Sampling requirements for predicting cattle diet quality using faecal near-infrared reflectance spectroscopy (F.NIRS) in heterogeneous tropical rangeland pastures

2010 ◽  
Vol 32 (4) ◽  
pp. 435 ◽  
Author(s):  
I. A. White ◽  
L. P. Hunt ◽  
D. P. Poppi ◽  
S. R. Petty
Keyword(s):  
Sample Size ◽  
Protein Content ◽  
Crude Protein ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Crude Protein Content ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Collection Method

Faecal near-infrared reflectance spectroscopy (F.NIRS) provides predictive information on cattle diets and nutritional levels, useful for livestock management or for research purposes. Potential errors exist throughout the entire F.NIRS process, including the collection method. The accepted collection method involves aggregating equal amounts of faecal material from 5 to 15 animals, mixing and removing a single sample for analysis. The adequacy of this method was tested by collecting and analysing up to 70 samples from individual cattle in different paddocks. Two methods were used to determine sample size based on observed variability in dietary attributes. Variability of dietary non-grass material and crude protein content increased with paddock size, so required sample size also increased. For dietary F.NIRS predictions to be used for research, our results suggest from 20 to 51 samples are needed in small to large paddocks to accurately predict the proportion of dietary non-grass material, from 12 to 50 samples for crude protein content and from 6 to 34 samples for dry matter digestibility. Composite samples from 15 cattle provided representative means in less than 50% of the situations investigated using biologically significant precision levels, but would be adequate for management of animal nutrition. Analysis of individual samples provided additional measures of range and variability which were also informative.

Prediction of crude protein content in field peas using near infrared reflectance spectroscopy

2006 ◽  
Vol 86 (1) ◽  
pp. 157-159 ◽  
Author(s):  
G. C. Arganosa ◽  
T. D. Warkentin ◽  
V. J. Racz ◽  
S. Blade ◽  
C. Phillips ◽  
...  
Keyword(s):  
Crude Protein ◽  
Near Infrared ◽  
Spectroscopic Method ◽  
Correlation Coefficients ◽  
Reflectance Spectroscopy ◽  
Field Pea ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy

A rapid, near-infrared spectroscopic method to predict the crude protein contents of 72 field pea lines grown in Saskatchewan, both whole seeds and ground samples, was established. Correlation coefficients between the laboratory and predicted values were 0.938 and 0.952 for whole seed and ground seed, respectively. Both methods developed are adequate to support our field pea breeding programme. Key words: Field pea, near-infrared reflectance spectroscopy, crude protein

Evaluation of seed components of wild soybean (Glycine soja) collected in Japan using near-infrared reflectance spectroscopy

2017 ◽  
Vol 16 (2) ◽  
pp. 94-102 ◽  
Author(s):  
Chi-Do Wee ◽  
Masatsugu Hashiguchi ◽  
Genki Ishigaki ◽  
Melody Muguerza ◽  
Chika Oba ◽  
...  
Keyword(s):  
Protein Content ◽  
Near Infrared ◽  
Glycine Soja ◽  
Wild Soybean ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Lipid Contents

AbstractSeed composition, including the protein, lipid and sucrose contents of 334 accessions of wild soybean (Glycine soja) collected in Japan, was evaluated using near-infrared reflectance spectroscopy (NIRS) technology. The distribution of protein, lipid and sucrose contents and correlations among these three classes of seed components were determined. Protein, lipid and sucrose levels ranged in accessions from 48.6 to 57.0, 9.0 to 14.3 and 1.24 to 3.53%, respectively. Average levels of protein, lipid and sucrose in the accessions were 54, 11 and 2.5%, respectively. High negative correlations were observed between the protein and lipid contents, and the protein and sucrose contents. Mean levels of the three constituents were compared among collection sites classified by climatic conditions. The total protein content of accessions from regions with a high annual mean temperature was high. The protein content of accessions from the II-1 region was higher than those from the III-3 region, and the sucrose content from the II-1 region was lower than those from regions III-2 and IV-3. The lipid content of plants from the II-1 region was lower than those from other regions, and the accessions in region II had a higher protein content and lower sucrose and lipid contents than the other regions. These results provide diverse and wide-ranged protein, lipid and sucrose contents information of Japanese wild soybean resources according to climatic region; thus, providing a foundation for the future development and selection of new soybean varieties with desired traits in global environmental changes.

scholarly journals Pengembangan Metode Pengujian Keaslian Beras Aceh Menggunakan Nirs Dengan Metode PCA

2019 ◽  
Vol 4 (1) ◽  
pp. 578-587
Author(s):  
Masyitah Masyitah ◽  
Syahrul Syahrul ◽  
Zulfahrizal Zulfahrizal
Keyword(s):  
Principal Component Analysis ◽  
Protein Content ◽  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Ft Ir

