scholarly journals The Estimation of Crude Protein and Dry Matter Degradability of Maize and Grass Silages by near Infrared Spectroscopy

1998 ◽  
Vol 6 (1) ◽  
pp. 145-151 ◽  
Author(s):  
Begoña De la Roza ◽  
Adela Martínez ◽  
Begoña Santos ◽  
Javier González ◽  
Guillermo Gómez
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Crude Protein ◽  
Dry Matter ◽  
Near Infrared ◽  
General Procedure ◽  
Good Method ◽  
Least Squares Regression ◽  
Ruminal Degradability ◽  
High Consistency

A total of 130 silages samples (53 of maize silages and 77 of grass silages), which were ensiled with or without silage additives, with different soil contamination levels, with different weed percentages and with or without wilting, were used to evaluate the dry matter (DM) and crude protein (CP) ruminal degradability. The ruminal degradability of the samples was calculated from the corresponding in situ degradation parameters and from the measured passage rates of the silages fed to each experimental animal. The DM and CP degradation parameters were obtained using the logistic model of Van Milgen and Baumont. The fitting of the models to the kinetics of degradation and particle passage was carried out by non-linear regression. The value of the effective degradability, considering in the rumen simultaneously an outflow compartment and a mixing–reduction compartment, were calculated in both cases from an adaptation of the general procedure proposed by Ørskov and McDonald. A NIRSystems 6500 spectrometer was used for the prediction of the DM and the CP degradation characteristics of the samples. Calibration equations were obtained by modified partial least squares regression, using reflectance spectra transformed into the second derivative. The results showed that near infrared spectroscopy is a good method for predicting the DM and CP degradation characteristics. The calibrations for effective degradability of maize and grass silages indicated a high consistency.

scholarly journals Rapid Estimation of Potato Quality Parameters by a Portable Near-Infrared Spectroscopy Device

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8222
Author(s):  
Olga Escuredo ◽  
Laura Meno ◽  
María Shantal Rodríguez-Flores ◽  
Maria Carmen Seijo
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Dry Matter ◽  
Near Infrared ◽  
Reducing Sugars ◽  
Quality Parameters ◽  
Portable Devices ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Potato Quality

The aim of the present work was to determine the main quality parameters on tuber potato using a portable near-infrared spectroscopy device (MicroNIR). Potato tubers protected by the Protected Geographical Indication (PGI “Patata de Galicia”, Spain) were analyzed both using chemical methods of reference and also using the NIR methodology for the determination of important parameters for tuber commercialization, such as dry matter and reducing sugars. MicroNIR technology allows for the attainment/estimation of dry matter and reducing sugars in the warehouses by directly measuring the tubers without a chemical treatment and destruction of samples. The principal component analysis and modified partial least squares regression method were used to develop the NIR calibration model. The best determination coefficients obtained for dry matter and reducing sugars were of 0.72 and 0.55, respectively, and with acceptable standard errors of cross-validation. Near-infrared spectroscopy was established as an effective tool to obtain prediction equations of these potato quality parameters. At the same time, the efficiency of portable devices for taking instantaneous measurements of crucial quality parameters is useful for potato processors.

Corrigendum to: Optimisation of dry matter and nutrients in feed rations through use of a near-infrared spectroscopy system mounted on a self-propelled feed mixer

2021 ◽  
Vol 61 (5) ◽  
pp. 540
Author(s):  
Ehab Mostafa ◽  
Philipp Twickler ◽  
Alexander Schmithausen ◽  
Christian Maack ◽  
Abdelkader Ghaly ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Crude Protein ◽  
Dry Matter ◽  
Near Infrared ◽  
Nutritional Composition ◽  
Preliminary Evaluation ◽  
Maize Silage ◽  
Nutrient Contents ◽  
Physical Form

Context Knowledge of the nutrient requirements of dairy cows, and the nutritional composition and physical form of the feed resources used to prepare the total mixed ration (TMR) of basic and concentrated feeds, is essential to achieving high milk yields, health and welfare in modern commercial herds. Grass and maize silage components can vary widely in composition depending on harvesting intervals and weather; thus, the distribution of dry matter (DM) and nutrients in silos may vary greatly, resulting in serious errors during sampling and analysis. In addition, the flow of information from the stored silage stops once the forages are stored in the silo. Aims The objective of this study was to develop a practical approach for measuring variations in DM and silage quality parameters (crude protein, fibre, ash and fat) during the feed-extraction process from a bunker silo by a self-propelled feed mixer, which would ultimately help farmers to optimise the TMR. Methods Near-infrared spectroscopy (NIRS) technology was used to estimate fodder DM and nutrient contents in the material flow. Wet chemical analyses were used for preliminary evaluation of grass and maize silage samples. A portable NIRS was developed to record the spectra of various silage samples. Key results The spans of calibration of sample DM content were 21.3–59.2% for grass and 26–46.7% for maize. Crude protein content had span values of 11.4–18.3% for the grass silage and 5.4–10.8% for the maize silage models. Conclusions NIRS technology was used successfully to estimate the DM and nutrient contents of the fodder. The location for the functional unit on the self-propelled feed mixer may need to be modified for series production because it is not fully accessible. Implications NIRS is a suitable method for measuring DM and nutrient contents continuously during feed extraction from the bunker silo and can be used to help farmers to optimise the TMR.

