scholarly journals The principles, practices and some future applications of near infrared spectroscopy for predicting the nutritive value of foods for animals and humans

1997 ◽  
Vol 10 (1) ◽  
pp. 83-114 ◽  
Author(s):  
D. I. Givens ◽  
J. L. De Boever ◽  
E. R. Deaville
Keyword(s):  
Egg Production ◽  
Nutritive Value ◽  
Near Infrared ◽  
Starch Content ◽  
Animal Health ◽  
Nir Spectroscopy ◽  
Vital Role ◽  
Future Application ◽  
Current Application ◽  
The Future

AbstractThe current application and future potential of near infrared (NIR) spectroscopy in the evaluation of foods for domesticated animals and humans is enormous. Where used, NIR spectroscopy has revolutionized the analysis and nutritional evaluation of animal feeds and human foods by providing a rapid means of examination. The availability of accurate and rapid methods of evaluation is becoming increasingly important to meet the nutritional requirements of animals for meat, milk, wool and egg production. This is essential for efficient and economic animal production, to maintain animal health and to minimize environmental impact. Accurate evaluation methods are also needed in relation to national and international legislation that regulates the circulation, trade and inspection of foods and feeds, aids effective functioning of the market and guards the safety of animals and humans. The aim of this review is to outline the theory and principles of NIR spectroscopy and to focus primarily on its application in the field of animal nutrition. The vital role NIR spectroscopy is playing in the prediction of biologically meaningful feed characteristics, including data derived in vivo, is demonstrated particularly through its application to forage evaluation, but also in the examination of raw materials and compound feeds. While the applications of NIR spectroscopy to different foods and drinks are extensive, this review gives an overview only of selected reported applications including its use for predicting nutritive value (mainly water, protein, fat, sucrose and starch content), monitoring food processing and for food authentication. The review provides clear evidence that the future application of NIR spectroscopy will undoubtedly increase, playing a vital role in the authentication of the quality and origin of foods and feeds and enabling the complex methods of feed evaluation required in the future to be put into widespread use.

Monitoring the performance of a broad-based calibration for measuring the nutritive value of two independent populations of pasture using near infrared reflectance (NIR) spectroscopy

1991 ◽  
Vol 31 (2) ◽  
pp. 205 ◽  
Author(s):  
KF Smith ◽  
PC Flinn
Keyword(s):  
Nutritive Value ◽  
Near Infrared ◽  
Linear Regression Analysis ◽  
Spectral Characteristics ◽  
Nir Spectroscopy ◽  
Cost Effective ◽  
Multiple Linear Regression Analysis ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance

Near infrared reflectance (NIR) spectroscopy is a rapid and cost-effective method for the measurement of organic constituents of agricultural products. NIR is widely used to measure feed quality around the world and is gaining acceptance in Australia. This study describes the development of an NIR calibration to measure crude protein (CP), predicted in vivo dry matter digestibility (IVDMD) and neutral detergent fibre (NDF) in temperate pasture species grown in south-western Victoria. A subset of 116 samples was selected on the basis of spectral characteristics from 461 pasture samples grown in 1987-89. Several grass and legume species were present in the population. Stepwise multiple linear regression analysis was used on the 116 samples to develop calibration equations with standard errors of 0.8,2.3 and 2.2% for CP, NDF and IVDMD, respectively. When these equations were tested on 2 independent pasture populations, a significant bias existed between NIR and reference values for 2 constituents in each population, indicating that the calibration samples did not adequately represent the new populations for these constituents. The results also showed that the H statistic alone was inadequate as an indicator of equation performance. It was confirmed that it was possible to develop a broad-based calibration to measure accurately the nutritive value of closed populations of temperate pasture species. For the resulting equations to be used for analysis of other populations, however, they must be monitored by comparing reference and NIR analyses on a small number of samples to check for the presence of bias or a significant increase in unexplained error.

