Synchronous front-face fluorescence spectroscopy for authentication of the adulteration of edible vegetable oil with refined used frying oil

Detection and quantification of extra virgin olive oil adulteration by other grades of olive oil using front-face fluorescence spectroscopy and different multivariate analysis techniques

This study explores the use of front-face fluorescence spectroscopy to detect extra virgin olive oil (EVOO) adulteration with lower-grade olive oils (virgin, ordinary virgin, lampante virgin, refined, and pomace) at 5-50 % adulteration levels. Emission spectra were analyzed using principal component analysis, factorial discriminant analysis, and partial least square discriminant analysis (PLS-DA) at excitation wavelengths of 270, 290, and 430 nm. PLS-DA at 430 nm provided the best results, achieving 100 % classification accuracy and perfectly separating 12 groups of pure and adulterated samples. For purity prediction, regression models (partial least squares, principal component, and support vector machine) applied to emission spectra data yielded high R2 values of 0.995, 0.96, and 0.98 at 430 nm, 290 nm, and 270 nm, respectively, with a low prediction error of 1.09 %. These findings confirm the method's high accuracy for detecting and quantifying EVOO adulteration.

Quantitative determination of acid value in palm oil during thermal oxidation using Raman spectroscopy combined with deep learning models

Accurate monitoring of acid value (AV) is critical for edible oil quality control, yet traditional chemometric methods often face limitations in handling complex spectral data. This study combines Raman spectroscopy with deep learning, including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Transformer, to explore their potential in improving the accuracy and efficiency of AV quantification during the thermal oxidation of palm oil. The results showed that all three deep learning models outperformed traditional chemometric methods in predictive accuracy. The CNN-LSTM model achieved the best performance, with a predicted coefficient of determination (Rp2) of 0.9978, a mean square error of prediction (RMSEP) of 0.0015, and a residual predictive deviation (RPD) of 21.21. This method demonstrates the effectiveness of Raman spectroscopy-driven deep learning for precise AV monitoring and holds promise for further validation with more diverse indicator datasets, providing a novel technical reference for edible oil quality control.

Using three-dimensional fluorescence spectroscopy and machine learning for rapid detection of adulteration in camellia oil

Camellia oil had been widely utilized in the realms of cooking, healthcare, and beauty. Nevertheless, merchants frequently adulterated pure camellia oil with low-priced oils to cut costs. This study was aimed at identifying the authenticity of camellia oil. Through the employment of three-dimensional fluorescence spectroscopy combined with the parallel factor analysis (PARAFAC) method, the characteristics of different vegetable oils were analyzed to establish a foundation for classification modeling. In the identification of pure vegetable oil types, methods such as partial least squares discriminant analysis (PLS-DA), k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) were adopted. The classification accuracy reached 100 %, demonstrating the effectiveness of feature extraction by PARAFAC. For the identification of camellia oil and its adulterants, traditional machine learning methods and convolutional neural network (CNN) models were introduced. The results indicated that traditional methods had limitations in the classification of single and binary adulterated oils. However, the optimized CaoCNN model achieved an accuracy of 97.78 % in identifying adulterated oil types, showcasing the potential of deep learning in adulterated oil detection. Further, feature visualization analysis verified the ability of CaoCNN to effectively capture and distinguish the characteristics of adulterated oils, providing an effective approach for the identification of camellia oil and its adulterated oils.

Reverse design of haptens based on antigen spatial conformation to prepare anti-capsaicinoids&gingerols antibodies for monitoring of gutter cooking oil

Rapid simultaneous detection of multi-component adulteration markers can improve the accuracy of identification of gutter cooking oil in edible oil, which is made possible by broad-spectrum antibody (bs-mAb). This study used capsaicinoids (CPCs) and gingerol derivatives (GDs) as adulteration markers, and two broad-spectrum haptens (bs-haptens) were designed and synthesized based on a reverse design strategy of molecular docking. Electrostatic potential (ESP) and monoclonal antibodies (mAbs) preparation verified the strategy's feasibility. To further investigate the recognition mechanism, five other reported antigens and mAbs were also used. Finally, the optimal combination (Hapten 5-OVA/1-F12) and key functional groups (f-groups) were determined. The half maximal inhibitory concentration (IC50) for CPCs-GDs was between 88.13 and 499.16 ng/mL. Meanwhile, a preliminary lateral flow immunoassay (LFIA) study made practical monitoring possible. The study provided a theoretical basis for the virtual screening of bs-haptens and simultaneous immunoassay of multiple exogenous markers to monitor gutter oil rapidly and accurately.

