Determination of total trans fat content in Pakistani cereal-based foods by SB-HATR FT-IR spectroscopy coupled with partial least square regression

2010 ◽  
Vol 123 (4) ◽  
pp. 1289-1293 ◽  
Author(s):  
S.A. Mahesar ◽  
Aftab A. Kandhro ◽  
L. Cerretani ◽  
A. Bendini ◽  
S.T.H. Sherazi ◽  
...  
Keyword(s):  
Ir Spectroscopy ◽  
Fat Content ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Trans Fat ◽  
Least Square Regression ◽  
Ft Ir ◽  
Ft Ir Spectroscopy

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 Sensitivity and Calibration of the FT-IR Spectroscopy on Concentration of Heavy Metal Ions in River and Borehole Water Sources

2020 ◽  
Vol 10 (21) ◽  
pp. 7785
Author(s):  
Matthew Mamera ◽  
Johan J. van Tol ◽  
Makhosazana P. Aghoghovwia ◽  
Elmarie Kotze
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Ir Spectroscopy ◽  
Water Sources ◽  
Partial Least Square ◽  
Least Square ◽  
Borehole Water ◽  
Ft Ir ◽  
Ir Calibration ◽  
Ft Ir Spectroscopy

Heavy metals in water sources can threaten human life and the environment. The analysis time, need for chemical reagents, and sample amount per analysis assist in monitoring contaminants. Application of the Fourier Transform Infrared (FT-IR) Spectroscopy for the investigation of heavy metal elements has significantly developed due to its cost effectiveness and accuracy. Use of chemometric models such as Partial Least Square (PLS) and Principle Component Regression Analysis (PCA) relate the multiple spectral intensities from numerous calibration samples to the recognized analytes. This study focused on the FT-IR calibration and quantification of heavy metals (Ag, Cd, Cu, Pb and Zn) in surveyed water sources. FT-IR measurements were compared with the atomic absorption spectrometer (AAS) measurements. Quantitative analysis methods, PCA and PLS, were used in the FT-IR calibration. The spectral analyses were done using the Attenuated Total Reflectance (ATR-FTIR) technique on three river and four borehole water sources sampled within two seasons in QwaQwa, South Africa (SA). The PLS models had good R2 values ranging from 0.95 to 1 and the PCA models ranged from 0.98 to 0.99. Significant differences were seen at 0.001 and 0.05 levels between the PLS and PCA models for detecting Cd and Pb in the water samples. The PCA models detected Ag concentrations more (˂0 mg L−1 on selected sites). Both the PLS and PCA models had lower detection only for Zn ions mostly above 45 mg L−1 deviating from the AAS measurements (<0.020 mg L−1). The FT-IR spectroscopy demonstrated good potential for heavy metal determination purposes.

scholarly journals PREDIKSI MODEL PENETAPAN KADAR FLAVONOID TOTAL PADA EKSTRAK Abelmoschus manihot L. MENGGUNAKAN SPEKTROSKOPI IR YANG DIKOMBINASIKAN DENGAN KEMOMETRIK

PHARMACON ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 480
Author(s):  
Juliandro Fangohoy ◽  
Sri Sudewi ◽  
Adithya Yudistira
Keyword(s):  
Ir Spectroscopy ◽  
Standard Error ◽  
Partial Least Square ◽  
Least Square ◽  
Total Flavonoid ◽  
Partial Least Square Regression ◽  
Total Flavonoid Content ◽  
Flavonoid Content ◽  
Least Square Regression ◽  
Error Calibration

