Review: Near infra‐red analysis of food

Davies, A.M.C.; Grant, Alex

doi:10.1111/j.1365-2621.1987.tb00479.x

Cited by 95 publications

(28 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…MSC reduces multi-colinearity and the confusing effects of baseline shift and curvature of the spectra arising from scattering effects because of physical effects. The use of first-and second-order derivatives increase the resolution of spectrum peaks, hence highlighting the signals related to the chemical composition of the meat samples (Davies and Grant, 1987). Partial least square regression type I (PLSR1) was used for predicting FA concentration using NIR spectra as independent variables.…”

Section: Discussionmentioning

confidence: 99%

Online prediction of fatty acid profiles in crossbred Limousin and Aberdeen Angus beef cattle using near infrared reflectance spectroscopy

Prieto

Ross

Navajas

et al. 2011

Animal

View full text Add to dashboard Cite

The objective of this study was to examine the online use of near infrared reflectance (NIR) spectroscopy to estimate the concentration of individual and groups of fatty acids (FA) as well as intramuscular fat (IMF) in crossbred Aberdeen Angus (AA3) and Limousin (LIM3) cattle. This was achieved by direct application of a fibre-optic probe to the muscle immediately after exposing the meat surface in the abattoir at 48 h post mortem. Samples of M. longissimus thoracis from 88 AA3 and 106 LIM3 were scanned over the NIR spectral range from 350 to 1800 nm and samples of the M. longissimus lumborum were analysed for IMF content and FA composition. Statistically significant differences (P , 0.001) were observed in most FA between the two breeds studied, with FA concentration being higher in AA3 meat mainly. NIR calibrations, tested by cross-validation, showed moderate to high predictability in LIM3 meat samples for C16:0, C16:1, C18:0, trans11 C18:1, C18:1, C18:2 n-6, C20:1, cis9, trans11 C18:2, SFA (saturated FA), MUFA (monounsaturated FA), PUFA (polyunsaturated FA) and IMF content with R 2 (SE CV , mg/ 100 g muscle) of 0.69 (146), 0.69 (28), 0.71 (62), 0.70 (8.1), 0.76 (192), 0.65 (13), 0.71 (0.9), 0.71 (2.9), 0.68 (235), 0.75 (240), 0.64 (17) and 0.75 (477), respectively. FA such as C14:0, C18:3 n-3, C20:4 n-6, C20:5 n-3, C22:6 n-3, n-6 and n-3 were more difficult to predict by NIR in these LIM3 samples (R 2 5 0.12 to 0.62; SE CV 5 0.5 to 26 mg/100 g muscle). In contrast, NIR showed low predictability for FA in AA3 beef samples. In particular for LIM3, the correlations of NIR measurements and several FA in the range from 0.81 to 0.87 indicated that the NIR spectroscopy is a useful online technique for the early, fast and relatively inexpensive estimation of FA composition in the abattoir.

show abstract

Section: Discussionmentioning

confidence: 99%

Online prediction of fatty acid profiles in crossbred Limousin and Aberdeen Angus beef cattle using near infrared reflectance spectroscopy

Prieto

Ross

Navajas

et al. 2011

Animal

View full text Add to dashboard Cite

show abstract

“…2 Since then, it has been used for rapid analysis of moisture, protein and fat contents of a wide variety of agricultural and food products. 3 Recent studies involving the NIR region have shown that NIRS is a suitable method for quantifying trace amounts of moisture in a rubber sheet, due to the strong combination of absorption bands for water at around 1940 nm and the¯rst, second and third overtones at 1450, 970 and 760 nm, respectively. 4 Another paper investigated the prediction of the dry rubber content in concentrated latex by using a portable NIRS (Avantes, the Netherlands) in the wavelength range 370-1085 nm.…”

Section: Introductionmentioning

confidence: 99%

Development of calibration models for rapid determination of moisture content in rubber sheets using portable near-infrared spectrometers

Puttipipatkajorn

2020

J. Innov. Opt. Health Sci.

View full text Add to dashboard Cite

Rubber sheets are one of the primary products of natural rubber and are the main raw material in various rubber industries. The quality of a rubber sheet can be visually examined by holding it against clear light to inspect for any specks and impurities inside, but its moisture content is difficult to evaluate based on a visual inspection and this might lead to unfair trading. Herein, we developed a rapid, robust and nondestructive near-infrared spectroscopy (NIRS)-based method for moisture content determination in rubber sheets. A set of 300 rubber sheets were divided into a calibration (200 samples) and prediction groups (100 samples). The calibration set was used to develop NIRS calibration equation using different calibration models, Partial Least Square Regression (PLSR), Least Square Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN). Among the models investigated, the ANN model with the first derivative of spectral preprocessing presented the best prediction with a coefficient of determination ([Formula: see text] of 0.993, root mean square error of calibration (RMSEC) of 0.126% and root mean square error of prediction (RMSEP) of 0.179%. The results indicated that the proposed NIRS-ANN model will be able to reduce human error and provide a highly accurate estimate of the moisture content in a rubber sheet compared to traditional wet chemistry estimation methods according to AOAC standards.

show abstract

“…NIRS technology was introduced in the industry in the 1970s, and has been used since then for moisture, protein, and fat content determination in many agricultural and food products (Davies & Grant, 1987;Gunasekaran & Irudayaraj, 2000;Nicolaï et al, 2014). The detection of these chemical components relies on the wavelength-specific NIR absorbance which is linearly proportional to the concentration of the absorbing constituents.…”

Section: Introductionmentioning

confidence: 99%

Bulk Optical Properties of Potato Flesh in the 500–1900 nm Range

López-Maestresalas

Aernouts

Beers

et al. 2015

Food Bioprocess Technol

View full text Add to dashboard Cite

In this study the optical properties of potato flesh tissue were estimated using double integrating sphere (DIS) measurements combined with an inverse adding doubling (IAD) light propagation model. Total reflectance, total transmittance and unscattered transmittance were measured for the wavelength range 500-1900 nm with 5 nm resolution. From these measurements, the bulk optical properties (absorption coefficient, scattering coefficient and anisotropy factor) of 53 potato tubers of the Hermes cultivar were estimated. The estimated absorption coefficient spectra were dominated by water and starch absorption bands, the main chemical components of potato tissue. Comparison of these values to those reported in literature for similar products showed comparable absorption profiles. The obtained scattering coefficient spectra showed a smooth decrease from 166 cm-1 to 160 cm-1 in the NIR spectral range with increasing wavelength, which is common for biological tissues. The anisotropy factor spectra obtained for the full wavelength range studied, ranged between 0.949 and 0.959 with a maximum variability of 0.009 among the set of samples used. The information obtained in this study is essential to understand the effects of absorption and scattering on the propagation of light through the potato tubers in order to design more efficient sensors for non-destructive quality evaluation.

show abstract

Review: Near infra‐red analysis of food

Cited by 95 publications

References 44 publications

Online prediction of fatty acid profiles in crossbred Limousin and Aberdeen Angus beef cattle using near infrared reflectance spectroscopy

Online prediction of fatty acid profiles in crossbred Limousin and Aberdeen Angus beef cattle using near infrared reflectance spectroscopy

Development of calibration models for rapid determination of moisture content in rubber sheets using portable near-infrared spectrometers

Bulk Optical Properties of Potato Flesh in the 500–1900 nm Range

Contact Info

Product

Resources

About