Total Synchronous Fluorescence Spectra Research Articles

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.

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.

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.

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.

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.

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.

Authentication of apple juice categories based on multivariate analysis of the synchronous fluorescence spectra

The usage of synchronous fluorescence as a means for discrimination between the commercial apple juice categories was studied. We recorded total synchronous fluorescence spectra (TSFS) of commercial apple juices belonging to the two categories: those produced directly - not from concentrate (NFC), and those reconstituted from concentrate (FC).An exploratory study of the spectra using the principal component analysis (PCA) revealed differentiation between the juices of the two categories based on their fluorescence.Partial least squares discriminant analysis (PLS-DA) was used for the development of the classification models. The analysis was performed for the unfolded TSFS (uTSFS) and individual synchronous fluorescence spectra (SFS) measured at particular emission-excitation wavelength offsets (Δλ), from 10 to 160 nm with 10 nm step. The best discrimination results were obtained for the model using uTSFS in the range of Δλ = 30–160 nm, with cross-validation and external validation error rates of 0.05 and 0.06, respectively. Models based on selected individual SFS also showed similarly good predictive ability, with cross-validation and external validation error rates of 0.08 and 0.05, respectively, for the SFS measured at Δλ = 70 and 90 nm. The analysis of significant variables using selectivity ratio (SR) suggests that the fluorescence of non-enzymatic browning products may significantly contribute to the differentiation of FC and NFC juices.

Multivariate curve resolution – Alternating least squares analysis of the total synchronous fluorescence spectra: An attempt to identify polyphenols contribution to the emission of apple juices

Presently we applied multivariate curve resolution – alternating least squares (MCR-ALS) method for the analysis of front-face total synchronous fluorescence spectra (TSFS) of differently processed apple juices. This analysis enabled extracting of the TSFS profiles of five fluorescent components with distinct spectral characteristics and different contributions to the fluorescence of individual juices. Based on the spectral profiles and quantitative relationship with the chemical parameters describing the antioxidant properties of juices, three of the resolved components may be tentatively attributed to phenolic compounds. The analysis using multiple linear regression (MLR) and partial least square (PLS) regression confirmed better performance of fluorescence for the prediction of the total flavonoid content (TFC) as opposed to the total phenolic content (TPC) and total antioxidant capacity (TAC).This study demonstrated that MCR-ALS decomposition of the TSFS may provide a selective tool for understanding and interpretation of the observed relationships between the fluorescence and the total antioxidant indices of the apple juices.

OIL IDENTIFICATION BASED ON TOTAL SYNCHRONOUS SPECTRA

Contribution of marine carriage in the global transport is in permanent developing. It leads to increase the unintentional oil spills, which may induce serious disturbances in functioning of the natural marine ecosystem, especially when it interferes with other technical activities in the marine space. That fact contributes to increase the interest in searching of new research techniques to protect the natural ecosystem. Therefore, the main task of marine international organisations concentrates on the effective and rapid detection of oil spill and on the possibility of identifying oil pollutants as well as on origin of pollution. The purpose of the study is to characterise the oil belonging to the vessel engine lubricate oils. We analyse the stage results of investigations based on one of types of fluorescence spectroscopy, namely: synchronous spectra. To characterise the oil, the Aqualog Horiba spectrofluorometer was applied, which allows performing precise measurement in a short time. Based on the measured excitation-emission spectra, total synchronous fluorescence spectra for oil were obtained using various wavelength intervals. Total synchronous fluorescence spectra of petroleum substances allow finding wavelength interval typical for particular type of oil. This approach could allow obtaining of complex mixtures, such as oils, more efficient description. We discuss the total synchronous fluorescence spectra for engine oil (Marinol type) dissolved in n-hexane in various concentrations of oil as a possible tool proposed to oil type identification. We present changes and variation of the total synchronous fluorescence spectra for oil with various oil concentrations.

Artificial neural networks for processing fluorescence spectroscopy data in skin cancer diagnostics

Over the years various optical spectroscopic techniques have been widely used as diagnostic tools in the discrimination of many types of malignant diseases. Recently, synchronous fluorescent spectroscopy (SFS) coupled with chemometrics has been applied in cancer diagnostics. The SFS method involves simultaneous scanning of both emission and excitation wavelengths while keeping the interval of wavelengths (constant-wavelength mode) or frequencies (constant-energy mode) between them constant. This method is fast, relatively inexpensive, sensitive and non-invasive. Total synchronous fluorescence spectra of normal skin, nevus and melanoma samples were used as input for training of artificial neural networks. Two different types of artificial neural networks were trained, the self-organizing map and the feed-forward neural network. Histopathology results of investigated skin samples were used as the gold standard for network output. Based on the obtained classification success rate of neural networks, we concluded that both networks provided high sensitivity with classification errors between 2 and 4%.

Rapid Quantitative Determination of Walnut Oil Adulteration with Sunflower Oil Using Fluorescence Spectroscopy

Adulteration of walnut oil with sunflower oil is a major issue for the walnut oil industry. In this paper, the potential use of total synchronous fluorescence (TSyF) spectra to differentiate walnut oil from sunflower oil and synchronous fluorescence spectra combined with multivariate analysis to assess the adulteration of walnut oil is demonstrated. TSyF spectra were acquired by varying the excitation wavelength in the region 250–700 nm and the wavelength interval (Δλ) in the region from 10 to 100 nm. TSyF contour plots for walnut oil, in contrast to sunflower oil, show an extra fluorescence region in the excitation wavelength lower than 280 nm. Fifty-one oil mixtures were prepared by adulterating walnut oil with sunflower oil at varying levels (0–100 %). The partial least-squares regression model was used for the quantification of adulteration using wavelength intervals of 20, 40, 60 and 80 nm. This technique is useful for the detection of sunflower oil in walnut oil at levels down to 0.3 % (v/v) in just 2.5 min using an 80-nm wavelength interval.

