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.

Review: The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation

2022 ◽  
pp. 096703352110572
Author(s):  
Nicholas T Anderson ◽  
Kerry B Walsh
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Short Wave ◽  
Least Squares Regression ◽  
Machine Learning Methods ◽  
Transmission Geometry ◽  
Modelling Techniques ◽  
Test Sets ◽  
Model Robustness

Short wave near infrared (NIR) spectroscopy operated in a partial or full transmission geometry and a point spectroscopy mode has been increasingly adopted for evaluation of quality of intact fruit, both on-tree and on-packing lines. The evolution in hardware has been paralleled by an evolution in the modelling techniques employed. This review documents the range of spectral pre-treatments and modelling techniques employed for this application. Over the last three decades, there has been a shift from use of multiple linear regression to partial least squares regression. Attention to model robustness across seasons and instruments has driven a shift to machine learning methods such as artificial neural networks and deep learning in recent years, with this shift enabled by the availability of large and diverse training and test sets.

The Effect of Multiplicative Scatter Correction (MSC) and Linearity Improvement in NIR Spectroscopy

1988 ◽  
Vol 42 (7) ◽  
pp. 1273-1284 ◽  
Author(s):  
Tomas Isaksson ◽  
Tormod Næs
Keyword(s):  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component Regression ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Data Sets ◽  
Prediction Errors ◽  
Regression Methods ◽  
Multiplicative Scatter Correction ◽  
Linearity Improvement

Near-infrared (NIR) reflectance spectra of five different food products were measured. The spectra were transformed by multiplicative scatter correction (MSC). Principal component regression (PCR) was performed, on both scatter-corrected and uncorrected spectra. Calibration and prediction were performed for four food constituents: protein, fat, water, and carbohydrates. All regressions gave lower prediction errors (7–68% improvement) by the use of MSC spectra than by the use of uncorrected absorbance spectra. One of these data sets was studied in more detail to clarify the effects of the MSC, by using PCR score, residual, and leverage plots. The improvement by using nonlinear regression methods is indicated.

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 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.

scholarly journals Evaluation of the physicochemical content and solid-state fermentation stage of Zhenjiang aromatic vinegar using near-infrared spectroscopy

2020 ◽  
Vol 16 (12) ◽  
Author(s):  
Suwan Fan ◽  
Tianhong Pan ◽  
Guoquan Li
Keyword(s):  
Solid State ◽  
Solid State Fermentation ◽  
Near Infrared ◽  
Evaluation Method ◽  
Nir Spectroscopy ◽  
Least Squares Regression ◽  
Total Acid ◽  
Fermentation Products ◽  
Fermentation Stage

AbstractAs one of the most famous traditional Chinese vinegars, the grains physicochemical content of Zhenjiang aromatic vinegar during solid-state fermentation (SSF) reflects the growth status of microorganisms and the quality of fermentation products. In addition, the time for grain-turning has a significant effect on the quality of fermentation products. In this study, a new evaluation method combined near-infrared (NIR) spectroscopy with partial least squares regression (PLSR) was proposed to predict the physicochemical content of grains and the fermentation stage. The performance of the PLSR models for the total acid and the nonvolatile acid were RMSEP = 0.0371, Rp2 = 0.9760, and RMSEP = 0.0216, Rp2 = 0.9646, respectively. The accuracy ratio of SSF stage judgment was 100%. Experimental results indicate that the proposed method can be used to guide on-site grain-turning and improve the quality of fermentation products.

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 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.

Integration of vis-NIR and pXRF spectroscopy for rapid measurement of soil lead concentrations

Soil Research ◽  
2020 ◽  
Vol 58 (3) ◽  
pp. 247 ◽  
Author(s):  
L. E. Pozza ◽  
T. F. A. Bishop ◽  
U. Stockmann ◽  
G. F. Birch
Keyword(s):  
Near Infrared ◽  
Prediction Interval ◽  
Fine Fraction ◽  
Model Averaging ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Model Fit ◽  
Combination Methods ◽  
Multi Stage

Heavy metals accumulate in soil over time and, with changing land use, humans may be exposed to elevated contaminant concentrations. It is therefore important to delineate contaminated sites in the most efficient and accurate manner. Sensors, such as portable X-ray fluorescence (pXRF) and visible near-infrared (vis-NIR) spectroscopy predict metal concentrations more rapidly and in a less hazardous manner compared to traditional laboratory analytical methods. The current study explored the potential for integrating vis-NIR and pXRF outputs to improve lead predictions in fine- (<62.5 µm) and whole-fraction (<2 mm) soil samples. A multi-stage approach was taken to compare different data treatments and combination methods for the prediction of whole-fraction lead content. Data treatment included principal component analysis, and combination methods included concatenation of pXRF and vis-NIR spectra before modelling, and Granger–Ramanathan model averaging of pXRF and vis-NIR model outputs. The most accurate predictions of whole-fraction lead were obtained by Granger–Ramanathan model averaging of vis-NIR Cubist predictions and Compton-normalised pXRF output: Lin’s Concordance Correlation Coefficient (LCCC) = 0.95, root mean square error (RMSE) = 86.4 mg kg–1, Bias < 0.001 mg kg–1 and ratio of performance to inter-quartile range (RPIQ) = 0.37. The most suitable modelling method was then used to predict fine-fraction lead, which provided a similarly accurate model fit (LCCC = 0.94, RMSE = 84.2 mg kg–1, Bias < 0.001 mg kg–1 and RPIQ = 0.34), indicating the potential to reduce the number of samples required for fine-fraction processing. In addition, the quality of the prediction interval estimates was examined – an important aspect in modelling which is underutilised in current literature related to soil spectroscopy.

Monitoring the Quality of Oils for Biodiesel Production Using Multivariate near Infrared Spectroscopy Models

2008 ◽  
Vol 16 (5) ◽  
pp. 445-454 ◽  
Author(s):  
Patrícia Baptista ◽  
Pedro Felizardo ◽  
José C. Menezes ◽  
M. Joana Neiva Correia
Keyword(s):  
Quality Control ◽  
Water Content ◽  
Vegetable Oils ◽  
Iodine Value ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Acid Number ◽  
Biodiesel Production ◽  
Least Squares Regression

Biodiesel is a mixture of fatty acid methyl esters, derived from vegetable oils or animal fats, which is usually produced by a transesterification reaction, where the oils or fats react with an alcohol in the presence of a catalyst. The quality of the oils used for biodiesel production strongly influences the final properties of biodiesel, namely its compliance to the European Standard. This work reports the use of near infrared (NIR) spectroscopy in the quality control of several oil properties, such as the iodine value, the water content and the acid number but, more importantly, the weight–weight percentages (wt%) of soybean, palm and rapeseed oil in mixtures. Principal component analysis was used to perform a qualitative analysis of the spectra, whereas partial least squares regression allowed the development of calibration models between analytical reference data and NIR spectra. The calibration ranges were 60–126 g I2 100 g−1 for the iodine value, 478–2500 mg kg−1 for the water content and 0.13-6.56 mg KOH g−1 for the acid number, whereas the validation errors were around 3.1 g I2 100 g−1, 111 mg kg−1 and 0.22 mg KOH g−1, respectively. The results obtained show that NIR spectroscopy is a promising technique to carry out the quality control of the commonly used vegetable oils for biodiesel production, namely the quality assurance and authenticity. Furthermore, it is of great value to have a simple, fast and reliable method to identify the composition of an oil mixture and/or some of its quality parameters, prior to storage or upon admission of a new lot of oil.

Sign in / Sign up

Export Citation Format

Share Document