Simultaneous Rapid Detection of Multiple Physicochemical Properties of Jet Fuel Using Near-Infrared Spectroscopy

Jet fuel is the primary fuel used in the aviation industry, and its quality has a direct impact on the safety and operational efficiency of aircraft. The accurate quantitative detection and analysis of various physicochemical property indicators are important for improving and ensuring the quality of jet fuel in the domestic market. This study used near-infrared (NIR) spectroscopy to establish a suitable model for the simultaneous and rapid detection of multiple physicochemical properties in jet fuel. Using more than 40 different sources of jet fuel, a rapid detection model was established by optimizing the spectral processing methods. The measurement models were separately built using the partial least-squares (PLS) and orthogonal PLS algorithms, and the model parameters were optimized. The results show that after the Savitzky-Golay second derivative preprocessing, the PLS model built using the feature spectra selected by the uninformative variable elimination wavelength algorithm achieved the best measurement performance. Compared with the PLS model without preprocessing, the range of the resulting accuracy improvement was at least 15.01%. Under the optimal model parameters, the calibration set regression coefficient (Rc2) of the 11 jet fuel property index models ranged from 0.9102 to 0.9763, with the root-mean-square error of calibration values up to 0.8468 °C (for flash points). The regression coefficient (Rp2) of the validation set ranged from 0.8239 to 0.9557, with the root-mean-square error of prediction values up to 1.1354 °C (for flash points). The ratios of prediction to deviation (RPD) values were all in the range of 1.9-3.0, indicating high accuracy and reliability of the model. The rapid NIR analysis method established in this study enables the simultaneous and rapid detection of multiple physicochemical properties of jet fuel, thereby providing effective technical support for ensuring the quality of jet fuel in the market.

Rapid quantitative evaluation of total polar materials (TPM) in frying oil based on an "off-on" fluorescence viscosity response probe

The content of total polar material (TPM) is considered as a comprehensive indicator to evaluate the quality of edible oils which should be discarded and no longer be used when TPM content exceeding 27 %. Nevertheless, there is currently a lack of a convenient and efficient TPM detection method, which is a meaningful challenge. With the increase of TPM content, the viscosity of frying oil grows, and the two maintain a satisfactory positive correlation. Consequently, an "off-on" fluorescence probe TCF-PR method based on viscosity-response has been developed. There exists a good linear relationship between the fluorescence intensity of the probe and the TPM content of soybean oil ((R2 = 0.9936) and salad oil (R2 = 0.9878), accompanying with the advantage of fast response (3 s), which means the rapid detection of TPM can be realized to determine the quality of frying oil in the field of food safety.

Ultraviolet-visible spectroscopy combined with machine learning as a rapid detection method to the predict adulteration of honey

Honey is often adulterated with inexpensive and artificial sweeteners. To overcome the time-consuming honey adulteration tests, which require precision, chemicals, and sample preparation, it is needful to develop trustworthy analytical methods to assure its authenticity. In the present study, the potential of ultraviolet-visible spectroscopy (UV-Vis) in predicting the sucrose content was evaluated by using Support Vector Regression (SVR) and Partial Least Square Regression (PLSR). To predict the sucrose content based on diagnostic wavelengths, a Point Spectro Transfer Function (PSTF) was evaluated using Multiple Linear Regression (MLR). For this purpose, the spectra of authentic (n = 12), commercial (n = 12), and adulterated (n = 16) honey samples were recorded. Four distinguished wavelengths from correlation analysis between sucrose content and spectra absorption were 216, 280, 316, and 603 nm. The SVR performed better calibration model than the PLSR estimations (RMSE = 0.97, and R2 = 0.98). The predictive models result revealed that both models had high accuracy for the sucrose content estimation. This study proved that UV-Vis spectroscopy provides an economical alternative for the rapid quantification of adulterated honey samples with sucrose.

