3D-front-face fluorescence spectroscopy and independent components analysis: A new way to monitor bread dough development

Garcia, Rebeca; Boussard, Aline; Rakotozafy, Lalatiana; Nicolas, J.; Potus, Jacques; Rutledge, Douglas N.; Cordella, Christophe

doi:10.1016/j.talanta.2015.10.002

Cited by 19 publications

(8 citation statements)

References 42 publications

(56 reference statements)

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“…This peak has the lowest intensity for whole wheat flour (Fig. d) due to the cascade effect as a result of the ferulic acid content (Garcia et al ., ). The third peak, which is attributed to the riboflavin content of the wheat flour, falls in the range of λ excitation 410–450 nm and λ emission 510–550 nm (Zandomeneghi et al ., ).…”

Section: Resultsmentioning

confidence: 97%

“…Each spectrum of the wheat flours shows three distinct peaks that can be attributed to the different types of the fluorophores. The highest peak was observed at λ excitation 310–370 nm and λ emission 410–470 nm, which can be attributed to ferulic acid (Garcia et al ., ) and NADH, which are naturally present in the wheat flour. Another peak was observed at λ excitation 270–310 nm and λ emission 310–370 nm, which belongs to the protein area of the spectra due to the fluorescence of tryptophan, phenylalanine and tyrosine (Grote et al ., ).…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, no sample preparation operations (freeze‐drying and powder formation) provide significant advantage over the previous contributions. Similarly, the hydration of the wheat flour has a strong impact on the fluorescence intensity in different regions of the spectra (Garcia et al ., ), which may be correlated with the estimation of moisture, whereas indirect determination of the glucose during the bio‐process (Ohadi et al ., ) and in figs (Jiang et al ., ) using this type of spectroscopy can be related to carbohydrate prediction. The other parameters, such as the protein, sucrose and salt contents, do not have a linear relationship (Tables and ).…”

Section: Resultsmentioning

confidence: 99%

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Estimation of the nutritional parameters of various types of wheat flours using fluorescence spectroscopy and chemometrics

Ahmad

Nache

Hinrichs

et al. 2016

Int J of Food Sci Tech

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The purpose of this study is to evaluate the potential of fluorescence spectroscopy to predict the nutritional parameters of twenty-six commercially available wheat flours from different vendors. Principal component analysis (PCA) was used to clearly identify the correlations among different types of flours. A partial least square regression (PLSR) model gives a good prediction for moisture, fat and carbohydrates using cross-validation, with a R 2 of 0.86, 0.88 and 0.89 respectively. However, the protein, sucrose and salt contents showed little correlation in PLSR. Locally weighted regression (LWR) provides a significant improvement in the prediction of all of the nutritional parameters. The error decreases with an increasing R 2 to 0.96, 0.93, 0.99, 0.98, 0.99, 0.88, 0.95 and 0.99 for the energetic value, protein, fat, moisture, carbohydrate, sucrose, salt and saturated fatty acid contents respectively, for different wheat flours. Hence, fluorescence, which is a non-invasive and rapid method, can be used to evaluate the nutritional parameters of different types of wheat flours. Estimation of nutritional parameters M. H. Ahmad et al. † Flour type 405 contains less than 0.5% mineral contents. ‡ Flour type 550 contains 0.5-0.63% mineral contents. § Flour type 1050 contains 0.91-1.2% mineral contents. ¶ Whole wheat flour contains 1.2-1.8% mineral contents. Estimation of nutritional parameters M. H. Ahmad et al. † Root mean square error of cross-validation. ‡ Coefficient of determination for cross-validation. Estimation of nutritional parameters M. H. Ahmad et al. † Root mean square error of cross-validation. ‡ Coefficient of determination for cross-validation. Estimation of nutritional parameters M. H. Ahmad et al.

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Section: Resultsmentioning

confidence: 97%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Estimation of the nutritional parameters of various types of wheat flours using fluorescence spectroscopy and chemometrics

Ahmad

Nache

Hinrichs

et al. 2016

Int J of Food Sci Tech

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“…The increasing use of this technique has been facilitated by improved instruments and new data analysis techniques such as multivariate and multiway chemometric tools. One of these tools is ICA, which is particularly useful to improve the interpretation of fluorescence data in cases of spectral overlap and changes caused by environmental and matrix effects [15][16][17][18][19][20][21][22][23][24][25]. The main application areas of ICA to handle fluorescence profiles are environmental monitoring and food control.…”

Section: Spectroscopymentioning

confidence: 99%

“…Multiple linear regression (MLR) was performed to estimate the added percentage of grapefruit juice from the IC proportions based on the dataset of juice mixtures and two commercial samples labeled with known composition of both juices. Another interesting example was provided by Garcia et al who applied 3D front-face fluorescence spectroscopy in combination with ICA to follow the development of dough [21].…”

Section: Spectroscopymentioning

confidence: 99%

Independent components analysis (ICA) at the “cocktail-party” in analytical chemistry

Monakhova

Rutledge

2020

Talanta

Self Cite

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Independent components analysis (ICA) is a probabilistic method, whose goal is to extract underlying component signals, that are maximally independent and non-Gaussian, from mixed observed signals. Since the data acquired in many applications in analytical chemistry are mixtures of component signals, such a method is of great interest. In this article recent ICA applications for quantitative and qualitative analysis in analytical chemistry are reviewed. The following experimental techniques are covered: fluorescence, UV-VIS, NMR, vibrational spectroscopies as well as chromatographic profiles. Furthermore, we reviewed ICA as a preprocessing tool as well as existing hybrid ICA-based multivariate approaches. Finally, further research directions are proposed. Our review shows that ICA is starting to play an important role in analytical chemistry, and this will definitely increase in the future.

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3DFluorescence Spectroscopy and Its Applications

Locquet

Aït‐Kaddour

Cordella

2018

Encyclopedia of Analytical Chemistry

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This article focuses on 3D fluorescence spectroscopy. After defining its principle, we then look at the factors which influence the phenomenon of fluorescence. There follows a brief discussion of the principal chemometric techniques implemented to manipulate and exploit fluorescence data, which have different and specific properties. A new article will describe the main endogenous fluorophores found in certain food, biological and environmental matrices, as well as exogenous fluorophores. Frontal fluorescence has seen considerable development during the past 15 years in a wide variety of fields, which is why we shall also be focusing on its principal applications in terms of the objectives of different studies: classification, authentication, quality control or process monitoring indicators. Fluorescence data generate a spectral fingerprint that can characterise samples within a very large space of variability, such as that which is inherent in food samples. Fluorescence spectroscopy can thus be used in a broad range of applications involving biological samples, animal tissues or environmental samples. Most of these applications are still qualitative, although quantitative methods are available. It is generally acknowledged that 3D fluorescence fingerprinting is more suitable for recognition, classification or detection processes when there is a need to save time and achieve optimum sensitivity. With the recent development of big data and BI, frontal fluorescence spectroscopy will soon benefit from the considerable power of artificial intelligence technologies.

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3D-front-face fluorescence spectroscopy and independent components analysis: A new way to monitor bread dough development

Cited by 19 publications

References 42 publications

Estimation of the nutritional parameters of various types of wheat flours using fluorescence spectroscopy and chemometrics

Estimation of the nutritional parameters of various types of wheat flours using fluorescence spectroscopy and chemometrics

Independent components analysis (ICA) at the “cocktail-party” in analytical chemistry

3DFluorescence Spectroscopy and Its Applications

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