Weighted partial least squares regression by variable grouping strategy for multivariate calibration of near infrared spectra

2010 ◽  
Vol 2 (3) ◽  
pp. 289 ◽  
Author(s):  
Heng Xu ◽  
Wensheng Cai ◽  
Xueguang Shao
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Infrared Spectra ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Least Squares Regression ◽  
Grouping Strategy ◽  
Near Infrared Spectra

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.

scholarly journals Rapid tannin profiling of tree fodders using untargeted mid-infrared spectroscopy and partial least squares regression

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Jordi Ortuño ◽  
Sokratis Stergiadis ◽  
Anastasios Koidis ◽  
Jo Smith ◽  
Chris Humphrey ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Multivariate Calibration ◽  
Plant Ecology ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy

Abstract Background The presence of condensed tannins (CT) in tree fodders entails a series of productive, health and ecological benefits for ruminant nutrition. Current wet analytical methods employed for full CT characterisation are time and resource-consuming, thus limiting its applicability for silvopastoral systems. The development of quick, safe and robust analytical techniques to monitor CT’s full profile is crucial to suitably understand CT variability and biological activity, which would help to develop efficient evidence-based decision-making to maximise CT-derived benefits. The present study investigates the suitability of Fourier-transformed mid-infrared spectroscopy (MIR: 4000–550 cm−1) combined with multivariate analysis to determine CT concentration and structure (mean degree of polymerization—mDP, procyanidins:prodelphidins ratio—PC:PD and cis:trans ratio) in oak, field maple and goat willow foliage, using HCl:Butanol:Acetone:Iron (HBAI) and thiolysis-HPLC as reference methods. Results The MIR spectra obtained were explored firstly using Principal Component Analysis, whereas multivariate calibration models were developed based on partial least-squares regression. MIR showed an excellent prediction capacity for the determination of PC:PD [coefficient of determination for prediction (R2P) = 0.96; ratio of prediction to deviation (RPD) = 5.26, range error ratio (RER) = 14.1] and cis:trans ratio (R2P = 0.95; RPD = 4.24; RER = 13.3); modest for CT quantification (HBAI: R2P = 0.92; RPD = 3.71; RER = 13.1; Thiolysis: R2P = 0.88; RPD = 2.80; RER = 11.5); and weak for mDP (R2P = 0.66; RPD = 1.86; RER = 7.16). Conclusions MIR combined with chemometrics allowed to characterize the full CT profile of tree foliage rapidly, which would help to assess better plant ecology variability and to improve the nutritional management of ruminant livestock.

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