scholarly journals Quantitative Determination of Calcium Hydroxide by Using Near-Infrared Spectroscopy

2018 ◽  
Vol 765 ◽  
pp. 300-304
Author(s):  
Tanyapa Sangpongpitthaya ◽  
Alfred A. Christy ◽  
Rein Terje Thorstensen
Keyword(s):  
Infrared Spectroscopy ◽  
Quantitative Determination ◽  
Calcium Hydroxide ◽  
Near Infrared Spectroscopy ◽  
Calcium Silicate ◽  
Near Infrared ◽  
Concrete Structures ◽  
Steel Structures ◽  
Hydration Reaction

Calcium hydroxide (CH) is a by-product from hydration reaction of cement along with calcium silicate hydrate (C-S-H) gel. It helps to protect the steel reinforcements in concrete structures from corrosion process due to carbonation. The presence of calcium hydroxide provides a basic environment (pH˃10) that induces the formation of passive oxide film and keeps steel structures from corrosion. The detection and quantification of calcium hydroxide in concrete structures are important to understand the nature and state of the steel structures in concretes. In this research work, the variation of calcium hydroxide to calcium silicate ratios in cement were measured by using near-infrared spectroscopy (NIR). The first overtone of the OH groups in calcium hydroxide absorbs at 7082 cm-1and this absorption peak can be used as a quantitative measure of calcium hydroxide in samples. Correlation plot between second derivative absorbance intensity at 7082 cm-1with different mixtures of calcium hydroxide in calcium silicate base. The amount of calcium hydroxide in calcium silicate base was established. This calibration plot was used as basis for determining calcium hydroxide content in unknown concrete samples. Concrete samples for the quantitative determination of calcium hydroxide were prepared from standard cement samples and cement samples with or without pozzolan along with various water to cement ratios. The results show that all samples analyzed in this work contain calcium hydroxide in varying amounts. This variation reflects the composition of the cement and concrete samples.

Influence of data pre-processing on the quantitative determination of the ash content and lipids in roasted coffee by near infrared spectroscopy

2004 ◽  
Vol 509 (2) ◽  
pp. 217-227 ◽  
Author(s):  
Consuelo Pizarro ◽  
Isabel Esteban-Dı́ez ◽  
Antonio-José Nistal ◽  
José-Marı́a González-Sáiz
Keyword(s):  
Infrared Spectroscopy ◽  
Quantitative Determination ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Ash Content ◽  
Roasted Coffee

scholarly journals Quantitative Determination of Fluorine Content in Blends of Polylactide (PLA)–Talc Using Near Infrared Spectroscopy

Sensors ◽  
2016 ◽  
Vol 16 (8) ◽  
pp. 1216 ◽  
Author(s):  
Elena Tamburini ◽  
Chiara Tagliati ◽  
Tiziano Bonato ◽  
Stefania Costa ◽  
Chiara Scapoli ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Quantitative Determination ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Fluorine Content

scholarly journals Quantitative Determination of Germinability ofPuccinia striiformisf. sp.triticiUrediospores Using Near Infrared Spectroscopy Technology

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yaqiong Zhao ◽  
Feng Qin ◽  
Pei Cheng ◽  
Xiaolong Li ◽  
Zhanhong Ma ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Quantitative Determination ◽  
Near Infrared Spectroscopy ◽  
Spectral Region ◽  
Near Infrared ◽  
Puccinia Striiformis ◽  
Support Vector ◽  
Near Infrared Reflectance ◽  
Preprocessing Method

Stripe rust caused byPuccinia striiformisf. sp.tritici(Pst) is an important disease on wheat. In this study, quantitative determination of germinability ofPsturediospores was investigated by using near infrared reflectance spectroscopy (NIRS) combined with quantitative partial least squares (QPLS) and support vector regression (SVR). The near infrared spectra of the urediospore samples were acquired using FT-NIR MPA spectrometer and the germination rate of each sample was measured using traditional spore germination method. The best QPLS model was obtained with vector correction as the preprocessing method of the original spectra and 4000–12000 cm−1as the modeling spectral region while the modeling ratio of the training set to the testing set was 4 : 1. The best SVR model was built when vector normalization was used as the preprocessing method, the modeling ratio was 5 : 1 and the modeling spectral region was 8000–11000 cm−1. The results showed that the effect of the best model built using QPLS or SVR was satisfactory. This indicated that quantitative determination of germinability ofPsturediospores using near infrared spectroscopy technology is feasible. A new method based on NIRS was provided for rapid, automatic, and nondestructive determination of germinability ofPsturediospores.

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