Quantitative analysis of two adulterants in Cynanchum stauntonii by near-infrared spectroscopy combined with multi-variate calibrations

2012 ◽  
Vol 66 (12) ◽  
Author(s):  
Wen-Jiang Dong ◽  
Yong-Nian Ni ◽  
Serge Kokot
Keyword(s):  
Infrared Spectroscopy ◽  
Quantitative Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Herbal Medicines ◽  
Least Squares Regression ◽  
Chinese Medicines ◽  
Calibration Models ◽  
Ann Models ◽  
Nir Reflectance Spectroscopy

AbstractAuthentication of traditional Chinese medicines (TCMs) has become important because they can be adulterated with relatively cheap herbal medicines similar in appearance. Detection of such adulterated samples is needed because their presence is likely to reduce the pharmacological potency of the original TCM and, in the worst cases, the samples may be harmful. The aim of this study was to develop a rapid near-infrared spectroscopy (NIRS) analytical method which was supported by multi-variate calibration, e.g. partial least squares regression (PLSR) and radial basis function artificial neural networks (RBF-ANN), in order to quantify the TCM and the adulterants. In this work, Cynanchum stauntonii (CS), a commonly used TCM, in mixtures with one or two adulterants — two morphological types of TCM, Cynanchum atrati (CA) and Cynanchum paniculati (CP), were determined using NIR reflectance spectroscopy. The three sample sets, CS adulterated with CA or CP, and CS with both CA and CP, were measured in the range of 800–2500 nm. Both PLSR and RBF-ANN calibration models provided satisfactory results, even at an adulteration level of 5 mass %, but the RBF-ANN models with better root mean square error of prediction (RMSEP) values for CS, CA, and CP arguably performed better. Consequently, this work demonstrates that the NIR method of sampling complex mixtures of similar substances such as CS adulterated by CA and/or CP is capable of producing data suitable for the quantitative analysis of mixtures consisting of the original TCM adulterated by one or two similar substances, provided the spectral data are interrogated by multi-variate methods of data analysis such as PLS or RBF-ANN.

Evaluation and comparison of NIR multi-product calibration methods for Brix prediction

2018 ◽  
Vol 85 (3) ◽  
pp. 184-190 ◽  
Author(s):  
Michael Kopf ◽  
Robin Gruna ◽  
Thomas Längle ◽  
Jürgen Beyerer
Keyword(s):  
Infrared Spectroscopy ◽  
Quantitative Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Sugar Content ◽  
Least Squares Regression ◽  
Local Approach ◽  
Single Product ◽  
Calibration Models ◽  
Calibration Methods

Abstract Near-infrared (NIR) spectroscopy is a widespread technology for fruit and vegetable quality assessment. New fields of application of this technology, like mobile food analysis with handheld low-cost spectrometers, increase the demand for chemometric calibration models that are able to deal with multiple products and varieties thereof at once (so-called multi-product calibration models). While there are well studied methods for single-product calibration as partial least squares regression (PLSR), multi-product calibration is still challenging. Conventional approaches that work well for single-product calibration can lead to high errors for multi-product calibration. However, nonlinear methods as local regression and artificial neural networks were found to be suitable E. Micklander, K. Kjeldahl, M. Egebo, and L. Norgaard. Multi-product calibration models of near-infrared spectra of foods. Journal of Near Infrared Spectroscopy, 14:395–402, 2006. L. R. Lopez, T. Behrens, K. Schmidt, A. Stevens, J. A. M. Dematte, and T. Scholten. The spectrum-based learner: A new local approach for modeling soil vis-NIR spectra of complex datasets. Geoderma, 195–196:268–279, 2013. . Preliminary studies in multi-product calibration for quantitative analysis of food with near-infrared spectroscopy showed good results for memory-based learning (MBL) and a classification prediction hierarchy (CPH) M. C. Kopf and R. Gruna. Examination of multiproduct calibration approaches for quantitative analysis of food with near infrared spectroscopy. Bachelor's thesis, Karlsruhe Institute of Technology KIT, 2016. . In this study, three varieties of apples, pears and tomatoes with known sugar content (in ○Brix) are analysed with NIR hyperspectral imaging spectroscopy in the range from 900 nm to 2400 nm. Predictive performance of a linear PLSR model, two nonlinear models (CPH and MBL) and different pre-processing techniques are tested and evaluated. For error estimation, leave-one-product-out and leave-one-out cross-validation are used.