Spectroscopic studies on thermal degradation and quantitative prediction on acid value of edible oil during frying by Raman spectroscopy

The health risks posed by harmful substances resulting from the thermal degradation of frying oils are of great concern. Characteristic peak intensity ratios (PIRs) screened from Raman spectra were used to characterize the thermal degradation. High correlation coefficients between PIRs and acid values (AVs) of 0.972 (linear fitting), 0.984 (logarithmic function fitting), and 0.954 (linear fitting) for fried soybean oil, canola oil, and palm oil, were obtained at the PIRs of I₁₂₆₇/I₁₇₄₉, I₁₂₆₇/I₁₆₅₉, and I₁₂₆₇/I₁₇₄₉, respectively. The highly correlated PIRs common to the three oils were determined by Pearson's correlation coefficient combined with heat maps. To accommodate both linear and nonlinear features, a global model for predicting AVs of multi-varieties frying oils was constructed using a least-squares support vector machine algorithm, and the results performed well with a root mean square error of prediction of 0.016 and a ratio of prediction to deviation of 11.351. The whole results demonstrate that Raman spectroscopy could characterize the thermal degradation and has excellent quantitative analysis ability for food control based on AV in frying oils, thus providing a new approach to quality control of frying oils.

Geographical Classification of Saffron (Crocus Sativus L.) Using Total and Synchronous Fluorescence Combined with Chemometric Approaches

There is an increasing interest in food science for high-quality natural products with a distinct geographical origin, such as saffron. In this work, the excitation-emission matrix (EEM) and synchronous fluorescence were used for the first time to geographically discriminate between Moroccan saffron from Taroudant, Ouarzazate, and Azilal. Moreover, to differentiate between Afghan, Iranian, and Moroccan saffron, a unique fingerprint was assigned to each sample by visualizing the EEM physiognomy. Moreover, principal component analysis (LDA) and linear discriminant analysis (LDA) were successfully applied to classify the synchronous spectra of samples. High fluorescence intensities were registered for Ouarzazate and Taroudant saffron. Yet, the Azilal saffron was distinguished by its low intensities. Furthermore, Moroccan, Afghan, and Iranian saffron were correctly assigned to their origins using PCA and LDA for different offsets (Δλ) (20-250 nm) such that the difference in the fluorescence composition of the three countries' saffron was registered in the following excitation/emission ranges: 250-325 nm/300-480 nm and 360-425 nm/500-550 nm. These regions are characterized by the high polyphenolic content of Moroccan saffron and the important composition of Afghan saffron, including vitamins and terpenoids. However, weak intensities of these compounds were found in Iranian saffron. Furthermore, a substantial explained variance (97-100% for PC1 and PC2) and an important classification rate (70-90%) were achieved. Thus, the non-destructive applied methodology of discrimination was rapid, straightforward, reliable, and accurate.

Establishment and evaluation of multiple adulteration detection of camellia oil by mixture design

Multiple adulteration is a common trick to mask adulteration detection methods. In this study, the representative multiple adulterated camellia oils were prepared according to the mixture design. Then, these representative oils were employed to build two-class classification models and validate one-class classification model combined with fatty acid profiles. The cross-validation results indicated that the recursive SVM model possessed higher classification accuracy (97.9%) than PLS-DA. In OCPLS model, the optimal percentage of RO, SO, CO and SUO was 2.8%, 0%, 7.2%, 0% respectively in adulterated camellia oil, which is the most similar to the authentic camellia oils. Further validation showed that five adulterated oils with the optimal percentage could be correctly identified, indicating that the OCPLS model could identify multiple adulterated oils with these four cheaper oils. Moreover, this study serves as a reference for one class classification model evaluation and a solution for multiple adulteration detection of other foods.

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.

Research on non-destructive testing of hotpot oil quality by fluorescence hyperspectral technology combined with machine learning

Eating repeatedly used hotpot oil will cause serious harm to human health. In order to realize rapid non-destructive testing of hotpot oil quality, a modeling experiment method of fluorescence hyperspectral technology combined with machine learning algorithm was proposed. Five preprocessing algorithms were used to preprocess the original spectral data, which realized data denoising and reduces the influence of baseline drift and tilt. The feature bands extracted from the spectral data showed that the best feature bands for the two-classification model and the six-classification model were concentrated between 469 and 962 nm and 534-809 nm, respectively. Using the PCA algorithm to visualize the spectral data, the results showed the distribution of the six types of samples intuitively, and indicated that the data could be classified. Based on the modeling analysis of the feature bands, the results showed that the best two-classification models and the best six-classification models were MF-RF-RF and MF-XGBoost-LGB models, respectively, and the classification accuracy reached 100 %. Compared with the traditional model, the error was greatly reduced, and the calculation time was also saved. This study confirmed that fluorescence hyperspectral technology combined with machine learning algorithm could effectively realize the detection of reused hotpot oil.

