Total Synchronous Fluorescence Research Articles

Identification of Goat Milk Adulterated with Cow Milk Based on Total Synchronous Fluorescence Spectroscopy Combined with CNN

Identification of Goat Milk Adulterated with Cow Milk Based on Total Synchronous Fluorescence Spectroscopy Combined with CNN

Monitoring small-scale bioreactor studies for media development using polarized total synchronous fluorescence spectroscopy (pTSFS) and synchronous light scattering (SyLS)

Biopharmaceutical process development often involves the use of small-scale bioreactors (SSBR) for optimizing media formulations and process conditions during scale up to commercial scale production. Two key process parameters (CPP) used in SSBR studies are protein titre and viable cell density (VCD). Here, we explore the efficacy of parallel polarized total synchronous fluorescence spectroscopy (TSFS||) and Synchronous Light Scattering (SyLS||) to qualitatively monitor these CPPs and quantitatively predict titre and VCD for a large-scale cell culture media optimization SSBR study. The study involved 71 different media formulations (50+ components each), and the bioprocess was run for 13 days or more. Samples were extracted at set times (Day 0, 3, 9, and 13) and clarified by centrifugation. TSFS|| spectra showed significant emission changes along with increased light scatter over the course of the bioprocess. SyLS|| measurements strongly correlated with particle size data obtained from Dynamic Light Scattering but did not correlate well with VCD probably because of the centrifugation-based sample preparation. Statistical and principal component analysis (PCA) of the pTSFS data showed that spectral variation was greater between media formulations than due to the evolving bioprocess. This prevented the development of accurate global prediction models for media performance (e.g., predicting product titre at day 9 from media spectra measured at day 0). However, classification methods were successfully used to select media subsets with better quantitative prediction accuracy based on spectral similarities. A practical binary (high/low performance) classification model based on Support Vector Machines was generated for media formulation screening. Combining emission and scatter measurements with multivariate data analysis provides a more holistic, multi-attribute bioprocess monitoring method that minimizes the need to use different offline analytical methods. This methodology can be used to monitor process trajectories and deviations, and ultimately be used to predict bioprocess CPPs when implemented on production scale processes where there is much less compositional variation in the media. We believe this SSBR-pTSFS/SyLS approach will provide a valuable resource to develop the design/parameter space for in-process monitoring at production scale from early-stage process/media development studies.

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 (Rp2) >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.

A practical application of front-face synchronous fluorescence spectroscopy to rapid, simultaneous and non-destructive determination of piperine and multiple adulterants in ground black and white pepper (Piper nigrum L.)

Based on the distinct fluorescence of piperine and tryptophan, and their different profiles in pepper and several possible adulterants, front-face synchronous fluorescence spectroscopy (FFSFS) was applied for the fast and non-invasive authentication of ground black pepper adulterated with papaya seed powder and buckwheat flour, and ground white pepper adulterated with whole wheat and maize flours. For either single adulterant or dual adulterants in the range of 10–40% w/w, prediction models were constructed 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 built PLS2 models produced suitable results, with most of the determination coefficients of prediction (Rp2) greater than 0.8, the root mean square error of prediction (RMSEP) < 5% and residual predictive deviation (RPD) greater than 2. The limits of detection (LODs) were 11.1, 5.5, 10.6 and 12.0% for papaya seed powder, buckwheat, whole wheat and maize flours, respectively. Most relative prediction errors for simulated blind samples were within ± 30%. Besides, piperine in ground black and white pepper was also determined with acceptable PLS results.

Front-face synchronous fluorescence spectroscopy: a rapid and non-destructive authentication method for Arabica coffee adulterated with maize and soybean flours.