Studies on the Absorption Characteristics of Triglyceride in Serum by near Infrared Spectroscopy

2011 ◽  
Vol 345 ◽  
pp. 124-127
Author(s):  
Dong Min Wang ◽  
Li Jun Yao
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Principal Components ◽  
Near Infrared ◽  
Relevant Information ◽  
Least Squares Regression ◽  
Noninvasive Measurements ◽  
Calibration Models ◽  
Spectral Ranges ◽  
Absorption Characteristics

To determine the spectral ranges and extract relevant information from near infrared spectroscopy of serum in the regions of 12000-4000cm-1 when predicting triglyceride concentrations, in this paper, the first three principal components, which represent not less than 94 percents spectral information, were calculated using principal components analysis. According to this, certain spectral ranges, which contained useful absorption information, were determined based on the absorption characteristics of triglyceride and the absorption interference of water in serum. At last, three calibration models were developed coupled with the method of partial least squares regression, the results shows that there are significant improvement preferment compared the calibration based on the whole overtones spectral ranges. The analysis described here provides a method of spectral ranges selectivity by which the interference of other constituents can be eliminated easily and is useful for noninvasive measurements of the biophysical constituents in blood.

Application of visible and near-infrared spectroscopy for evaluation of ewes milk with different feeds

2019 ◽  
Vol 59 (6) ◽  
pp. 1190 ◽  
Author(s):  
A. Bahri ◽  
S. Nawar ◽  
H. Selmi ◽  
M. Amraoui ◽  
H. Rouissi ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Random Forest ◽  
Near Infrared Spectroscopy ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Principal Component ◽  
Least Squares Regression ◽  
Random Forest Regression ◽  
Milk Samples

Rapid measurement optical techniques have the advantage over traditional methods of being faster and non-destructive. In this work visible and near-infrared spectroscopy (vis-NIRS) was used to investigate differences between measured values of key milk properties (e.g. fat, protein and lactose) in 30 samples of ewes milk according to three feed systems; faba beans, field peas and control diet. A mobile fibre-optic vis-NIR spectrophotometer (350–2500 nm) was used to collect reflectance spectra from milk samples. Principal component analysis was used to explore differences between milk samples according to the feed supplied, and a partial least-squares regression and random forest regression were adopted to develop calibration models for the prediction of milk properties. Results of the principal component analysis showed clear separation between the three groups of milk samples according to the diet of the ewes throughout the lactation period. Milk fat, protein and lactose were predicted with good accuracy by means of partial least-squares regression (R2 = 0.70–0.83 and ratio of prediction deviation, which is the ratio of standard deviation to root mean square error of prediction = 1.85–2.44). However, the best prediction results were obtained with random forest regression models (R2 = 0.86–0.90; ratio of prediction deviation = 2.73–3.26). The adoption of the vis-NIRS coupled with multivariate modelling tools can be recommended for exploring to differences between milk samples according to different feed systems, and to predict key milk properties, based particularly on the random forest regression modelling technique.

Evaluation of the higher heating value, volatile matter, fixed carbon and ash content of ground bamboo using near infrared spectroscopy

2017 ◽  
Vol 25 (5) ◽  
pp. 301-310 ◽  
Author(s):  
Jetsada Posom ◽  
Panmanas Sirisomboon
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Model Development ◽  
Volatile Matter ◽  
Ash Content ◽  
Least Squares Regression ◽  
Fixed Carbon ◽  
Bomb Calorimetry ◽  
Heating Value

This research aimed to determine the higher heating value, volatile matter, fixed carbon and ash content of ground bamboo using Fourier transform near infrared spectroscopy as an alternative to bomb calorimetry and thermogravimetry. Bamboo culms used in this study had circumferences ranging from 16 to 40 cm. Model development was performed using partial least squares regression. The higher heating value, volatile matter, fixed carbon and ash content were predicted with coefficients of determination (r2) of 0.92, 0.82, 0.85 and 0.51; root mean square error of prediction (RMSEP) of 122 J g−1, 1.15%, 1.00% and 0.77%; ratio of the standard deviation to standard error of validation (RPD) of 3.66, 2.55, 2.62 and 1.44; and bias of 14.4 J g−1, −0.43%, 0.03% and −0.11%, respectively. This report shows that near infrared spectroscopy is quite successful in predicting the higher heating value, and is usable with screening for the determination of fixed carbon and volatile matter. For ash content, the method is not recommended. The models should be able to predict the properties of bamboo samples which are suitable for achieving higher efficiency for the biomass conversion process.

Linear transformations of multivariate calibration models in near infrared spectroscopy: A comparative study

2017 ◽  
Vol 25 (4) ◽  
pp. 223-230 ◽  
Author(s):  
Joseph Dubrovkin
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Cetane Number ◽  
Total Protein Content ◽  
Prediction Errors ◽  
Calibration Data ◽  
Linear Transformations ◽  
Calibration Models

It was shown that linear transformations are suitable for use in multivariate calibration in near infrared spectroscopy as data compression tools. Partial Least Squares calibration models were built using spectral data transformed by expansion in the series of classical orthogonal polynomials, Fourier and wavelet harmonics. These models allowed effective prediction of the cetane number of diesel fuels, Brix and pol parameters of syrup in sugar production and fat and total protein content in milk. Depending on the compression ratio, prediction errors were no larger than 30% of corresponding errors obtained by the use of the non-transformed models. Although selection of the most suitable transformation depends on the calibration data and on the cross-validation method, in many cases Fourier transform gave satisfactory results.

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