Fourier transform mid-infrared (MIR) and near-infrared (NIR) spectroscopy for rapid quality assessment of Chinese medicine preparation Honghua Oil

Characterization and quantification of individual whey proteins is crucial in many industrial dairy processes. With labor-intensive wet-chemical methods still being used as reference, a rapid quantification method for individual whey proteins is yet to be established. In this work Fourier Transform Mid-Infrared (FT-MIR) spectroscopy and Fourier Transform Near-Infrared (FT-NIR) spectroscopy were investigated as rapid methods for the analysis of the two main whey proteins (Î²-lactoglobulin and Î±-lactalbumin) in complex aqueous whey solutions simulating production process streams. MIR and NIR spectra were obtained on whey samples with known and varying amounts of the proteins of interest and regressed onto the spectra using Partial Least Squares (PLS). Selecting wavelengths allowed for prediction of Î²-lactoglobulin and Î±-lactalbumin concentrations with very high precision and accuracy (RMSECV of 0.6% and R2 of 0.99), suggesting the possibility of implementing systematic rapid in-line monitoring of protein composition in industrial whey streams. While the protein secondary structure fingerprint in the MIR region is well established in literature, this paper goes a step further by identifying the most informative parts of the NIR spectra in relation to protein secondary structure analysis through application of two-dimensional MIR-NIR correlation spectroscopy. Multivariate Curve Resolution (MCR) was applied to the MIR data to resolve mixture spectra and to elucidate the spectral ranges that were most useful in distinguishing between the two main whey proteins. The study concludes that NIR spectroscopy is a potential technology for accurate protein structural quantification, and that the method proposed here opens the possibilities for novel in-line industrial applications.

Download Full-text

Major ergot alkaloids in naturally contaminated cool-season barley grain grown under a cold climate condition in western Canada, explored with near-infrared (NIR) and fourier transform mid-infrared (ATR-FT/MIR) spectroscopy

Food Control ◽

10.1016/j.foodcont.2019.03.025 ◽

2019 ◽

Vol 102 ◽

pp. 221-230 ◽

Cited By ~ 5

Author(s):

Haitao Shi ◽

Warren Schwab ◽

Na Liu ◽

Peiqiang Yu

Keyword(s):

Fourier Transform ◽

Near Infrared ◽

Ergot Alkaloids ◽

Barley Grain ◽

Cold Climate ◽

Western Canada ◽

Climate Condition ◽

Cool Season ◽

Mid Infrared ◽

Mir Spectroscopy

Download Full-text

Rapid Determination of Heartwood Extractives in Larix sp. by Means of Fourier Transform near Infrared Spectroscopy

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.336 ◽

2002 ◽

Vol 10 (3) ◽

pp. 203-214 ◽

Cited By ~ 69

Author(s):

N. Gierlinger ◽

M. Schwanninger ◽

B. Hinterstoisser ◽

R. Wimmer

Keyword(s):

Fourier Transform ◽

Near Infrared ◽

Cross Validation ◽

Nir Spectroscopy ◽

Solid Wood ◽

Hot Water ◽

Extractive Content ◽

Fast Method ◽

Wood Powder ◽

Test Set

The feasibility of Fourier transform near infrared (FT-NIR) spectroscopy to rapidly determine extractive and phenolic content in heartwood of larch trees ( Larix decidua MILL., L. leptolepis (LAMB.) CARR. and the hybrid L. x eurolepis) was investigated. FT-NIR spectra were collected from wood powder and solid wood using a fibre-optic probe. Partial Least Squares (PLS) regression analyses were carried out describing relationships between the data sets of wet laboratory chemical data and the FT-NIR spectra. Besides cross and test set validation the established models were subjected to a further evaluation step by means of additional wood samples with unknown extractive content. Extractive and phenol contents of these additional samples were predicted and outliers detected through Mahalanobis distance calculations. Models based on the whole spectral range and without data pre-processing performed well in cross-validation and test set validation, but failed in the evaluation test, which is based on spectral outlier detection. But selection of data pre-processing methods and manual as well as automatic restriction of wavenumber ranges considerably improved the model predictability. High coefficients of determination ( R2) and low root mean square errors of cross-validation ( RMSECV) were obtained for hot water extractives ( R2 = 0.96, RMSECV = 0.86%, range = 4.9–20.4%), acetone extractives ( R2 = 0.86, RMSECV = 0.32%, range = 0.8–3.6%) and phenolic substances ( R2 = 0.98, RMSECV = 0.21%, range = 0.7–4.9%) from wood powder. The models derived from wood powder spectra were more precise than those obtained from solid wood strips. Overall, NIR spectroscopy has proven to be an easy to facilitate, reliable, accurate and fast method for non-destructive wood extractive determination.

