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.

Measurement of the Concentrations of Mannosyl Erythritol Lipid and Soybean Oil in the Glycolipid Fermentation Process Using near Infrared Spectroscopy

2002 ◽  
Vol 10 (1) ◽  
pp. 53-61 ◽  
Author(s):  
Kazuhiro Nakamichi ◽  
Ken-Ichiro Suehara ◽  
Yasuhisa Nakano ◽  
Koji Kakugawa ◽  
Masahiro Tamai ◽  
...  
Keyword(s):  
Soybean Oil ◽  
Ethyl Acetate ◽  
Spectral Data ◽  
Standard Error ◽  
Fermentation Process ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Raw Material ◽  
Second Derivative ◽  
Calibration Equation

In a glycolipid fermentation, mannosyl erythritol lipid (MEL) is produced from soybean oil added to a medium as a source of carbon. A measurement system for the concentrations of MEL and soybean oil in the fermentation process has been developed using near infrared (NIR) spectroscopy. MEL and soybean oil in the culture broth were extracted with ethyl acetate. NIR spectra of the ethyl acetate extract were measured in the wavelength range between 400 and 2500 nm at 2 nm intervals. The absorption caused by MEL was observed at 1436, 1920 and 2052 nm. To obtain a calibration equation, a multiple linear regression (MLR) was carried out between the second derivative NIR spectral data at 2040 and 1312 nm and MEL concentrations obtained using thin-layer chromatography with a flame-ionisation detector (TLC/FID) method. The values of the regression coefficient ( R) and the standard error of calibration ( SEC) were 0.994 and 0.48 g L−1, respectively. The absorption caused by soybean oil was observed at 1208, 1716, 1766, 2182 and 2302 nm. A calibration equation for soybean oil was formulated with the second derivative NIR spectral data at 2178 and 2090 nm. The values of R and SEC were 0.974 and 0.77 g L−1, respectively. After validation of the calibration equation, good agreement was observed between the results of the TLC/FID method and those of the NIR method for both constituents. The values of the correlation coefficient ( r) for MEL and the standard error of prediction ( SEP) were 0.994 and 0.45 g L−1, respectively. The values of r and SEP for soybean oil were 0.979 and 0.56 g L−1, respectively. The NIR method was applied to the measurement of the concentrations of MEL and soybean oil in an actual fermentation process and good results were obtained. The study indicates that NIR spectroscopy is a useful method for the measurement of the raw material and product in glycolipid fermentation.

scholarly journals Prediction of Starch, Moisture, and Sugar in Sweetpotato by Near Infrared Transmittance

HortScience ◽  
1996 ◽  
Vol 31 (6) ◽  
pp. 1003-1006 ◽  
Author(s):  
Kenji Katayama ◽  
Katsumi Komaki ◽  
Seiji Tamiya
Keyword(s):  
Standard Error ◽  
Ipomoea Batatas ◽  
Near Infrared ◽  
Sugar Content ◽  
Multiple Correlation Coefficient ◽  
Infrared Analysis ◽  
Multiple Correlation ◽  
Storage Roots ◽  
Calibration Equation ◽  
Infrared Transmittance

Near infrared analysis was used to predict the starch, moisture, and sugar content in sliced fresh sweetpotato [Ipomoea batatas (L.) Lam.] storage roots. Samples were collected in each of three growing years. The best calibration equation for starch from combined samples (1989 to 1991) showed a multiple correlation coefficient (R) of 0.949, a standard error of calibration (sec) of 2.01, and a standard error of prediction (sep) of 1.91. The R, sec, and sep for moisture and sugar were 0.930, 1.85, and 2.00, and 0.837, 1.30, and 1.21, respectively. Calibrations based on samples from a given year adequately predicted the variables but could not account for variances introduced by samples from other years. Multiyear calibrations based on several years of data adequately predicted starch and moisture content in root slices. Thus, multiyear calibrations with annual bias adjustments can be applied to screening sweetpotato breeding germplasm for these two variables.

