Can Grassland Chemical Quality Be Quantified Using Transform Near-Infrared Spectroscopy?

Near-infrared spectroscopy (NIRS) and closed spectroscopy methods have been applied to analyse the quality of forage and animal feed. However, grasslands are linked to variability factors (e.g., site, year, occurring species, etc.) which restrict the prediction capacity of the NIRS. The aim of this study is to test the Fourier transform NIRS application in order to determine the chemical characteristics of fresh, undried and unground samples of grassland located in north-central Apennine. The results indicated the success of FT-NIRS models for dry matter (DM), crude protein (CP), acid detergent fibre (ADF), neutral detergent fibre (NDF) and acid detergent lignin (ADL) on fresh grassland samples (R2 > 0.90, in validation). The model can be used to quantitatively determine CP and ADF (residual prediction deviation-RPD > 3 and range error ratio- RER > 10), followed by DM and NDF that maintain a RER > 10, and are sufficient for screening for the lignin fraction (RPD = 2.4 and RER = 8.8). On the contrary, models for both lipid and ash seem not to be usable at a practical level. The success of FT-NIRS quantification for the main chemical parameters is promising from the practical point of view considering both the absence of samples preparation and the importance of these parameters for diet formulation.

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An overview of Near Infrared spectroscopy: from an application's point of view

Fresenius Journal of Analytical Chemistry ◽

10.1007/bf00324506 ◽

1991 ◽

Vol 339 (2) ◽

pp. 65-67 ◽

Cited By ~ 4

Author(s):

Cynthia Kradjel

Keyword(s):

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Point Of View

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Characterization of the variation in the daily excretion of faecal constituents and digestibility predictions in beef cattle fed feedlot diets using near-infrared spectroscopy

Canadian Journal of Animal Science ◽

10.1139/cjas-2015-0193 ◽

2016 ◽

Vol 96 (4) ◽

pp. 532-549 ◽

Cited By ~ 3

Author(s):

L.J. Jancewicz ◽

G.B. Penner ◽

M.L. Swift ◽

J.J. McKinnon ◽

C.L. Waldner ◽

...

Keyword(s):

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Acid Detergent Lignin ◽

Faecal Samples ◽

Acid Detergent Fibre ◽

Feeding Regimes ◽

Daily Excretion ◽

Composite Samples

Six heifers were individually housed and assigned to once (FF1) or twice (FF2) daily feeding regimes over backgrounding and finishing periods. Following adaptation, total faecal collections were conducted at 4-h intervals and at 24-h intervals over 4 d, and near-infrared spectroscopy (NIRS) was used to predict faecal organic matter (OM), starch, nitrogen (N), neutral detergent fibre (NDF), acid detergent fibre (ADF), and acid detergent lignin (ADL). At each interval, NIRS calibrations were used to estimate faecal constituents and ADL to calculate apparent (aTTD) and estimated (eTTD) total tract digestibility. Faecal dry matter (DM) (%), NDF, and ADF varied among 4-h interval samples in the backgrounding period and faecal DM, starch, NDF, ADF, and ADL in the finishing period. Faecal starch was able to predict aTTD during both feeding periods (backgrounding: R2 = 0.96, P < 0.01; finishing: R2 = 0.98, P < 0.01). The NIRS calibrations for predicting aTTD using the 4-h interval samples or the 4-d–24-h composite were least accurate for NDF and ADF. Most 4-h interval samples could be used to predict eTTD of nutrients, and aside from starch in the finishing period, there were no differences in eTTD using faecal samples collected over 4-h intervals versus those collected over 4 d. Spot faecal samples collected at any time point from multiple cattle have the potential to predict digestibility. Timing of sampling after feeding must be standardized to predict starch digestibility during the finishing period, with samples between 0–4 h and 8–16 h generating estimates of both starch concentration and digestibility that were closest to that derived from 4-d–24-h composite samples.

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Determination of fatty acid content in sheep milk by means of near infrared spectroscopy

Acta Veterinaria Brno ◽

10.2754/avb201483s10s27 ◽

2014 ◽

Vol 83 (10) ◽

pp. S27-S34 ◽

Cited By ~ 3

Author(s):

Táňa Lužová ◽

Květoslava Šustová ◽

Jan Kuchtík ◽

Jiří Mlček ◽

Lenka Vorlová ◽

...

Keyword(s):

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Fatty Acid Content ◽

Correlation Coefficients ◽

Standard Errors ◽

Sheep Milk ◽

Calibration Models ◽

The Fourier Transform

The study focused on the use of the Fourier transform near infrared spectroscopy in determining the content of selected fatty acids in raw non-homogenized sheep milk. The raw sheep milk sample spectra were scanned in reflectance mode using the FT NIR Antaris spectrophotometer. The reliable functional calibration models were created for estimation of the contents of myristic, oleic, lauric, palmitic, and stearic acids (with calibration correlation coefficients of R = 0.999; 0.999; 0.993; 0.992; 0.858) and with standard errors SEC = 0.056; 0.152; 0.066; 0.367; 1.36%.

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Recent Developments in Instrumentation of Functional Near-Infrared Spectroscopy Systems

Applied Sciences ◽

10.3390/app10186522 ◽

2020 ◽

Vol 10 (18) ◽

pp. 6522

Author(s):

Murad Althobaiti ◽

Ibraheem Al-Naib

Keyword(s):

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Continuous Wave ◽

Infrared Region ◽

Point Of View ◽

Electromagnetic Spectrum ◽

Functional Near Infrared Spectroscopy ◽

Recent Developments ◽

In The Beginning

In the last three decades, the development and steady improvement of various optical technologies at the near-infrared region of the electromagnetic spectrum has inspired a large number of scientists around the world to design and develop functional near-infrared spectroscopy (fNIRS) systems for various medical applications. This has been driven further by the availability of new sources and detectors that support very compact and wearable system designs. In this article, we review fNIRS systems from the instrumentation point of view, discussing the associated challenges and state-of-the-art approaches. In the beginning, the fundamentals of fNIRS systems as well as light-tissue interaction at NIR are briefly introduced. After that, we present the basics of NIR systems instrumentation. Next, the recent development of continuous-wave, frequency-domain, and time-domain fNIRS systems are discussed. Finally, we provide a summary of these three modalities and an outlook into the future of fNIRS technology.

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