This article describes a novel front-face synchronous fluorescence spectroscopy (FFSFS) method for the fast and non-invasive authentication of ground roasted Arabica coffee adulterated with roasted maize and soybean flours. The detection was based on the different composition of fluorescent Maillard reaction products and caffeine in roasted coffee and cereal flours. For each roasted maize or soybean adulterant flour (5–40 wt%), principal component analysis coupled with linear discriminant analysis (PCA–LDA) was used for qualitative discrimination. Quantitative prediction models were constructed based on the combination of unfolded total synchronous fluorescence spectra and partial least square regression (PLSR), followed by fivefold cross-validation and external validation. The PLSR models produced suitable results, with the determination coefficient of prediction (Rp2) > 0.9, root mean square error of prediction (RMSEP) < 5%, relative error of prediction (REP) < 25% and residual predictive deviation (RPD) > 3. The limits of detection (LOD) were both 10% for roasted maize and soybean flours. Most relative errors for the prediction of simulated blind samples were between -30% and + 30%. The benefits of this strategy are simplicity, rapidity, and non-destructive detection. However, owing to the high similarity between roasted coffee and roasted cereal flours and the influence of the roasting degree on fluorescent Maillard reaction products, its application is limited to the preliminary screening of roasted coffee with the same roasting degree, adulterated with relatively large amounts of roasted cereal flours which are roasted to analogous color to the coffee.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00003-022-01396-8.

Investigations of potential ionic liquid phases for chromatographic processes using spectroscopic and thermal techniques

The thermal degradation of ionic liquids 1-octyl-3-methyl imidazolium trifluoromethanesulfonate ([OMIM][TfO]) and 1-octyl-3-methyl imidazolium bis(trifluoromethylsulfonyl)imide ([OMIM][Tf2N]) were investigated using spectroscopic and thermal techniques to predict gas chromatographic phase stability. For differential scanning calorimetry, lowering the heating rate from 10 to 2 °C/min decreased the decomposition temperature of the ionic liquids ∼ 23 °C for [OMIM][TfO] and ∼ 13 °C for [OMIM][Tf2N]. In addition, lowering the heating rate gave better resolution and showed anion thermal decomposition occurred several degrees before cation decomposition for the ionic liquids. Experimental measurements showed that decomposition of the ionic liquids was indicated at lower temperatures when using spectroscopic methods compared to thermal scanning methods. Total synchronous fluorescence spectroscopy was used to monitor the formation of ionic liquid degradation products during thermal stress experiments. Decomposition temperatures determined by differential scanning calorimetry were approximately 140 °C higher compared to measurements by fluorescence spectroscopy.

Vision transformer for quality identification of sesame oil with stereoscopic fluorescence spectrum image

Sesame oil (SO), as a high-priced edible oil, is often counterfeited and adulterated. A new method for SO quality identification using Vision Transformer (ViT) network based on stereoscopic images of Excitation-emission matrix fluorescence (EEMF) and Total synchronous fluorescence (TSyF) spectroscopy was proposed. The basic samples including pure, counterfeit and adulterated SOs were characterized by fluorescence spectroscopy. A data augmentation strategy including linear interpolation, shift and noise injection was selected for few sample learning. All fluorescence spectral data were visualized as stereoscopic images with rich spectral characteristics. The ViT network architecture based on attention mechanism was designed and trained to establish four SO quality identification models. The macro averages of precision, recall and F1-score on the validation set were greater than 0.99. The values of these indicators on the test samples were equal to one. In conclusion, deep learning based on ViT using stereoscopic fluorescence spectrum image provided a new method for sesame oil identification.

Evaluating the interaction of human serum albumin (HSA) and 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) liposomes in different aqueous environments using anisotropy resolved multi-dimensional emission spectroscopy (ARMES)

