Faecal Indices Based on near Infrared Spectroscopy to Assess Intake, in vivo Digestibility and Chemical Composition of the Herbage Ingested by Sheep (Crude Protein, Fibres and Lignin Content)

2007 ◽  
Vol 15 (2) ◽  
pp. 107-113 ◽  
Author(s):  
A. Fanchone ◽  
M. Boval ◽  
Ph. Lecomte ◽  
H. Archimède
Keyword(s):  
Chemical Composition ◽  
Crude Protein ◽  
Near Infrared ◽  
Cross Validation ◽  
Small Ruminants ◽  
Partial Least Square ◽  
Least Square ◽  
Faecal Excretion ◽  
Faecal Samples

The aim of this study was to evaluate the potential of faecal indices based on near infrared (NIR) spectroscopy to assess chemical composition and functional properties (intake and in vivo digestibility) of fresh grass ingested by sheep. Reference data and faecal spectra were obtained from a pen experiment with 12 ewes individually housed and fed fresh Digitaria decumbens at varying stages of re-growth (14–63 days) during a period of 49 days. The amount of herbage offered, refused and faecal excretion were measured per ewe daily. Organic matter (OM) content, crude protein (CP) content, neutral and acid detergent fibre (NDF, ADF) and acid detergent lignin (ADL) content were dosed in offered, refused and faecal samples. OM digestibility (OMD), intake (OMI) and chemical composition of the herbage ingested (OMi, CPi, NDFi, ADFi, ADLi, % dry matter) were calculated per ewe and per seven days. Faecal samples were bulked within each seven days of measurement period, per ewe. Eighty four dried and milled faecal samples were scanned using a monochromator. Faecal spectra were used to calibrate and cross-validate equations for predicting the various parameters using the modified partial least square (MPLS) procedure. For the CP content of the herbage really ingested (CPi), derived standard error of cross-validation ( SECV) and cross-validation R2 ( R2cv) were 0.61% and 0.98. For NDFi, ADFi and ADLi, the values of SEC-V and R2 cv were, respectively, 1.64% and 0.45, 0.78% and 0.91 and 0.34% and 0.77. For OMD, the values of SECV and R2 cv were 2.02% and 0.77, whereas lower calibrations statistics were obtained for OMI (11.04 g kg BW–0.75 and 0.45). These values confirmed the potential of NIR Spectra of faeces as a technology for reliably predicting the in vivo digestibility and chemical quality of herbage really ingested and estimating the herbage intake by small ruminants.

Application of Wavelet Transform in the Prediction of Chemical Composition of Sunflower Seeds by Near Infrared Reflectance Spectroscopy

2011 ◽  
Vol 460-461 ◽  
pp. 599-604
Author(s):  
Rui Zhen Han ◽  
Shu Xi Cheng ◽  
Yong He
Keyword(s):  
Wavelet Transform ◽  
Chemical Composition ◽  
Infrared Spectra ◽  
Crude Protein ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Sunflower Seeds ◽  
Near Infrared Reflectance ◽  
Near Infrared Spectra

In this paper, a method based on wavelet transform, which is used to analyze near infrared spectra, is discussed with the purpose of prediction of the content of oil, crude protein(CP) and moisture in sunflower seeds. By using different decomposing levels of Daubechies 2 wavelet transform, the near infrared spectra signals obtained from 105 intact sunflower seed samples were de-noised. Calibration equations were developed by partial least square regression (PLS) using the reconstructed spectra data with internal cross validation. It was indicated that the prediction effects varied when different wavelet decomposing level were employed. At the wavelet decomposing level 5, the best prediction effect was obtained, with the coefficient of correlation(R)and root mean square error prediction (RMSEP) being 0.953 and 0.466% for moisture;0.963 and 1.259% for crude protein; 0.801 and 1.874% for oil on a dry weight basis. It was concluded that the near infrared spectral model de-noised by means of wavelet transform can be used for the prediction of chemical composition in sunflower seeds for rapid pre-screening of quality characteristics on breeding programs.

scholarly journals In-Situ Screening of Soybean Quality with a Novel Handheld Near-Infrared Sensor

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6283
Author(s):  
Didem Peren Aykas ◽  
Christopher Ball ◽  
Amanda Sia ◽  
Kuanrong Zhu ◽  
Mei-Ling Shotts ◽  
...  
Keyword(s):  
Amino Acids ◽  
Fatty Acid ◽  
Moisture Content ◽  
Real Time ◽  
Crude Protein ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Sensor Technology