ABSTRACT This study aims to determine the validation of IR spectroscopy method in determining the total flavonoid level in Abelmoschus manihot L., can meet the requirements and can be applied. The method for determining the total flavonoid content model using a combination of IR Spectroscopy and Chemometrics Partial Least Square Regression (PLSR). The calorimetrics method was used to determine the total flavonoid content in the green gedi leaves axtracts onn eight samples of growth where Bitung Was 2.64 mg QE/g extract  ± 0.035, Minahasa Selatan is  1.91 mg QE/g extract ± 0.027, Kotamobagu is 4.84 mg QE/g extract  ± 0.03, Minahasa Utara is  4.40 mg QE/g extract ± 0.091, Manado is 3.45 mg QE/g extract  ± 0.012, Minahasa Tenggara is  1.72 mg QE/g extract ± 0.006, Minahasa is 3.67 mg QE/g extract ± 0.033, Tomohon is 3.40 mg QE/g extract ± 0.003. This combination is involves involving x-variables (FTIR measurement results) and y-variables (data from the results of the calorimetric method analysis). Error value [standard error calibration (SEC=0.003), standard error of prediction (SEP = 0.052)] and cslibration r value 0.999, and r validation 0.975. Keywords: Flavonoids, Green Gedi Leaves, FTIR Spectrofotometry, UV-VIS Spectrofotometry, Chemometrics ABSTRAK Penelitian ini bertujuan untuk mengetahui Validasi Metode Spektroskopi IR Pada Penetapan kadar Flavonoid Total   pada   Abelmoschus manihot L Dapat Memenuhi Persyaratan dan Dapat di Aplikasikan. Metode penentuan model  kandungan flavonoid total  menggunakan kombinasi Spektroskopi IR dan Kemometrik Partial Least Square Regression (PLSR). Metode Kalorimetrik digunakan untuk mengetahui kandungan Flavonoid Total pada pada Ekstra Daun Gedi Hijau pada 8 sampel tempat tumbuh yaitu Bitung sebesar 2.64 mg QE/g ekstrak  ± 0.035, Minahasa Selatan sebesar  1.91 mg QE/g ekstrak ± 0.027, Kotamobagu sebesar 4.84 mg QE/g ekstrak ± 0.03, Minahasa Utara sebesar  4.40 mg QE/g ekstrak ± 0.091, Manado sebesar 3.45 mg QE/g ekstrak  ± 0.012, Minahasa Tenggara sebesar  1.72 mg QE/g ekstrak ± 0.006, Minahasa sebesar 3.67 mg QE/g ekstrak ± 0.033, Tomohon sebesar 3.40 mg QE/g ekstrak ± 0.003. Kombinasi ini melibatkan melibatkan variabel x (hasil pengukuran FTIR) dan variabel y (data hasil analisis metode Kalorimetrik). Nilai kesalahan (standar error calibration (SEC = 0.003), standard error of prediction (SEP = 0.052)) dan nilai r kalibrasi 0.999, serta r validasi 0.975. Kata Kunci: Total Flavonoid, Daun Gedi Hijau, Spektrofotometri FTIR, Spektrofotometri UV-VIS,  Kemometrik.

Application of MALDI-TOFMS Combined with Partial Least Square Regression for the Determination of Mercury and Copper in Canned Tuna, Using Dithizone as the Complexing Agent and Ag(I) as Internal Standard

2018 ◽  
Vol 11 (10) ◽  
pp. 2835-2846 ◽  
Author(s):  
Manuel Mendez Garcia ◽  
Kazimierz Wrobel ◽  
Alejandra Sarahi Ramirez Segovia ◽  
Eunice Yanez Barrientos ◽  
Alma Rosa Corrales Escobosa ◽  
...  
Keyword(s):  
Internal Standard ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Complexing Agent ◽  
Canned Tuna ◽  
Least Square Regression

Mechanical Parameter Analysis of Rock Material via PLSR Model

2011 ◽  
Vol 467-469 ◽  
pp. 1826-1831 ◽  
Author(s):  
Zao Bao Liu ◽  
Wei Ya Xu ◽  
Fei Xu ◽  
Lin Wei Wang
Keyword(s):  
Regression Model ◽  
Principal Component ◽  
Partial Least Square ◽  
Rock Material ◽  
Least Square ◽  
Parameter Analysis ◽  
Partial Least Square Regression ◽  
Mechanical Parameter ◽  
Least Square Regression

Mechanical parameter analysis is a complicated issue since it is influenced by many factors. Closely related with the influencing factors of compressibility coefficients of rock material (sandstone), this article first introduces the way to process partial least square regression (PLSR) analysis. The process of carrying out PLSR is divided into six steps as for analysis and prediction of the regression model, which are data preparation, principle collection, regression model for first principle component, secondary principle analysis, establishment of final regression model and number determination of principal component l. And then introduces PLSR for application of analysis and prediction of compressibility coefficients with 30 experiment samples. Seven prediction samples are carried out by PLSR with the training process of 30 samples. The result shows PLSR has good accuracy in prediction under the condition that the model is properly deprived based on certain experimental samples. Finally, some conclusions are made for further study on both mechanical parameters and partial least square regression method.

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