Determination of Major Compounds of Alcoholic Fermentation by Middle-Infrared Spectroscopy: Study of Temperature Effects and Calibration Methods

1996 ◽  
Vol 50 (10) ◽  
pp. 1325-1330 ◽  
Author(s):  
Philippe Fayolle ◽  
Daniel Picque ◽  
Bruno Perret ◽  
Eric Latrille ◽  
Georges Corrieu

The potential of Fourier transform middle-infrared spectroscopy has been demonstrated for the quantitative analysis of substrates (glucose and fructose) and metabolites (glycerol and ethanol) involved in alcoholic fermentation. Temperature variations between samples and water background reference caused changes in absorbance, and therefore the prediction of concentrations with partial least-squares (PLS) regressions was affected. The same temperatures for the calibration, validation, and prediction sets gave the smallest standard error of prediction (SEP): SEPglucose = 3.9 g L−1; SEPfructose = 4.3 g L−1; SEPglycerol = 0.5 g L−1; SEPethanol = 1.3 g L−1. In order to take different working temperatures (18, 25, and 35 °C) into account, an artificial neural network was used to create a nonlinear multivariate model. Compared to PLS regression, this method provided better results, especially for glycerol and ethanol, where SEP decreased by 0.3 g L−1 and 0.4 g L−1, respectively.

2011 ◽  
Vol 460-461 ◽  
pp. 357-362 ◽  
Author(s):  
Peng Cheng Nie ◽  
Yan Yang ◽  
Yong He

Middle infrared spectroscopy combined with chemometrics was investigated for the fast determination of protein of mushroom. 140 samples (35 for each variety) were selected randomly for the calibration set, whereas, 40 samples for the validation set. After some spectrum preprocessing, linear modeling method (PLS) and nonlinear modeling LS-SVM were constructed. Different latent variables were used as inputs of LS-SVM. The optimal models were obtained with 8 LVs based on LS-SVM. The correlation coefficient,,root mean square error of prediction for the best prediction by LV-LS-SVM were 0.9275, 0.25961. The result indicated that middle infrared spectroscopy combined with LV-LS-SVM could be applied as a high precision and fast way for determination protein of mushroom.


2010 ◽  
Vol 682 (1-2) ◽  
pp. 37-47 ◽  
Author(s):  
Luiz Alberto Pinto ◽  
Roberto Kawakami Harrop Galvão ◽  
Mário César Ugulino Araújo

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1981 ◽  
Author(s):  
Zhengyan Xia ◽  
Yiming Sun ◽  
Chengyong Cai ◽  
Yong He ◽  
Pengcheng Nie

The feasibility of near-infrared spectroscopy (NIR) to detect chlorogenic acid, luteoloside and 3,5-O-dicaffeoylquinic acid in Chrysanthemum was investigated. An NIR spectroradiometer was applied for data acquisition. The reference values of chlorogenic acid, luteoloside, and 3,5-O-dicaffeoylquinic acid of the samples were determined by high-performance liquid chromatography (HPLC) and were used for model calibration. The results of six preprocessing methods were compared. To reduce input variables and collinearity problems, three methods for variable selection were compared, including successive projections algorithm (SPA), genetic algorithm-partial least squares regression (GA-PLS), and competitive adaptive reweighted sampling (CARS). The selected variables were employed as the inputs of partial least square (PLS), back propagation-artificial neural networks (BP-ANN), and extreme learning machine (ELM) models. The best performance was achieved by BP-ANN models based on variables selected by CARS for all three chemical constituents. The values of rp2 (correlation coefficient of prediction) were 0.924, 0.927, 0.933, the values of RMSEP were 0.033, 0.018, 0.064 and the values of RPD were 3.667, 3.667, 2.891 for chlorogenic acid, luteoloside, and 3,5-O-dicaffeoylquinic acid, respectively. The results indicated that NIR spectroscopy combined with variables selection and multivariate calibration methods could be considered as a useful tool for rapid determination of chlorogenic acid, luteoloside, and 3,5-O-dicaffeoylquinic acid in Chrysanthemum.


2012 ◽  
Vol 60 (25) ◽  
pp. 6341-6348 ◽  
Author(s):  
Stefan Castritius ◽  
Mirko Geier ◽  
Gerold Jochims ◽  
Ulf Stahl ◽  
Diedrich Harms

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