This study evaluates a novel handheld sensor technology coupled with pattern recognition to provide real-time screening of several soybean traits for breeders and farmers, namely protein and fat quality. We developed predictive regression models that can quantify soybean quality traits based on near-infrared (NIR) spectra acquired by a handheld instrument. This system has been utilized to measure crude protein, essential amino acids (lysine, threonine, methionine, tryptophan, and cysteine) composition, total fat, the profile of major fatty acids, and moisture content in soybeans (n = 107), and soy products including soy isolates, soy concentrates, and soy supplement drink powders (n = 15). Reference quantification of crude protein content used the Dumas combustion method (AOAC 992.23), and individual amino acids were determined using traditional protein hydrolysis (AOAC 982.30). Fat and moisture content were determined by Soxhlet (AOAC 945.16) and Karl Fischer methods, respectively, and fatty acid composition via gas chromatography-fatty acid methyl esterification. Predictive models were built and validated using ground soybean and soy products. Robust partial least square regression (PLSR) models predicted all measured quality parameters with high integrity of fit (RPre ≥ 0.92), low root mean square error of prediction (0.02–3.07%), and high predictive performance (RPD range 2.4–8.8, RER range 7.5–29.2). Our study demonstrated that a handheld NIR sensor can supplant expensive laboratory testing that can take weeks to produce results and provide soybean breeders and growers with a rapid, accurate, and non-destructive tool that can be used in the field for real-time analysis of soybeans to facilitate faster decision-making.

Nondestructive Method for Analysis of the Soybean Quality

2012 ◽  
Vol 8 (4) ◽  
Author(s):  
Yan Dong ◽  
Shi You Qu
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Crude Protein ◽  
Near Infrared ◽  
Nondestructive Method ◽  
Partial Least Square ◽  
Least Square ◽  
Mean Square ◽  
Pre Treatment

Abstract Fourier transform near infrared (NIR) spectra combined with chemometric methods was proposed to the analysis of the crude protein and fat contents in whole-kernel soybean. The calibration models were established by partial least square. After optimizing spectral pre-treatment, the determination coefficient (R2) of the crude protein and fat were 0.971, 0.970, and root mean square error of calibration (RMSEC) were 0.610, 0.365，respectively. For the prediction samples of the crude protein and fat, root mean square error of prediction (RMSEP) were 0.766, 0.420, respectively. The analytical results showed that NIR spectra had significant potential as a rapid and nondestructive method for the crude protein and fat contents in soybean.

scholarly journals Determination and Quantification of the Distribution of CN-NL Nanoparticles Encapsulating Glycyrrhetic Acid on Novel Textile Surfaces with Hyperspectral Imaging

2020 ◽  
Vol 11 (2) ◽  
pp. 32 ◽  
Author(s):  
Kudirat A. Obisesan ◽  
Simona Neri ◽  
Elodie Bugnicourt ◽  
Inmaculada Campos ◽  
Laura Rodriguez-Turienzo
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Cross Validation ◽  
Imaging Techniques ◽  
Antioxidant Properties ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Glycyrrhetic Acid ◽  
Chemometric Tools

Chitin Lignin nanoparticles (CN-NL), standalone and encapsulating glycyrrhetic acid (GA), were applied on novel substrates for textiles to obtain antibacterial, antioxidant properties. Their homogeneous application is an important parameter that can strongly influence the final performance of the investigated textiles for its cosmetic and medical use. In this paper, hyperspectral imaging techniques combined with chemometric tools were investigated to study the distribution and quantification of CN-NL/GA on chitosan and CN-NL on pullulan substrates. To do so, samples of chitosan and pullulan impregnated with CN-NL/GA and CN-NL were analysed through Short Wave Infrared (SWIR) and Visible-Near Infrared (VisNIR) hyperspectral cameras. Two different chemometric tools for qualitative and quantitative analysis have been applied, principal component analysis (PCA) and partial least square regression (PLSR) models. Promising results were obtained in the VisNIR range, which made it possible for us to visualize the CN-NL/GA compound on chitosan and CN-NL on pullulan substrates. Additionally, the PLSR model results had determination coefficient ( R C 2 ) for calibration and cross-validation ( R C V 2 )   values of 0.983 and 0.857, respectively. Minimum values of root-mean-square error for calibration (RMSEC) and cross-validation (RMSECV) of CN-NL/GA were 0.333 and 0.993 g, respectively. The results demonstrate that hyperspectral imaging combined with chemometrics offers a powerful tool for studying the distribution on chitosan and pullulan substrates and to quantify the content of CN-NL/GA compounds on chitosan substrates.