scholarly journals Estimating dry matter and fat content in blocks of Swiss cheese during production using on-line near infrared spectroscopy

2019 ◽  
Vol 27 (4) ◽  
pp. 293-301
Author(s):  
Carl Emil Eskildsen ◽  
Karen Wahlstrøm Sanden ◽  
Sileshi Gizachew Wubshet ◽  
Petter Vejle Andersen ◽  
Jorun Øyaas ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Dry Matter ◽  
Near Infrared ◽  
Least Squares Regression ◽  
Reference Analysis ◽  
Critical Control Points ◽  
Infrared Measurements ◽  
On Line ◽  
Fresh Cheese

Modern dairy factories produce thousands of cheese blocks per day. Cheese quality is partly defined by the concentration of dry matter and fat. In this study, we evaluated three different near infrared spectroscopy instruments for on-line determination of fat and dry matter in cheese blocks of approx. size 35 × 28 × 12 cm: scanning reflection (908–1676 nm), scanning interaction (760–1040 nm), and imaging interaction measurements (760–1040 nm). The near infrared measurements were performed on fresh cheese blocks in a pilot plant at three different critical control points (CCP): (CCP1) before pressing, (CCP2) after pressing, and (CCP3) after salting. A total of 160 cheeses from 10 production batches were measured. Whereas near infrared measurements were obtained from the surface of the cheese blocks, the reference analysis was done on a cross-section of the cheese blocks. In general, good results were obtained regressing the reference values onto the near infrared measurements using partial least squares regression. For example, using near infrared scanning reflection at CCP2 yielded root mean squared errors of cross-validation on 0.44% and 0.64% for fat and dry matter, respectively. Hence, surface chemistry of cheese blocks were representative for the average chemistry of the blocks. Furthermore, this study finds that it is possible to predict fat and dry matter at CCP3 based on near infrared measurements obtained at CCP1 earlier in the process. This enables improved control of the cheese making process, as it is possible to detect deviations from target quality early in the production process.

Prediction of crude protein and neutral detergent fibre concentration in residues of in situ ruminal degradation of pasture samples by near-infrared spectroscopy (NIRS)

2016 ◽  
Vol 56 (9) ◽  
pp. 1504 ◽  
Author(s):  
J. P. Keim ◽  
H. Charles ◽  
D. Alomar
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Crude Protein ◽  
Near Infrared ◽  
External Validation ◽  
Neutral Detergent Fibre ◽  
Insufficient Amount ◽  
Ruminal Degradation ◽  
Animal Feedstuffs

An important constraint of in situ degradability studies is the need to analyse a high number of samples and often with insufficient amount of residue, especially after the longer incubations of high-quality forages, that impede the study of more than one nutritional component. Near-infrared spectroscopy (NIRS) has been established as a reliable method for predicting composition of many entities, including forages and other animal feedstuffs. The objective of this work was to evaluate the potential of NIRS for predicting the crude protein (CP) and neutral detergent fibre (NDF) concentration in rumen incubation residues of permanent and sown temperate pastures in a vegetative stage. In situ residues (n = 236) from four swards were scanned for their visible-NIR spectra and analysed for CP and NDF. Selected equations developed by partial least-squares multivariate regression presented high coefficients of determination (CP = 0.99, NDF = 0.95) and low standard errors (CP = 4.17 g/kg, NDF = 7.91 g/kg) in cross-validation. These errors compare favourably to the average concentrations of CP and NDF (146.5 and 711.2 g/kg, respectively) and represent a low fraction of their standard deviation (CP = 38.2 g/kg, NDF = 34.4 g/kg). An external validation was not as successful, with R2 of 0.83 and 0.82 and a standard error of prediction of 14.8 and 15.2 g/kg, for CP and NDF, respectively. It is concluded that NIRS has the potential to predict CP and NDF of in situ incubation residues of leafy pastures typical of humid temperate zones, but more robust calibrations should be developed.