scholarly journals Near-Infrared Spectroscopy and Geostatistical Analysis for Modeling Spatial Distribution of Analytical Constituents in Bulk Animal By-Product Protein Meals

2016 ◽  
Vol 71 (3) ◽  
pp. 520-532 ◽  
Author(s):  
José A. Adame-Siles ◽  
Tom Fearn ◽  
José E. Guerrero-Ginel ◽  
Ana Garrido-Varo ◽  
Francisco Maroto-Molina ◽  
...  
Keyword(s):  
Near Infrared ◽  
Raw Materials ◽  
Animal Health ◽  
Real Life ◽  
Nir Spectroscopy ◽  
Geostatistical Analysis ◽  
Structure Characterization ◽  
Sampling Point ◽  
Fiber Optic Probe ◽  
Effective Manner

Control and inspection operations within the context of safety and quality assessment of bulk foods and feeds are not only of particular importance, they are also demanding challenges, given the complexity of food/feed production systems and the variability of product properties. Existing methodologies have a variety of limitations, such as high costs of implementation per sample or shortcomings in early detection of potential threats for human/animal health or quality deviations. Therefore, new proposals are required for the analysis of raw materials in situ in a more efficient and cost-effective manner. For this purpose, a pilot laboratory study was performed on a set of bulk lots of animal by-product protein meals to introduce and test an approach based on near-infrared (NIR) spectroscopy and geostatistical analysis. Spectral data, provided by a fiber optic probe connected to a Fourier transform (FT) NIR spectrometer, were used to predict moisture and crude protein content at each sampling point. Variographic analysis was carried out for spatial structure characterization, while ordinary Kriging achieved continuous maps for those parameters. The results indicated that the methodology could be a first approximation to an approach that, properly complemented with the Theory of Sampling and supported by experimental validation in real-life conditions, would enhance efficiency and the decision-making process regarding safety and adulteration issues.

Taking near infrared spectroscopy beyond feedstuff analysis to enhance animal production profitability

2013 ◽  
Vol 53 (11) ◽  
pp. 1179 ◽  
Author(s):  
Hadden Graham ◽  
Chris Piotrowski ◽  
Robert Van Barneveld
Keyword(s):  
Nutritive Value ◽  
Near Infrared ◽  
Animal Feed ◽  
Nir Spectroscopy ◽  
Near Infrared Reflectance ◽  
Feed Ingredients ◽  
Feed Industry ◽  
Recent Developments ◽  
Feed Costs ◽  
Line Analysis

Near infrared reflectance (NIR) spectroscopy allows a cheap and rapid estimation of composition, and thus is widely used in the animal feed industry for the quality control and quality assurance of feeds and particularly feed ingredients. However, such analyses are often limited to a few variables not closely related to the nutritive value of the particular feed ingredients, and are often retrospective. This paper discusses recent developments in both hardware and software, which now allow real-time and in-line analysis of feed ingredients and feeds, and how these can be used to save substantial costs in the feed industry worldwide by reducing feed costs and giving more predictable animal performance. We also discuss how laboratory, hand-held and in-line NIR equipment could be widely used in the future for the purchase of feed ingredients and the manufacture of animal feeds.

Changes in Carbohydrate Content during Wheat Maturation—What is Measured by near Infrared Spectroscopy?

2005 ◽  
Vol 13 (1) ◽  
pp. 9-17 ◽  
Author(s):  
Szilveszter Gergely ◽  
András Salgó
Keyword(s):  
Near Infrared ◽  
Starch Content ◽  
Nir Spectroscopy ◽  
Water Soluble ◽  
Starch Accumulation ◽  
Soluble Carbohydrates ◽  
Spectroscopic Techniques ◽  
Absorption Bands ◽  
Physiological Processes ◽  
Water Soluble Carbohydrates