The prediction of rumen fermentation characteristics in sheep given grass silage diets

1998 ◽  
Vol 66 (1) ◽  
pp. 163-173 ◽  
Author(s):  
N. W. Offer ◽  
D. S. Percival
Keyword(s):  
Lactic Acid ◽  
Chemical Composition ◽  
Cross Validation ◽  
Live Weight ◽  
Rumen Fermentation ◽  
Partial Least Square ◽  
Metabolizable Energy ◽  
Least Square ◽  
Total N ◽  
Grass Silage

AbstractEight change-over design experiments (each a duplicated 3 × 3 Latin square design using six rumen-fistulated wether sheep, live weight 50 to 60 kg) measured rumen fermentation patterns for 24 perennial ryegrass silages. Sheep were offered 800 g dry matter (DM) per day of each silage in two equal meals at 09.00 and 17.00 h. Samples of rumen liquor were taken on days 19 and 21 of each 21-day period, at 08.50 h and at 1·5-h intervals until 16.30 h. Rumen samples were analysed by gas chromatography; silages by high-performance liquid chromatography and by near infra-red reflectance spectroscopy (NIRS) using samples scanned after drying at 100°C (NIRSdry) or in the fresh state (NIRSwet).Mean intake of DM was 737 g/day. The range of silage composition was as follows (mean, range, s.d., g/kg DM unless specified): metabolizable energy (ME MJ/kg DM) 11·1, 8·8 to 12·6, 0·81; pH 4·0, 3·6 to 5·0, 0·34; lactic acid 86, 4 to 139, 42·6; butyric acid 4·7, 0·1 to 46·7,10·2. Rumen measurements varied substantially both diurnally and between silages. Mean diurnal rumen values for the 24 silages (mean, range, s.d.) were: pH 6·76, 6·55 to 7·09, 0·155; ammonia (mg/l) 132, 70 to 247, 47·7; total volatile fatty acids (TVFA mmol/l) 58·2, 45·8 to 72·0, 8·97; (acetate+butyrate)/propionate (ABP) 3·2, 2·2 to 4·8, 0·56.Partial least-square models were developed to predict rumen fermentation (means for six sampling times) using either the silage chemical composition (CHEM g/kg DM unless specified: DM, ME (MJ/kg DM), crude protein (CP), ammonia (NH3, g N per kg total N), neutralizingvalue (meq per kg DM), sugar, lactic, formic, acetic, propionic and butyric acids and ethanol) or silage NIRSdry or NIRSwet. Prediction performance was assessed comparing values for R2, standard error of cross validation (SECV) and SD/SECV (s.d. of reference population! SECV) obtained by the ‘leave one out’ cross validation method. NIRSwet gave slightly better prediction accuracy overall than NIRSdry but both were superior to prediction from chemical composition. Values for R2, SECV and SD/SECV for pH were 0·23, 0·14, 1·09; 0·76, 0·08, 2·01 and 0·72, 0·08, 1·89 for CHEM, NIRSdry and NIRSwet respectively. Corresponding values for rumen ammonia (mg/l) were 0·48, 35·2, 1·35; 0·69, 27·1, 1·76 and 0·70, 26·3, 1·81; for TVFA 0·52, 6·73, 1·33; 0·80, 4·06, 2·21 and 0·93, 2·47, 3·63; for rumen ABP ratio 0·69, 0·32, 1·78; 0·76, 0·30,1·88; 0·72, 0·30,1·85. The silage predictors with greatest influence in the CHEM model for rumen ABP ratio were sugar, CP and lactic acid (negative) and butyrate and ethanol (positive). NIRS shows considerable promise as a means of predicting rumen fermentation of animals given grass silage diets.

On-line characterization of wood chip brightness and chemical composition by means of visible and near-infrared spectroscopy

Holzforschung ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Guillaume Hans ◽  
Bruce Allison
Keyword(s):  
Chemical Composition ◽  
Near Infrared ◽  
Wood Chip ◽  
Pulp Mill ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
External Parameter ◽  
Contact Measurement ◽  
On Line

Abstract Historically, on-line and real-time measurement of wood chip properties in the pulp and paper industry has been a challenge and has hampered the development of advanced process control strategies. In this study, visible and near-infrared (VIS-NIR) spectroscopy is investigated as a means to characterize wood chip brightness and chemical composition (i.e. extractives, lignin and holocellulose content) on-line. The estimated standard error on the holocellulose reference measurement was significantly reduced using data reconciliation. VIS-NIR calibration models were developed using partial least square regression. Derivative and baseline correction were found to be the most appropriate pre-processing methods. Model desensitization to the influence of moisture content and temperature by means of external parameter orthogonalization resulted in more robust models critical for on-line applications under harsh industrial conditions. Wavelength selection improved model accuracy for all properties. A comparison of two different spectrometer and probe combinations demonstrated that, after wavelengths selection, a non-contact measurement of wood chips performs as well as a contact measurement of wood powder for monitoring chemical composition. On-line prediction of wood chip brightness and chemical composition using the developed VIS-NIR models was demonstrated over 7 months in a kraft pulp mill processing both hardwood and softwood chips.