Application of visible and near-infrared spectroscopy for evaluation of ewes milk with different feeds

2019 ◽  
Vol 59 (6) ◽  
pp. 1190 ◽  
Author(s):  
A. Bahri ◽  
S. Nawar ◽  
H. Selmi ◽  
M. Amraoui ◽  
H. Rouissi ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Random Forest ◽  
Near Infrared Spectroscopy ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Principal Component ◽  
Least Squares Regression ◽  
Random Forest Regression ◽  
Milk Samples

Rapid measurement optical techniques have the advantage over traditional methods of being faster and non-destructive. In this work visible and near-infrared spectroscopy (vis-NIRS) was used to investigate differences between measured values of key milk properties (e.g. fat, protein and lactose) in 30 samples of ewes milk according to three feed systems; faba beans, field peas and control diet. A mobile fibre-optic vis-NIR spectrophotometer (350–2500 nm) was used to collect reflectance spectra from milk samples. Principal component analysis was used to explore differences between milk samples according to the feed supplied, and a partial least-squares regression and random forest regression were adopted to develop calibration models for the prediction of milk properties. Results of the principal component analysis showed clear separation between the three groups of milk samples according to the diet of the ewes throughout the lactation period. Milk fat, protein and lactose were predicted with good accuracy by means of partial least-squares regression (R2 = 0.70–0.83 and ratio of prediction deviation, which is the ratio of standard deviation to root mean square error of prediction = 1.85–2.44). However, the best prediction results were obtained with random forest regression models (R2 = 0.86–0.90; ratio of prediction deviation = 2.73–3.26). The adoption of the vis-NIRS coupled with multivariate modelling tools can be recommended for exploring to differences between milk samples according to different feed systems, and to predict key milk properties, based particularly on the random forest regression modelling technique.

Evaluation of the higher heating value, volatile matter, fixed carbon and ash content of ground bamboo using near infrared spectroscopy

2017 ◽  
Vol 25 (5) ◽  
pp. 301-310 ◽  
Author(s):  
Jetsada Posom ◽  
Panmanas Sirisomboon
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Model Development ◽  
Volatile Matter ◽  
Ash Content ◽  
Least Squares Regression ◽  
Fixed Carbon ◽  
Bomb Calorimetry ◽  
Heating Value

This research aimed to determine the higher heating value, volatile matter, fixed carbon and ash content of ground bamboo using Fourier transform near infrared spectroscopy as an alternative to bomb calorimetry and thermogravimetry. Bamboo culms used in this study had circumferences ranging from 16 to 40 cm. Model development was performed using partial least squares regression. The higher heating value, volatile matter, fixed carbon and ash content were predicted with coefficients of determination (r2) of 0.92, 0.82, 0.85 and 0.51; root mean square error of prediction (RMSEP) of 122 J g−1, 1.15%, 1.00% and 0.77%; ratio of the standard deviation to standard error of validation (RPD) of 3.66, 2.55, 2.62 and 1.44; and bias of 14.4 J g−1, −0.43%, 0.03% and −0.11%, respectively. This report shows that near infrared spectroscopy is quite successful in predicting the higher heating value, and is usable with screening for the determination of fixed carbon and volatile matter. For ash content, the method is not recommended. The models should be able to predict the properties of bamboo samples which are suitable for achieving higher efficiency for the biomass conversion process.

scholarly journals Rapid Diagnosis of HIV-1 virus by Near Infrared Spectroscopy: based on Partial least squares regression

2021 ◽  
Vol 271 ◽  
pp. 03067
Author(s):  
Xiaohong He ◽  
Zhihong Song ◽  
Haifei Shang ◽  
Silang Yang ◽  
Lujing Wu ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
False Negative ◽  
Cost Effective ◽  
Rapid Diagnosis ◽  
Enzyme Linked Immunosorbent Assay ◽  
Least Squares Regression ◽  
Early Screening ◽  
Hiv 1

Currently, the laboratory diagnostic tests available for HIV-1 viral infection are mainly based on serological testing which relies on enzyme-linked immunosorbent assay (ELISA) for blood HIV antigen detection and reverse transcription polymerase chain reaction (RT-PCR) for HIV specific RNA sequence identification. However, these methods are expensive and time-consuming, and suffer from false positive and/or false negative results. Thus, there is an urgent need for developing a cost effective, rapid and accurate diagnostic method for HIV-1 infection. In order to reduce the barriers for effective diagnosis, a near-infrared spectroscopy (NIR) method was used to detect the HIV-1 virus in human serum, specifically, three absorption peaks with dose-dependent at 1582nm, 1810nm and 2363nm were found by multiple FBiPLSR test analysis for HIV-nano and HIV-EGFP, but not for MLV. Therefore, we recommend the use of 1582nm, 1810nm and 2363nm as the characteristic spectrum peak, for early screening and rapid diagnosis of serum HIV.

scholarly journals Hemodialysis monitoring using mid- and near-infrared spectroscopy with partial least squares regression

2018 ◽  
Vol 11 (7) ◽  
pp. e201700365 ◽  
Author(s):  
Raphael Henn ◽  
Christian G. Kirchler ◽  
Zora L. Schirmeister ◽  
Andreas Roth ◽  
Werner Mäntele ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared Spectroscopy ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Least Squares Regression
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