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

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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Rapid Measurement of Cellulose, Hemicellulose, and Lignin Content in Sargassum horneri by Near-Infrared Spectroscopy and Characteristic Variables Selection Methods

Molecules ◽

10.3390/molecules27020335 ◽

2022 ◽

Vol 27 (2) ◽

pp. 335

Author(s):

Ning Ai ◽

Yibo Jiang ◽

Sainab Omar ◽

Jiawei Wang ◽

Luyue Xia ◽

...

Keyword(s):

Near Infrared ◽

Lignin Content ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Sargassum Horneri ◽

Calibration Model ◽

Selection Methods ◽

Calibration Models ◽

Variables Selection

Near-infrared (NIR) spectroscopy and characteristic variables selection methods were used to develop a quick method for the determination of cellulose, hemicellulose, and lignin contents in Sargassum horneri. Calibration models for cellulose, hemicellulose, and lignin in Sargassum horneri were established using partial least square regression methods with full variables (full-PLSR). The PLSR calibration models were established by four characteristic variables selection methods, including interval partial least square (iPLS), competitive adaptive reweighted sampling (CARS), correlation coefficient (CC), and genetic algorithm (GA). The results showed that the performance of the four calibration models, namely iPLS-PLSR, CARS-PLSR, CC-PLSR, and GA-PLSR, was better than the full-PLSR calibration model. The iPLS method was best in the performance of the models. For iPLS-PLSR, the determination coefficient (R2), root mean square error (RMSE), and residual predictive deviation (RPD) of the prediction set were as follows: 0.8955, 0.8232%, and 3.0934 for cellulose, 0.8669, 0.4697%, and 2.7406 for hemicellulose, and 0.7307, 0.7533%, and 1.9272 for lignin, respectively. These findings indicate that the NIR calibration models can be used to predict cellulose, hemicellulose, and lignin contents in Sargassum horneri quickly and accurately.

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

Chemistry Proceedings ◽

10.3390/csac2021-10622 ◽

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.

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

Anais da Academia Brasileira de Ciências ◽

10.1590/s0001-37652001000400005 ◽

2001 ◽

Vol 73 (4) ◽

pp. 519-524 ◽

Cited By ~ 5

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.

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Prediction of Soil Organic Carbon for Ethiopian Highlands Using Soil Spectroscopy

ISRN Soil Science ◽

10.1155/2013/720589 ◽

2013 ◽

Vol 2013 ◽

pp. 1-11 ◽

Cited By ~ 9

Author(s):

Tadele Amare ◽

Christian Hergarten ◽

Hans Hurni ◽

Bettina Wolfgramm ◽

Birru Yitaferu ◽

...

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Diffuse Reflectance ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Calibration Models ◽

The Stability

Soil spectroscopy was applied for predicting soil organic carbon (SOC) in the highlands of Ethiopia. Soil samples were acquired from Ethiopia’s National Soil Testing Centre and direct field sampling. The reflectance of samples was measured using a FieldSpec 3 diffuse reflectance spectrometer. Outliers and sample relation were evaluated using principal component analysis (PCA) and models were developed through partial least square regression (PLSR). For nine watersheds sampled, 20% of the samples were set aside to test prediction and 80% were used to develop calibration models. Depending on the number of samples per watershed, cross validation or independent validation were used. The stability of models was evaluated using coefficient of determination (R2), root mean square error (RMSE), and the ratio performance deviation (RPD). The R2 (%), RMSE (%), and RPD, respectively, for validation were Anjeni (88, 0.44, 3.05), Bale (86, 0.52, 2.7), Basketo (89, 0.57, 3.0), Benishangul (91, 0.30, 3.4), Kersa (82, 0.44, 2.4), Kola tembien (75, 0.44, 1.9), Maybar (84. 0.57, 2.5), Megech (85, 0.15, 2.6), and Wondo Genet (86, 0.52, 2.7) indicating that the models were stable. Models performed better for areas with high SOC values than areas with lower SOC values. Overall, soil spectroscopy performance ranged from very good to good.

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Determine of Moisture Content for Rough Rice Single Kernel by Visible/NIR Spectroscopy

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.931-932.1549 ◽

2014 ◽

Vol 931-932 ◽

pp. 1549-1554 ◽

Cited By ~ 1

Author(s):

Adcha Heman ◽

Ching Lu Hsieh

Keyword(s):

Moisture Content ◽

Nir Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Regression Methods ◽

Average Accuracy ◽

Rough Rice ◽

Calibration Models ◽

Plsr Model

Moisture content (MC) of rough rice directly affects rice quality and its market value. This study applied spectroscopy both in visible 400-700 nm and NIR 700-1050 nm bands to record spectrum of rough rice single kernel (SK). Tainan No.11 medium rice randomly collected from field. After machine harvested, it was used in the tests and they were conditioned by oven to five MC levels ranging from 10.2 to 35.9%. Two regression methods, multiple linear regressions (MLR) and partial least square regression (PLSR), were applied to develop calibration models. Among 7 tested models were found that PLSR model of first differential with 21 gap points, which are rc=0.98, SEC=1.1% for calibration and rp=0.96, SEP=1.9% for prediction. The results suggested average accuracy for the best model was about 98.4% in 400-1050 nm wavelength.

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PLS-R Calibration Models for Wine Spirit Volatile Phenols Prediction by Near-Infrared Spectroscopy

Sensors ◽

10.3390/s22010286 ◽

2021 ◽

Vol 22 (1) ◽

pp. 286

Author(s):

Ofélia Anjos ◽

Ilda Caldeira ◽

Tiago A. Fernandes ◽

Soraia Inês Pedro ◽

Cláudia Vitória ◽

...

