scholarly journals Rapid Determination of Nutritional Parameters of Pasta/Sauce Blends by Handheld Near-Infrared Spectroscopy

Molecules ◽  
2019 ◽  
Vol 24 (11) ◽  
pp. 2029 ◽  
Author(s):  
Marina D. G. Neves ◽  
Ronei J. Poppi ◽  
Heinz W. Siesler
Keyword(s):  
Near Infrared ◽  
Rapid Determination ◽  
Analytical Data ◽  
Nir Spectroscopy ◽  
Nutritional Parameters ◽  
Direct To Consumer ◽  
Calibration Models ◽  
Food And Beverage ◽  
The Individual ◽  
Near Future

Nowadays, near infrared (NIR) spectroscopy has experienced a rapid progress in miniaturization (instruments < 100 g are presently available), and the price for handheld systems has reached the < $500 level for high lot sizes. Thus, the stage is set for NIR spectroscopy to become the technique of choice for food and beverage testing, not only in industry but also as a consumer application. However, contrary to the (in our opinion) exaggerated claims of some direct-to-consumer companies regarding the performance of their “food scanners” with “cloud evaluation of big data”, the present publication will demonstrate realistic analytical data derived from the development of partial least squares (PLS) calibration models for six different nutritional parameters (energy, protein, fat, carbohydrates, sugar, and fiber) based on the NIR spectra of a broad range of different pasta/sauce blends recorded with a handheld instrument. The prediction performance of the PLS calibration models for the individual parameters was double-checked by cross-validation (CV) and test-set validation. The results obtained suggest that in the near future consumers will be able to predict the nutritional parameters of their meals by using handheld NIR spectroscopy under every-day life conditions.

scholarly journals Rapid determination of hyaluronic acid concentration in fermentation broth with near-infrared spectroscopy

2014 ◽  
Vol 07 (06) ◽  
pp. 1450012 ◽  
Author(s):  
Qin Dong ◽  
Hengchang Zang ◽  
Lixuan Zang ◽  
Aihua Liu ◽  
Yanli Shi ◽  
...  
Keyword(s):  
Hyaluronic Acid ◽  
Fermentation Process ◽  
Near Infrared ◽  
Fermentation Broth ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Promising Tool ◽  
Calibration Models ◽  
Order Polynomial

Hyaluronic acid (HA) concentration is an important parameter in fermentation process. Currently, carbazole assay is widely used for HA content determination in routine analysis. However, this method is time-consuming, environment polluting and has the risk of microbial contamination, as well as the results lag behind fermentation process. This paper attempted the feasibility to predict the concentration of HA in fermentation broth by using near infrared (NIR) spectroscopy in transmission mode. In this work, a total of 56 samples of fermentation broth from 7 batches were analyzed, which contained HA in the range of 2.35–9.69 g/L. Different data preprocessing methods were applied to construct calibration models. The final optimal model was obtained with first derivative using Savitzky–Golay smoothing (9 points window, second-order polynomial) and partial least squares (PLS) regression with leave-one-block-out cross validation. The correlation coefficient and Root Mean Square Error of prediction set is 0.98 and 0.43 g/L, respectively, which show the possibility of NIR as a rapid method for microanalysis and to be a promising tool for a rapid assay in HA fermentation.

scholarly journals Influence of environmental factors on the detection of blood in sheep faeces using visible–near-infrared spectroscopy as a measure of Haemonchus contortus infection

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Elise A. Kho ◽  
Jill N. Fernandes ◽  
Andrew C. Kotze ◽  
Glen P. Fox ◽  
Maggy T. Sikulu-Lord ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Haemonchus Contortus ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Diagnostic Methods ◽  
Calibration Models ◽  
Good Prediction Accuracy

