Near-Infrared Spectroscopy and Chemometrics for the Routine Detection of Bilberry Extract Adulteration and Quantitative Determination of the Anthocyanins

Consumers must be assured that bought food supplements contain both bilberry extract and the anthocyanin amounts that match the declared levels. Therefore, a Fourier transform near-infrared (FT-NIR) spectroscopic method was validated based on principal component scores for the prediction of bilberry extract adulteration and partial least squares regression model for total anthocyanin evaluation. Anthocyanins have been quantified individually in 71 commercial bilberry extracts by HPLC-DAD, and 6 of them were counterfeit. The anthocyanin content in bilberry extracts was in the range 18–34%. Authentic bilberry extracts (n=65) were divided into two parts: one for calibration (n=38) and the other for the validation set (n=27). Spectra were recorded in the range of 4000–12500 cm−1, and a good prediction model was obtained in the range of 9400–6096 and 5456–4248 cm−1withr2of 99.5% and a root-mean-square error of 0.3%. The adulterated extracts subjected to NIR analysis were recognized as noncompliant, thus confirming the results obtained by chromatography. The FT-NIR spectroscopy is an economic, powerful, and fast methodology for the detection of adulteration and quantification of the total anthocyanin in bilberry extracts; above all, it is a rapid, low cost, and nondestructive technique for routine analysis.

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Improvement of NIR models for quality parameters of leech and earthworm medicines using outlier multiple diagnoses

Journal of Innovative Optical Health Sciences ◽

10.1142/s1793545817500092 ◽

2017 ◽

Vol 11 (01) ◽

pp. 1750009 ◽

Cited By ~ 3

Author(s):

Chunyan Wu ◽

Jiashan Chen ◽

Mengru Li ◽

Yongjiang Wu ◽

Xuesong Liu

Keyword(s):

Near Infrared ◽

Nir Spectroscopy ◽

Principal Component ◽

Solid Content ◽

Detection Methods ◽

Soluble Solid Content ◽

Relative Standard Error ◽

Diffuse Reflection Spectroscopy ◽

Nondestructive Technique ◽

Relative Standard

Leeches and earthworms are the main ingredients of Shuxuetong injection compositions, which are natural biomedicines. Near infrared (NIR) diffuse reflection spectroscopy has been used for quality assurance of Chinese medicines. In the present work, NIR spectroscopy was proposed as a rapid and nondestructive technique to assess the moisture content (MC), soluble solid content (SSC) and hypoxanthine content (HXC) of leeches and earthworms. This study goal was to improve NIR models for accurate quality control of leech and earthworm using outlier multiple diagnoses (OMD). OMD was composed of four outlier detection methods: spectrum outlier diagnostic (MD), leverage diagnostic (LD), principal component scores diagnostic (PCSD) and factor loading diagnostic (FLD). Conventional outlier diagnoses (MD, LD) and OMD were compared, and the best NIR models were those based on OMD. The correlation coefficients ([Formula: see text]) for leech were 0.9779, 0.9616 and 0.9406 for MC, SSC and HXC, respectively. The values of relative standard error of prediction (RSEP) for leech were 2.3%, 5.1% and 9.0% for MC, SSC and HXC, respectively. The values of [Formula: see text] for earthworm were 0.9478, 0.9991 and 0.9605 for MC, SSC and HXC, respectively. The values of RSEP for earthworm were 8.8%, 2.4% and 12% for MC, SSC and HXC, respectively. The performance of the NIR models was certainly improved by OMD.

