scholarly journals Investigations into the Performance of a Novel Pocket-Sized Near-Infrared Spectrometer for Cheese Analysis

Molecules ◽  
2019 ◽  
Vol 24 (3) ◽  
pp. 428 ◽  
Author(s):  
Verena Wiedemair ◽  
Dominik Langore ◽  
Roman Garsleitner ◽  
Klaus Dillinger ◽  
Christian Huck
Keyword(s):  
Data Analysis ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Analysis Software ◽  
Infrared Spectrometer ◽  
Automated Tools ◽  
Near Infrared Spectrometer ◽  
Data Analysis Software

The performance of a newly developed pocket-sized near-infrared (NIR) spectrometer was investigated by analysing 46 cheese samples for their water and fat content, and comparing results with a benchtop NIR device. Additionally, the automated data analysis of the pocket-sized spectrometer and its cloud-based data analysis software, designed for laypeople, was put to the test by comparing performances to a highly sophisticated multivariate data analysis software. All developed partial least squares regression (PLS-R) models yield a coefficient of determination (R2) of over 0.9, indicating high correlation between spectra and reference data for both spectrometers and all data analysis routes taken. In general, the analysis of grated cheese yields better results than whole pieces of cheese. Additionally, the ratios of performance to deviation (RPDs) and standard errors of prediction (SEPs) suggest that the performance of the pocket-sized spectrometer is comparable to the benchtop device. Small improvements are observable, when using sophisticated data analysis software, instead of automated tools.

Use of a handheld near infrared spectrometer and partial least squares regression to quantify metanil yellow adulteration in turmeric powder

2020 ◽  
Vol 28 (2) ◽  
pp. 81-92 ◽  
Author(s):  
Isaac R Rukundo ◽  
Mary-Grace C Danao ◽  
Curtis L Weller ◽  
Randy L Wehling ◽  
Kent M Eskridge
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Prediction Performance ◽  
Least Squares Regression ◽  
Calibration Models ◽  
Infrared Spectrometer ◽  
Metanil Yellow ◽  
Near Infrared Spectrometer

The efficacy of using a handheld near infrared spectrometer to predict metanil yellow (MY) adulteration levels (0-30% w/w) in dried turmeric powder was tested against a benchtop near infrared spectrometer using partial least squares regression models. The differences between near infrared instruments were resolution (i.e., 1 nm (handheld) vs. 0.5 nm (benchtop)) and sample container during scanning (plastic pouch (handheld) vs. quartz glass cup (benchtop)). Prediction performance of the calibration models developed was evaluated using number of model factors ([Formula: see text]), coefficients of determination of calibration and validation ([Formula: see text] and[Formula: see text], respectively), root-mean-square errors of calibration, cross-validation, and validation (RMSEC, RMSECV, and RMSEP), ratio of prediction error to standard deviation (RPD), and limits of detection (LOD) and quantification (LOQ). The best benchtop calibration models were based on spectral data preprocessed with Savitzky–Golay first derivative algorithm for the benchtop near infrared and standard normal variate for the handheld near infrared, yielding low[Formula: see text], high [Formula: see text] and[Formula: see text], low RMSEC, RMSECV, RMSEP, and high RPD. The LOD and LOQ for both spectrometers were 0.33 and 1.10%, respectively, and no significant difference was found between the predicted [Formula: see text] values by the benchtop and handheld near infrared spectrometers. The models were, in general, not sensitive to sample source and size of the validation set. When spectra from the benchtop near infrared were standardized using a reverse standardization strategy, calibrated against[Formula: see text], and transferred to the handheld near infrared, prediction performance dropped, from [Formula: see text] of 0.99 to 0.98, RMSEP increased from 0.96% to 1.53%, and [Formula: see text] decreased from 10.1 to 6.3. Despite the reduced prediction performance, the handheld near infrared with a transferred calibration model from the benchtop near infrared was still useful for screening, quality control, and process control applications.

Investigating the total antioxidant capacity of gluten-free grains with miniaturized near-infrared spectrometer

NIR news ◽  
2019 ◽  
Vol 30 (5-6) ◽  
pp. 35-38
Author(s):  
Verena Wiedemair ◽  
Christian Wolfgang Huck
Keyword(s):  
Antioxidant Capacity ◽  
Total Antioxidant Capacity ◽  
High Performance ◽  
Near Infrared ◽  
Least Squares Regression ◽  
Test Set ◽  
Total Antioxidant ◽  
Infrared Spectrometer ◽  
Near Infrared Spectrometer ◽  
Infrared Instruments

The use of ever smaller near-infrared instruments is becoming more and more prevalent, since they are cheaper, more versatile and often advertised as high-performance spectrometer. The last claim is rarely verified by independent researchers, which is why the presented work evaluates the performance of three hand-held spectrometers in comparison to a benchtop instrument. Seventy-seven samples comprising buckwheat, millet and oat were investigated for their total antioxidant capacity using Folin–Ciocalteu and near-infrared spectroscopy. Partial least squares regression models were established using cross- and test set validation. Results showed that all instruments were able to predict total antioxidant capacity to some extent. The coefficients of determinations ranged from 0.823 to 0.951 for cross-validated and from 0.849 to 0.952 for test set validated models. Errors for cross-validated models ranged from 1.11 to 2.08 mgGAE/g and for test set validated models from 1.02 to 1.86 mgGAE/g.