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 Feasibility of Determination of Foodborne Microbe Contamination of Fresh-Cut Shredded Cabbage Using SW-NIR

2019 ◽  
Vol 1 (2) ◽  
pp. 246-256
Author(s):  
Benjamaporn Matulaprungsan ◽  
Chalermchai Wongs-Aree ◽  
Pathompong Penchaiya ◽  
Phonkrit Maniwara ◽  
Sirichai Kanlayanarat ◽  
...  
Keyword(s):  
Quantitative Measurement ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Sample Type ◽  
Least Squares Regression ◽  
Calibration Equation ◽  
E Coli ◽  
Total Bacteria

Shredded cabbage is widely used in much ready-to-eat food. Therefore, rapid methods for detecting and monitoring the contamination of foodborne microbes is essential. Short wavelength near infrared (SW-NIR) spectroscopy was applied on two types of solutions, a drained solution from the outer surface of the shredded cabbage (SC) and a ground solution of shredded cabbage (GC) which were inoculated with a mixture of two bacterial suspensions, Escherichia coli and Salmonella typhimurium. NIR spectra of around 700 to 1100 nm were collected from the samples after 0, 4, and 8 h at 37 °C incubation, along with the growth of total bacteria, E. coli and S. typhimurium. The raw spectra were obtained from both sample types, clearly separated with the increase of incubation time. The first derivative, a Savitzky–Golay pretreatment, was applied on the GC spectra, while the second derivative was applied on the SC spectra before developing the calibration equation, using partial least squares regression (PLS). The obtained correlation (r) of the SC spectra was higher than the GC spectra, while the standard error of cross-validation (SECV) was lower. The ratio of prediction of deviation (RPD) of the SC spectra was higher than the GC spectra, especially in total bacteria, quite normal for the E. coli but relatively low for the S. typhimurium. The prediction results of microbial spoilage were more reliable on the SC than on the GC spectra. Total bacterial detection was best for quantitative measurement, as E. coli contamination could only be distinguished between high and low values. Conversely, S. typhimurium predictions were not optimal for either sample type. The SW-NIR shows the feasibility for detecting the existence of microbes in the solution obtained from SC, but for a more specific application for discrimination or quantitation is needed, proving further research in still required.

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 PENDUGAAN KANDUNGAN ENERGI BRUTO TEPUNG IKAN MENGGUNAKAN TEKNOLOGI Near Infrared (NIR) (Prediction of the Gross Energy for Fishmeal using Near Infrared Reflectant (NIR) Technology)

2016 ◽  
Vol 1 (1) ◽  
pp. 33
Author(s):  
Ati Atul Quddus
Keyword(s):  
Standard Error ◽  
Coefficient Of Variation ◽  
Near Infrared ◽  
Animal Feed ◽  
Principal Component Regression ◽  
Principal Component ◽  
Calibration Equation ◽  
Bomb Calorimeter ◽  
Gross Energy ◽  
Pcr Method

Abstrak Penelitian ini bertujuan untuk menduga kandungan energi bruto tepung ikan untuk bahan pakan ternak menggunakan teknologi Near Infrared (NIR). Tepung ikan yang digunakan dalam penelitian ini diperoleh dari poultry shop yang ada di beberapa daerah di Indonesia dan industri pakan ternak. Penelitian ini menggunakan 50 tepung ikan. Tiga puluh lima sampel digunakan untuk kalibrasi, sedangkan 15 sampel digunakan untuk validasi. Pengukuran NIR reflektan menggunakan sistem NIR. Energi bruto diukur menggunakan bomb calorimeter. Data dianalisis dengan menggunakan regresi linier berganda (RLB) dan Principal Component Regression (PCR). Persamaan kalibrasi dari reflektan dianalisis menggunakan 29 panjang gelombang untuk memprediksi energi bruto. Hasil dari validasi menunjukkan akurasi yang tinggi dengan standar eror dan koefisien variasi untuk energi bruto yaitu 6,6 Kkal/Kg dan 0,2%. Persamaan kalibrasi dari metode PCR menggunakan data absorban. Hasil dari validasinya menunjukkan kurang akurasi dengan nilai standar eror dan koefisien variasi yaitu 119,2 Kkal/kg dan 4,16%. Kata kunci : energi bruto, NIR, RLB, PCR Abstract This experiment was aimed to predict gross energy (GE) content of fishmeal by using Near Infrared (NIR) technology. Fishmeal that was used in this experiment was obtained from the poultry shop in several regions in Indonesia and from animal feed industries. This experiment was conducted by using 50 fishmeals. Thirty five samples out of 50 samples fishmeal was used to develop the NIR of calibration and the rest 15 samples was used to test the accuracy of the calibration. NIR reflectant was measured by NIR system. Gross energy was measured by bomb calorimeter. Collected data were analyzed by using multivariate linier regression (MLR) and principal component regression (PCR). Calibration equation of reflectant was analyzed by using 29 wavelengths for predicting GE. The results of the validation indicated high accuracy with standard error and coefficient of variation for GE: SEp = 6.6 Kkal/Kg, CV = 0.2 % . Calibration equation was obtained from PCR method by using absorbent data. The result of the validation indicated less accuracy with standard error and coefficient of variation for GE: SEp = 119.92 Kkal/Kg, CV = 4.16% . Keywords : Gross Energy, Near infrared Reflectant (NIR), fishmeal, Multivariate Linier Regression (MLR), Principal Component Regression (PCR)