Download Full-text

Identification of celluloses with Fourier-Transform (FT) mid-infrared, FT-Raman and near-infrared spectrometry

Journal of Pharmaceutical and Biomedical Analysis ◽

10.1016/0731-7085(95)01298-y ◽

1995 ◽

Vol 13 (4-5) ◽

pp. 409-414 ◽

Cited By ~ 100

Author(s):

F.W. Langkilde ◽

A. Svantesson

Keyword(s):

Fourier Transform ◽

Near Infrared ◽

Infrared Spectrometry ◽

Mid Infrared ◽

Near Infrared Spectrometry ◽

Ft Raman

Download Full-text

Quality assessment of pharmaceutical tablet samples using Fourier transform near infrared spectroscopy and multivariate analysis

Infrared Physics & Technology ◽

10.1016/j.infrared.2017.07.016 ◽

2017 ◽

Vol 85 ◽

pp. 300-306 ◽

Cited By ~ 11

Author(s):

Lalit Mohan Kandpal ◽

Jagdish Tewari ◽

Nishanth Gopinathan ◽

Jessica Stolee ◽

Rick Strong ◽

...

Keyword(s):

Multivariate Analysis ◽

Fourier Transform ◽

Infrared Spectroscopy ◽

Quality Assessment ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Pharmaceutical Tablet

Download Full-text

Vibrational spectroscopy in practice: Detection of mineral oil in sunflower oil with near- and mid-infrared spectroscopy

NIR news ◽

10.1177/0960336018763196 ◽

2018 ◽

Vol 29 (3) ◽

pp. 6-11 ◽

Cited By ~ 1

Author(s):

Michael K-H Pfister ◽

Bettina Horn ◽

Janet Riedl ◽

Susanne Esslinger ◽

Carsten Fauhl-Hassek

Keyword(s):

Fourier Transform ◽

Infrared Spectroscopy ◽

Sunflower Oil ◽

Near Infrared ◽

Limit Of Detection ◽

Model Performance ◽

Mineral Oil ◽

Food Ingredient ◽

Mid Infrared ◽

Mid Infrared Spectroscopy

Fourier transform infrared spectroscopy becomes increasingly important for detecting adulterations in food due to a minimal sample preparation and a fast nondestructive measurement. Sunflower oil is a popular food ingredient, which might be contaminated or even adulterated by compounds with health concerns such as mineral oil. In this context a feasibility study was performed to compare the suitability of near- and mid-infrared spectroscopy for detecting mineral oil in sunflower oil. For this purpose, sunflower oils spiked with mineral oil in the concentration range of 0.001–1.0% w/w were analyzed by Fourier transform near- and mid-infrared spectroscopy, respectively, and spectra data were preprocessed prior to partial least squares regression. Hereby, the data preparation was optimized for each technique to account for model performance influences. The model performance was fairly similar for both approaches with a slightly better precision and thus limit of detection (near infrared 0.12% w/w, mid infrared 0.16% w/w) for the near-infrared-based model compared to the mid-infrared model. Consequently, both techniques are considered suitable for the determination of mineral oil in sunflower oil in the context of food authentication.

