Application of partial least squares near-infrared spectral classification in diabetic identification

2014 ◽  
Author(s):  
Wen-juan Yan ◽  
Ming Yang ◽  
Guo-quan He ◽  
Lin Qin ◽  
Gang Li
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Spectral Classification ◽  
Infrared Spectral

N-way partial least squares combined with new self-construction strategy—A promising approach of using near infrared spectral data for quantitative determination of multiple compounds

2019 ◽  
Vol 28 (2) ◽  
pp. 113-121
Author(s):  
Xiang-Zhi Zhang ◽  
Ai-Jun Ma ◽  
Na Feng ◽  
Bao Qiong Li
Keyword(s):  
Quantitative Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Least Squares Method ◽  
Three Dimensional ◽  
Near Infrared Spectra ◽  
Infrared Spectral ◽  
Construction Strategy ◽  
Partial Least Squares Method

Because of the complexity of near infrared spectral data, effective strategies are necessary proposed for accurate quantitative analysis purpose. This work explores a new self-construction strategy for the arrangement of conventional near infrared two-dimensional spectra into new self-constructed three-dimensional spectra, and investigate the feasibility of N-way partial least squares combined with the new self-constructed three-dimensional near infrared spectra for obtaining accurate quantitative determination results. A proof-of-concept model system, the quantitative analysis of four components (moisture, oil, protein, and starch) in corn samples, was applied to evaluate the performance of the proposed strategy. The ability of the newly proposed approach to predict the target compounds was checked with test samples. The established models have good predictive power for the target compounds with acceptable values of Rp (range from 0.82 to 0.997) and RMSEP (range from 0.03 to 0.47). Compared with partial least squares method on pretreated near infrared spectra and N-way partial least squares method on the basis of near infrared self-constructed three-dimensional spectra, the proposed method is competitive.

Selection of near infrared spectral information based on section combination moving window partial least squares

2017 ◽  
Vol 38 (1) ◽  
pp. 590-594
Author(s):  
Chen Yueyang ◽  
Gao Zhishan ◽  
Yu Xiaohui ◽  
Zhu Dan ◽  
Chen Ming ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Spectral Information ◽  
Moving Window ◽  
Infrared Spectral ◽  
Selection Of

scholarly journals Multivariate and machine learning approaches for honey botanical origin authentication using near infrared spectroscopy

2019 ◽  
Vol 27 (1) ◽  
pp. 65-74 ◽  
Author(s):  
Vittoria Bisutti ◽  
Roberta Merlanti ◽  
Lorenzo Serva ◽  
Lorena Lucatello ◽  
Massimo Mirisola ◽  
...  
Keyword(s):  
Machine Learning ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Canonical Discriminant Analysis ◽  
Learning Approaches ◽  
Botanical Origin ◽  
Infrared Spectral

In this work the feasibility of near infrared spectroscopy was evaluated combined with chemometric approaches, as a tool for the botanical origin prediction of 119 honey samples. Four varieties related to polyfloral, acacia, chestnut, and linden were first characterized by their physical–chemical parameters and then analyzed in triplicate using a near infrared spectrophotometer equipped with an optical path gold reflector. Three different classifiers were built on distinct multivariate and machine learning approaches for honey botanical classification. A partial least squares discriminant analysis was used as a first approach to build a predictive model for honey classification. Spectra pretreatments named autoscale, standard normal variate, detrending, first derivative, and smoothing were applied for the reduction of scattering related to the presence of particle size, like glucose crystals. The values of the descriptive statistics of the partial least squares discriminant analysis model allowed a sufficient floral group prediction for the acacia and polyfloral honeys but not in the cases of chestnut and linden. The second classifier, based on a support vector machine, allowed a better classification of acacia and polyfloral and also achieved the classification of chestnut. The linden samples instead remained unclassified. A further investigation, aimed to improve the botanical discrimination, exploited a feature selection algorithm named Boruta, which assigned a pool of 39 informative averaged near infrared spectral variables on which a canonical discriminant analysis was assessed. The canonical discriminant analysis accounted a better separation of samples according to the botanical origin than the partial least squares discriminant analysis. The approach used has permitted to achieve a complete authentication of the acacia honeys but not a precise segregation of polyfloral ones. The comparison between the variables important in projection and the Boruta pool showed that the informative wavelengths are partially shared especially in the middle and far band of the near infrared spectral range.

scholarly journals Identifying camellia oil adulteration with selected vegetable oils by characteristic near-infrared spectral regions

2018 ◽  
Vol 11 (02) ◽  
pp. 1850006 ◽  
Author(s):  
Xuan Chu ◽  
Wei Wang ◽  
Chunyang Li ◽  
Xin Zhao ◽  
Hongzhe Jiang
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Vegetable Oils ◽  
Near Infrared ◽  
Corn Oil ◽  
Correlation Coefficients ◽  
Full Spectrum ◽  
Camellia Oil ◽  
Infrared Spectral ◽  
Mean Square Errors

In this paper, a methodology based on characteristic spectral bands of near infrared spectroscopy (1000–2500[Formula: see text]nm) and multivariate analysis was proposed to identify camellia oil adulteration with vegetable oils. Sunflower, peanut and corn oils were selected to conduct the test. Pure camellia oil and that adulterated with varying concentrations (1–10% with the gradient of 1%, 10–40% with the gradient of 5%, 40–100% with the gradient of 10%) of each type of the three vegetable oils were prepared, respectively. For each type of adulterated oil, full-spectrum partial least squares partial least squares (PLS) models and synergy interval partial least squares (SI-PLS) models were developed. Parameters of these models were optimized simultaneously by cross-validation. The SI-PLS models were proved to be better than the full-spectrum PLS models. In SI-PLS models, the correlation coefficients of predition set (Rp) were 0.9992, 0.9998 and 0.9999 for adulteration with sunflower oil, peanut oil and corn oil seperately; the corresponding root mean square errors of prediction set (RMSEP) were 1.23, 0.66 and 0.37. Furthermore, a new generic PLS model was built based on the characteristic spectral regions selected from the intervals of the three SI-PLS models to identify the oil adulterants, regardless of the adultrated oil types. The model achieved with Rp[Formula: see text] 0.9988 and RMSEP [Formula: see text] 1.52. These results indicated that the characteristic near infrared spectral regions could determine the level of adulteration in the camellia oil.

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