Studying the interaction between plasma proteins and liposomes is critical, particularly for their use as drug delivery systems. Here, the efficacy of anisotropy resolved multidimensional emission spectroscopy (ARMES) for investigating the interaction of human serum albumin (HSA) with liposomes was explored and compared to conventional spectroscopic techniques. Dynamic Light Scattering (DLS) and absorbance spectroscopy (with Multivariate Curve Resolution (MCR) modeling) indicated that the highest degree of liposome rupturing, and aggregation occurred in water, with less in ammonium bicarbonate buffer (ABC) and phosphate buffered saline (PBS). Fluorescence emission spectra of HSA-liposome mixtures revealed significant hypsochromic shifts for water and ABC, but much less in PBS, where the data suggests a non-penetrating protein layer was formed. Average fluorescence lifetimes decreased upon liposome interaction in water (6.2→5.2 ns) and ABC buffer (6.3→5.6 ns) but increased slightly for PBS (5.6→5.8 ns). ARMES using polarized Total Synchronous Fluorescence Scan measurements with parallel factor (PARAFAC) analysis resolved intrinsic HSA fluorescence into two components for interactions in water and ABC buffer, but only one component for PBS. These components, in water and ABC buffer, corresponded to two different HSA populations, one blue-shifted and penetrating the liposomes (λex/em = ~ 280/320 nm) and a second, similar to free HSA in solution (λex/em = ~ 282/356 nm). PARAFAC scores for water and ABC buffer suggested that a large proportion of HSA interacted in an end on configuration. ARMES provides a new way for investigating protein-liposome interactions that exploits the full intrinsic emission space of the protein and thus avoids the use of extrinsic labels. The use of multivariate data analysis provided a comprehensive and structured framework to extract a variety of useful information (resolving different fluorescent species, quantifying their signal contribution, and extracting light scatter signals) all of which can be used to discriminate between interaction mechanisms.

Fluorescence quenching by competitive absorption between solid foods: Rapid and non-destructive determination of maize flour adulterated in turmeric powder

Fluorescence quenching induced by competitive absorption between different components of solid foods was observed for the first time. By using front-face synchronous fluorescence spectroscopy (FFSFS) and fluorescence titration, competitive absorption between maize flour and turmeric powder was proven to occur between phenolic acids in maize flour and curcumin in turmeric powder. FFSFS was applied for the rapid and non-destructive determination of maize flour adulterated in turmeric powder. Prediction models were constructed by partial least square (PLS) regression based on unfolded total synchronous fluorescence spectra, and were validated by five-fold cross-validation and external validation, with the determination coefficient of prediction (Rp2) greater than 0.95, root mean square error of prediction (RMSEP) < 6%, relative error of prediction (REP) < 15% and residual predictive deviation (RPD) greater than 5. The limit of detection (LOD) of maize flour was approximately 9%. In addition, most relative errors for test samples were from −20% to 20%.

Rapid, simultaneous and non-destructive determination of maize flour and soybean flour adulterated in quinoa flour by front-face synchronous fluorescence spectroscopy

Quinoa is a naturally gluten-free pseudocereal receiving growing attentions worldwide. Herein, front-face synchronous fluorescence spectroscopy (FFSFS) was applied for the fast and non-invasive authentication of quinoa flour adulteration with maize and soybean flours. The cases of both single and dual adulterants (6–50 wt%) were investigated. For single adulteration with either maize or soybean flour, principal component analysis coupled with linear discriminant analysis (PCA–LDA) was used for preliminary qualitative discrimination. Then first-order prediction models were constructed based on the combination of unfolded total synchronous fluorescence spectra and partial least square (PLS) regression, and were validated by five-fold cross-validation and external validation. For dual adulterants, a general PLS2 model for the simultaneous determination of maize and soybean flours was developed, which produced suitable results, with the determination coefficient of prediction (Rp2) > 0.9, root mean square error of prediction (RMSEP) < 5% and residual predictive deviation (RPD) > 3. The limits of detection (LODs) are 9.0% and 6.9% for maize flour and soybean flour, respectively. Furthermore, most spike recoveries are in the range of 80–120%. This study not only has important applications in the authentication of quinoa flour adulteration but may also shed light on the quantitative component analysis of other cereal flour mixtures from different botanical origins.

Development of a rapid polarized total synchronous fluorescence spectroscopy (pTSFS) method for protein quantification in a model bioreactor broth.