Keyword(s):

Near Infrared ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Volatile Phenols ◽

Near Ir ◽

Liquid Liquid Extraction ◽

Calibration Models ◽

First Time

Near-infrared spectroscopic (NIR) technique was used, for the first time, to predict volatile phenols content, namely guaiacol, 4-methyl-guaiacol, eugenol, syringol, 4-methyl-syringol and 4-allyl-syringol, of aged wine spirits (AWS). This study aimed to develop calibration models for the volatile phenol’s quantification in AWS, by NIR, faster and without sample preparation. Partial least square regression (PLS-R) models were developed with NIR spectra in the near-IR region (12,500–4000 cm−1) and those obtained from GC-FID quantification after liquid-liquid extraction. In the PLS-R developed method, cross-validation with 50% of the samples along a validation test set with 50% of the remaining samples. The final calibration was performed with 100% of the data. PLS-R models with a good accuracy were obtained for guaiacol (r2 = 96.34; RPD = 5.23), 4-methyl-guaiacol (r2 = 96.1; RPD = 5.07), eugenol (r2 = 96.06; RPD = 5.04), syringol (r2 = 97.32; RPD = 6.11), 4-methyl-syringol (r2 = 95.79; RPD = 4.88) and 4-allyl-syringol (r2 = 95.97; RPD = 4.98). These results reveal that NIR is a valuable technique for the quality control of wine spirits and to predict the volatile phenols content, which contributes to the sensory quality of the spirit beverages.

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

Atmospheric Measurement Techniques ◽

10.5194/amt-9-441-2016 ◽

2016 ◽

Vol 9 (2) ◽

pp. 441-454 ◽

Cited By ~ 10

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.

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Chemometric Analysis of UV-Visible Spectral Fingerprints for the Discrimination and Quantification of Clinical Anthracycline Drug Preparation Used in Oncology

BioMed Research International ◽

10.1155/2021/5580102 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Aimen El Orche ◽

Casimir Adade Adade ◽

Hafid Mefetah ◽

Amine Cheikh ◽

Khalid Karrouchi ◽

...

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Drug Preparation ◽

Mean Square ◽

Chemotherapeutic Drugs ◽

Uv Visible

In clinical treatment, the analytical quality assessment of the delivery of chemotherapeutic preparations is required to guarantee the patient’s safety regarding the dose and most importantly the appropriate anticancer drug. On its own, the development of rapid analytical methods allowing both qualitative and quantitative control of the formulation of prepared solutions could significantly enhance the hospital’s workflow, reducing costs, and potentially providing optimal patient care. UV-visible spectroscopy is a nondestructive, fast, and economical technique for molecular characterization of samples. A discrimination and quantification study of three chemotherapeutic drugs doxorubicin, daunorubicin, and epirubicin was conducted, using clinically relevant concentration ranges prepared in 0.9% NaCl solutions. The application of the partial least square discriminant analysis PLS-DA method on the UV-visible spectral data shows a perfect discrimination of the three drugs with a sensitivity and specificity of 100%. The use of partial least square regression PLS shows high quantification performance of these molecules in solution represented by the low value of root mean square error of calibration (RMSEC) and root mean square error of cross validation (RMSCECV) on the one hand and the high value of R -square on the other hand. This study demonstrated the viability of UV-visible fingerprinting (routine approach) coupled with chemometric tools for the classification and quantification of chemotherapeutic drugs during clinical preparation.

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Near-Infrared Spectroscopy for Measuring Urea in Hemodialysis Fluids

Clinical Chemistry ◽

10.1093/clinchem/47.7.1279 ◽

2001 ◽

Vol 47 (7) ◽

pp. 1279-1286 ◽

Cited By ~ 34

Author(s):

Christopher V Eddy ◽

Mark A Arnold

Keyword(s):

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Spectral Range ◽

Near Infrared ◽

Factor Model ◽

Partial Least Square ◽

Least Square ◽

Calibration Model ◽

Calibration Models ◽

Sensitivity And Selectivity

Abstract Background: Near-infrared spectroscopy is proposed as a method for providing real-time urea concentrations during hemodialysis treatments. The feasibility of such noninvasive urea measurements is evaluated in undiluted dialysate fluid. Methods: Near-infrared spectra were collected from calibration solutions of urea prepared in dialysate fluid. Spectra were collected over three distinct spectral regions, and partial least-squares calibration models were optimized and compared for each. Selectivity for urea was demonstrated with two-component samples composed of urea and glucose in the dialysate matrix. The clinical significance of this approach was assessed by measuring urea in real hemodialysate samples. Results: Urea absorptions within the combination and short-wavelength, near-infrared spectral regions provided sufficient spectral information for sound calibration models in the dialysate matrix. The combination spectral region had SEs of calibration (SEC) and prediction (SEP) of 0.38 mmol/L and 0.26 mmol/L, respectively, over the 4720–4600 cm−1 spectral range with 5 partial least-square factors. A second calibration model was established over the combination region from a series of solutions prepared with independently variable concentrations of urea and glucose. The best calibration model for urea in the presence of variable glucose concentrations had a SEC of 0.6 mmol/L and a SEP of 0.4 mmol/L for a 5-factor model over the 4600–4350 cm−1 spectral range. There was no significant decrease in SEP when the 4720–4600 cm−1 calibration model was used to measure urea in real samples collected during actual hemodialysis. Conclusions: Urea can be determined with sufficient sensitivity and selectivity for clinical measurements within the matrix of the hemodialysis fluid.

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

Journal of Fluorescence ◽

10.1007/s10895-020-02640-w ◽

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.

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