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

Molecules ◽  
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.

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

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Claudio Gardana ◽  
Antonio Scialpi ◽  
Christian Fachechi ◽  
Paolo Simonetti
Keyword(s):  
Near Infrared ◽  
Spectroscopic Method ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Good Prediction ◽  
Anthocyanin Content ◽  
Least Squares Regression ◽  
Nondestructive Technique ◽  
Total Anthocyanin ◽  
Bilberry Extract

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.

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.

Fungal decay of spruce and beech wood assessed by near-infrared spectroscopy in combination with uni- and multivariate data analysis

Holzforschung ◽  
2007 ◽  
Vol 61 (6) ◽  
pp. 680-687 ◽  
Author(s):  
Karin Fackler ◽  
Manfred Schwanninger ◽  
Cornelia Gradinger ◽  
Ewald Srebotnik ◽  
Barbara Hinterstoisser ◽  
...  
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Lignin Content ◽  
Beech Wood ◽  
Multivariate Data ◽  
Principal Component ◽  
Brown Rot ◽  
White Rot ◽  
Least Squares Regression

Abstract Wood is colonised and degraded by a variety of micro-organisms, the most efficient ones are wood-rotting basidiomycetes. Microbial decay processes cause damage to wooden constructions, but also have great potential as biotechnological tools to change the properties of wood surfaces and of sound wood. Standard methods to evaluate changes in infected wood, e.g., EN350-1 1994, are time-consuming. Rapid FT-NIR spectroscopic methods are also suitable for this purpose. In this paper, degradation experiments on surfaces of spruce (Picea abies L. Karst) and beech (Fagus silvatica L.) were carried out with white rot basidiomycetes or the ascomycete Hypoxylon fragiforme. Experiments with brown rot or soft rot caused by Chaetomium globosum were also performed. FT-NIR spectra collected from the degraded wood were subjected to principal component analysis. The lignin content and mass loss of the specimens were estimated based on univariate or multivariate data analysis (partial least squares regression).

Determination of soil organic carbon and nitrogen at the field level using near-infrared spectroscopy

2002 ◽  
Vol 82 (4) ◽  
pp. 413-422 ◽  
Author(s):  
P D Martin ◽  
D F Malley ◽  
G. Manning ◽  
L. Fuller
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Analytical Data ◽  
Principal Component ◽  
Soil Samples ◽  
Organic N ◽  
Least Squares Regression ◽  
Total N ◽  
Organic C

This study explored the use of near-infrared spectroscopy (NIRS) for the rapid analysis of organic C (Corg) and organic N (Norg) in the A horizon of soil within a single field. Soil was sampled throughout a field in Manitoba, Canada to capture soil variability associated with topography. The soil samples were oven-dried and treated with acid to remove carbonates, after which C and N were determined by dry combustion. In this study, portions of the dried soil samples not treated with acid were scanned with a near-infrared scanning spectrophotometer between 1100 and 2500 nm. Correlating the spectral and the chemical analytical data using multiple linear regression or principal component analysis/partial least squares regression gave useful correlations for Corg. Over the range of 0–40 mg g-1 Corg, NIR-predicted values explained 75–78% of the variance in the chemical results. Results were improved to 80% for calibrations developed for the 0–20 mg g-1 organic C range. Useful results were not obtained for Norg although the literature shows that total N in soil is predictable using NIRS. It is likely that the acid treatment altered the composition of the samples in an inconsistent manner such that the chemically analyzed samples and those scanned by NIRS were different from each other in Norg concentration or composition. Extrapolation of these Corg results to the landscape scale implies that NIRS has potential to be a suitable method for mapping C for the purposes of monitoring C sequestration. Key words: Near-infrared spectroscopy, soil, carbon, nitrogen, topography, soil monitoring

DWT-PLS Regression on Near-Infrared Spectra for Moisture Determination of Coal

2013 ◽  
Vol 827 ◽  
pp. 209-212
Author(s):  
Xiao Li Yang ◽  
Fan Wang ◽  
Wen Chao Wang ◽  
Yun Xiu Chen ◽  
Ji Shu Chen
Keyword(s):  
Bituminous Coal ◽  
Near Infrared ◽  
Principal Component ◽  
Discrete Wavelet ◽  
Moisture Determination ◽  
Least Squares Regression ◽  
Compression Performance ◽  
Moisture Analysis ◽  
Near Infrared Spectra ◽  
Compressed Data

We studied moisture determination in bituminous coal and lignitic coal samples using near-infrared (NIR) spectra. This research was developed by applying partial least squares regression (PLS) and discrete wavelet transform (DWT). Firstly, the NIR spectra were pre-processed by DWT for fitting and compression. Then, the compressed data were used to build regression model with PLS for moisture determination in coal samples. Compression performance at different resolution scales was investigated. Using the compressed data, PLS can obtain more accurate result than using raw spectra. The number of principal component in PLS model was investigated too. The results show DWT-PLS can obtain satisfactory determination performance for moisture analysis in bituminous coal and lignitic coal.