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Screening of grated cheese authenticity by nir spectroscopy

Journal of Agricultural Engineering ◽

10.4081/jae.2013.295 ◽

2013 ◽

Vol 44 (2s) ◽

Cited By ~ 3

Author(s):

Chiara Cevoli ◽

Angelo Fabbri ◽

Alessandro Gori ◽

Maria Fiorenza Caboni ◽

Adriano Guarnieri

Keyword(s):

Near Infrared ◽

Nir Spectroscopy ◽

Principal Component ◽

Classification Performance ◽

Integrating Sphere ◽

Least Squares Regression ◽

Test Set ◽

Spectra Analysis ◽

Protected Designation Of Origin ◽

Parmigiano Reggiano

Parmigiano–Reggiano (PR) cheese is one of the oldest traditional cheeses produced in Europe, and it is still one of the most valuable Protected Designation of Origin (PDO) cheeses of Italy. The denomination of origin is extended to the grated cheese when manufactured exclusively from whole Parmigiano-Reggiano cheese wheels that respond to the production standard. The grated cheese must be matured for a period of at least 12 months and characterized by a rind content not over 18%. In this investigation the potential of near infrared spectroscopy (NIR), coupled to different statistical methods, were used to estimate the authenticity of grated Parmigiano Reggiano cheese PDO. Cheese samples were classified as: compliance PR, competitors, non-compliance PR (defected PR), and PR with rind content greater then 18%. NIR spectra were obtained using a spectrophotometer Vector 22/N (Bruker Optics, Milan, Italy) in the diffuse reflectance mode. Instrument was equipped with a rotating integrating sphere. Principal Component Analysis (PCA) was conducted for an explorative spectra analysis, while the Artificial Neural Networks (ANN) were used to classify spectra, according to different cheese categories. Subsequently the rind percentage and month of ripening were estimated by a Partial Least Squares regression (PLS). Score plots of the PCA show a clear separation between compliance PR samples and the rest of the sample was observed. Competitors samples and the defected PR samples were grouped together. The classification performance for all sample classes, obtained by ANN analysis, was higher of 90%, in test set validation. Rind content and month of ripening were predicted by PLS a with a determination coefficient greater then 0.95 (test set). These results showed that the method can be suitable for a fast screening of grated cheese authenticity.

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Laboratory-Scale Preparation and Characterization of Dried Extract of Muirapuama (Ptychopetalum olacoides Benth) by Green Analytical Techniques

Molecules ◽

10.3390/molecules25051095 ◽

2020 ◽

Vol 25 (5) ◽

pp. 1095

Author(s):

Ester Paulitsch Trindade ◽

Franklin Teixeira Regis ◽

Gabriel Araújo da Silva ◽

Breno Nunes Aguillar ◽

Marcelo Vítor de Paiva Amorim ◽

...

Keyword(s):

Near Infrared ◽

Moisture Absorption ◽

Spectroscopic Method ◽

Herbal Medicines ◽

Morphological Characteristics ◽

Principal Component ◽

Ethanolic Extract ◽

Analytical Techniques ◽

Least Squares Regression ◽

Sem Images

This work reports on the preparation of a drying process from the ethanolic extract of Muirapuama and its characterization through green analytical techniques. The spray-drying processes were performed by using ethanolic extract in a ratio of 1:1 extract/excipient and 32 factorial design. The properties of dried powder were investigated in terms of total flavonoid content, moisture content, powder yield, and particle size distribution. An analytical eco-scale was applied to assess the greenness of the developed protocol. Ultra-high performance liquid chromatography (UHPLC)with reduced solvent consumption in the analysis was compared to the conventional HPLC method. A Fourier transform near-infrared (FT-NIR) spectroscopic method was applied based on the principal component scores for the prediction of extract/excipient mixtures and partial least squares regression model for quantitative analysis. NIR spectroscopy is an economic, powerful, and fast methodology for the detection of excipient in muirapuama dried extracts, generating no residue in the analysis. Scanning electron microscopy (SEM) images showed samples with a higher concentration of excipient, presenting better morphological characteristics and a lower moisture absorption rate. An eco-scale score value of 85 was achieved for UHPLC and 100 was achieved for NIR (excellent green analysis). Above all, these methods are rapid and green for the routine analysis of herbal medicines based on dried extracts.

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Application of the Non-Destructive NIR Technique for the Evaluation of Strawberry Fruits Quality Parameters

Foods ◽

10.3390/foods9040441 ◽

2020 ◽

Vol 9 (4) ◽

pp. 441 ◽

Cited By ~ 2

Author(s):

Manuela Mancini ◽

Luca Mazzoni ◽

Francesco Gagliardi ◽

Francesca Balducci ◽

Daniele Duca ◽

...

