therefore, it is important to monitor serum retinol concentrations. To simplify and accelerate the evaluation of serum retinol concentrations in cattle, we developed a new predictive method using excitation-emission matrix (EEM) fluorescence spectrophotometry. For analytical comparison, the concentration of serum retinol was also measured using the conventional HPLC method. We examined excitation (Ex) and emission (Em) wavelengths of cattle serum, which were 250–450 and 250–600 nm, respectively. Parallel factor analysis separated four components from EEM data, one of which was related to retinol. Next, a partial least square regression model was created using the obtained EEMs as explanatory variables and accrual measurement values as objective variables. The determination coefficient value (R2), root mean squared error of prediction (RMSEP), and the ratio of performance to deviation (RPD) of the model were determined. A comparison with reference values found that R2, RMSEP, and RPD of the calibration model were 0.95, 6.4 IU/dl, and 4.2, respectively. This implies that EEM can estimate the serum retinol concentration with high accuracy. Additionally, the fluorescent peaks that contributed to the calibration, which were extracted from the regression coefficient and variable importance in projection plots, were Ex/Em = 320/390 and 330/520 nm. Thus, we assume that this method observes not only free retinol, but also retinol-binding protein. In conclusion, multidimensional fluorescence analysis can accurately and quickly determine serum retinol concentrations in fattening cattle.

scholarly journals Development and Validation of a Simple, Green Infrared Spectroscopic Method for Quantitation of Sildenafil Citrate in Siloflam Tablets of Unknown Manufacturing Formula

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Van Trung Bui ◽  
Cao Son Doan ◽  
Thi Thanh Vuong Tong ◽  
Dinh Chi Le
Keyword(s):  
Spectroscopic Method ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Sildenafil Citrate ◽  
Calibration Model ◽  
Mass Content ◽  
Exact Mass ◽  
Infrared Spectroscopic ◽  
Development And Validation

A simple, easy-to-implement, and green infrared spectroscopic method was developed and validated for the quantitative determination of sildenafil citrate in tablets of unknown manufacturing formula. Homogenized tablet powder with known mass content (%, m/m) of sildenafil citrate was mixed with paracetamol to form standard mixtures with different percentages of sildenafil citrate on the total quantity of sildenafil citrate and paracetamol (designated as R). Unknown tablet samples were finely ground and mixed with paracetamol to form test mixtures having R values about 50%. Infrared spectra of standard mixtures, measured in attenuated total reflectance mode, in the wavenumber zone from 1800 cm−1 to 1300 cm−1 were selected and processed by partial least square regression to form the calibration model for quantitation of sildenafil citrate in unknown samples. Spectral responses of test mixtures and the calibration model were used to determine the exact mass content (%, m/m) of sildenafil citrate in the powder of unknown tablet samples. The method was fully validated in terms of linearity, precision, and accuracy according to the requirements of current guidelines and was proved as reliable and suitable for the intended application.

scholarly journals The Use of Partial Least Square Regression and Spectral Data in UV-Visible Region for Quantification of Adulteration in Indonesian Palm Civet Coffee

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Diding Suhandy ◽  
Meinilwita Yulia
Keyword(s):  
Visible Region ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Calibration Model ◽  
Simple Method ◽  
Calibration Models ◽  
Visible Spectra ◽  
Palm Civet ◽  
Uv Visible

Asian palm civet coffee or kopi luwak (Indonesian words for coffee and palm civet) is well known as the world’s priciest and rarest coffee. To protect the authenticity of luwak coffee and protect consumer from luwak coffee adulteration, it is very important to develop a robust and simple method for determining the adulteration of luwak coffee. In this research, the use of UV-Visible spectra combined with PLSR was evaluated to establish rapid and simple methods for quantification of adulteration in luwak-arabica coffee blend. Several preprocessing methods were tested and the results show that most of the preprocessing spectra were effective in improving the quality of calibration models with the best PLS calibration model selected for Savitzky-Golay smoothing spectra which had the lowest RMSECV (0.039) and highest RPDcal value (4.64). Using this PLS model, a prediction for quantification of luwak content was calculated and resulted in satisfactory prediction performance with high both RPDp and RER values.

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