Development of Quantitative Analysis Techniques for Saccharification Reactions Using Raman Spectroscopy

2018 ◽  
Vol 72 (11) ◽  
pp. 1606-1612 ◽  
Author(s):  
Anggara Maharadika ◽  
Bibin B. Andriana ◽  
AB Susanto ◽  
Hiroko Matsuyoshi ◽  
Hidetoshi Sato
Keyword(s):  
Raman Spectroscopy ◽  
Quantitative Analysis ◽  
Conventional Method ◽  
Prediction Models ◽  
Plant Tissues ◽  
Least Squares Regression ◽  
Specific Enzyme ◽  
Raman Analysis ◽  
Good Agreement

A technique for the analysis of saccharification reactions by a specific enzyme was developed on the basis of Raman spectroscopy using multivariate analysis. It is a microvolume, quantitative, and in situ technique, which can be used for studying saccharification processes in plant tissues. Prediction models for quantitative analysis of maltose, glucose, and starch were built with partial least squares regression (PLSR) analysis to monitor the saccharification process caused by α-amylase. We examined the reliability of the prediction models built using seven test samples. The spectral regions used to build the models were optimized for each sugar and were selected in such a manner that they did not overlap with strong protein and lipid bands that generally exist in plant tissues. The models were validated by monitoring the composition of reduced sugars and starch in a reactor and by comparing the results with those obtained by a conventional method. The results of Raman analysis and the conventional method showed good agreement for the reaction with α-amylase; however, it is not perfect for reactions with a different enzyme, especially β-amylase. The results suggest that the present Raman technique is reliable and useful for sugar analysis. However, the prediction model built for a specific enzyme is valid only for that enzyme.

In situ quantitative analysis of individual H2O–CO2 fluid inclusions by laser Raman spectroscopy

2007 ◽  
Vol 237 (3-4) ◽  
pp. 255-263 ◽  
Author(s):  
Tristan Azbej ◽  
Matthew J. Severs ◽  
Brian G. Rusk ◽  
Robert J. Bodnar
Keyword(s):  
Raman Spectroscopy ◽  
Quantitative Analysis ◽  
Fluid Inclusions ◽  
Laser Raman Spectroscopy ◽  
Laser Raman

scholarly journals High-Throughput Phenotyping Approach for Screening Major Carotenoids of Tomato by Handheld Raman Spectroscopy Using Chemometric Methods

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3723 ◽  
Author(s):  
Hacer Akpolat ◽  
Mark Barineau ◽  
Keith A. Jackson ◽  
Mehmet Z. Akpolat ◽  
David M. Francis ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
High Performance ◽  
Prediction Models ◽  
Least Squares Regression ◽  
Family Farms ◽  
Non Invasive ◽  
Uv Visible ◽  
The Ohio State University ◽  
Non Destructive ◽  
Β Carotene

Our objective was to develop a rapid technique for the non-invasive profiling and quantification of major tomato carotenoids using handheld Raman spectroscopy combined with pattern recognition techniques. A total of 106 samples with varying carotenoid profiles were provided by the Ohio State University Tomato Breeding and Genetics program and Lipman Family Farms (Naples, FL, USA). Non-destructive measurement from the surface of tomatoes was performed by a handheld Raman spectrometer equipped with a 1064 nm excitation laser, and data analysis was performed using soft independent modelling of class analogy (SIMCA)), artificial neural network (ANN), and partial least squares regression (PLSR) for classification and quantification purposes. High-performance liquid chromatography (HPLC) and UV/visible spectrophotometry were used for profiling and quantification of major carotenoids. Seven groups were identified based on their carotenoid profile, and supervised classification by SIMCA and ANN clustered samples with 93% and 100% accuracy based on a validation test data, respectively. All-trans-lycopene and β-carotene levels were measured with a UV-visible spectrophotometer, and prediction models were developed using PLSR and ANN. Regression models developed with Raman spectra provided excellent prediction performance by ANN (rpre = 0.9, SEP = 1.1 mg/100 g) and PLSR (rpre = 0.87, SEP = 2.4 mg/100 g) for non-invasive determination of all-trans-lycopene in fruits. Although the number of samples were limited for β-carotene quantification, PLSR modeling showed promising results (rcv = 0.99, SECV = 0.28 mg/100 g). Non-destructive evaluation of tomato carotenoids can be useful for tomato breeders as a simple and rapid tool for developing new varieties with novel profiles and for separating orange varieties with distinct carotenoids (high in β-carotene and high in cis-lycopene).