Protein quantification during bioprocess monitoring is essential for biopharmaceutical manufacturing and is complicated by the complex chemical composition of the bioreactor broth. Here we present the early-stage development and optimization of a polarized total synchronous fluorescence spectroscopy (pTSFS) method for protein quantification in a hydrolysate-protein model (mimics clarified bioreactor broth samples) using a standard benchtop laboratory fluorometer. We used UV transmitting polarizers to provide wider range pTSFS spectra for screening of the four different TSFS spectra generated by the measurement: parallel (||), perpendicular (⊥), unpolarized (T) intensity spectra and anisotropy maps. TSFS|| (parallel polarized) measurements were the best for protein quantification compared to standard unpolarized measurements and the Bradford assay. This was because TSFS|| spectra had a better analyte signal to noise ratio (SNR), due to the anisotropy of protein emission. This meant that protein signals were better resolved from the background emission of small molecule fluorophores in the cell culture media. SNR of >5000 was achieved for concentrations of bovine serum albumin/yeastolate 1.2/10 g L-1 with TSFS|| . Optimization using genetic algorithm and interval partial least squares based variable selection enabled reduction of spectral resolution and number of excitation wavelengths required without degrading performance. This enables fast (<3.5 min) online/at-line measurements, and the method had an LOD of 0.18 g L-1 and high accuracy with a predictive error of <9%.

Total synchronous fluorescence spectroscopy coupled with deep learning to rapidly identify the authenticity of sesame oil.

The quality of sesame oil (SO) has been paid more and more attention. In this study, total synchronous fluorescence (TSyF) spectroscopy and deep neural networks were utilized to identify counterfeit and adulterated sesame oils. Firstly, typical samples including pure SO, counterfeit sesame oil (CSO) and adulterated sesame oil (ASO) were characterized by TSyF spectra. Secondly, three data augmentation methods were selected to increase the number of spectral data and enhance the robustness of the identification model. Then, five deep network architectures, including Simple Recurrent Neural Network (Simple RNN), Long Short-Term Memory (LSTM) network, Gated Recurrent Unit (GRU) network, Bidirectional LSTM (BLSTM) network and LSTM fortified with Convolutional Neural Network (LSTMC), were designed to identify the CSO and trace the source with 100% accuracy. Finally, ASO samples were also 100% correctly identified by training these network architectures. These results supported the feasibility of the novel method.

Exploration of total synchronous fluorescence spectroscopy combined with pre-trained convolutional neural network in the identification and quantification of vegetable oil

In order to distinguish different vegetable oils, adulterated vegetable oils, and to identify and quantify counterfeit vegetable oils, a method based on a small sample size of total synchronous fluorescence (TSyF) spectra combined with convolutional neural network (CNN) was proposed. Four typical vegetable oils were classified by three ways of fine-tuning the pre-trained CNN, the pre-trained CNN as a feature extractor, and traditional chemometrics. The pre-trained CNN was combined with support vector machines to distinguish adulterated sesame oil and counterfeit sesame oil separately with 100% correct classification rates. The pre-trained CNN combined with partial least square regression was used to predict the level of counterfeit sesame oil. The coefficient of determination for calibration (Rc2) values were all greater than 0.99, and the root mean square errors of validation were 0.81% and 1.72%, respectively. These results show that it is feasible to combine TSyF spectra with CNN for vegetable oil identification.

Analysis of Carcinogenic Polycyclic Aromatic Hydrocarbons in the Complex Background of the Petroleum Fluorescence Using Multivariate Curve Resolution Alternating Least Square and Total Synchronous Fluorescence Spectroscopic Technique.

Multivariate curve resolution alternating least square (MCR-ALS) analysis allows the simultaneous retrieval of pure concentration and spectral profiles for each of the analysed chemical components from the composite spectrum even in the presence of unknown interferences. Total synchronous fluorescence spectroscopy (TSFS), a multidimensional fluorescence technique that describes the variation of synchronous fluorescence profile acquired as a function of increasing offset, has become a useful analytical technique. Suitably arranged TSFS data set can be easily processed using MCR-ALS and thereby a simple and sensitive analytical tool could be developed. The present work successfully used the combination of the MCR-ALS and TSFS to analyse the three carcinogenic and mutagenic polycyclic aromatic hydrocarbons (PAHs) namely Benzo[a]Pyrene, Chrysene and Pyrene in the presence of complex fluorescence background originated from petroleum product. MCR-ALS assisted TSFS can be used for the routine analyses of these carcinogenic PAHs to ensure the quality of water and other samples belonging to different part of the ecosystem.