Calibration transfer across near infrared spectrometers for measuring hematocrit in the blood of grazing cattle

2017 ◽  
Vol 25 (1) ◽  
pp. 15-25 ◽  
Author(s):  
Xuan Luo ◽  
Akifumi Ikehata ◽  
Kunio Sashida ◽  
Shanji Piao ◽  
Tsutomu Okura ◽  
...  
Keyword(s):  
Latent Variables ◽  
Near Infrared ◽  
Least Squares Regression ◽  
Calibration Transfer ◽  
Grazing Cattle ◽  
Robust Model ◽  
Infrared Spectrometer ◽  
Near Infrared Spectrometer ◽  
Direct Standardization

For fast diagnosis of anemia for cattle farmed on pastures, portable short wavelength near infrared spectrometer instrument was built, and the feasibility of estimating hematocrit by partial least squares regression modeling was validated in our previous work. As a follow-up, the present study reports calibration transfer from one master instrument to two slave instruments. Different approaches, i.e. making robust models, skew and bias correction, and piecewise direct standardization, were compared. Our results show that making more use of simple models based on appropriate preprocessing, number of latent variables, and wavelength selection, can sometimes be as effective as applying standardization algorithm (e.g. piecewise direct standardization) when tackling the spectral differences between instruments. The importance of a robust model during the process of calibration transfer is shown. Application of standardization such as piecewise direct standardization could be risky when the model to be transferred is not robust enough.

Oil Content Estimation of Individual Kernels of Quercus Ilex Subsp. Rotundifolia [(Lam) O. Schwarz] Acorns by Fourier Transform near Infrared Spectroscopy and Partial Least Squares Regression

2007 ◽  
Vol 15 (4) ◽  
pp. 247-260 ◽  
Author(s):  
Cristina Sousa-Correia ◽  
Ana Alves ◽  
José C. Rodrigues ◽  
Suzana Ferreira-Dias ◽  
José M. Abreu ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Least Squares ◽  
Partial Least Squares ◽  
Oil Content ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Association Studies ◽  
Extraction Procedure ◽  
Coefficient Of Determination ◽  
Least Squares Regression

The aim of this work was to use Fourier transform near infrared (FT-NIR) spectroscopy and partial least squares regression (PLSR) to estimate the oil content of individual Holm oak ( Quercus sp.) acorn kernels from different trees, sites and years that should be used in the future for molecular marker association studies. Sampling of acorns in two consecutive years (2003 and 2004) and from different sites in Portugal provided independent sample sets. A total of 89 samples (acorn kernels) representative of the natural oil content range were extracted. The results of the analyses performed by three people revealed accuracy of the oil extraction procedure ( n-hexane) and the precision (repeatability) of this method, assessed during a four-day period, gave a standard deviation of 0.1%. Careful wavenumber selection and several steps of validation of the PLSR models led to a final robust model that allowed the precise prediction of the oil content of individual acorns. By using the wavenumber ranges from 5995 to 5323 cm−1 and from 4478 to 4177 cm−1 of the vector normalised spectra, a PLSR model with a coefficient of determination ( r 2) of 0.992 and a root mean square error of cross-validation ( RMSECV) of 0.37% was achieved. The RPD value of about 10 and a bias of almost zero showed that the developed models are good for process control, development, and applied research. Oil content estimation of individual Quercus sp. acorns by FT-NIR and PLSR was shown to be possible. The varying water content detected in the spectra of the milled kernels after drying in similar conditions, within and especially between years, could be handled.

Rapid detection of the component contents in caryophylli flos by a handheld near infrared spectrometer based on digital light processing technology

2018 ◽  
Vol 26 (6) ◽  
pp. 389-397
Author(s):  
Hui Yan ◽  
Yue Ma ◽  
Bangxing Han
Keyword(s):  
Standard Error ◽  
Near Infrared ◽  
Processing Technology ◽  
Quantitative Detection ◽  
Least Squares Regression ◽  
Digital Light Processing ◽  
Standard Normal Variate ◽  
Eugenyl Acetate ◽  
Infrared Spectrometer ◽  
Near Infrared Spectrometer

