scholarly journals Prediction of anthocyanin concentrations during red wine fermentation: development of Fourier transform infrared spectroscopy partial least squares models

OENO One ◽  
2019 ◽  
Vol 53 (4) ◽  
Author(s):  
Clément Miramont ◽  
Michael Jourdes ◽  
Torben Selberg ◽  
Henrik Vilstrup Juhl ◽  
Lars Nørgaard ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Ir Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
High Performance ◽  
Red Wine ◽  
Fourier Transform Infrared ◽  
Ft Ir ◽  
Predicted Values ◽  
Ft Ir Spectroscopy

Aim: The aim of the present study was to use Fourier transform infrared (FT–IR) spectroscopy with chemometrics to develop partial least squares (PLS) models to predict the concentrations of various anthocyanins during red wine fermentation.Methods and results: Must and wine samples were collected during fermentation. To maximize diversity, 12 different fermentations, of two different vintages and two different varieties, were followed. The anthocyanin composition of the samples was characterized by using different methods described in the literature: the concentration of free anthocyanins was determined by bisulphite bleaching, the concentration of molecular anthocyanins was determined by high-performance liquid chromatography with ultraviolet–visible detection, and the ratio of monomeric anthocyanins to polymeric anthocyanins was determined using the Adams–Harbertson assay. Finally, the data were analysed statistically by PLS regression to quantify laboratory-determined anthocyanin from FT–IR spectra. The correlations obtained showed good results for a large percentage of parameters studied, with the determination coefficient (R2) for both calibration and cross-validation exceeding 0.8. The models for molecular anthocyanins appeared to overestimate their prediction, owing to intercorrelation with other parameters. Comparison of the data for each vintage indicated no apparent matrix effect per year, and data for other vintages should be compared to validate this hypothesis. The best models were those for monomeric or polymeric pigments and free anthocyanins.Conclusions: By using FT–IR spectroscopy coupled with chemometrics, it is possible to create predictive models to estimate concentrations of anthocyanins and changes in global anthocyanin parameters during winemaking.Significance and impact of the study: These results improve our understanding of anthocyanin prediction using FT–IR spectroscopy with chemometrics and pave the way for its use as a process control tool for the winemaker. They also highlight the propensity of chemometrics to overestimate certain predicted values when close parameters intercorrelate.

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.

Determination of ß-Carotene and Lutein in Green Tea Using Fourier Transform Infrared Spectroscopy

2019 ◽  
Vol 62 (1) ◽  
pp. 75-81 ◽  
Author(s):  
Yong He ◽  
Yong He ◽  
Yiying Zhao ◽  
Chu Zhang ◽  
Chanjun Sun ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Ir Spectroscopy ◽  
Least Squares ◽  
Fourier Transform Infrared Spectroscopy ◽  
Green Tea ◽  
Fourier Transform Infrared ◽  
Support Vector ◽  
Ft Ir ◽  
Ft Ir Spectroscopy

Abstract. The feasibility of using Fourier transform infrared (FT-IR) spectroscopy combined with chemometrics to determine the ß-carotene and lutein contents in green tea was investigated in this study. The relationship between pigment contents and spectral responses was explored by partial least squares (PLS), least squares support vector machine (LS-SVM), and extreme learning machine (ELM) methods. Next, 30 and 29 effective wavenumbers (EWs) for ß-carotene and lutein, respectively, were selected according to the weighted regression coefficients of the PLS regression models, and simplified determinant models were built on the extracted EWs. The ELM models based on the EWs obtained the best results, with correlation coefficients of calibration (rc) and prediction (rp), and residual prediction deviation (RPD) of 0.977, 0.946, and 2.84, respectively, for ß-carotene and 0.975, 0.937, and 2.88, respectively, for lutein. The overall results indicate that FT-IR spectroscopy combined with chemometrics could be a rapid and accurate alternative method for determining carotenoid pigments in green tea. Keywords: ß-carotene, Chemometrics, Fourier transform infrared spectroscopy, Green tea, Lutein.

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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