A simple and rapid homogeneous fluorescence polarization immunoassay for rapid identification of gutter cooking oil by detecting capsaicinoids

In order to address the widespread concerns with food safety such as adulteration and forgery in the edible oil field, this study developed a fluorescence polarization immunoassay (FPIA) based on a monoclonal antibody in a homogeneous solution system for determination of capsaicinoids in gutter cooking oil by using chemically stable capsaicinoids as an adulteration marker. The prepared fluoresceinthiocarbamyl ethylenediamine (EDF) was coupled with capsaicinoid hapten C, and the synthesized tracer was purified by thin-layer chromatography (TLC) and showed good binding to the monoclonal antibody CPC Ab-D8. The effects of concentration of tracer and recognition components, type and pH of buffer and incubation time on the performance of FPIA were studied. The linear range (IC₂₀ to IC₈₀) was 3.97-97.99 ng/mL, and the half maximal inhibitory concentration (IC₅₀) was 19.73 ng/mL, and the limit of detection (LOD) was 1.56 ng/mL. The recovery rates of corn germ oil, soybean oil and peanut blend oil were in the range of 94.7-132.3%. The experimental results showed that the fluorescence polarization detection system could realize the rapid detection of capsaicinoids, and had the potential to realize on-site identification of gutter cooking oil. As a universal monoclonal antibody, CPC Ab-D8 can also specifically identify capsaicin and dihydrocapsaicin, so the proposed method can be used to quickly monitor for the presence of gutter cooking oil in normal cooking oil.

Rapid quantification of acid value in frying oil using iron tetraphenylporphyrin fluorescent sensor coupled with density functional theory and multivariate analysis

Metalloporphyrin-based fluorescent sensor was developed for the acid value in frying oil. The electronic and structural performances of iron tetraphenylporphyrin (FeTPP) were theoretically investigated using time-dependent density functional theory (TD-DFT) and DFT at the B3LYP/LANL2DZ level. The quantified FeTPP-based fluorescent sensor results revealed its excellent performance in discriminating different analytes. In the present work, the acid value of palm olein was determined after every single frying cycle. A total of 10 frying cycles were conducted each day for 10 consecutive days. The FeTPP-based fluorescent sensor was used to quantify the acid value and the results were compared with the chemical data obtained by conventional titration method. The synchronous fluorescence spectrum for each sample was recorded. Parallel factor analysis (PARAFAC) was used to decompose the three-dimensional spectrum data. Then, the support vector regression (SVR), partial least squares (PLS), and back-propagation artificial neural network (BP-ANN) methods were applied to build the regression models. After the comparison of the constructed models, the SVR models exhibited the highest correlation coefficients among all models, with 0.9748 and 0.9276 for the training and test set, respectively. The findings suggested the potential of FeTPP-based fluorescent sensor in rapid monitoring of the used frying oil quality and perhaps also in other foods with higher oil content.

Comparison of four classification statistical methods for characterising virgin olive oil quality during storage up to 18 months

This study examines the ability of fluorescence spectroscopy for monitoring the quality of 70 Moroccan virgin olive oils belonging to three varieties and originating from three regions of Morocco. By applying principal component analysis and factorial discriminant analysis to the emission spectra acquired after excitation wavelengths set at 270, 290, and 430 nm, a clear differentiation between samples according to their storage time was observed. The obtained results were confirmed following the application of four multivariate classification methods: partial least squares regression, principal component regression, support vector machine, and multiple linear regression on the emission spectra. The best prediction model of storage time was obtained by applying partial least squares regression since a coefficient of determination (R²) and a root mean square error of prediction (RMSEP) of 0.98 and 24.85 days were observed, respectively. The prediction of the chemical parameters allowed to obtain excellent validation models with R² ranging between 0.98 and 0.99 for free acidity, peroxide value, chlorophyll level, k₂₃₂, and k₂₇₀.

