scholarly journals Prediction of the Quality Trait in Rice (Oryza sativa L.) by Near-infrared Spectroscopy Using Different Statistical Methods

2002 ◽  
Vol 52 (1) ◽  
pp. 65-70 ◽  
Author(s):  
Bo-Jein Kuo ◽  
Kun-Chi Su ◽  
Mei-Chu Hong ◽  
Fu-Sheng Thseng
Keyword(s):  
Oryza Sativa ◽  
Infrared Spectroscopy ◽  
Statistical Methods ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Oryza Sativa L ◽  
Quality Trait

scholarly journals Statistical methods for characterizing transfusion-related changes in regional oxygenation using near-infrared spectroscopy (NIRS) in preterm infants

2018 ◽  
Vol 28 (9) ◽  
pp. 2710-2723 ◽  
Author(s):  
Ying Guo ◽  
Yikai Wang ◽  
Terri Marin ◽  
Kirk Easley ◽  
Ravi M Patel ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Statistical Methods ◽  
Preterm Infants ◽  
Near Infrared Spectroscopy ◽  
Technology Use ◽  
Near Infrared ◽  
Estimation Methods ◽  
Red Blood Cell Transfusion ◽  
Testing Procedures ◽  
The Mean

Near infrared spectroscopy (NIRS) is an imaging-based diagnostic tool that provides non-invasive and continuous evaluation of regional tissue oxygenation in real-time. In recent years, NIRS has shown promise as a useful monitoring technology to help detect relative tissue ischemia that could lead to significant morbidity and mortality in preterm infants. However, some issues inherent in NIRS technology use on neonates, such as wide fluctuation in signals, signal dropout and low limit of detection of the device, pose challenges that may obscure reliable interpretation of the NIRS measurements using current methods of analysis. In this paper, we propose new nonparametric statistical methods to analyze mesenteric rSO2(regional oxygenation) produced by NIRS to evaluate oxygenation in intestinal tissues and investigate oxygenation response to red blood cell transfusion (RBC) in preterm infants. Specifically, we present a mean area under the curve (MAUC) measure and a slope measure to capture the mean rSO2level and temporal trajectory of rSO2, respectively. We develop estimation methods for the measures based on multiple imputation and spline smoothing and further propose novel nonparametric testing procedures to detect RBC-related changes in mesenteric oxygenation in preterm infants. Through simulation studies, we show that the proposed methods demonstrate improved accuracy in characterizing the mean level and changing pattern of mesenteric rSO2and also increased statistical power in detecting RBC-related changes, as compared with standard approaches. We apply our methods to a NIRS study in preterm infants receiving RBC transfusion from Emory University to evaluate the pre- and post-transfusion mesenteric oxygenation in preterm infants.

scholarly journals Classification option for Korean traditional paper based on type of raw materials, using near-infrared spectroscopy and multivariate statistical methods

BioResources ◽  
2020 ◽  
Vol 15 (4) ◽  
pp. 9045-9058
Author(s):  
Kyung Ju Jang ◽  
Tae Young Heo ◽  
Seon Hwa Jeong
Keyword(s):  
Infrared Spectroscopy ◽  
Statistical Methods ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Raw Materials ◽  
Classification Performance ◽  
Support Vector ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Material Classification

Depending on the different types of raw materials used to produce hanji, a Korean traditional handmade paper, there can be significant differences in the durability and mechanical properties of the final product. In this study, near-infrared spectroscopy (NIR) combined with multivariate statistical methods were used to confirm the classification possibility of hanji based on the various type of raw materials. The hanji papers were prepared from paper mulberry trees, cooking agents, and mucilage. Altogether, a total of 60 hanji spectra were collected by NIR. Then, the 60 spectra were grouped into four categories: the control, paper mulberry, cooking agent, and mucilage type based on each of the types of raw materials contained in the hanji. Three different classification algorithms – partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), and random forest (RF) – were used to classify the hanji types. The best hanji material classification performance was obtained when the hanji samples were classified according to paper mulberry type, wherein the prediction accuracies of PLS-DA, SVM, and RF were 100%, 100%, and 98%, respectively. These results suggested that NIR in combination with multivariate statistical methods can be used for hanji material classification.

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