A handheld and inexpensive near infrared spectrometer based on digital light processing technology was used to investigate the potential of fast quantitative detection of eugenol, beta-caryophyllene and eugenyl acetate in caryophylli flos. Gas chromatography was used to determine the reference values. The diffuse reflectance spectra of caryophylli flos powder were recorded and were pretreated by different methods, and then the partial least squares regression was applied to develop calibration models; furthermore, the competitive adaptive reweighted sampling was used for the wavelength selection to improve the performance of models. The results show that the best performance of the pretreatment methods is seen with the combination of the first derivative and standard normal variate, and the performance of calibration is improved by the competitive adaptive reweighted sampling. For the eugenol, the standard error of calibration and standard error of prediction are 0.46% and 0.60%, respectively, and the corresponding R-squares are 0.955 and 0.89; for beta-caryophyllene, the standard error of calibration and standard error of prediction are 0.11% and 0.14%, respectively, and the corresponding R-squares are 0.89 and 0.86; for eugenyl acetate, the standard error of calibration and standard error of prediction are 0.30% and 0.38%, respectively, and the corresponding R-squares are 0.89 and 0.80. The overall results of this work revealed the feasibility of the use of handheld near infrared spectrometers as a method for the quantitative on-site determination of eugenol, beta-caryophyllene and eugenyl acetate in caryophylli flos.

Rapid detection of infrared inactive sodium chloride content in frozen tuna fish for determining commercial value using short wavelengths

2019 ◽  
Vol 27 (6) ◽  
pp. 424-431 ◽  
Author(s):  
Suttahatai Pochanagone ◽  
Ronnarit Rittiron
Keyword(s):  
Sodium Chloride ◽  
Least Squares ◽  
Partial Least Squares ◽  
Rapid Detection ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Chloride Content ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Tuna Fish

The sodium chloride content in the flesh of tuna fish is one of the factors for determining the price in the fishing industry. Titration is a standard method for the analysis of salt and this is time consuming. Near infrared spectroscopy is a potential alternative method for rapid detection without the need for wet chemical assay. Although sodium chloride is infrared inactive, this study investigated the influence of salt on the absorbance of near infrared energy and showed that the sodium chloride content can be determined using changes in the water band at 970 nm. Calibration equations were developed from frozen fish pieces and ground samples using multiple linear regression for the wavelength region of 700–1000 nm. The best result was achieved from frozen samples with a coefficient of determination for the calibration set ([Formula: see text]) = 0.71, standard error of calibration (SEC) = 0.20%, coefficient of determination for the validation set ([Formula: see text]) = 0.64, standard error of prediction (SEP) = 0.26% and bias = − 0.00%. In order to verify the significant variables used to determine infrared inactive sodium chloride, partial least squares regression was performed on frozen samples. The important variable in multiple linear regression and partial least squares regression was the absorbance band at 976 nm attributed to water molecules. The result from partial least squares calibration showed [Formula: see text] = 0.83, SEC = 0.20%, [Formula: see text] = 0.54, SEP = 0.25% and bias = 0.00%. The salt values predicted using the near infrared models were not significantly different from the reference values obtained by the standard titration method at the 95% confidence interval.

scholarly journals Rapid tannin profiling of tree fodders using untargeted mid-infrared spectroscopy and partial least squares regression

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Jordi Ortuño ◽  
Sokratis Stergiadis ◽  
Anastasios Koidis ◽  
Jo Smith ◽  
Chris Humphrey ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Multivariate Calibration ◽  
Plant Ecology ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy

Abstract Background The presence of condensed tannins (CT) in tree fodders entails a series of productive, health and ecological benefits for ruminant nutrition. Current wet analytical methods employed for full CT characterisation are time and resource-consuming, thus limiting its applicability for silvopastoral systems. The development of quick, safe and robust analytical techniques to monitor CT’s full profile is crucial to suitably understand CT variability and biological activity, which would help to develop efficient evidence-based decision-making to maximise CT-derived benefits. The present study investigates the suitability of Fourier-transformed mid-infrared spectroscopy (MIR: 4000–550 cm−1) combined with multivariate analysis to determine CT concentration and structure (mean degree of polymerization—mDP, procyanidins:prodelphidins ratio—PC:PD and cis:trans ratio) in oak, field maple and goat willow foliage, using HCl:Butanol:Acetone:Iron (HBAI) and thiolysis-HPLC as reference methods. Results The MIR spectra obtained were explored firstly using Principal Component Analysis, whereas multivariate calibration models were developed based on partial least-squares regression. MIR showed an excellent prediction capacity for the determination of PC:PD [coefficient of determination for prediction (R2P) = 0.96; ratio of prediction to deviation (RPD) = 5.26, range error ratio (RER) = 14.1] and cis:trans ratio (R2P = 0.95; RPD = 4.24; RER = 13.3); modest for CT quantification (HBAI: R2P = 0.92; RPD = 3.71; RER = 13.1; Thiolysis: R2P = 0.88; RPD = 2.80; RER = 11.5); and weak for mDP (R2P = 0.66; RPD = 1.86; RER = 7.16). Conclusions MIR combined with chemometrics allowed to characterize the full CT profile of tree foliage rapidly, which would help to assess better plant ecology variability and to improve the nutritional management of ruminant livestock.

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