Keyword(s):

Near Infrared ◽

Nir Spectroscopy ◽

Principal Component ◽

Infrared Region ◽

Quality Parameters ◽

Soluble Solids ◽

Electromagnetic Spectrum ◽

Least Squares Regression ◽

Solids Content ◽

Non Destructive

The determination of strawberry fruit quality through the traditional destructive lab techniques has some limitations related to the amplitude of the samples, the timing and the applicability along all phases of the supply chain. The aim of this study was to determine the main qualitative characteristics through traditional lab destructive techniques and Near Infrared Spectroscopy (NIR) in fruits of five strawberry genotypes. Principal Component Analysis (PCA) was applied to search for spectral differences among all the collected samples. A Partial Least Squares regression (PLS) technique was computed in order to predict the quality parameters of interest. The PLS model for the soluble solids content prediction was the best performing—in fact, it is a robust and reliable model and the validation values suggested possibilities for its use in quality applications. A suitable PLS model is also obtained for the firmness prediction—the validation values tend to worsen slightly but can still be accepted in screening applications. NIR spectroscopy represents an important alternative to destructive techniques, using the infrared region of the electromagnetic spectrum to investigate in a non-destructive way the chemical–physical properties of the samples, finding remarkable applications in the agro-food market.

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Using near Infrared Spectroscopy for Predicting Process Conditions. A Laboratory Study from Pulp Production

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.557 ◽

2005 ◽

Vol 13 (5) ◽

pp. 265-276 ◽

Cited By ~ 3

Author(s):

Heidi Henriksen ◽

Tormod Næs ◽

Vegard Segtnan ◽

Are Aastveit

Keyword(s):

Data Storage ◽

Large Scale ◽

Near Infrared ◽

Model Development ◽

Nir Spectroscopy ◽

Principal Component ◽

Process Conditions ◽

Multivariate Techniques ◽

Least Squares Regression ◽

Statistical Process

Most industries face a growing challenge concerning data handling due to the large data storage capacity available today. In many cases, it is difficult to navigate through these amounts of data in search of relevant information. An important tool in this context is statistical process control (SPC), which enables the discovery of possible process drift or other problems as early as possible. In this work the potential of using near infrared (NIR) spectroscopy as a multifunction tool for SPC in the context of process monitoring has been investigated. Both principal component analysis (PCA) and partial least squares regression (PLS) are tested as tools for extracting useful information from NIR spectra. The two methods have been compared based on interpretation of score plots and explained variance. We have also tested classification tools for prediction of classes and various types of validation, since these data came from designed experiments. It has been demonstrated that PLS is a useful tool both for forward and backward predictions. Another topic considered is discovery of instrument drift and outlier detection. It has been demonstrated that PLS is a useful tool in both contexts. The robustness of PLS predictions has been investigated and it was found that PLS score plots can reveal useful information early in the process. This study was a feasibility study and the models can not be used directly in any large scale installations. This work has, however, demonstrated the usefulness of multivariate techniques in such processes and found a good basis for further model development.

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Applying NIR spectroscopy to evaluate quality of whey protein supplements available on the Polish market

Zywnosc Nauka Technologia Jakosc/Food Science Technology Quality ◽

10.15193/zntj/2018/115/233 ◽

2018 ◽

Vol 115 (2) ◽

pp. 59-70

Author(s):

Krzysztof Wójcicki

Keyword(s):