Support Vector Machine on Fluorescence Landscapes for Breast Cancer Diagnostics

Excitation-emission matrices (EEM) and total synchronous fluorescence spectra (SFS) of normal and malignant breast tissue specimens are measured in UV-VIS spectral region to serve as data inputs in development of Support Vector Machine (SVM) based breast cancer diagnostics tool. Various input data combinations are tested for classification accuracy using SVM prediction against histopathology findings to discover the best combination regarding diagnostics sensitivity and specificity. It is shown that with EEM data SVM provided 67% sensitivity and 62% specificity diagnostics. With SFS data SVM provided 100% sensitivity and specificity for a several input data combinations. Among these combinations those that require minimal data inputs are identified.

Classification of Intact Cereal Flours by Front-Face Synchronous Fluorescence Spectroscopy

Synchronous fluorescence spectroscopy, a technique that measures both the absorption and the emission properties of a sample in a single measurement, was used for the analysis and classification of intact cereal flours (wheat, corn, rye, buckwheat, rice, and barley). Total synchronous fluorescence spectra recorded in constant wavelength mode show clear differences in the emission spectra of different flours due to variances in intrinsic fluorophore concentrations and their microenvironments. Principal component analysis, cluster analysis, and partial least squares discriminant analysis are used to assess the ability of synchronous fluorescence measurements to differentiate and classify intact samples of different flour types. The flour specimens were obtained directly from a market in Belgrade and had different expiration dates to provide a more representative set of samples. The results of the current analysis suggest that chemometric methods applied on synchronous fluorescence data can discriminate and classify flour types and that the best results are achieved using a combination of synchronous fluorescence measurements at synchronous intervals of 7 and 20 nm. The quality of results, the high speed of measurements, and the avoidance of extensive sample preparation make synchronous fluorescence spectroscopy a promising technique for cereal research.

Synchronous Fluorescence Spectroscopy: Tool for Monitoring Thermally Stressed Edible Oils

Total synchronous fluorescence spectra are proposed for monitoring edible oils during thermal stress. Synchronous fluorescence spectra obtained at an 80 nm wavelength interval combined with principal component analysis are suitable for classification of vegetable oil deterioration. Spectroscopic features in the range of 300-500 nm have been used for extra virgin olive, olive pomace, and sesame oil and the range of 320-520 nm has been used for corn, soybean, sunflower, and a commercial blend of oils. The score in the first two principal components explains 91.1% of the data matrix variance for extra virgin olive, sesame, and olive pomace oil and 89.3% for corn, soybean, sunflower, and the commercial blend of oils. The objective of this study is to develop a rapid method for the prediction of edible oil quality during thermal stress. Spectroscopic changes are indicative of oxidative deterioration as measured through wet chemistry methods: peroxide value, p-anisidine value, totox value, and radical-scavenging capacity.

Rapid synchronous fluorescence method for virgin olive oil adulteration assessment

This paper describes the differentiation of virgin olive from olive-pomace, corn, sunflower, soybean, rapeseed and walnut oils using total synchronous fluorescence (TSyF) spectra. TSyF spectra are acquired by varying the excitation wavelength in the region 250–720 nm and the wavelength interval (Δ λ) in the region from 20 to 120 nm. It is shown that adulterants can be discriminated from virgin olive oil using a wavelength interval of 20 nm and excitation wavelength region 315–400, 315–392, 315–375, 315–365, 315–375 and 315–360 for olive-pomace, corn, sunflower, soybean, rapeseed and walnut oils, respectively. Thirty one virgin olive oil mixtures with each potential adulterant were prepared at varying levels with emphasis at low concentrations. The partial least-squares regression model was used to quantify adulteration. This technique is useful for detection of olive-pomace, corn, sunflower, soybean, rapeseed and walnut oil in virgin olive oil at levels of 2.6%, 3.8%, 4.3%, 4.2%, 3.6%, and 13.8% (w/w), respectively.

Synchronous fluorescence spectroscopy for quantitative determination of virgin olive oil adulteration with sunflower oil

Adulteration of extra virgin olive oil with sunflower oil is a major issue for the olive oil industry. In this paper, the potential of total synchronous fluorescence (TSyF) spectra to differentiate virgin olive oil from sunflower oil and synchronous fluorescence (SyF) spectra combined with multivariate analysis to assess the adulteration of virgin olive oil are demonstrated. TSyF spectra were acquired by varying the excitation wavelength in the region 270-720 nm and the wavelength interval (Deltalambda) in the region from 20 to 120 nm. TSyF contour plots for sunflower, in contrast to virgin olive oil, show a fluorescence region in the excitation wavelength range 325-385 nm. Fifteen different virgin olive oil samples were adulterated with sunflower oil at varying levels (0.5-95%) resulting in one hundred and thirty six mixtures. The partial least-squares regression model was used for quantification of the adulteration using wavelength intervals of 20 and 80 nm. This technique is useful for detection of sunflower oil in virgin olive oil at levels down to 3.4% (w/v) in just two and a half minutes using an 80-nm wavelength interval.