Leukemia Early Screening by Using NIR Spectroscopy and LAR-PLS Regression Model

2017 ◽  
pp. 193-201
Author(s):  
Ying Qi ◽  
Zhenbing Liu ◽  
Xipeng Pan ◽  
Weidong Zhang ◽  
Shengke Yan ◽  
...  
Keyword(s):  
Regression Model ◽  
Nir Spectroscopy ◽  
Pls Regression ◽  
Early Screening

A simple multiple linear regression model in near infrared spectroscopy for soluble solids content of pomegranate arils based on stability competitive adaptive re-weighted sampling

2021 ◽  
pp. 096703352098236
Author(s):  
Zhaoqiong Jiang ◽  
Yiping Du ◽  
Fangping Cheng ◽  
Feiyu Zhang ◽  
Wuye Yang ◽  
...  
Keyword(s):  
Variable Selection ◽  
Regression Model ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Soluble Solids ◽  
Pls Regression ◽  
Variable Selection Method ◽  
Solids Content ◽  
Pomegranate Arils ◽  
Mlr Model

The objective of this study was to develop a multiple linear regression (MLR) model using near infrared (NIR) spectroscopy combined with chemometric techniques for soluble solids content (SSC) in pomegranate samples at different storage periods. A total of 135 NIR diffuse reflectance spectra with the wavelength range of 950-1650 nm were acquired from pomegranate arils. Based upon sampling error profile analysis (SEPA), outlier diagnosis was conducted to improve the stability of the model, and four outliers were removed. Several pretreatment and variable selection methods were compared using partial least squares (PLS) regression models. The overall results demonstrated that the pretreatment method of the first derivative (1D) was very effective and the variable selection method of stability competitive adaptive re-weighted sampling (SCARS) was powerful for extracting feature variables. The equilibrium performance of 1D-SCARS-PLS regression model for ten times was similar to 1D-PLS regression model, so that the advantage of wavelength selection was inconspicuous in PLS regression model. However, the number of variables selected by 1D-SCARS was less to 9, which was enough to establish a simple MLR model. The performance of MLR model for SSC of pomegranate arils based on 1D-SCARS was receivable with the root-mean-square error of calibration set (RMSEC) of 0.29% and prediction set (RMSEP) of 0.31%. This strategy combining variable selection method with MLR may have a broad prospect in the application of NIR spectroscopy due to its simplicity and robustness.

scholarly journals A PLS regression model using NIR spectroscopy for on-line monitoring of the biodiesel production reaction

Fuel ◽  
2011 ◽  
Vol 90 (11) ◽  
pp. 3268-3273 ◽  
Author(s):  
Mario H.M. Killner ◽  
Jarbas J.R. Rohwedder ◽  
Celio Pasquini
Keyword(s):  
Regression Model ◽  
Nir Spectroscopy ◽  
Biodiesel Production ◽  
Pls Regression ◽  
On Line

Determination of fat content in chicken hamburgers using NIR spectroscopy and the Successive Projections Algorithm for interval selection in PLS regression ( i SPA-PLS)

2018 ◽  
Vol 189 ◽  
pp. 300-306 ◽  
Author(s):  
Gabriela Krepper ◽  
Florencia Romeo ◽  
David Douglas de Sousa Fernandes ◽  
Paulo Henrique Gonçalves Dias Diniz ◽  
Mário César Ugulino de Araújo ◽  
...  
Keyword(s):  
Nir Spectroscopy ◽  
Fat Content ◽  
Pls Regression ◽  
Successive Projections Algorithm ◽  
Interval Selection ◽  
Successive Projections

scholarly journals Vis-NIR Spectroscopy and PLS Regression with Waveband Selection for Estimating the Total C and N of Paddy Soils in Madagascar

2017 ◽  
Vol 9 (10) ◽  
pp. 1081 ◽  
Author(s):  
Kensuke Kawamura ◽  
Yasuhiro Tsujimoto ◽  
Michel Rabenarivo ◽  
Hidetoshi Asai ◽  
Andry Andriamananjara ◽  
...  
Keyword(s):  
Nir Spectroscopy ◽  
Paddy Soils ◽  
Pls Regression ◽  
C And N ◽  
Selection For

scholarly journals Estimating Chlorophyll Content of Leafy Green Vegetables from Adaxial and Abaxial Reflectance

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4059 ◽  
Author(s):  
Lu ◽  
Bu ◽  
Lu
Keyword(s):  
Regression Model ◽  
Chlorophyll Content ◽  
Pls Regression ◽  
Edge Region ◽  
Spectral Indices ◽  
Romaine Lettuce ◽  
Leaf Chlorophyll ◽  
Chinese White ◽  
White Cabbage ◽  
Leaf Surfaces

As a primary pigment of leafy green vegetables, chlorophyll plays a major role in indicating vegetable growth status. The application of hyperspectral remote sensing reflectance offers a quick and nondestructive method to estimate the chlorophyll content of vegetables. Reflectance of adaxial and abaxial leaf surfaces from three common leafy green vegetables: Pakchoi var. Shanghai Qing (Brassica chinensis L. var. Shanghai Qing), Chinese white cabbage (Brassica campestris L. ssp. Chinensis Makino var. communis Tsen et Lee), and Romaine lettuce (Lactuca sativa var longifoliaf. Lam) were measured to estimate the leaf chlorophyll content. Modeling based on spectral indices and the partial least squares regression (PLS) was tested using the reflectance data from the two surfaces (adaxial and abaxial) of leaves in the datasets of each individual vegetable and the three vegetables combined. The PLS regression model showed the highest accuracy in estimating leaf chlorophyll content of pakchoi var. Shanghai Qing (R2 = 0.809, RMSE = 62.44 mg m−2), Chinese white cabbage (R2 = 0.891, RMSE = 45.18 mg m−2) and Romaine lettuce (R2 = 0.834, RMSE = 38.58 mg m−2) individually as well as of the three vegetables combined (R2 = 0.811, RMSE = 55.59 mg m−2). The good predictability of the PLS regression model is considered to be due to the contribution of more spectral bands applied in it than that in the spectral indices. In addition, both the uninformative variable elimination PLS (UVE-PLS) technique and the best performed spectral index: MDATT, showed that the red-edge region (680–750 nm) was effective in estimating the chlorophyll content of vegetables with reflectance from two leaf surfaces. The combination of the PLS regression model and the red-edge region are insensitive to the difference between the adaxial and abaxial leaf structure and can be used for estimating the chlorophyll content of leafy green vegetables accurately.

True Accuracy of near Infrared Spectroscopy and its Dependence on Precision of Reference Data

2002 ◽  
Vol 10 (1) ◽  
pp. 15-25 ◽  
Author(s):  
L.K. Sørensen
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Reference Data ◽  
Nir Spectroscopy ◽  
Limited Resources ◽  
Standard Errors ◽  
Calibration Data ◽  
Pls Regression ◽  
Calibration Equations ◽  
Very High

A more precise estimate of the accuracy of near infrared (NIR) spectroscopy is obtained when the measured standard errors of cross validation ( SECV) and prediction ( SEP) are corrected for imprecision of the reference data. The significance of correction increases with increasing imprecision of reference data. Very high precision of reference data obtained through replicate analyses under reproducibility conditions may not be the optimal goal for the development of calibration equations. In a situation of limited resources, the precision of the reference data should be related to the obtainable accuracy of the spectroscopic system. Investigation of several routine applications based on the partial least-squares (PLS) regression technique showed that increased precision of calibration data only resulted in marginal improvements in true accuracy if the total standard error of reference results from the beginning was less than the estimated true accuracy of the corresponding NIR calibration.

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