The role of bread, pasta and related products produced from milled wheat seeds is important to the human diet, so monitoring changes of starch content in developing grain is essential. Immature wheat grains are also used as a functional food, particularly as a source of water-soluble carbohydrates. The amount and variation in content of different carbohydrates changes considerably during maturation and these changes were non-destructively monitored in developing grain using near infrared (NIR) spectroscopy. Characteristic changes in three carbohydrate absorption bands [1585–1595 nm (Carbohydrate I), 2270–2280 nm (Carbohydrate II) and 2325–2335 nm (Carbohydrate III)] were identified and it was concluded that the different dynamics of carbohydrates (starch accumulation as well as synthesis/decomposition of water-soluble carbohydrates) could be followed sensitively by monitoring these three different regions of NIR spectra. Carbohydrate I represents the effect of starch accumulation during maturation based on the vibrations of intermolecular hydrogen bonded O–H groups in polysaccharides. Carbohydrate II is the manifestation of O–H stretching and C–C stretching vibrations existing unengaged in water-soluble carbohydrates while Carbohydrate III describes the changes in C–H stretching and deformation band of poly- and mono-oligosaccharides. NIR spectroscopic techniques are shown to be effective in monitoring plant physiological processes and the spectra have hidden information for predicting the stage of growth in wheat seed.

Prediction of Phenolics and Tannins in Forage Legumes by near Infrared Reflectance

1998 ◽  
Vol 6 (1) ◽  
pp. 175-181 ◽  
Author(s):  
A.V. Goodchild ◽  
F.J. El Haramein ◽  
A. Abd El Moneim ◽  
H.P.S. Makkar ◽  
P.C. Williams
Keyword(s):  
Biological Activity ◽  
Condensed Tannins ◽  
Nutritive Value ◽  
Near Infrared ◽  
Cross Validation ◽  
Nir Spectroscopy ◽  
Gas Production ◽  
Near Infrared Reflectance ◽  
Rumen Microbes

Near infrared (NIR) spectroscopy calibrations for measures of tannins and nutritive value were made on a set of 40 hays and straws of Vicia and Lathyrus spp. by the modified partial least squares (MPLS) method and were evaluated by cross-validation. They successfully predicted, in the dry matter, 4.6–34.1 g kg−1 total phenolics with a cross-validation R2 of 0.95 and a SECV of 1.68 g kg−1, 1.3–23.1 g kg−1 total tannins ( R2 = 0.89, SECV = 1.84 g kg−1) and 0.5–30.3 g kg−1 condensed tannins ( R2 = 0.93, SECV = 2.34 g kg−1). In multiple regression and MPLS calibrations, a wavelength close to 2.144 μm was common to all measures of tannins, and was attributed to condensed tannins and its flavanoid precursors. The biological activity of tannins on rumen microbes, measured as a 0–6.9% effect on gas production with rumen liquor in vitro, was less precisely predicted by MPLS ( R2 = 0.49, SECV = 1.49%). The biological activity per gram of chemical tannins could not be predicted by NIR spectroscopy in the material studied. Acid detergent fibre, neutral detergent fibre, crude protein and gas production in vitro were predicted with R2 = 0.95 to 0.96 ( SECV = 18.2, 24.8, 10.1 g kg−1 or 7.2 mL g−1).

scholarly journals Estimation of the Constituent Properties of Red Delicious Apples Using a Hybrid of Artificial Neural Networks and Artificial Bee Colony Algorithm

Agronomy ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 267 ◽  
Author(s):  
Yousef Abbaspour-Gilandeh ◽  
Sajad Sabzi ◽  
Brahim Benmouna ◽  
Ginés García-Mateos ◽  
José Luis Hernández-Hernández ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Spectral Range ◽  
Artificial Bee Colony ◽  
Near Infrared ◽  
Effective Properties ◽  
Starch Content ◽  
Nir Spectroscopy ◽  
Bee Colony ◽  
Artificial Neural

