Front-face fluorescence excitation-emission matrices in combination with three-way chemometrics for the discrimination and prediction of phenolic response to vineyard agronomic practices

What Can Fluorescence Tell Us About Wine?

Rapid and cost-effective measurements of the autofluorescence of wine can provide valuable information on the brand, origin, age, and composition of wine and may be helpful for the authentication of wine and detection of forgery. The list of fluorescent components of wines includes flavonoids, phenolic acids, stilbenes, some vitamins, aromatic amino acids, NADH, and Maillard reaction products. Distinguishing between various fluorophores is not simple, and chemometrics are usually employed to analyze the fluorescence spectra of wines. Front-face fluorescence is especially useful in the analysis of wine, obviating the need for sample dilution. Front-face measurements are possible using most plate readers, so they are commonly available. Additionally, the use of fluorescent probes allows for the detection and quantification of specific wine components, such as resveratrol, oxygen, total iron, copper, hydrogen sulfite, and haze-forming proteins. Fluorescence measurements can thus be useful for at least a preliminary rapid evaluation of wine properties.

Rapid, simultaneous and non-destructive determination of multiple adulterants in Panax notoginseng powder by front-face total synchronous fluorescence spectroscopy

The authentication of traditional herbal medicines in powder form is of great significance, as they are always of high values but vulnerable to adulteration. Based on the distinct fluorescence of protein tryptophan, phenolic acids and flavonoids, front-face synchronous fluorescence spectroscopy (FFSFS) was applied for the fast and non-invasive authentication of Panax notoginseng powder (PP) adulterated with the powder of rhizoma curcumae (CP), maize flour (MF) and whole wheat flour (WF). For either single or multiple adulterants in the range of 5-40% w/w, prediction models were built based on the combination of unfolded total synchronous fluorescence spectra and partial least square (PLS) regression, and were validated by both five-fold cross-validation and external validation. The constructed PLS2 models simultaneously predicted the contents of multiple adulterants in PP and gave suitable results, with most of the determination coefficients of prediction (R_p²) >0.9, the root mean square error of prediction (RMSEP) no >4% and residual predictive deviation (RPD) >2. The limits of detections (LODs) were 12.0, 9.1 and 7.6% for CP, MF and WF, respectively. All the relative prediction errors for simulated blind samples were between -22% and + 23%. FFSFS offers a novel alternative to the authentication of powdered herbal plants.

Multispectral fluorescence sensitivity to acidic and polyphenolic changes in Chardonnay wines - The case study of malolactic fermentation

In this study, stationary and time-resolvedfluorescence signatures, were statistically and chemometrically analyzed among three typologies of Chardonnay wines (A, B and C) with the objectives to evaluate their sensitivity to acidic and polyphenolic changes. For that purpose, a dataset was built using Excitation Emission Matrices of fluorescence (N = 103) decomposed by a Parallel Factor Analysis (PARAFAC), andfluorescence decays (N = 22), mathematically fitted, using the conventional exponential modeling and the phasor plot representation. Wine PARAFAC component C4 coupledwith its phasor plot g and s values enable the description of malolactic fermentation (MLF) occurrence in Chardonnay wines. Such proxies reflect wine concentration modifications in total acidity, malic/lactic and phenol acids.Lower g values among fresh MLF + wines compared to MLF- wines are explained by a quenching effect on wine fluorophores by both organic and phenolic acids.The combination of multispectral fluorescence parametersopens a novel routinely implementable methodology to diagnose fermentative processes.

Direct quantification of red wine phenolics using fluorescence spectroscopy with chemometrics

Phenolic compounds are secondary metabolites known to play crucial roles in important chemical reactions impacting the mouthfeel, colour and ageing potential of red wine. Their complexity has resulted in a number of advanced analytical methods, which often prevent routine phenolic analysis in winemaking. Fluorescence spectroscopy could be an alternative to current spectrophotometric techniques and its combination with chemometrics was investigated for its suitability in directly quantifying phenolic content of unaltered red wine and fermenting samples. Front-face fluorescence was optimised and used to build predictive models for total phenols, total condensed tannins, total anthocyanins, colour density and polymeric pigments. Machine learning algorithms were used for model development. The most successful models were built for total phenols, total condensed tannins and total anthocyanins with coefficient of determination (R²cal) and RMSECV of 0.81, 0.89, 0.80 and 5.71, 104.03 mg/L, 60.67 mg/L, respectively. The validation results showed R²val values of 0.77, 0.8 and 0.77, and RMSEP values of 7.6, 172.37 mg/L and 76.57 mg/L, respectively. A novel approach for the classification of South African red wine cultivars based on unique fluorescent fingerprints was also successful with an overall cross validation score of 0.8. The best classification ability (validation score = 0.93) was shown for the data set containing only fermenting wines for the most widely represented cultivars (>20 samples). This approach may provide a useful tool for authentication and quality control by regulatory bodies.

Front-face synchronous fluorescence spectroscopy for rapid and non-destructive determination of free capsanthin, the predominant carotenoid in chili (Capsicum annuum L.) powders based on aggregation-induced emission

Capsanthin is the major natural carotenoid pigment in red chili pepper possessing important bioactivity. Its conventional determination method is high performance liquid chromatography (HPLC) with complex and tedious sample pretreatment. In this study, synchronous front-face fluorescence spectroscopy (FFFS) was applied for the fast and non-invasive detection of free capsanthin in chili powders. Although capsanthin was only weak fluorescent in solution state, it showed strong fluorescence in two separated regions in front-face geometry which could also be clearly observed in chili powders. The mechanisms of these emissions are revealed to be aggregation-induced emission (AIE) and J-aggregate formation (JAF). The free capsanthin in 85 chili powder samples were determined by HPLC as in the range of 0.6-3.0 mg/g. The total synchronous FFFS spectra of these samples were scanned. Simple first-order models were built by partial least square regression (PLSR), and were validated by 5-fold cross-validation and external validation. The coefficients of determination (R²) were higher than 0.9, and the root mean square errors (RMSE) were less than 0.2 mg/g. The relative error of prediction (REP) was 9.9%, and the residual predictive deviation (RPD) was 3.7. The method was applied for the estimation of free capsanthin in several real-world samples with satisfactory analytical results. The average relative error to HPLC reference values was -11.8%.

A GA-BP method of detecting carbamate pesticide mixture based on three-dimensional fluorescence spectroscopy

In this paper, we have proposed a method to detect a mixture of carbamate pesticides using a back propagation network (BP), which is optimized by genetic algorithm (GA) for quantitative analysis. This method aims to combine the advantages of BP and GA to remedy their drawbacks. The training samples were taken as input, some performance indexes such as the predicted values, iteration time, mean squared error, correlation coefficient and recovery rate were compared between BP neural network and the constructed GA-BP model to evaluate the performance of two neural networks. Results show that the optimized GA-BP model can effectively predict the concentrations, the mean squared error and recovery rate are better. In addition, the correlation coefficient has a significant improvement. This study can provide a new way for detection of the pesticides mixture and help to analysis in a reliable way.