Using NIR and ATR-FTIR spectroscopy to rapidly detect compression wood inPinus radiata

2014 ◽  
Vol 44 (7) ◽  
pp. 820-830 ◽  
Author(s):  
J. Paul McLean ◽  
Guangwu Jin ◽  
Maree Brennan ◽  
Michél K. Nieuwoudt ◽  
Philip J. Harris
Keyword(s):  
Chemical Composition ◽  
Compression Wood ◽  
Near Infrared ◽  
Water Saturation ◽  
Nir Spectroscopy ◽  
Analytical Techniques ◽  
Gradual Change ◽  
Least Squares Regression ◽  
Dimensional Changes ◽  
Discriminatory Analysis

Compression wood has undesirable properties for structural timber and for paper production. Traditional methods of detecting it are often time consuming and subjective. This study aimed to rapidly and impartially detect compression wood through the use of attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) and diffuse reflectance near infrared (NIR) spectroscopy. Compression wood and non-compression wood samples were obtained from young Pinus radiata D. Don trees grown in New Zealand. Longitudinal dimensional changes were measured during drying or water saturation of the samples; lignin and galactose contents were determined using conventional analytical techniques. Chemical composition was here a more reliable discriminator between wood types than longitudinal dimensional changes. It was shown that partial least-squares regression (PLS-R) or discriminatory analysis (PLS-DA) could be used to build models on the training samples that could discriminate between wood types of the independently grown validation samples. Ultimately, both types of spectroscopies could be used to discriminate between compression wood and non-compression wood either through prediction or discriminatory analysis with equal success. Investigation into spectral differences between wood types, including sequential mixtures of wood types, showed that for the mid-IR region absorbance at a well-resolved lignin band could be used to discriminate compression wood from non-compression wood. For NIR, a similar investigation showed that absorbance values at four separate wavenumbers or the 6000–5600 cm−1region of the first derivative spectra were required for that discrimination. It is proposed that if there is a gradual change in the chemical composition of compression wood with its severity, then IR spectroscopy could feasibly be used to rapidly determine compression wood severity.

105 NEAR-INFRARED SPECTROSCOPY AND AQUAPHOTOMICS ANALYSIS OF SERUM FROM MARES EXPOSED TO THE FUNGAL MYCOTOXIN ZEARALENONE

2017 ◽  
Vol 29 (1) ◽  
pp. 160
Author(s):  
C. K. Vance ◽  
K. R. Counsell ◽  
L. A. Agcanas ◽  
N. Shappell ◽  
S. Bowers ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Spectral Features ◽  
Serum Chemistry ◽  
Least Squares Regression ◽  
Aqueous Mixtures ◽  
Estrogenic Potential ◽  
Spectral Profiles

Aquaphotomics is a branch of near-infrared (NIR) spectroscopy in which bond vibrations from organic molecules and water create unique spectral absorbance patterns to profile complex aqueous mixtures. Aquaphotomics has been shown to detect virus infected soybean plants from extracts, classify probiotic bacteria, and detect contamination of aquatic ecosystems. We have used aquaphotomics to characterise serum profiles from horses in various phases of the reproductive cycle such as oestrus and diestrus. Because serum is a complex solution of biomolecules, various modes of serum processing (e.g. large protein removal for proteomics or mass spectrometry) may provide different NIR spectral profiles for quantitative analysis of specific compounds or their effects. Zearalenone is a fungal mycotoxin that may have estrogenic potential in mares and is found in feedstuffs. The objectives of this study were to (1) establish NIR spectral profiles of serum and protein-precipitated serum (PPS) collected at peak oestrus from mares; (2) determine if NIR profiles correlate and quantify E2 concentrations in serum or PPS; and (3) determine if NIR can detect differences in serum chemistry of zearalenone-treated mares. Mares were fed zearalenone daily at low (2 mg, 2 mares, 5 cycles) and high (8 mg, 1 mare, 3 cycles) concentrations, plus control (0 mg, 1 mare, 3 cycles). Oestrus cycles were monitored by ultrasound and serum hormone analysis. Serum collected at peak oestrus had E2 values determined by radioimmunoassay (range 0.02–16.87 pg mL−1). Protein precipitated serum had high and medium MW proteins removed with acetonitrile. NIR spectra, collected in triplicate with a 1 mm quartz cuvette and ASD FieldSpec®3 (Boulder, CO, USA), were pre-treated with a Savitsky-Golay 1st derivative for inspection of spectral features, principal component analysis, and partial least-squares regression (PLS) to investigate spectral correlations to E2 concentrations and zearalenone treatment effects. The NIR profiles contrasting serum and PPS at oestrus had distinct spectral features differing significantly at 1320, 1491, 1536, and 1566 nm in the NIR water spectrum, and principal component-1 accounted for 97% of principal component analysis variance in spectra from serum compared to PPS. In the PLS cross-validation linear fit regression model, NIR predicted E2 concentrations (validated by RIA) from serum (slope = 0.89, SECV = 1.92, R2 = 0.81, 3 factors), and from PPS (slope = 0.61; SECV = 1.84, R2 = 0.76, 4 factors). Spectral predictions were poorest at the low E2 threshold, E2 = 0.02 pg mL−1. The PLS model validation metrics of zearalenone dose-dependent effects were also evident in serum (slope = 0.88, SECV = 1.26, R2 = 0.86) and in PPS (slope = 0.67, SECV = 1.96, R2 = 0.66). Correlations of quantitative values of E2 and zearalenone were both better for spectra taken of serum compared to PPS. In summary, NIR spectral profiles of serum chemistry may be able to map E2 hormone levels during reproductive cycling, and these spectra may also have correlations that reflect exposure of mares to estrogenic toxins such as zearalenone. Research was supported by USDA-ARS Biophotonics grant #58-6402-3-018.