Near Infrared ◽

Protein Hydrolysate ◽

Scatter Correction ◽

Protein A ◽

Nir Spectroscopy ◽

Principal Component ◽

Protein Isolate ◽

Least Squares Regression ◽

Protein Supplements

The objective of the research study was to apply near infrared (NIR) spectroscopy to evaluate the quality of protein supplements available in the Polish shops and gyms. The evaluation was performed on the basis of the determination of the protein quantity contained in the individual samples by a Kjeldahl method and then the evaluation results were correlated with the measured NIR spectra using an appropriate chemometric method. The research material consisted of fifteen protein supplement samples for athletes, which included the following types: WPI (protein isolate), WPC (protein concentrate), WPH (protein hydrolysate), and mixtures thereof. The obtained NIR spectra of protein supplements were characterized by a similar shape of the bands. Depending on the type of protein, a different intensity of absorption of individual bands could be observed. A Principal Component Analysis (PCA) was used to distinguish the samples based on the spectra measured. Unfortunately, owing to the varying composition of the protein mixtures, it was not possible to find characteristic arrangement of the samples depending on their types. The spectra were correlated with the protein contents determined in the samples using a Partial Least Squares regression method (PLS regression) and various mathematic transformations of the NIR spectral data. The obtained regression models were analysed and the analysis results confirmed that it was possible to apply NIR spectra to estimate the content of proteins in protein supplements. The best result was obtained in a spectrum region between 9401 and 5448 cm-1 and after the first derivative was applied with Multiplicate Scatter Correction (MSC) as a mathematical pre-treatment. On the basis of the results obtained, it was proved that the NIR spectra applied together with the chemometric analysis could be used to quickly evaluate the products studied.

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Spectral Range and Wood Surface Impacts on Equilibrium Moisture Content Estimation in Thermally Modified Beech Wood by FT-NIR Spectroscopy

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1208/1/012022 ◽

2021 ◽

Vol 1208 (1) ◽

pp. 012022

Author(s):

Nebojša Todorović

Keyword(s):

Moisture Content ◽

Equilibrium Moisture Content ◽

Cross Section ◽

Near Infrared ◽

Prediction Models ◽

Beech Wood ◽

Nir Spectroscopy ◽

Principal Component ◽

Least Squares Regression ◽

The Cross

Abstract Fourier transform near-infrared (FT-NIR) spectroscopy and partial least squares regression (PLS-R) were tested for the possibility of equilibrium moisture content (EMC) prediction in thermally modified beech wood (Fagus moesiaca C.). The samples were modified for 4h at temperatures of 170, 190 and 210 °C. After thermal modification, the samples were kept in a climatic chamber until EMC was reached. FT-NIR spectra (100 scans and 4 cm-1) were collected on the cross-section and radial surfaces at four points. PLS – R models were developed for four spectral regions: the first overtone, the second overtone, the third overtone and the combination band region. Applied thermal treatment caused a decrease of EMC by 42 % at 170 °C, by 53 % at 190 °C, and by 62 % at 210 °C. Principal component analysis (PCA) indicated that there is a difference both between treatments and between wood surfaces. The results of the spectra taken from the radial surface were, in all models, better than the spectra of the cross-section. Related to chemical changes, the first and second overtone region play an important role in the calibrations. The best prediction models for EMC of thermally modified beech wood were obtained from radial surface spectra in the first (Rp2=0.86, RPD=2.69) and second overtone region (Rp2=0.87, RPD=2.70). The obtain results could contribute to the development of predictive models in monitoring of EMC which could significantly improve the quality of industrial production of thermally modified wood.

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Quantitative Analysis ofPanax ginsengby FT-NIR Spectroscopy

Journal of Analytical Methods in Chemistry ◽

10.1155/2014/741571 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6 ◽

Cited By ~ 4

Author(s):

Xin-fang Xu ◽

Li-xing Nie ◽

Li-li Pan ◽

Bian Hao ◽

Shao-xiong Yuan ◽

...

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Multivariate Regression ◽

Near Infrared ◽

Nir Spectroscopy ◽

Principal Component ◽

Mean Square ◽

Least Squares Regression ◽

Regression Methods

Near-infrared spectroscopy (NIRS), a rapid and efficient tool, was used to determine the total amount of nine ginsenosides inPanax ginseng. In the study, the regression models were established using multivariate regression methods with the results from conventional chemical analytical methods as reference values. The multivariate regression methods, partial least squares regression (PLSR) and principal component regression (PCR), were discussed and the PLSR was more suitable. Multiplicative scatter correction (MSC), second derivative, and Savitzky-Golay smoothing were utilized together for the spectral preprocessing. When evaluating the final model, factors such as correlation coefficient (R2) and the root mean square error of prediction (RMSEP) were considered. The final optimal results of PLSR model showed that root mean square error of prediction (RMSEP) and correlation coefficients (R2) in the calibration set were 0.159 and 0.963, respectively. The results demonstrated that the NIRS as a new method can be applied to the quality control ofGinseng Radix et Rhizoma.

