Blend uniformity end-point determination using near-infrared spectroscopy and multivariate calibration

2011 ◽  
Vol 55 (3) ◽  
pp. 429-434 ◽  
Author(s):  
Yusuf Sulub ◽  
Michele Konigsberger ◽  
James Cheney
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
End Point ◽  
Blend Uniformity ◽  
Point Determination

Modeling strategies for pharmaceutical blend monitoring and end-point determination by near-infrared spectroscopy

2014 ◽  
Vol 473 (1-2) ◽  
pp. 219-231 ◽  
Author(s):  
Benoît Igne ◽  
Anna de Juan ◽  
Joaquim Jaumot ◽  
Jordane Lallemand ◽  
Sébastien Preys ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
End Point ◽  
Point Determination

Linear transformations of multivariate calibration models in near infrared spectroscopy: A comparative study

2017 ◽  
Vol 25 (4) ◽  
pp. 223-230 ◽  
Author(s):  
Joseph Dubrovkin
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Cetane Number ◽  
Total Protein Content ◽  
Prediction Errors ◽  
Calibration Data ◽  
Linear Transformations ◽  
Calibration Models

It was shown that linear transformations are suitable for use in multivariate calibration in near infrared spectroscopy as data compression tools. Partial Least Squares calibration models were built using spectral data transformed by expansion in the series of classical orthogonal polynomials, Fourier and wavelet harmonics. These models allowed effective prediction of the cetane number of diesel fuels, Brix and pol parameters of syrup in sugar production and fat and total protein content in milk. Depending on the compression ratio, prediction errors were no larger than 30% of corresponding errors obtained by the use of the non-transformed models. Although selection of the most suitable transformation depends on the calibration data and on the cross-validation method, in many cases Fourier transform gave satisfactory results.

scholarly journals Use of near infrared spectroscopy and chemometrics to evaluate the shelf-life of cloudy sonicated apple juice

2019 ◽  
Vol 27 (1) ◽  
pp. 75-85 ◽  
Author(s):  
Lorenzo Serva ◽  
Stefania Balzan ◽  
Vittoria Bisutti ◽  
Filomena Montemurro ◽  
Giorgio Marchesini ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Shelf Life ◽  
Near Infrared Spectroscopy ◽  
Storage Time ◽  
Apple Juice ◽  
Near Infrared ◽  
Partial Least Square ◽  
Feasibility Trial ◽  
End Point

Fresh products, such as cloudy apple juice, could be preserved from early spoilage through the application of non-thermal processes such as sonication. However, shelf-life analyses based on microbiological and sensory evaluations are expensive and time consuming. Few studies have applied near infrared spectroscopy to evaluate the quality and decay of apple juices. Here, a feasibility trial was conducted to study the spectral behaviour at 1300–2500 nm combined with chemometric approaches. The shelf-life was monitored during two experiments, a challenge test with juices inoculated with spoilage yeasts (inoculated non-sonicated (INS)) and then submitted to sonication treatments (inoculated sonicated (IS)), and a storage test to evaluate the spoilage on non-inoculated juices (non-inoculated non-sonicated (NINS)) and sonicated non-inoculated juices (non-inoculated sonicated (NIS)). These experiments were investigated at six different refrigeration times 7, 14, 21, 28 and 60 days. Two functions were modelled to describe the behaviours of the first principal component according to the storage time. In agreement with a previous chemical and sensory evaluation, this approach allowed us to highlight shelf-life end points of 7 and 14 days for non-sonicated and sonicated samples, respectively. Three different models were evaluated for classification purposes: (1) sonicated versus non-treated samples, (2) end-point shelf-life evaluation at seven days for the NINS and INS juices and (3) end-point shelf-life discrimination at 14 days for IS and NIS samples. A partial least square-discriminant analysis enabled a group classification with accuracy values ranging from 0.63 to 1.00. The application of a variable importance in projection index to interpret the wavelengths of the spectral features suggests a contribution of organic acids and lipids to the prediction of decay. A canonical discriminant analysis provided a clearer separation of samples according to the storage time, especially in relation to the two time thresholds of 7 and 14 days.

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