Abstract Background Existing diagnostic methods for the parasitic gastrointestinal nematode, Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, thereby preventing losses in production and flock welfare. We previously demonstrated the ability of visible–near-infrared (Vis–NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we report our investigation of whether variation in sheep type and environment affect the prediction accuracy of Vis–NIR spectroscopy in quantifying blood in faeces. Methods Visible–NIR spectra were obtained from worm-free sheep faeces collected from different environments and sheep types in South Australia (SA) and New South Wales, Australia and spiked with various sheep blood concentrations. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387–609 nm) using partial least squares regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected sheep faeces from Queensland (QLD). Samples from QLD were quantified using Hemastix® test strip and FAMACHA© diagnostic test scores. Results Principal component analysis showed that location, class of sheep and pooled versus individual samples were factors affecting the Hb predictions. The models successfully differentiated ‘healthy’ SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity 57–94%, specificity 44–79%). The models were not predictive for blood in the naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of the QLD samples, however, identified a difference between samples containing high and low quantities of blood. Conclusion This study demonstrates the potential of Vis–NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture sufficient environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas where H. contortus is endemic.

scholarly journals Comparisons between SCIAMACHY and ground-based FTIR data for total columns of CO, CH<sub>4</sub>, CO<sub>2</sub> and N<sub>2</sub>O

2006 ◽  
Vol 6 (7) ◽  
pp. 1953-1976 ◽  
Author(s):  
B. Dils ◽  
M. De Mazière ◽  
J. F. Müller ◽  
T. Blumenstock ◽  
M. Buchwitz ◽  
...  
Keyword(s):  
Near Infrared ◽  
Polynomial Interpolation ◽  
Global Network ◽  
Target Species ◽  
Time Period ◽  
Order Polynomial ◽  
Temporal And Spatial ◽  
The Individual ◽  
Near Future ◽  
Total Column

Abstract. Total column amounts of CO, CH4, CO2 and N2O retrieved from SCIAMACHY nadir observations in its near-infrared channels have been compared to data from a ground-based quasi-global network of Fourier-transform infrared (FTIR) spectrometers. The SCIAMACHY data considered here have been produced by three different retrieval algorithms, WFM-DOAS (version 0.5 for CO and CH4 and version 0.4 for CO2 and N2O), IMAP-DOAS (version 1.1 and 0.9 (for CO)) and IMLM (version 6.3) and cover the January to December 2003 time period. Comparisons have been made for individual data, as well as for monthly averages. To maximize the number of reliable coincidences that satisfy the temporal and spatial collocation criteria, the SCIAMACHY data have been compared with a temporal 3rd order polynomial interpolation of the ground-based data. Particular attention has been given to the question whether SCIAMACHY observes correctly the seasonal and latitudinal variability of the target species. The present results indicate that the individual SCIAMACHY data obtained with the actual versions of the algorithms have been significantly improved, but that the quality requirements, for estimating emissions on regional scales, are not yet met. Nevertheless, possible directions for further algorithm upgrades have been identified which should result in more reliable data products in a near future.

Near infrared spectroscopy as an alternative method for rapid determination of the solidification point of 2,4,6-trinitrotoluene in production

2019 ◽  
Vol 27 (4) ◽  
pp. 286-292
Author(s):  
Chongchong She ◽  
Min Li ◽  
Yunhui Hou ◽  
Lizhen Chen ◽  
Jianlong Wang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Quality Parameter ◽  
Solidification Point ◽  
Fast Analysis ◽  
Online Application

The solidification point is a key quality parameter for 2,4,6-trinitrotoluene (TNT). The traditional solidification point measurement method of TNT is complicated, dangerous, not environmentally friendly and time-consuming. Near infrared spectroscopy (NIR) analysis technology has been applied successfully in the chemical, petroleum, food, and agriculture sectors owing to its characteristics of fast analysis, no damage to the sample and online application. The purpose of this study was to study near infrared spectroscopy combined with chemometric methods to develop a fast and accurate quantitative analysis method for the solidification point of TNT. The model constructed using PLS regression was successful in predicting the solidification point of TNT ([Formula: see text] = 0.999, RMSECV = 0.19, RPDCa = 33.5, [Formula: see text] = 0.19, [Formula: see text] = 0.999). Principal component analysis shows that the model could identify samples from different reactors. The results clearly demonstrate that the solidification point can be measured in a short time by NIR spectroscopy without any pretreatment for the sample and skilled laboratory personnel.