Inner filter effect mediated red-shift in synchronous and total synchronous fluorescence spectra as a tool to monitor quality of oils and petrochemicals

In this study, we propose that the Inner filter effect (IFE) mediated concentration dependent red-shift (CDRS) can be used as a tool towards analysing optically dense multi-fluorophoric systems like oils and petrochemicals. It has been shown recently that as the optical density (OD) of the sample, at the excitation wavelength, reaches around 0.87–1 (the exact value is instrument specific), the IFE becomes significant and CDRS shows up; with further increase in OD, the CDRS effect is prominently observed. Moreover, the conventionally used fluorescence measurement procedures are not effective in analysing optically dense systems. It is reasonable to consider IFE as intrinsic part of such a system and use the IFE induced red-shift as a parameter towards analysing the system as a whole. This approach is also rational because IFE is directly related to the OD of the sample matrix. We have verified the proposed approach, using total synchronous fluorescence spectra (TSFS) and synchronous fluorescence spectra (SFS), considering three different kinds of optically dense multi-fluorophoric systems such as accelerated thermal aging of ester oil (Eoil; a potential transformer insulant), quantification of diesel-biodiesel and petrol-ethanol mixtures. The CDRS response with varying sample concentration is captured by linear regression tools like N-way partial least square (N-PLS), unfolded-PLS and PLS models. The proposed approach can invariably be applied to any optically dense multi-fluorophoric systems where CDRS is observed while using normal and synchronous fluorescence measurements.

Ozonation of natural organic matter and aquatic humic substances: the effects of ozone on the structural characteristics and subsequent trihalomethane formation potential

Abstract The effect of ozonation on the structural and chemical characteristics of natural organic matter (NOM) and its isolated humic fractions, humic acid (HA) and fulvic acid, were studied using Fourier transform infrared coupled to attenuated total reflectance (FTIR-ATR), ultraviolet/visible (UV/Vis) spectroscopy, and synchronous scanning fluorescence (SSF) spectroscopy. The results were linked to the effect of ozonation on trihalomethane formation potential (THMfp) reduction for water standards with high THM precursors. Results showed that ozonation at a dose of 1 mg ozone/mg dissolved organic carbon (DOC) was capable of reducing DOC, UV absorbance at 254 nm (UV254), and THMfp by up to 42%, 95%, and 89% for the HA water standard, respectively. The study of UV/Vis, FTIR-ATR, and SSF revealed trends showing that ozone can alter the composition of DOC in the water standards, causing a significant reduction in aromaticity. The reduction of UV254 for each ozonated sample also affirms that ozone mainly targets aromatic moieties contained in NOM. FTIR-ATR results showed that the reduction of unsaturated functional groups, including aromatic rings and C = C bonds in the water standards tested, were the main components impacted by ozone application. SSF results also revealed that ozonation decreases the fluorescence intensity of the maximum peak – as well as the whole spectra.

Application of multidimensional and conventional fluorescence techniques for classification of beverages originating from various berry fruit

The objectives of this study were to characterize fluorescence of beverages from berry fruit, including chokeberry, blackcurrant, raspberry and strawberry, and to develop classification models based on different types of fluorescence spectra to identify beverages depending on the fruit species. Total fluorescence spectra (excitation-emission matrices, EEMs) and total synchronous fluorescence spectra (TSFS) were recorded for a series of commercial berry fruit beverages. An analysis of EEMs using parallel factor analysis (PARAFAC) revealed four components characterized by the excitation/emission maxima at 275/326, 319/410, 414/600, and 360/460 nm, respectively. Based on the spectral profiles, these components were assigned to various groups of phenolic compounds. Partial least squares discriminant analysis was used to develop the classification models. The analysis was performed on PARAFAC scores, unfolded EEMs (uEEMs), unfolded TSFS (uTSFS), and additionally on conventional emission spectra (EMS) measured at particular excitation wavelengths and single synchronous fluorescence spectra (SFS). The classification models with the same average classification error of 4.86% were obtained for the analysis of both the entire uEEMs and uTSFS. Among models based on the individual spectra, the lowest error of 4.42% was obtained for SFS measured at Δλ = 40 nm, and an error of 7.64% was obtained for EMS measured at the excitation wavelength of 360 nm. The classification model based on the PARAFAC scores had the highest error of 15.27%. The present results show good potential of fluorescence as rapid and reagent-free tool for authenticity evaluation of berry beverages.