Abstrak. Tujuan dari penelitian ini adalah membangun model pendugaan untuk menilai keaslian beras Aceh berdasarkan spektrum NIRS yang dihasilkan. Pendeteksian keaslian beras Aceh secara cepat dan efesien dapat diwujudkan melalui pengembangan teknologi Near Infrared Reflectance Spectroscopy (NIRS). Penelitian ini menggunakan beras varietas Sigupai (Aceh Barat Daya), varietas  Sanbay (Simeulue) dan varietas Ciherang. Jumlah sampel yang digunakan pada penelitian ini adalah 45 sampel. Pengukuran spektrum beras menggunakan Self developed FT-IR IPTEK T-1516. Klasifikasi data spektrum beras menggunakan Principal Component Analysis (PCA) dengan dua  pretreatment yaitu De-trending dan Multiplicative Scatter Correction. Hasil penelitian ini diperoleh yaitu: Spektrum NIRS beras menunjukkan keberadaan kandungan lemak pada panjang gelombang 2355 nm - 2462 nm. Kandungan karbohidrat pada panjang gelombang 2256 nm - 2321 nm.  Kandungan protein pada panjang gelombang 2056 nm - 2166 nm. Kandungan kadar air pada panjang gelombang 1910 nm-1980 nm dan panjag gelombang 1411 nm - 1492 nm menunjukkan kandungan protein dan kadar air. NIRS dengan metode PCA mampu membedakan pencampuran beras Sigupai dengan beras Ciherang dimana pembedaan terbaik terjadi dalam bentuk dua macam pengelompokan yaitu beras  Sigupai ≥ 75 dan beras Sigupai ≤50 dan pretreatment de-trending merupakan pretreatment terbaik dalam mengklasifikasi beras Aceh (Sigupai dan Sanbay) dengan beras Nasional (Ciherang).Development of Methods for Testing the Authenticity of Aceh Rice Using NIRS with the PCA MethodAbstract. The purpose of this study is to develop a prediction model to assess the authenticity of Aceh rice based on the NIRS spectrum produced. The detection of the authenticity of Aceh rice quickly and efficiently can be realized through technological development Near Infrared Reflectance Spectroscopy (NIRS). This study uses Sigupai rice varieties (Aceh Barat Daya), Sanbay (Simeulue) and Ciherang. The number of samples used in this study was 45 samples. Measurement of rice spectrum  using Self developed FT-IR IPTEK T-1516. Rice spectrum data classification uses the Principal Component Analysis (PCA) with two pretreatments, namely De-trending and Multiplicative Scatter Correction. The results of this study were obtained: NIRS spectrum of rice showed the presence of fat content at a wavelength of 2355 nm - 2462 nm. Carbohydrate content at wavelength 2256 nm - 2321 nm. Protein content at wavelength 2056 nm - 2166 nm. The content of water content at a wavelength of 1910 nm-1980 nm and wave length of 1411 nm - 1492 nm shows the protein content and water content. NIRS with the PCA method was able to distinguish the mixing of Sigupai rice from Ciherang rice where the best differentiation occurred in the form of two types of grouping namely Sigupai rice ≥ 75 and Sigupai rice ≤ 50 and de-trending pretreatment was the best pretreatment in classifying Aceh rice (Sigupai and Sanbay) with National rice (Ciherang).

ANALYSIS OF HAY, HAYLAGE AND CORN SILAGE SAMPLES BY NEAR INFRARED REFLECTANCE SPECTROSCOPY

1985 ◽  
Vol 65 (3) ◽  
pp. 753-760 ◽  
Author(s):  
E. V. VALDES ◽  
L. G. YOUNG ◽  
I. McMILLAN ◽  
J. E. WINCH
Keyword(s):  
Crude Protein ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Corn Silage ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Mineral Contents ◽  
Infrared Reflectance Spectroscopy ◽  
Filter Type

Separate calibrations for hay, haylage and corn silage were developed to predict chemical composition by near infrared reflectance spectroscopy (NIR). A scanning type of NIR instrument was used to select the best set of wavelengths (λ) while a filter type was used to evaluate the calibrations. Reflectance (R) was recorded as log (1/R). Bias (nonrandom error) was corrected for each set of samples before the NIR analysis. Percent crude protein (CP), acid detergent fiber (ADF), calcium (Ca) and phosphorus (P) were studied in the hay samples. In addition, potassium (K) and magnesium (Mg) were included for the haylage and corn silage samples. Six hundred samples, including calibration (C) and prediction sets (PRE1 and PRE2) were used. PRE1 samples came from the same population as the C samples, whereas PRE2 samples were obtained in a different year. Accuracy of the predictions was assessed by the coefficients of determination (r2), standard error of the estimate (SEE), and coefficients of variation (CV). Crude protein was the parameter best predicted by NIR with r2, SEE and CV ranging from 0.72 to 0.96, 0.43 to 1.17 and 5.6 to 10.4, respectively. The highest SEE for crude protein were associated with the PRE2 samples for haylage and hay samples (1.09 and 1.17), respectively. NIR predictions of ADF had r2, SEE and CV values ranging from 0.21 to 0.92, 1.44 to 2.53 and 5.3% to 7.9%, respectively. Corn silage had the lowest SEE for ADF in both C and PRE1 sets. Predicting mineral contents by NIR gave high CV (10.5%–34.5%) and low r2 values (0.02–0.75). Calcium predictions had the highest variability, and P and Mg predictions the lowest.These results indicate that CP was successfully predicted by NIR. The higher SEE values for ADF may have been due to variation in the wet chemistry values of some samples. Minerals were not adequately predicted by NIR as assessed by r2, SEE and CV values. Key words: Near infrared reflectance spectroscopy, forage, chemical analysis

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