Prediction of Yellow-Poplar (Liriodendron Tulipifera) Veneer Stiffness and Bulk Density Using near Infrared Spectroscopy and Multivariate Calibration

2008 ◽  
Vol 16 (5) ◽  
pp. 487-496 ◽  
Author(s):  
Oluwatosin Emmanuel Adedipe ◽  
Ben Dawson-Andoh
Keyword(s):  
Bulk Density ◽  
Spectral Range ◽  
Predictive Models ◽  
Spectral Region ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
Yellow Poplar ◽  
First Derivative ◽  
Calibration Models

This study investigated the feasibility of using near infrared (NIR) spectroscopy and multivariate calibration to predict bulk density and stiffness of 3.2 mm thick yellow poplar veneer strips. Full-range (800–2500 nm) raw NIR spectra and spectra pre-treated using the first derivative method, along with spectra from three other different wavelength windows of 1200–2400 nm, 1800–2400 nm and 1400–2000 nm were regressed against the bulk density (kg m−3) values and the dynamic modulus of elasticity (stiffness; GPa) of the veneers using the projection to latent structures (PLS) method to develop calibration models. All predictive models developed performed well in the prediction of bulk density and stiffness of new test samples that were not included in the calibration models. R2 values ranged from 0.67-0.78 and 0.56-0.72, respectively, for bulk density and stiffness. There was significant improvement in models developed with first derivative spectra over models developed with raw spectra. The models developed using the first derivative used fewer latent variables to achieve predictive models with higher R2 values, lower root mean square errors of prediction (RMSEP) and standard errors of prediction (SEP). Models developed using the full NIR spectral range (800–2500 nm) and the NIR spectral region of 1200–2400 nm performed better than models developed using the restricted NIR wavelength regions of 1800–2400 nm and 1400–2000 nm. However, there was no clear distinction between models developed using the full NIR spectral range and the NIR spectral region of 1200–2400 nm. Overall, models developed with the first derivative pre-processed spectra using the whole NIR spectrum performed best in predictability. The results of this study show the potential of using multivariate data analysis coupled with NIR spectroscopy for on-line sorting and assessment of veneer stiffness prior to the lay-up process in the manufacturing of veneer-based engineered wood products such as plywood, Parallam and laminated veneer lumber.

The rapid determination of total polyphenols content and antioxidant activity in Dendrobium officinale using near-infrared spectroscopy

2016 ◽  
Vol 8 (23) ◽  
pp. 4584-4589 ◽  
Author(s):  
Longhui Ma ◽  
Zhimin Zhang ◽  
Xingbing Zhao ◽  
Sufeng Zhang ◽  
Hongmei Lu
Keyword(s):  
Antioxidant Activity ◽  
Infrared Spectroscopy ◽  
Near Infrared ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Dendrobium Officinale ◽  
Chemometric Methods ◽  
Total Polyphenols ◽  
Rapid Quantification

NIR spectroscopy coupled with chemometric methods for rapid quantification of total polyphenols content and antioxidant activity inDendrobium officinale.

scholarly journals Soluble Solids and Simple Sugars Measurement in Intact Mango Using Near Infrared Spectroscopy

2008 ◽  
Vol 18 (3) ◽  
pp. 410-416 ◽  
Author(s):  
Stephen R. Delwiche ◽  
Weena Mekwatanakarn ◽  
Chien Y. Wang
Keyword(s):  
Standard Error ◽  
Quality Evaluation ◽  
Near Infrared ◽  
Wavelength Region ◽  
Nir Spectroscopy ◽  
Nondestructive Method ◽  
Soluble Solids ◽  
Least Squares Regression ◽  
Different Temperatures ◽  
The Individual

A rapid, reliable, and nondestructive method for quality evaluation of mango (Magnifera indica) fruit is important to the mango industry for international trade. The objective of this study was to determine the potential of near-infrared (NIR) spectroscopy to predict soluble solids content (SSC) and individual and combined concentrations of sucrose, glucose, and fructose nondestructively in mango. Mature mangoes at two different temperatures (15 °C and 20 °C) were measured by NIR interactance (750–1088 nm wavelength region analyzed) over an 11-day period, starting when the fruit were underripe and extending to a few days past optimal ripeness. Partial least squares regression was used to develop models for SSC, individual sugar concentration, and the sum of the concentrations of the three sugars. Such analyses yielded calibration equations with R2 = 0.77 to 0.88 (SSC), 0.75 (sucrose), 0.67 (glucose), 0.70 (fructose), and 0.82 (sum); standard error of calibration = 0.56 to 0.90 (SSC), 10.0 (sucrose), 0.9 (glucose), 4.5 (fructose), and 10.4 (sum); and standard error of cross-validation = 0.93 to 1.10 (SSC), 15.6 (sucrose), 1.4 (glucose), 6.9 (fructose), and 16.8 (sum). When the SSC calibration was applied to a separate validation set, the standard error of performance ranged from 0.94% to 1.72%. These results suggest that for assessment of mango ripeness, NIR SSC calibrations are superior to the NIR calibrations for any of the individual sugars. This nondestructive technology can be used in the screening and grading of mangoes and in quality evaluation at wholesale and retail levels.