Non-destructive estimation of the constituent properties of fruits and vegetables has led to a dramatic change in the agriculture and food industry, allowing accurate and efficient sorting of the products based on their internal properties. Therefore, the present study utilized visible (VIS) and near-infrared (NIR) spectroscopy data in the range from 200 to 1100 nm for the estimation of several properties of Red Delicious apples, namely Brix minus acid (BrimA), firmness, acidity and starch content, using a hybrid of Artificial Neural Networks and Artificial Bee Colony (ANN–ABC) algorithm. Furthermore, the hybrid Artificial Neural Network–Particle Swarm Optimization (ANN–PSO) algorithm was utilized to select the most effective properties to estimate these characteristics. The results indicated that there are different peaks within this spectral range, and the spectral range for each peak gives different results. To ensure the stability of the proposed method, 1000 replications were performed for each estimate. The highest coefficients of determination, R2, for estimating the studied properties among the 1000 replicates were 0.898 for BrimA, 0.8 for firmness, 0.825 for acidity and 0.973 for starch content. The selection of the most effective wavelengths for estimating the properties produced five effective wavelengths for BrimA, nine for firmness, seven for acidity and five for starch content. In this case, the best R2 of the hybrid ANN–ABC among the 1000 iterations were 0.828, 0.738, 0.9 and 0.923, respectively.

Development of near Infrared Analysis of Faeces to Estimate Non-Grass Proportions in Diets Selected by Cattle Grazing Tropical Pastures

2008 ◽  
Vol 16 (5) ◽  
pp. 471-480 ◽  
Author(s):  
David B. Coates ◽  
Rob M. Dixon
Keyword(s):  
Standard Error ◽  
Nutritive Value ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Grass Species ◽  
Infrared Analysis ◽  
Tropical Pastures ◽  
Calibration Equation ◽  
Ruminant Diets ◽  
Modified Formula

Grass (monocots) and non-grass (dicots) proportions in ruminant diets are important nutritionally because the non-grasses are usually higher in nutritive value, particularly protein, than the grasses, especially in tropical pastures. For ruminants grazing tropical pastures where the grasses are C4 species and most non-grasses are C3 species, the ratio of 13C/12C in diet and faeces, measured as δ13C‰, is proportional to dietary non-grass%. This paper describes the development of a faecal near infrared (NIR) spectroscopy calibration equation for predicting faecal δ13C from which dietary grass and non-grass proportions can be calculated. Calibration development used cattle faeces derived from diets containing only C3 non-grass and C4 grass components, and a series of expansion and validation steps was employed to develop robustness and predictive reliability. The final calibration equation contained 1637 samples and faecal δ13C range (‰) of [12.27]–[27.65]. Calibration statistics were: standard error of calibration (SEC) of 0.78, standard error of cross-validation (SECV) of 0.80, standard deviation ( SD) of reference values of 3.11 and R2 of 0.94. Validation statistics for the final calibration equation applied to 60 samples were: standard error of prediction ( SEP) of 0.87, bias of −0.15, R2 of 0.92 and RPD of 3.16. The calibration equation was also tested on faeces from diets containing C4 non-grass species or temperate C3 grass species. Faecal δ13C predictions indicated that the spectral basis of the calibration was not related to 13C/12C ratios per se but to consistent differences between grasses and non-grasses in chemical composition and that the differences were modified by photosynthetic pathway. Thus, although the calibration equation could not be used to make valid faecal δ13C predictions when the diet contained either C3 grass or C4 non-grass, it could be used to make useful estimates of dietary non-grass proportions. It could also be used to make useful estimates of non-grass in mixed C3 grass/non-grass diets by applying a modified formula to calculate non-grass from predicted faecal δ13C. The development of a robust faecal-NIR calibration equation for estimating non-grass proportions in the diets of grazing cattle demonstrated a novel and useful application of NIR spectroscopy in agriculture.

