Simultaneous determination of human serum albumin, γ-globulin, and glucose in a phosphate buffer solution by near-infrared spectroscopy with moving window partial least-squares regression

The Analyst ◽  
2003 ◽  
Vol 128 (12) ◽  
pp. 1471-1477 ◽  
Author(s):  
Sumaporn Kasemsumran ◽  
Yi-ping Du ◽  
Koichi Murayama ◽  
Martin Huehne ◽  
Yukihiro Ozaki
Keyword(s):  
Infrared Spectroscopy ◽  
Human Serum Albumin ◽  
Near Infrared Spectroscopy ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Phosphate Buffer Solution ◽  
Moving Window ◽  
Least Squares Regression ◽  
Buffer Solution

Determination of sulfur dioxide content in osmotically dehydrated papaya and its classification by near infrared spectroscopy

2018 ◽  
Vol 26 (6) ◽  
pp. 359-368 ◽  
Author(s):  
Bumrungrat Rongtong ◽  
Thongchai Suwonsichon ◽  
Pitiporn Ritthiruangdej ◽  
Sumaporn Kasemsumran
Keyword(s):  
Infrared Spectroscopy ◽  
Sulfur Dioxide ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared Spectroscopy ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Moving Window ◽  
Least Squares Regression ◽  
Near Infrared Spectra

Sulfur dioxide (SO2) is used as a preservative in osmotically dehydrated papaya to improve product quality and extend shelf-life. The potential of near infrared spectroscopy, as a rapid method, was investigated to determine sulfur dioxide in osmotically dehydrated papaya. Commercial and laboratory osmotically dehydrated papaya samples were selected to determine the sulfur dioxide content using the Monier–Williams method. From the total of 350 samples, subsets were selected randomly for the calibration set (n=250) and validation set (n = 100). Near infrared spectra in the region 800–2400 nm were measured on the samples of osmotically dehydrated papaya. Quantitative analyses of sulfur dioxide in the osmotically dehydrated papaya and their qualitative analyses were carried out using multivariate analysis. Before developing models, a second derivative spectral pretreatment was applied to the original spectral data. Subsequently, two wavelength interval selection methods, namely moving window partial least squares regression (MWPLSR) and searching combination moving window partial least squares (SCMWPLS), were applied to determine the suitable input wavelength variables. For quantitative analysis, three linear models (partial least squares regression, MWPLSR and SCMWPLS) and a non-linear artificial neural network model were applied to develop predictive models. The results showed that the artificial neural network model produced the best performance, with correlation coefficient (R) and root mean square error of prediction values of 0.937 and 114.53 mg SO2 kg−1, respectively. Qualitative models were developed using partial least squares-discriminant analysis and soft independent modeling of class analogy (SIMCA) for the optimized combination of informative regions of the near infrared spectra to classify osmotically dehydrated papaya into three groups based on sulfur dioxide. The SIMCA in combination with SCMWPLS model had the highest correct classification rate (96%). The study demonstrated that near infrared spectroscopy combined with SCMWPLS is a powerful procedure for both quantitative and qualitative analyses of osmotically dehydrated papaya. Therefore, it was demonstrated that near infrared spectroscopy could be effective tools for food quality and safety evaluation in food industry.

Sign in / Sign up

Export Citation Format

Share Document