scholarly journals Influence of environmental factors on the detection of blood in sheep faeces using visible-near infrared spectroscopy as a measure of Haemonchus contortus infection

2020 ◽  
Author(s):  
Elise Ai Hwee Kho ◽  
Jill N. Fernandes ◽  
Andrew C. Kotze ◽  
Glen P. Fox ◽  
Maggy T. Sikulu-Lord ◽  
...  
Keyword(s):  
Haemonchus Contortus ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Diagnostic Methods ◽  
Calibration Model ◽  
Good Prediction ◽  
Blood Concentrations ◽  
Calibration Models ◽  
Good Prediction Accuracy

Abstract Background: Existing diagnostic methods for the parasitic gastrointestinal nematode, Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, preventing production and welfare loss in the flock. We previously demonstrated the ability of visible-near infrared (vis-NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we investigate whether variation in sheep type and environment affect the prediction accuracy of vis-NIR spectroscopy in quantifying blood in faeces.Methods: Vis-NIR spectra were obtained from worm-free sheep faeces from different environments in South Australia (SA) and New South Wales (NSW), Australia and spiked with various sheep blood concentrations collected. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387 – 609 nm) using partial least squares (PLS) regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected Queensland (QLD) faeces. Naturally occurring blood in QLD samples was quantified using Hemastix® and FAMACHA© scores.Results: PCA showed that location, class of sheep and pooled/individual samples were factors affecting the Hb predictions in sheep faeces. The calibration models successfully differentiated ‘healthy’ SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity: 57 – 94%, specificity: 44 – 79%). The models were not predictive for naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of QLD samples, however, identified a difference between samples containing high and low quantities of blood.Conclusion: This study demonstrates the potential of vis-NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture enough environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas where H. contortus is endemic for the accurate prediction of H. contortus infections in these regions.

scholarly journals Influence of environmental factors on the detection of blood in sheep faeces using visible-near infrared spectroscopy as a measure of Haemonchus contortus infection

2020 ◽  
Author(s):  
Elise Ai Hwee Kho ◽  
Jill N. Fernandes ◽  
Andrew C. Kotze ◽  
Glen P. Fox ◽  
Maggy T. Sikulu-Lord ◽  
...  
Keyword(s):  
Haemonchus Contortus ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Diagnostic Methods ◽  
Calibration Model ◽  
Good Prediction ◽  
Blood Concentrations ◽  
Calibration Models ◽  
Good Prediction Accuracy

Abstract Background: Existing diagnostic methods for the parasitic gastrointestinal nematode, Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, preventing production and welfare loss in the flock. We previously demonstrated the ability of visible-near infrared (vis-NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we investigate whether variation in sheep type and environment affect the prediction accuracy of vis-NIR spectroscopy in quantifying blood in faeces. Methods: Vis-NIR spectra were obtained from worm-free sheep faeces collected from different environments and sheep types in South Australia (SA) and New South Wales (NSW), Australia and spiked with various sheep blood concentrations. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387 – 609 nm) using partial least squares (PLS) regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected sheep faeces from Queensland (QLD). QLD samples were quantified using Hemastix® and FAMACHA © scores. Results: PCA showed that location, class of sheep and pooled/individual samples were factors affecting the Hb predictions. The models successfully differentiated ‘healthy’ SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity: 57 – 94%, specificity: 44 – 79%). The models were not predictive for blood in naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of QLD samples, however, identified a difference between samples containing high and low quantities of blood. Conclusion: This study demonstrates the potential of vis-NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture enough environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas where H. contortus is endemic.

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