Early warning of disturbances in a laboratory-scale MSW biogas process

2002 ◽  
Vol 45 (10) ◽  
pp. 255-260 ◽  
Author(s):  
M. Hannsson ◽  
Å Nordberg ◽  
I. Sundh ◽  
B. Mathisen
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Early Warning ◽  
Near Infrared ◽  
Total Concentration ◽  
Principal Component ◽  
Before And After ◽  
On Line ◽  
Biogas Process ◽  
Line Analysis

The use of near-infrared spectroscopy (NIR) to monitor the dynamics of a biogas process was evaluated using multivariate data analysis. The digester was a completely stirred 8 l tank reactor fed with the organic fraction of source-sorted MSW. Intermittently the digester was overloaded with feed. Before and after overload on-line monitoring of NIR spectra and off-line analysis in the liquid and the gas phase of traditional chemical variables and microbial biomass, determined as total concentration of phospholipid fatty acids (PLFA and PLEL), were done. The dynamics that occurred due to overloading could be followed using principal component analysis of the obtained NIR-spectra. In addition, the response to changes in the digester fluid was reproducible and could be detected within five minutes, which can be considered as real-time monitoring. Selected wavelengths in the region 800–2,000 nm were used to make a PLS1-regression with propionate. The regression resulted in a good correlation for propionate (R = 0.94 and RMSEP of 0.21 g/l in the range of 0.3–3 g/l). The results indicate the possibility to develop an early warning biogas control system based on near-infrared spectroscopy monitoring of propionate.

Potential of near Infrared Spectroscopy for On-Line Analysis at the Milking Parlour Using a Fibre-Optic Probe Presentation

NIR news ◽  
2011 ◽  
Vol 22 (7) ◽  
pp. 11-13 ◽  
Author(s):  
Hoang Nam Nguyen ◽  
Frédéric Dehareng ◽  
Mohamed Hammida ◽  
Vincent Baeten ◽  
Eric Froidmont ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Fibre Optic ◽  
Fibre Optic Probe ◽  
On Line ◽  
Optic Probe ◽  
Line Analysis

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.

Identification of Liquor Brands Based on near Infrared Spectroscopy

2013 ◽  
Vol 834-836 ◽  
pp. 935-938
Author(s):  
Lian Shun Zhang ◽  
Chao Guo ◽  
Bao Quan Wang
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Principal Components ◽  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component ◽  
Component Analysis ◽  
Correction Method ◽  
Variance Contribution

In this paper, the liquor brands were identified based on the near infrared spectroscopy method and the principal component analysis. 60 samples of 6 different brands liquor were measured by the spectrometer of USB4000. Then, in order to eliminate the noise caused by the external factors, the smoothing method and the multiplicative scatter correction method were used. After the preprocessing, we got the revised spectra of the 60 samples. The difference of the spectrum shape of different brands is not much enough to classify them. So the principal component analysis was applied for further analysis. The results showed that the first two principal components variance contribution rate had reached 99.06%, which can effectively represent the information of the spectrums after preprocessing. From the scatter plot of the two principal components, the 6 different brands of liquor were identified more accurate and easier than the spectra curves.

Sign in / Sign up

Export Citation Format

Share Document