A feasibility study for rapid evaluation of emulsion oxidation using synchronous fluorescence spectroscopy coupled with chemometrics

A rapid method based on three-dimensional synchronous fluorescence spectroscopy was developed for emulsion oxidation evaluation. This method was selected because of its high sensitivity to dissolved organic matter typically occurring in the lipid oxidation. Spectral signal and chemical reference measurements were recorded for each emulsion sample as input and output data for the model construction. Characteristic values were extracted from the spectral data by the application of parallel factor (PARAFAC) analysis. Partial least squares regression (PLSR) was then used to construct a regression model for the rapid determination of emulsion oxidation. The correlation coefficient of the calibration and prediction sets were used as the performance parameters for the PLSR models as follows: R = 0.929, 0.973 for emulsion samples stored at 25℃; R = 0.897, 0.903 for emulsion samples stored at 70℃. The overall results demonstrated that the fluorescence spectroscopy, coupled with PARAFAC and PLSR algorithms, could be successfully used as a rapid method for the emulsion oxidation evaluation.

Molecular Approaches to Agri-Food Traceability and Authentication: An Updated Review

In the last decades, the demand for molecular tools for authenticating and tracing agri-food products has significantly increased. Food safety and quality have gained an increased interest for consumers, producers, and retailers, therefore, the availability of analytical methods for the determination of food authenticity and the detection of major adulterations takes on a fundamental role. Among the different molecular approaches, some techniques such as the molecular markers-based methods are well established, while some innovative approaches such as isothermal amplification-based methods and DNA metabarcoding have only recently found application in the agri-food sector. In this review, we provide an overview of the most widely used molecular techniques for fresh and processed agri-food authentication and traceability, showing their recent advances and applications and discussing their main advantages and limitations. The application of these techniques to agri-food traceability and authentication can contribute a great deal to the reassurance of consumers in terms of transparency and food safety and may allow producers and retailers to adequately promote their products.

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%.

State-of-the-Art of Analytical Techniques to Determine Food Fraud in Olive Oils

The benefits of the food industry compared to other sectors are much lower, which is why producers are tempted to commit fraud. Although it is a bad practice committed with a wide variety of foods, it is worth noting the case of olive oil because it is a product of great value and with a high percentage of fraud. It is for all these reasons that the authenticity of olive oil has become a major problem for producers, consumers, and legislators. To avoid such fraud, it is necessary to develop analytical techniques to detect them. In this review, we performed a complete analysis about the available instrumentation used in olive fraud which comprised spectroscopic and spectrometric methodology and analyte separation techniques such as liquid chromatography and gas chromatography. Additionally, other methodology including protein-based biomolecular techniques and analytical approaches like metabolomic, hhyperspectral imaging and chemometrics are discussed.

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.

Intelligent evaluation of total polar compounds (TPC) content of frying oil based on fluorescence spectroscopy and low-field NMR

The purpose of this study was to construct a fusion model using probe-based and non-probe-based fluorescence spectroscopy and low-field nuclear magnetic resonance spectroscopy (Low-field NMR) for rapid quality evaluation of frying oil. Iron tetraphenylporphyrin (FeTPP) was selected as the probe to detect polar compounds in frying oil samples. Non-probe-based fluorescence spectroscopy and low-field NMR were employed to determine the fluorescence changes of antioxidants, triglycerides and fatty acids in frying oil samples. Compared to the models constructed using non-fusion data, the fusion-data models achieved a better regression prediction performance and correlation coefficients with values of 0.9837 and 0.9823 for the training and test sets, respectively. This study suggested that the multiple data fusion method was capable to construct better regression models to rapidly evaluate the quality of frying oil and other food with high oil contents.

Spectroscopic Techniques for Monitoring Thermal Treatments in Fish and Other Seafood: A Review of Recent Developments and Applications

Cooking is an important processing method, that has been used since ancient times in order to both ensure microbiological safety and give desired organoleptic properties to the cooked food. Fish and other seafood products are highly sensitive to thermal treatments and the application of severe heat can result in negative consequences on sensory and nutritional parameters, as well as other quality attributes of the thermally processed products. To avoid such undesired effects and to extend the shelf life of these perishable products, both the heat processing methods and the assessment techniques used to monitor the process should be optimized. In this review paper, the most common cooking methods and some innovative ones will first be presented with a brief discussion of their impact on seafood quality. The main methods used for monitoring heat treatments will then be reviewed with a special focus on spectroscopic techniques, which are known to be rapid and non-destructive methods compared to traditional approaches. Finally, viewpoints of the current challenges will be discussed and possible directions for future applications and research will be suggested. The literature presented in this review clearly demonstrates the potential of spectroscopic techniques, coupled with chemometric tools, for online monitoring of heat-induced changes resulting from the application of thermal treatments of seafood. The use of fluorescence hyperspectral imaging is especially promising, as the technique combines the merits of both fluorescence spectroscopy (high sensitivity and selectivity) and hyperspectral imaging (spatial dimension). With further research and investigation, the few current limitations of monitoring thermal treatments by spectroscopy can be addressed, thus enabling the use of spectroscopic techniques as a routine tool in the seafood industry.