Using polarized Total Synchronous Fluorescence Spectroscopy (pTSFS) with PARAFAC analysis for characterizing intrinsic protein emission

Using polarized Excitation Emission Matrix (pEEM) spectroscopy to measure the intrinsic emission of proteins offers a potentially useful methodology for a wide variety of potential applications. However, the presence of Rayleigh light scatter causes significant problems when attempting to use Parallel Factor (PARAFAC) and for anisotropy calculations. The use of polarized Total Synchronous Fluorescence Spectroscopy (pTSFS) can minimize Rayleigh scatter and avoid the use of complex data correction methods. Here, we investigated for the first time the use of pTSFS and PARAFAC to analyze the intrinsic emission of an Immunoglobulin (IgG) type protein in its native state. To enable PARAFAC analysis however, TSFS data (which is not trilinear) must first be transformed into an EEM like layout (t-EEM) and this generated a region with no experimentally acquired information (<8%). Here we critically evaluated several data handling methods and determined that interpolation was the best solution for dealing with the spectral regions with no experimentally acquired data at the blue edge of the emission.There were only subtle structural changes measured over the temperature range (15–35 °C) analyzed and PARAFAC only resolved two emitting components. A Trp emission component (average signal from all Trp present) which represented >92% of the explained variance, and a much weaker, mostly Tyr related emission with ~3% of the explained variance. The recovery of this Tyr component was only possible because pTSFS measurements were less contaminated by Rayleigh scattering. Changes in Tyr-to-Trp energy transfer rates caused by thermal motion were detected as an increase in Tyr contribution, which could not be resolved with the equivalent pEEM measurements due to light scatter contamination. The increased selectivity, sensitivity, and reproducibility of pTSFS measurements shows that this is a better option than pEEM for fluorescence emission based monitoring of protein structural change or lot-to-lot variance of IgG type proteins.

Application of Akaike information criterion assisted probabilistic latent semantic analysis on non-trilinear total synchronous fluorescence spectroscopic data sets: Automatizing fluorescence based multicomponent mixture analysis

The lack of trilinear structure is known to limit the application of chemometric techniques on the total synchronous fluorescence spectroscopy (TSFS) data sets. To overcome this limitation, the present work successfully proposes application of Akaike information criterion (AIC) assisted probabilistic latent semantic analysis (pLSA) algorithm on TSFS data sets. The present work also discusses various practical and theoretical aspects that need to be considered while applying AIC assisted pLSA algorithm on TSFS data sets.

Rapid and non-destructive prediction of methylxanthine and cocoa solid contents in dark chocolate by synchronous front-face fluorescence spectroscopy and PLSR

To circumvent the limitation of conventional high performance liquid chromatography (HPLC) method, synchronous front-face fluorescence spectroscopy was applied for the rapid and non-destructive determination of two major methylxanthines (theobromine and caffeine) and cocoa solid contents in dark chocolate for the first time. Total synchronous fluorescence spectra at wavelength interval (Δλ) between 30–100 nm of 21 brands of commercial dark chocolate bars with labeled cocoa solids of 50–100% were directly obtained at front-face geometry. The contents of the two methylxanthines were determined by HPLC. Partial least squares regression (PLSR) models for quantification were constructed, optimized, and validated by both full (leave-one-out) cross-validation and external validation. The coefficients of determination (R2) values are larger than 0.8, and the relative errors of prediction were ca. 10%. The built models were applied to the detection of four real samples and compared with HPLC method. The combination of front-face fluorescence and PLSR provides a fast and non-invasive procedure for the quantification of theobromine and caffeine in dark chocolate, as an effective alternative for the traditional HPLC method. The further application of the proposed strategy is the rough estimation of cocoa solids in dark chocolate.