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

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.

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).

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 Application of visible-near infrared spectroscopy to evaluate the quality of button mushrooms

2018 ◽  
Vol 27 (1) ◽  
pp. 38-45 ◽  
Author(s):  
Valentina Giovenzana ◽  
Alessio Tugnolo ◽  
Andrea Casson ◽  
Riccardo Guidetti ◽  
Roberto Beghi
Keyword(s):  
Moisture Content ◽  
Near Infrared ◽  
Distribution Channel ◽  
Nir Spectroscopy ◽  
Quality Standard ◽  
Quality Parameters ◽  
Soluble Solids ◽  
Least Squares Regression ◽  
Physical Damage ◽  
Reference Quality

The Agaricus bisporus mushroom is one of the most cultivated and consumed mushrooms in the world, thanks to its delicacy, nutritional value and flavour. The quality evaluation of the A. bisporus during the harvest is generally established by a visual check by trained operators. This method complies with the request of the Distribution Channel (DC) to retailers and guarantees very low physical damage to the mushrooms; nevertheless, it is subjective and it does not guarantee the highest quality standard for the consumer. The aim of this study was to test the use of visible/near infrared (vis/NIR) reflectance spectroscopy (400–1000 nm) to objectively evaluate the quality parameters of A. bisporus mushrooms. A total of 167 samples of A. bisporus mushrooms were harvested according to the main DC purchasing standards. The vis/NIR analyses were performed the day of sampling just before the physico-chemical analyses (sizes, firmness, soluble solids content and moisture content) used as reference quality parameters. The vis/NIR spectra were correlated to reference measures in order to build predictive models using the partial least squares regression method. Calculated models gave positive results regarding the prediction of the moisture content (r2(pred) = 0.78) and firmness (r2(pred) = 0.78). Results of this explorative study could be considered encouraging and demonstrate the applicability of vis/NIR spectroscopy on A. bisporus as a rapid technique (i) to monitor the productive process directly at the company, (ii) to standardize the harvest moment, and (iii) to support DC’s buyers’ choices, nowadays exclusively based on product external characteristics.

A survey of moisture distribution in two sets of Scots pine logs by NIR-spectroscopy

Holzforschung ◽  
2008 ◽  
Vol 62 (4) ◽  
Author(s):  
Kyösti Karttunen ◽  
Asta Leinonen ◽  
Matti-Paavo Sarén
Keyword(s):  
Moisture Content ◽  
Scots Pine ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Optical Probe ◽  
Moisture Distribution ◽  
Least Squares Regression ◽  
Step Procedure ◽  
Surface Drying ◽  
Automated Scanning

Abstract Moisture content distributions of Scots pine logs in the green state were measured by a novel multi-step procedure. After sample preparation, the transverse sections of the wood surfaces were scanned by an automated scanning device with a fiber optical probe connected to a Fourier transform near-infrared spectroscope. In the course of the measurement sequences, several issues were addressed, such as surface drying, measurement geometry, ease of automation and interconnected data handling. The near-infrared (NIR) data were first modeled separately for heartwood and sapwood by means of multivariate partial least squares regression. The models for moisture content were evaluated by root mean square error of prediction, the result being 0.8% for heartwood and 10% for sapwood. The two models were then applied to the NIR data collected from sets of disks cut from nine logs. The results of the calculated moisture contents were evaluated by methods of descriptive statistics, and they indicated clear differences and trends in the distribution of moisture content in transverse or longitudinal regions of a log. Additionally, inter-tree variation in moisture content was detected.

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.

Estimating mechanical properties and specific gravity for five-year-old Eucalyptus tereticornis having broad moisture content range by NIR spectroscopy

Holzforschung ◽  
2011 ◽  
Vol 65 (5) ◽  
Author(s):  
Vimal Kothiyal ◽  
Aasheesh Raturi
Keyword(s):  
Specific Gravity ◽  
Moisture Content ◽  
Near Infrared ◽  
Wood Property ◽  
Nir Spectroscopy ◽  
Modulus Of Rupture ◽  
Bending Test ◽  
Coefficient Of Determination ◽  
Eucalyptus Tereticornis ◽  
Least Squares Regression

Abstract Near infrared spectroscopy coupled with multivariate data analysis has been used to predict the specific gravity, modulus of rupture, modulus of elasticity, and fiber stress at elastic limit in bending tests on radial and tangential strip wood samples obtained from five-year-old Eucalyptus tereticornis. Moisture content of samples was 6–21% for bending test and 7–16% for specific gravity. Partial least squares regression calibrations were developed for each wood property. Calibrations had good relationships between values measured in laboratory and NIR predicted values obtained from small clear samples. The coefficient of determination (R2) for calibration ranged from 0.76 to 0.83 and for prediction (Rp 2) it was between 0.58 and 0.77. Both radial and tangential faces are equally suited for calibration (for radial face R2 was 0.77–0.83 and for tangential it was 0.76–0.83). Standard errors of predictions were slightly higher compared to standard error of calibration.

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

Foods ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 441 ◽  
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.

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

2005 ◽  
Vol 13 (5) ◽  
pp. 265-276 ◽  
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.

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

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.

scholarly journals Sorghum Grains Grading for Food, Feed, and Fuel Using NIR Spectroscopy

2021 ◽  
Vol 12 ◽  
Author(s):  
Irsa Ejaz ◽  
Siyang He ◽  
Wei Li ◽  
Naiyue Hu ◽  
Chaochen Tang ◽  
...  
Keyword(s):  
Near Infrared ◽  
Prediction Models ◽  
Low Cost ◽  
Nir Spectroscopy ◽  
Grain Morphology ◽  
Whole Grains ◽  
Least Squares Regression ◽  
Sorghum Grains ◽  
Sorghum Grain ◽  
Non Destructive

Near-infrared spectroscopy (NIR) is a non-destructive, fast, and low-cost method to measure the grain quality of different cereals. However, the feasibility for determining the critical biochemicals, related to the classifications for food, feed, and fuel products are not adequately investigated. Fourier-transform (FT) NIR was applied in this study to determine the eight biochemicals in four types of sorghum samples: hulled grain flours, hull-less grain flours, whole grains, and grain flours. A total of 20 hybrids of sorghum grains were selected from the two locations in China. Followed by FT-NIR spectral and wet-chemically measured biochemical data, partial least squares regression (PLSR) was used to construct the prediction models. The results showed that sorghum grain morphology and sample format affected the prediction of biochemicals. Using NIR data of grain flours generally improved the prediction compared with the use of NIR data of whole grains. In addition, using the spectra of whole grains enabled comparable predictions, which are recommended when a non-destructive and rapid analysis is required. Compared with the hulled grain flours, hull-less grain flours allowed for improved predictions for tannin, cellulose, and hemicellulose using NIR data. This study aimed to provide a reference for the evaluation of sorghum grain biochemicals for food, feed, and fuel without destruction and complex chemical analysis.

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