scholarly journals Screening of grated cheese authenticity by nir spectroscopy

2013 ◽  
Vol 44 (2s) ◽  
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.

scholarly journals Feasibility Study of the Use of Handheld NIR Spectrometer for Simultaneous Authentication and Quantification of Quality Parameters in Intact Pineapple Fruits

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Charles L. Y. Amuah ◽  
Ernest Teye ◽  
Francis Padi Lamptey ◽  
Kwasi Nyandey ◽  
Jerry Opoku-Ansah ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nearest Neighbor ◽  
Multivariate Calibration ◽  
Principal Component ◽  
Quality Parameters ◽  
Soluble Solids ◽  
Least Squares Regression ◽  
Total Soluble Solids ◽  
Linear Discriminant ◽  
Identification Rate

The potential of predicting maturity using total soluble solids (TSS) and identifying organic from inorganic pineapple fruits based on near-infrared (NIR) spectra fingerprints would be beneficial to farmers and consumers alike. In this study, a portable NIR spectrometer and chemometric techniques were combined to simultaneously identify organically produced pineapple fruits from conventionally produced ones (thus organic and inorganic) and also predict total soluble solids. A total of 90 intact pineapple fruits were scanned with the NIR spectrometer while a digital refractometer was used to measure TSS from extracted pineapple juice. After attempting several preprocessing techniques, multivariate calibration models were built using principal component analysis (PCA), K-nearest neighbor (KNN), and linear discriminant analysis (LDA) to identify the classes (organic and conventional pineapple fruits) while partial least squares regression (PLSR) method was used to determine TSS of the fruits. Among the identification techniques, the MSC-PCA-LDA model accurately identified organic from conventionally produced fruits at 100% identification rate. For quantification of TSS, the MSC-PLSR model gave Rp = 0.851 and RMSEC = 0.950 °Brix, and Rc = 0.854 and RMSEP = 0.842 °Brix at 5 principal components in the calibration set and prediction set, respectively. The results generally indicated that portable NIR spectrometer coupled with the appropriate chemometric tools could be employed for rapid nondestructive examination of pineapple quality and also to detect pineapple fraud due to mislabeling of conventionally produced fruits as organic ones. This would be helpful to farmers, consumers, and quality control officers.

Reflectance Near-Infrared Spectroscopic Method with Chemometric Techniques for Simultaneous Determination of Chondroitin, Glucosamine, and Methyl Sulfonyl Methane

2012 ◽  
Vol 95 (4) ◽  
pp. 1035-1042
Author(s):  
Alaa El-Gindy ◽  
Khalid Abdel-Salam Attia ◽  
Mohammad Wafaa Nassar ◽  
Hamed Hamed Abu Seda ◽  
Maisra Al-Shabrawi
Keyword(s):  
Simultaneous Determination ◽  
Near Infrared ◽  
Spectroscopic Method ◽  
External Validation ◽  
Pharmaceutical Preparations ◽  
Principal Component ◽  
Pharmaceutical Products ◽  
Pharmaceutical Product ◽  
Calibration Model

Abstract Reflectance near-IR (RNIR) spectroscopy was used for the simultaneous determination of chondroitin (CH), glucosamine (GO), and methyl sulfonyl methane (MSM) in tablets. Simple sample preparation was done by grinding, sieving, and compression of the tablets for improving RNIR spectra. Principal component regression and partial least squares (PLS-1 and PLS-2) were successfully applied to quantify the three components in the studied mixture using information included in RNIR spectra in the range of 4350–9100 cm–1. The calibration model was developed with drug concentration ranges of 14.5–44.2% (w/w) for CH, 18.4–55.3% (w/w) for GO, and 6–18.6% (w/w) for MSM with addition of tablet excipients to the calibration set in the same ratio as in the tested tablets. The calibration models were evaluated by internal validation, cross-validation, and external validation using synthetic and pharmaceutical preparations. The proposed method was applied for analysis of six batches of the pharmaceutical product. The results of the proposed method were compared with the results of the pharmacopoeial method for the same batch of the pharmaceutical product. No significant differences between the results were found. The RNIR method is accurate and precise, and can be used for QC of pharmaceutical products.

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