The SMRI-NIRS technology Part 2: Improving factory performance

2021 ◽  
pp. 406-413
Author(s):  
Shaun Madho ◽  
Bryan Barker
Keyword(s):  
South Africa ◽  
Infrared Spectroscopy ◽  
Decision Support ◽  
South African ◽  
Analytical Method ◽  
Near Infrared ◽  
Ph Value ◽  
Analytical Data ◽  
Process Stream ◽  
Sugarcane Processing

The Sugar Milling Research Institute NPC (SMRI) has developed a Near Infrared Spectroscopy (NIRS) analytical method for use in sugarcane factories, initially for use in South Africa, in place of conventional analytical methods. Details on the development, validation and benefit of the SMRI-NIRS analytical method are reported in Part 1 of this paper (Walford 2019). By 2019 all South African sugarcane processing factories had discontinued conventional analyses in favour of the SMRI-NIRS method for factory control purposes. The SMRI-NIRS method predicts analytical results of dry solids, polarimetric sugar, sucrose (HPLC), glucose, fructose, conductivity ash contents as well as ICUMSA colour and pH value from a single NIRS scan of any suitably diluted sugarcane process stream sample. Final molasses dry solids can also be predicted. In addition to improved laboratory output, the additional analytical data can be used to improve factory performance. This paper gives examples of where the SMRI-NIRS technology, the analytical method and the associated decision-support toolkits, have been used in South African factories, to improve factory sucrose recoveries and the reporting of factory performance figures.

Determination of soil organic carbon and nitrogen at the field level using near-infrared spectroscopy

2002 ◽  
Vol 82 (4) ◽  
pp. 413-422 ◽  
Author(s):  
P D Martin ◽  
D F Malley ◽  
G. Manning ◽  
L. Fuller
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Analytical Data ◽  
Principal Component ◽  
Soil Samples ◽  
Organic N ◽  
Least Squares Regression ◽  
Total N ◽  
Organic C

This study explored the use of near-infrared spectroscopy (NIRS) for the rapid analysis of organic C (Corg) and organic N (Norg) in the A horizon of soil within a single field. Soil was sampled throughout a field in Manitoba, Canada to capture soil variability associated with topography. The soil samples were oven-dried and treated with acid to remove carbonates, after which C and N were determined by dry combustion. In this study, portions of the dried soil samples not treated with acid were scanned with a near-infrared scanning spectrophotometer between 1100 and 2500 nm. Correlating the spectral and the chemical analytical data using multiple linear regression or principal component analysis/partial least squares regression gave useful correlations for Corg. Over the range of 0–40 mg g-1 Corg, NIR-predicted values explained 75–78% of the variance in the chemical results. Results were improved to 80% for calibrations developed for the 0–20 mg g-1 organic C range. Useful results were not obtained for Norg although the literature shows that total N in soil is predictable using NIRS. It is likely that the acid treatment altered the composition of the samples in an inconsistent manner such that the chemically analyzed samples and those scanned by NIRS were different from each other in Norg concentration or composition. Extrapolation of these Corg results to the landscape scale implies that NIRS has potential to be a suitable method for mapping C for the purposes of monitoring C sequestration. Key words: Near-infrared spectroscopy, soil, carbon, nitrogen, topography, soil monitoring

scholarly journals pH value monitoring during human albumin purification with near infrared spectroscopy and chemometrics

2018 ◽  
Vol 11 (03) ◽  
pp. 1850009 ◽  
Author(s):  
Qiaofeng Sun ◽  
Zhongyu Sun ◽  
Fei Wang ◽  
Lian Li ◽  
Ronghua Liu ◽  
...  
Keyword(s):  
Quality Control ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Ph Value ◽  
Acid Precipitation ◽  
Human Albumin ◽  
Precipitation Process ◽  
Least Squares Regression

Human albumin (HA) is a very important blood product which requires strict quality control strategy. Acid precipitation is a key step which has a great effect on the quality of final product. Therefore, a new method based on quality by design (QbD) was proposed to investigate the feasibility of realizing online quality control with the help of near infrared spectroscopy (NIRS) and chemometrics. The pH value is the critical process parameter (CPP) in acid precipitation process, which is used as the end-point indicator. Six batches, a total of 74 samples of acid precipitation process, were simulated in our lab. Four batches were selected randomly as calibration set and remaining two batches as validation set. Then, the analysis based on material information and three different variable selection methods, including interval partial least squares regression (iPLS), competitive adaptive reweighted sampling (CARS) and correlation coefficient (CC) were compared for eliminating irrelevant variables. Finally, iPLS was used for variables selection. The quantitative model was built up by partial least squares regression (PLSR). The values of determination coefficients ([Formula: see text] and [Formula: see text]), root mean squares error of prediction (RMSEP), root mean squares error of calibration (RMSEC) and root mean squared error of cross validation (RMSECV) were 0.969, 0.953, 0.0496, 0.0695 and 0.0826, respectively. The paired [Formula: see text] test and repeatability test showed that the model had good prediction ability and stability. The results indicated that PLSR model could give accurate measurement of the pH value.

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