Dual sensing and synchronous fluorescence spectroscopic monitoring of Cr3+and Al3+ using a luminescent Schiff base: Extraction and DFT studies

A well designed, new pyrene based small molecule (L) was synthesized from 1:1 condensation reaction of 1-aminopyrene and 6-(1,3-benzodioxal-5-yl)-2-pyridine carboxaldehyde which was characterized by absorption, emission spectrometry, FTIR, NMR and mass studies. Interestingly the UV-vis and fluorescence spectroscopic studies revealed that the ligand (L) works as a dual turn-on luminescent chemosensor for chromium(III) (Cr³⁺) and aluminium(III) (Al³⁺) in aqueous environment which were further supported by DFT and TDDFT studies. L shows a significant colour change from pale yellow to reddish yellow with a detection limit of ~10^-9 M in the presence of Cr³⁺ and Al³⁺ whereas there were no noteworthy changes in the presence of other monovalent and divalent metal ions. The molecular signaling in the presence of Cr³⁺, Al³⁺, Fe³⁺ and EDTA was compared with advanced level combinational INHIBIT gate based on 4 input logic gates. Herein, first derivative constant wavelength synchronous fluorescence spectroscopy (1^st DCWSFS) was applied for the determination of Cr³⁺, Al³⁺ ion concentrations in a mixture via increment of spectral resolution of the respective overlapping peaks. 1^st DCWSFS is reported to be used in pharmaceuticals but very few works have been done for determination of metal ion concentration in environmental sample without prior separation. The individual Cr³⁺and Al³⁺ ion concentrations in a mixture were determined through liquid-liquid extraction process and the efficiencies were compared with 1^st derivative SFS method. It was observed that 1^st derivative SFS process is more efficient than conventional liquid-liquid extraction process. Therefore, 1^st DCWSFS method using sensor L might be useful as a diagnostic tool for detection of individual metal ion concentrations (Cr³⁺ and Al³⁺) from a mixture which will be cost-effective, time saving and more precise.

Authentication of Eucommia ulmoides Seed Oil Using Fourier Transform Infrared and Synchronous Fluorescence Spectroscopy Combined with Chemometrics

Eucommia ulmoides is a traditional Chinese herb whose seeds can be used to produce edible oils. Fourier transform infrared (FTIR) and synchronous fluorescence spectroscopic (SyFS) spectra of Eucommia ulmoides seed oil (EUSO) are lacking. The relevant functional and fluorescent groups were determined by FTIR and SyFS techniques for discriminating adulteration of EUSO, respectively. FTIR and SyFS spectra of EUSO and six common-used vegetable oils were recorded from 4000-400 cm^-1 and 250-700 nm at wavelength interval of 60 nm, respectively. Principal component analysis (PCA), linear discriminant analysis (LDA), cluster analysis (CA) and partial least square (PLS) regression was used for qualitative and quantitative calibration of EUSO adulteration. The FTIR spectral regions of 1429-1377 cm^-1 and 1128-1110 cm^-1 based on PCA, LDA, and CA, and the PCA of SyFS spectral regions of 600-700 nm and 300-500 nm were evaluated for qualitative differentiation of EUSO adulteration. The recognition rate of PCA validation was found to be 100% by FTIR regions. PLS calibration was optimal by the spectral normalization vector treatment in the two FTIR spectral regions and SyFS spectra were combined with characteristic absorption peak area, which can achieve quantitative detection of EUSO adulteration. The two techniques are useful for EUSO adulteration detection at levels down to 1% and 0.48% (w/w), respectively. The results indicated that spectral information obtained by FTIR and SyFS of EUSO can be used for qualitative and quantitative analysis of EUSO adulteration with the advantages of high sensitivity, simplicity, and rapidness.