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Total Anthocyanin Content in Intact Açaí (Euterpe oleracea Mart.) and Juçara (Euterpe edulis Mart.) Fruit Predicted by Near Infrared Spectroscopy

HortScience ◽

10.21273/hortsci.50.8.1218 ◽

2015 ◽

Vol 50 (8) ◽

pp. 1218-1223 ◽

Cited By ~ 3

Author(s):

Gustavo H. de A. Teixeira ◽

Valquiria G. Lopes ◽

Luís C. Cunha Júnior ◽

José D.C. Pessoa

Keyword(s):

Near Infrared ◽

Nir Spectroscopy ◽

Nondestructive Method ◽

Data Sets ◽

Anthocyanin Content ◽

Euterpe Oleracea ◽

Total Anthocyanin ◽

Euterpe Edulis ◽

On Line ◽

Aoac Method

Açaí (Euterpe oleraceae Mart.) and juçara (Euterpe edulis Mart.) palms are native to Brazil and these species are rich in anthocynanins. The methods applied to determine anthocyanins are time-consuming, generate chemical residues, and do not fit in modern on-line grading machines. As near infrared (NIR) spectroscopy has been used as a nondestructive method to determine anthocyanin, the objective of this study was to use NIR spectroscopy to predict total anthocyanin (TA) in intact açaí and juçara fruits. Spectra were collected using a Fourier transform (FT)-NIR spectrophotometer in the diffuse reflectance (4,000–10,000 cm−1) and TA reference data were obtained using the Association of Official Analytical Chemists (AOAC) method. Different treatments were applied to spectra and spectral data sets were correlated with TA by using partial least squares (PLSs) regression algorithm. The global-PLS model obtained with açaí and juçara spectra resulted in a root mean standard error of prediction (RMSEP) of 10.05 g·kg−1. However, this model was not adequate for TA levels found in açaí fruits, therefore individual models were developed. The açaí-PLS model proved to be more adequate, as RMSEP was reduced to 3.56 g·kg−1. On the other hand, the RMSEP obtained with the juçara-PLS model (6.59 g·kg−1) was almost the same of the global model. NIR spectroscopy can be used to adequately predict TA content in intact açaí and juçara fruits and this method could be used as an analytical procedure to monitor their quality.

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Analysis of Selected Properties of Fibreboard Panels Manufactured from Wood and Leather Using the Near Infrared Spectroscopy

International Journal of Spectroscopy ◽

10.1155/2015/691796 ◽

2015 ◽

Vol 2015 ◽

pp. 1-6 ◽

Cited By ~ 4

Author(s):

Kerstin Wagner ◽

Thomas Schnabel ◽

Marius-Catalin Barbu ◽

Alexander Petutschnigg

Keyword(s):

Infrared Spectroscopy ◽

Data Analysis ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Bending Strength ◽

Raw Materials ◽

Nir Spectroscopy ◽

Physical And Mechanical Properties ◽

Principal Component ◽

Least Squares Regression

This paper deals with the characterization of the properties of wood fibres leather shavings composite board by using the near infrared spectroscopy (NIRS) and multivariate data analysis. In this study fibreboards were manufactured with different leather amounts by using spruce fibres, as well as vegetable and mineral tanned leather shavings (wet white and wet blue). The NIR spectroscopy was used to analyse the raw materials as well as the wood leather fibreboards. Moreover, the physical and mechanical features of the wood leather composite fibreboards were determined to characterize their properties for the further data analysis. The NIR spectra were analysed by univariate and multivariate methods using the Principal Component Analysis (PCA) and the Partial Least Squares Regression (PLSR) method. These results demonstrate the potential of FT-NIR spectroscopy to estimate the physical and mechanical properties (e.g., bending strength). This phenomenon provides a possibility for quality assurance systems by using the NIRS.

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