Quantitative Raman Spectroscopy when the Signal-to-Noise is Below the Limit of Quantitation due to Fluorescence Interference: Advantages of a Moving Window Sequentially Shifted Excitation Approach

2016 ◽  
Vol 70 (9) ◽  
pp. 1489-1501 ◽  
Author(s):  
Sarah Marshall ◽  
John B. Cooper
Keyword(s):  
Raman Spectroscopy ◽  
Quantitative Analysis ◽  
Signal To Noise Ratio ◽  
Raman Signal ◽  
Moving Window ◽  
Linear Calibration ◽  
Signal To Noise ◽  
Raman Analysis ◽  
Binary Model ◽  
Limit Of Quantitation

Raman spectroscopy is a useful analytical tool. However, its application is often limited because shot noise from fluorescence obscures the Raman signal. In such cases, quantitative analysis is not possible when the signal-to-noise ratio (SNR) drops below two. A method is described for performing quantitative Raman spectroscopy that not only removes fluorescence backgrounds, but also results in a significant improvement in the SNR. The Raman data is extracted using a moving window sequentially shifted excitation algorithm. To demonstrate the capabilities of the method, a binary mixture of two analytes at varying concentrations is quantified in the presence of a highly fluorescent dye. Linear calibration plots were constructed and validated for the binary model using individual Raman peaks with SNR ranging from 0.073–12.6; r2 values are greater than 0.96 in all cases, with all but the weakest peaks yielding values greater than 0.997. The presented method demonstrates a universal and autonomous approach for the quantitative analysis of highly fluorescent samples via Raman spectroscopy. The lower limit on the SNR ratio for quantitative Raman analysis with the described method is 0.1. In order to assess the effectiveness of the presented method, the entire set of experiments was also processed using the more common shifted excitation Raman difference spectroscopy (SERDS) approach. The advantages of the proposed method over SERDS are demonstrated for both the detection limit and the SNR of the processed spectra.

Growth units and growth habit of α-BaB2O4crystal

2007 ◽  
Vol 40 (4) ◽  
pp. 725-729 ◽  
Author(s):  
Songming Wan ◽  
Xia Zhang ◽  
Sijie Zhao ◽  
Qingli Zhang ◽  
Jinglin You ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Crystal Growth ◽  
High Temperature ◽  
Growth Habit ◽  
Fundamental Problem ◽  
Bond Chain ◽  
Good Agreement

The structure of the melt near a crystal–melt interface is a fundamental problem in the dynamics of crystal growth. In this work, high-temperature Raman spectroscopy was applied to investigatein situthe structure of the melt near the α-BaB2O4(α-BBO) crystal–melt interface. A structured melt was found in this region: (B3O6)3−groups form near the interface and vanish towards the bulk melt. The crystal growth habit was then explained by the periodic bond chain (PBC) theory. At the α-BBO crystal–melt interface, the growth units, namely the (B3O6)3−anion groups and Ba2+cations, stack mainly along four types of PBCs. These four PBCs constitute three potential F faces: {10\bar{1} 2}, {01\bar{1} 4} and {10\bar{1} 10}. The predicted results are in good agreement with the observed growth habit of α-BBO crystal.

scholarly journals Evidence of a new phase in gypsum–anhydrite transformations under microwave heating by in situ dielectric analysis and Raman spectroscopy

2020 ◽  
Vol 22 (47) ◽  
pp. 27713-27723
Author(s):  
Angel M. López-Buendía ◽  
Beatriz García-Baños ◽  
M. Mar Urquiola ◽  
José M. Catalá-Civera ◽  
Felipe L. Peñaranda-Foix
Keyword(s):  
Raman Spectroscopy ◽  
Microwave Heating ◽  
Dielectric Analysis ◽  
Raman Analysis ◽  
Mineral Transformations ◽  
New Phase

New insights into mineral transformations under microwave heating through advanced in situ dielectric and Raman analysis.

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