Air pollution impact on architectural heritage of Morocco: Combination of synchronous fluorescence and ATR-FTIR spectroscopies for the analyses of black crusts deposits

The work is focusing on air pollution impacts on historical limestone buildings located in urban areas in Morocco. Black crusts sampled on the façades of two ancient limestone monuments, dating back to the 12th and 20th centuries edified in the cities of Salé and Casablanca, have been analyzed by means of ATR-FTIR and synchronous fluorescence spectroscopies. Infrared analyses revealed degradation products, mainly gypsum due to calcite sulphation under wetness and SO₂ rich oil fired soot, and oxalates due to ancient biological weathering. Synchronous fluorescence permitted the identification of the most hazardous PAHs along with other non-identified fluorescent organics; this technique appeared efficient and suitable for the analysis of fluorescent pollutants entrapped in black crusts. Such results keeping track of air pollution causing disfigurement of architectural heritage must alarm both cultural heritage and environmental decision makers.

Stimulated Brillouin scattering in combination with visible absorption spectroscopy for authentication of vegetable oils and detection of olive oil adulteration

Vegetable oils provide high nutritional value in the human diet. Specifically, extra virgin olive oil (EVOO) possesses a higher price than that of other vegetable oils. Adulteration of pure EVOO with other types of vegetable oils has attracted increasing attentions. In this work, a stimulated Brillouin scattering (SBS) combined with visible absorption spectroscopy method is proposed for authentication of vegetable oils and detection of olive oil adulteration. The results provided here have demonstrated that the different vegetable oils and adulteration oils exhibit significant differences in normalized absorbance values of two relevant wavelengths (455 and 670 nm) and frequency shifts of SBS. The normalized absorbance values of all spectra at the two relevant wavelengths of 670 nm and 455 nm linearly decrease with the increase of the adulteration concentration. The Brillouin frequency shifts exponentially increase with the increase of the adulteration concentration. Due to non-destructive and requiring no sample pretreatment procedure, this method can be effectively employed for authentication and detection of oils adulteration.

Hue Parameter Fluorescence Identification of Edible Oils with a Smartphone

Food fraud can be highly lucrative, and high accuracy authentication of various foodstuffs is becoming essential. Olive oil is one of the most investigated food matrices, due to its high price and low production globally, with recent food fraud examples showing little or no high quality olive oil in the tested oils. Here a simple method using a 405 nm LED flashlight and a smartphone is developed for edible oil authentication. Identification is fingerprinted by intrinsic fluorescent compounds in the oils, such as chlorophylls and polyphenols. This study uses the hue parameter of HSV-colorspace to authenticate 24 different edible oils of 9 different types and 15 different brands. For extra virgin olive oil, all nine samples are well separated from the other oil samples. The rest of the samples were also well type-distinguished by the hue parameter, which is complemented by hue-histogram analysis. This opens up opportunities for low-cost and high-throughput smartphone field-testing of edible oils on all levels of the production and supply chain.

Detection of Adulteration in Canola Oil by Using GC-IMS and Chemometric Analysis

The aim of the present study was to detect adulteration of canola oil with other vegetable oils such as sunflower, soybean, and peanut oils and to build models for predicting the content of adulterant oil in canola oil. In this work, 147 adulterated samples were detected by gas chromatography-ion mobility spectrometry (GC-IMS) and chemometric analysis, and two methods of feature extraction, histogram of oriented gradient (HOG) and multiway principal component analysis (MPCA), were combined to pretreat the data set. The results evaluated by canonical discriminant analysis (CDA) algorithm indicated that the HOG-MPCA-CDA model was feasible to discriminate the canola oil adulterated with other oils and to precisely classify different levels of each adulterant oil. Partial least square analysis (PLS) was used to build prediction models for adulterant oil level in canola oil. The model built by PLS was proven to be effective and precise for predicting adulteration with good regression (R²>0.95) and low errors (RMSE ≤ 3.23).

Effects of low sulfur dioxide concentrations on bioactive compounds and antioxidant properties of Aglianico red wine

This study analyzed the effect of low sulfur dioxide concentrations on the chromatic properties, phytochemical composition and antioxidant activity of Aglianico red wines with respect to wines produced from conventional winemaking. We determined the phytochemical composition by spectrophotometric methods and HPLC-DAD analysis and the in vitro antioxidant activity of different wine samples by the ORAC assay. The main important classes of fluorophore molecules in red wine were identified by Front-Face fluorescence spectroscopy, and the emission intensity trend was investigated at various sulfur dioxide concentrations. Lastly, we tested the effects of both conventional and low sulfite wines on ex vivo human erythrocytes under oxidative stimulus by the cellular antioxidant activity (CAA) assay and the hemolysis test. The addition of sulfur dioxide, which has well-known side effects, increased the content of certain bioactive components but did not raise the erythrocyte antioxidant capacity.