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.

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.

Detection of Variety Contamination in Milled Japanese Rice Using a Single Kernel near Infrared Technique in Transmittance Mode

2005 ◽  
Vol 13 (1) ◽  
pp. 19-25 ◽  
Author(s):  
Ronnarit Rittiron ◽  
Innapa Saranwong ◽  
Sumio Kawano
Keyword(s):  
Protein Content ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Measuring System ◽  
Protein Distribution ◽  
Milled Rice ◽  
Protein Determination ◽  
Fibre Optics ◽  
Calibration Equation ◽  
System A

An authentication system for identifying the purity of a milled rice bulk using near infrared (NIR) protein determination was established. NIR spectra from 1100 nm to 1800 nm of single kernels acquired with fibre optics in transmittance mode were used. First, the effect of the degree of milling (DOM) on the NIR protein determination was examined. Then, to obtain a robust and accurate protein measuring system, a calibration equation was developed with variety and DOM compensation. Finally, by examining the normal distribution of the protein content measured by the single kernel NIR spectroscopy, a blended bulk could be separated from a pure bulk as it did not exhibit a normal protein distribution while a pure one did. However, the system would have less capability if the average protein content of the contrasting variety was close to that of the main variety. In the naturally acquired protein histogram, the system proposed had the capability to detect the blending when the Koshihikari bulk included 5% or more Akitakomachi kernels.

scholarly journals Non-invasive assessment of pineapple and mango fruit quality using near infra-red spectroscopy

1997 ◽  
Vol 37 (2) ◽  
pp. 253 ◽  
Author(s):  
J. Guthrie ◽  
K. Walsh
Keyword(s):  
Fruit Quality ◽  
Standard Error ◽  
Monochromatic Light ◽  
Nir Spectroscopy ◽  
Ananas Comosus ◽  
Coefficient Of Determination ◽  
Mango Fruit ◽  
Calibration Equation ◽  
Non Invasive ◽  
Infra Red

Summary. The potential of near infra-red (NIR) spectroscopy for non-invasive measurement of fruit quality of pineapple (Ananas comosus var. Smooth Cayenne) and mango (Magnifera indica var. Kensington) fruit was assessed. A remote reflectance fibre optic probe, placed in contact with the fruit skin surface in a light-proof box, was used to deliver monochromatic light to the fruit, and to collect NIR reflectance spectra (760–2500 nm). The probe illuminated and collected reflected radiation from an area of about 16 cm2. The NIR spectral attributes were correlated with pineapple juice Brix and with mango flesh dry matter (DM) measured from fruit flesh directly underlying the scanned area. The highest correlations for both fruit were found using the second derivative of the spectra (d2 log 1/R) and an additive calibration equation. Multiple linear regression (MLR) on pineapple fruit spectra (n = 85) gave a calibration equation using d2 log 1/R at wavelengths of 866, 760, 1232 and 832 nm with a multiple coefficient of determination (R2) of 0.75, and a standard error of calibration (SEC) of 1.21 °Brix. Modified partial least squares (MPLS) regression analysis yielded a calibration equation with R2 = 0.91, SEC = 0.69, and a standard error of cross validation (SECV) of 1.09 oBrix. For mango, MLR gave a calibration equation using d2 log 1/R at 904, 872, 1660 and 1516 nm with R2 = 0.90, and SEC = 0.85% DM and a bias of 0.39. Using MPLS analysis, a calibration equation with R2 = 0.98, SEC = 0.54 and SECV = 1.19 was obtained. We conclude that NIR technology offers the potential to assess fruit sweetness in intact whole pineapple and DM in mango fruit, respectively, to within 1° Brix and 1% DM, and could be used for the grading of fruit in fruit packing sheds.

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