The use of visible (VIS) and near infrared (NIR) reflectance spectroscopy to predict fibre diameter in both clean and greasy wool samples

2005 ◽  
Vol 80 (3) ◽  
pp. 333-337 ◽  
Author(s):  
D. Cozzolino ◽  
F. Montossi ◽  
R. San Julian
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Model Development ◽  
Fibre Diameter ◽  
Reflectance Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Calibration Models ◽  
Nir Reflectance Spectroscopy

AbstractAbstract Visible (VIS) and near infrared (NIR) reflectance spectroscopy combined with multivariate data analysis were explored to predict fibre diameter in both clean and greasy Merino wool samples. Fifty clean and 400 greasy wool samples were analysed. Samples were scanned in a large cuvette using a NIRSystems 6500 monochromator instrument by reflectance in the VIS and NIR regions (400 to 2500 nm). Partial least square (PLS) regression was used to develop a number of calibration models between the spectral and reference data. Different mathematical treatments were used during model development. Cross validation was used to assess the performance and avoid overfitting of the models. The NIR calibration models gave a coefficient of determination in calibration (R2) > 0·90 for clean wool samples and a R2 < 0·50 for greasy wool samples. The values for the residual predictive value, RPD (ratio of standard deviation (s. d.) to the root mean square of the standard error of cross validation (RMSECV)) were 3 for clean and 0·6 for greasy wool samples, respectively. The results indicated that fibre diameter in greasy wool samples was poorly predicted with NIR, while clean wool showed good relationships.More research is required to improve the calibration on greasy wool samples if the technology is to be used for rapid analysis to assist in the selection of animals in breeding programmes.

Prediction of internal compositions change in potato during storage using visible/near-infrared (Vis/NIR) spectroscopy

2020 ◽  
Vol 16 (4) ◽  
Author(s):  
Roya Farhadi ◽  
Amir H. Afkari-Sayyah ◽  
Bahareh Jamshidi ◽  
Ahmad Mousapour Gorji
Keyword(s):  
Moisture Content ◽  
Cold Storage ◽  
Near Infrared ◽  
Cross Validation ◽  
Nir Spectroscopy ◽  
Reducing Sugar ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Reliable Technique

AbstractVisible/Near-infrared (Vis/NIR) spectroscopy at a range of 450–1000 nm was used to predict the values of three qualitative variables (starch, reducing sugar, and moisture content) on 200 potato tubers from 2 potato genotypes (‘Agria’ and ‘Clone 397009–8’) stored in both traditional and cold storages. After spectroscopy measurements, these variables were measured using reference methods. Then, Partial Least Square (PLS) models were developed. To evaluate developed models, Root Mean Square Error of calibration and cross validation (RMSEC and RMSECV), as well as coefficient of determination for calibration and cross validation (R2C and R2CV), and Residual Predictive Deviation (RPD) were used. The best prediction belonged to reducing sugar with statistical values of R2C = 0.99, R2CV = 0.98, RMSEC = 0.029, RMSECV = 0.037, and RPD = 7.57 in ‘Clone’ genotype stored under cold storage. The weakest prediction was related to moisture content with statistical values of R2C = 0.93, R2CV = 0.92, RMSEC = 0.268, RMSECV = 0.279, and RPD = 6.45 in stored ‘Clone’ genotypes under cold storage. Results of the study showed that, Vis/NIR spectroscopy as a non-destructive, fast, and reliable technique can be used for prediction of inner compositions of stored potatoes.

scholarly journals Gross calorific and ash content assessment of recycled sawdust from mushroom cultivation using near infrared spectroscopy

2018 ◽  
Vol 192 ◽  
pp. 03021 ◽  
Author(s):  
Jetsada Posom ◽  
Jirawat phuphanutada ◽  
Ravipat Lapcharoensuk
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Ash Content ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mushroom Cultivation

The aim of this study was to use the near infrared spectroscopy for predicting the gross calorific value (GCV) and ash content (AC) of recycled sawdust from mushroom cultivation. The wavenumber was in range of 12500-4000 cm-1 with the diffuse reflection mode was used. The NIR models was established using partial least square regression (PLSR) and was validated via using full cross validation. GCV model provided the coefficient of determination (R2), root mean square error of cross validation (RMSECV), ratio of prediction to deviation (RPD), and bias of 0.90, 445 J/g, 3.19 and 4 J/g, respectively. The AC model gave the R2, RMSECV, RPD and bias of 0.83, 1.7000 %wt, 2.44 and 0.0059 %wt, respectively. For prediction of unknow samples, GCV model provided the standard error of prediction (SEP) and bias of 670 J/g and -654 J/g, respectively. The AC model gave the SEP and bias of 1.84 %wt and 0.912 %wt, respectively. The result represented that the GCV and AC model probably used as the rapid method and non-destructive method.

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