Fourier Transform Infrared (FT-IR) Spectroscopy and Improved Principal Component Regression (PCR) for Quantification of Solid Analytes in Microalgae and Bacteria

2011 ◽  
Vol 65 (4) ◽  
pp. 442-453 ◽  
Author(s):  
Rebecca B. Horton ◽  
Edward Duranty ◽  
Morgan McConico ◽  
Frank Vogt
Keyword(s):  
Fourier Transform ◽  
Ir Spectroscopy ◽  
Principal Component Regression ◽  
Principal Component ◽  
Fourier Transform Infrared ◽  
Ft Ir ◽  
Ft Ir Spectroscopy

scholarly journals High-Throughput Metabolic Fingerprinting of Legume Silage Fermentations via Fourier Transform Infrared Spectroscopy and Chemometrics

2004 ◽  
Vol 70 (3) ◽  
pp. 1583-1592 ◽  
Author(s):  
Helen E. Johnson ◽  
David Broadhurst ◽  
Douglas B. Kell ◽  
Michael K. Theodorou ◽  
Roger J. Merry ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Lactic Acid ◽  
Ir Spectroscopy ◽  
High Throughput ◽  
Function Analysis ◽  
Principal Component ◽  
Fourier Transform Infrared ◽  
Metabolic Fingerprinting ◽  
Ft Ir ◽  
Ft Ir Spectroscopy

ABSTRACT Silage quality is typically assessed by the measurement of several individual parameters, including pH, lactic acid, acetic acid, bacterial numbers, and protein content. The objective of this study was to use a holistic metabolic fingerprinting approach, combining a high-throughput microtiter plate-based fermentation system with Fourier transform infrared (FT-IR) spectroscopy, to obtain a snapshot of the sample metabolome (typically low-molecular-weight compounds) at a given time. The aim was to study the dynamics of red clover or grass silage fermentations in response to various inoculants incorporating lactic acid bacteria (LAB). The hyperspectral multivariate datasets generated by FT-IR spectroscopy are difficult to interpret visually, so chemometrics methods were used to deconvolute the data. Two-phase principal component-discriminant function analysis allowed discrimination between herbage types and different LAB inoculants and modeling of fermentation dynamics over time. Further analysis of FT-IR spectra by the use of genetic algorithms to identify the underlying biochemical differences between treatments revealed that the amide I and amide II regions (wavenumbers of 1,550 to 1,750 cm−1) of the spectra were most frequently selected (reflecting changes in proteins and free amino acids) in comparisons between control and inoculant-treated fermentations. This corresponds to the known importance of rapid fermentation for the efficient conservation of forage proteins.

scholarly journals Application of Fourier Transform Infrared Spectroscopy and Multivariate Analysis Methods for the Non-Destructive Evaluation of Phenolics Compounds in Moringa Powder

Agriculture ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 10
Author(s):  
Rahul Joshi ◽  
Ramaraj Sathasivam ◽  
Sang Un Park ◽  
Hongseok Lee ◽  
Moon S. Kim ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Phenolic Compounds ◽  
Ir Spectroscopy ◽  
High Performance ◽  
Principal Component ◽  
Fourier Transform Infrared ◽  
Least Squares Regression ◽  
Ft Ir ◽  
Non Destructive ◽  
Ft Ir Spectroscopy

This study performed non-destructive measurements of phenolic compounds in moringa powder using Fourier Transform Infrared (FT-IR) spectroscopy within a spectral range of 3500–700 cm−1. Three major phenolic compounds, namely, kaempferol, benzoic acid, and rutin, were measured in five different varieties of moringa powder, which was approved with respect to the high-performance liquid chromatography (HPLC) method. The prediction performance of three different regression methods, i.e., partial least squares regression (PLSR), principal component regression (PCR), and net analyte signal (NAS)-based methodology, called hybrid linear analysis (HLA/GO), were compared to achieve the best prediction model. The obtained results for the PLS regression method resulted in better performance for the prediction analysis of phenolic compounds in moringa powder. The PLSR model attained a correlation coefficient () value of 0.997 and root mean square error of prediction (RMSEP) of 0.035 mg/g, respectively, which is comparatively higher than the other two regression models. Based on the results, it can be concluded that FT-IR spectroscopy in conjugation with a suitable regression analysis method could be an effective analytical tool for the non-destructive prediction of phenolic compounds in moringa powder.

scholarly journals Bacterial Typing and Identification Based on Fourier Transform Infrared Spectroscopy

2020 ◽  
Author(s):  
Huayan Yang ◽  
Fangling Wu ◽  
Fuxin Xu ◽  
Keqi Tang ◽  
Chuanfan Ding ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Ir Spectroscopy ◽  
Bacterial Culture ◽  
Clinical Laboratory ◽  
Fourier Transform Infrared ◽  
Identification Accuracy ◽  
Bacterial Typing ◽  
Typical Experiment ◽  
Ft Ir ◽  
Ft Ir Spectroscopy

Abstract Fourier transform infrared (FT-IR) spectroscopy is a label-free and highly sensitive technique that provides complete information on the chemical composition of biological samples. The bacterial FT-IR signals are extremely specific and highly reproducible fingerprint-like patterns, making FT-IR an efficient tool for bacterial typing and identification. Due to the low cost and high flux, FT-IR has been widely used in hospital hygiene management for infection control, epidemiological studies, and routine bacterial determination of clinical laboratory values. However, the typing and identification accuracy could be affected by many factors, and the bacterial FT-IR data from different laboratories are usually not comparable. A standard protocol is required to improve the accuracy of FT-IR-based typing and identification. Here, we detail the principles and procedures of bacterial typing and identification based on FT-IR spectroscopy, including bacterial culture, sample preparation, instrument operation, spectra collection, spectra preprocessing, and mathematical data analysis. Without bacterial culture, a typical experiment generally takes <2 h.

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