Download Full-text

A New Plant Indicator (Artemisia lavandulaefolia DC.) of Mercury in Soil Developed by Fourier-Transform Near-Infrared Spectroscopy Coupled with Least Squares Support Vector Machine

Journal of Analytical Methods in Chemistry ◽

10.1155/2019/3240126 ◽

2019 ◽

Vol 2019 ◽

pp. 1-6

Author(s):

Lu Xu ◽

Qiong Shi ◽

Bang-Cheng Tang ◽

Shunping Xie

Keyword(s):

Support Vector Machine ◽

Fourier Transform ◽

Least Squares ◽

Near Infrared ◽

Mercury Level ◽

Reference Method ◽

Nir Spectroscopy ◽

Classification Model ◽

Support Vector ◽

Reference Analysis

A rapid indicator of mercury in soil using a plant (Artemisia lavandulaefolia DC., ALDC) commonly distributed in mercury mining area was established by fusion of Fourier-transform near-infrared (FT-NIR) spectroscopy coupled with least squares support vector machine (LS-SVM). The representative samples of ALDC (stem and leaf) were gathered from the surrounding and distant areas of the mercury mines. As a reference method, the total mercury contents in soil and ALDC samples were determined by a direct mercury analyzer incorporating high-temperature decomposition, catalytic adsorption for impurity removal, amalgamation capture, and atomic absorption spectrometry (AAS). Based on the FT-NIR data of ALDC samples, LS-SVM models were established to distinguish mercury-contaminated and ordinary soil. The results of reference analysis showed that the mercury level of the areas surrounding mercury mines (0–3 kilometers, 7.52–88.59 mg/kg) was significantly higher than that of the areas distant from mercury mines (>5 kilometers, 0–0.75 mg/kg). The LS-SVM classification model of ALDC samples was established based on the original spectra, smoothed spectra, second-derivative (D2) spectra, and standard normal transformation (SNV) spectra, respectively. The prediction accuracy of D2-LS-SVM was the highest (0.950). FT-NIR combined with LS-SVM modeling can quickly and accurately identify the contaminated ALDC. Compared with traditional methods which rely on naked eye observation of plants, this method is objective and more sensitive and applicable.

Download Full-text

Monitoring of CO2 Absorption Solvent in Natural Gas Process Using Fourier Transform Near-Infrared Spectrometry

International Journal of Analytical Chemistry ◽

10.1155/2020/9830685 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Mohd Yusop Nurida ◽

Dolmat Norfadilah ◽

Mohd Rozaiddin Siti Aishah ◽

Chan Zhe Phak ◽

Syafiqa M. Saleh

Keyword(s):

Fourier Transform ◽

Near Infrared ◽

Nir Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Infrared Spectrometry ◽

Calibration Model ◽

Co2 Absorption ◽

Processing Unit

The analytical methods for the determination of the amine solvent properties do not provide input data for real-time process control and optimization and are labor-intensive, time-consuming, and impractical for studies of dynamic changes in a process. In this study, the potential of nondestructive determination of amine concentration, CO2 loading, and water content in CO2 absorption solvent in the gas processing unit was investigated through Fourier transform near-infrared (FT-NIR) spectroscopy that has the ability to readily carry out multicomponent analysis in association with multivariate analysis methods. The FT-NIR spectra for the solvent were captured and interpreted by using suitable spectra wavenumber regions through multivariate statistical techniques such as partial least square (PLS). The calibration model developed for amine determination had the highest coefficient of determination (R2) of 0.9955 and RMSECV of 0.75%. CO2 calibration model achieved R2 of 0.9902 with RMSECV of 0.25% whereas the water calibration model had R2 of 0.9915 with RMSECV of 1.02%. The statistical evaluation of the validation samples also confirmed that the difference between the actual value and the predicted value from the calibration model was not significantly different and acceptable. Therefore, the amine, CO2, and water models have given a satisfactory result for the concentration determination using the FT-NIR technique. The results of this study indicated that FT-NIR spectroscopy with chemometrics and multivariate technique can be used for the CO2 solvent monitoring to replace the time-consuming and labor-intensive conventional methods.

Download Full-text