Prediction of Starch Content and Ethanol Yields of Sorghum Grain Using near Infrared Spectroscopy

2015 ◽  
Vol 23 (2) ◽  
pp. 85-92 ◽  
Author(s):  
Junhui Li ◽  
Mary-Grace C. Danao ◽  
Shih-Fang Chen ◽  
Song Li ◽  
Vijay Singh ◽  
...  
Keyword(s):  
Near Infrared ◽  
Ethanol Yield ◽  
Starch Content ◽  
Nir Spectroscopy ◽  
High Ratio ◽  
Fermentation Products ◽  
Number Of Factors ◽  
Ethanol Yields ◽  
Theoretical Ethanol Yield ◽  
Sorghum Grain

A rapid quantification method was developed and validated for non-destructive measurement of starch content, theoretical ethanol yield and actual ethanol yield of 48 cultivars of sorghum grain using Fourier transform near infrared (FT-NIR) spectroscopy in diffuse reflectance mode. Multiplicative scatter correction, Savitzky–Golay derivative smoothing and mean centring were used for processing the spectra of ground sorghum grain. The processed spectra were correlated with starch content, theoretical ethanol yield and ethanol produced through simultaneous saccharification and fermentation using partial least-squares regression (PLSR). The spectral range and number of factors were optimised for the low number of factors, high coefficients of determination for calibration ( R2) and validation ( r2), low root mean square error of prediction ( RMSEP), high ratio of performance to deviation ( RPD) and high ratio of the standard error of prediction to the range ( RER). The best PLSR model for starch content utilised the 4000–6000 cm−1 wavebands and had the following values: R2 = r2 = 0.97, RMSEP = 5.5 g kg−1 grain, RPD = 5.9 and RER = 15. Likewise, the model for theoretical ethanol yield utilised the 4000–8000 cm−1 wavebands and had R2 and r2 values of >0.90, RMSEP = 4.9 g kg−1 grain, RPD = 4.47 and RER = 12.8. It was more difficult to predict actual ethanol yield using FT-NIR spectroscopy given the small data set, and spectra were collected prior to the fermentation step. Resulting PLSR models had R2 and r2 values of <0.60, RMSEP = 11.2–21.4 g kg−1, RPD < 3 and RER < 6. These results demonstrated that FT-NIR spectroscopy may be a practical method for rough screening of sorghum cultivars for desirable starch content and theoretical ethanol yield. The models may be improved by including more cultivars in the model and additional compositional information, such as tannin and free amino nitrogen contents, in the chemometric analysis and using FT-NIR scans of the fermentation products to predict actual ethanol yields.

Calibration Transfer and Measurement Stability of Near-Infrared Spectrometers

1992 ◽  
Vol 46 (5) ◽  
pp. 764-771 ◽  
Author(s):  
Yongdong Wang ◽  
Bruce R. Kowalski
Keyword(s):  
Near Infrared ◽  
Multivariate Calibration ◽  
Process Analysis ◽  
Starch Content ◽  
Industrial Process ◽  
Nir Spectroscopy ◽  
Calibration Model ◽  
Calibration Transfer ◽  
Small Set ◽  
Transfer Method

Near-infrared (NIR) spectroscopy has been widely accepted as a quantitative technique in which multivariate calibration plays an important role. The application of NIR to process analysis, however, has been largely limited by a problem identified as calibration transfer, the attempt to transfer a well-established calibration model from one instrument (e.g., located in the central laboratory) to another instrument of the same type (e.g., located on an industrial process). A calibration transfer method called piecewise direct standardization (PDS) is applied to a set of gasoline samples measured on two different NIR spectrometers. On the basis of the measurement of a small set of transfer samples on both instruments, a structured transformation matrix can be determined and applied to transform spectra between two instruments, enabling the transfer of calibration models. The effect of spectrum preprocessing on standardization is studied with the use of a set of gasoline samples. In a separate study, the day-to-day instrument variation as observed from the change in the polystyrene spectrum is related to the prediction of moisture, oil, protein, and starch content in corn samples, and then the possibility of using such generic standards to replace real samples in a transfer set is explored. In all cases, a standard error for prediction comparable to full set cross-validation is obtained through standardization.

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