Portable Detection and Quantification of Olive Oil Adulteration by 473-nm Laser-Induced Fluorescence

Enhanced quantification of chlorophyll a and its degradation products in olive oil using time-resolved laser-induced fluorescence fingerprint analysis

Potential errors in the fluorescence analysis of chlorophylls and their degradation products, primarily due to spectral overlap and inner filter, are widely acknowledged. This study aimed to devise a sensitivity-enhanced technique for the concurrent quantification of chlorophyll a and its degradation products while minimizing effects from type-B chlorophylls. Initially, a time-resolved laser-induced fluorescence spectroscopic system was designed and tested on stardard chlorophyll samples. The origins, implications, and mitigation strategies of spectral overlap and the inner filter effect on the measured fluorescence intensity were thoroughly examined. Then, this methodology was proved to be efficacious within complex liquid matrices derived from olive oil. The experimental outcomes not only shed additional light on the mechanisms of chlorophyll fluorescence overlap and the inner filter effect, but also establish a general framework for developing spectrally and timely resolved fluorescence fingerprint analysis for the simultaneous quantification of chlorophylls and their degradation products at high concentrations.

Design & development of adulteration detection system by fumigation method & machine learning techniques

A novel method for discovery of adulteration in edible oil is proposed based on concept of refractive index and electronic sensors. The research work focusses on two distinct methodologies like employing datasets and implementing a fumigation technique that integrates real-time hardware for testing Edible oil Impurities. In the first method, the dataset taken into consideration contains spectral data collected using Advanced ATR-MIR Spectroscopy for pure oil and various levels of adulteration with Vegetable oil. Each and every edible oil has a certain value of refractive index. When such oils are contemned in a change adding adulterants, the value of its refractive indices also changes. This value of refractive index serves as a feature for testing the oil and helps us in detecting the adulteration. If Oil is adulterated with vegetable oils, the refractive index will be lower and with animal fats, the refractive index will be higher than that of pure Oil. While in Fumigation Method a hardware module is develop in which adulterated & pure oil samples are heated at 40-50 °C for 4.66 min and the volatiles that are generated by varying gas concentrations are forcefully passed through to the MEMS Gas Sensor-MISC-2714 and Multichannel Gas sensor. The conductance of the sensors changes according to the gases sensed by the sensors contributes to features extraction. The conductance value serves as a feature for the classifier to determine whether the sample is highly, moderately, or lowly contaminated. Thus, in proposed methods we use different algorithms based on machine learning like KNN, Random Forest, CATBOOST and XGBOOST to accurately reveal the adulteration. Amongst all the applied algorithm Random Forest (RF) Classifier & XGBOOST algorithm outperform well and gives 100% accuracy. The proposed work is used for identifying food adulteration in edible food products which helps us to feed Society with high-quality food.

Recent advances in artificial intelligence towards the sustainable future of agri-food industry

Artificial intelligence has the potential to alter the agricultural and food processing industries, with significant ramifications for sustainability and global food security. The integration of artificial intelligence in agriculture has witnessed a significant uptick in recent years. Therefore, comprehensive understanding of these techniques is needed to broaden its application in agri-food supply chain. In this review, we explored cutting-edge artificial intelligence methodologies with a focus on machine learning, neural networks, and deep learning. The application of artificial intelligence in agri-food industry and their quality assurance throughout the production process is thoroughly discussed with an emphasis on the current scientific knowledge and future perspective. Artificial intelligence has played a significant role in transforming agri-food systems by enhancing efficiency, sustainability, and productivity. Many food industries are implementing the artificial intelligence in modelling, prediction, control tool, sensory evaluation, quality control, and tackling complicated challenges in food processing. Similarly, artificial intelligence applied in agriculture to improve the entire farming process, such as crop yield optimization, use of herbicides, weeds identification, and harvesting of fruits. In summary, the integration of artificial intelligence in agri-food systems offers the potential to address key challenges in agriculture, enhance sustainability, and contribute to global food security.

A novel method for determination of peroxide value and acid value of extra-virgin olive oil based on fluorescence internal filtering effect correction

Peroxide value (PV) and acid value (AV) are widely used indicators for evaluating oxidation degree of olive oils. Fluorescence spectroscopy has been extensively studied on the detection of oil oxidation, however, the detection accuracy is limited due to internal filtering effect (IFE). Due to the primary and secondary IFE, at least two wavelengths of absorption information are required. Least squares support vector regression (LSSVR) models for PV and AV were established based on two absorption coefficients (μa) at 375 nm and emission wavelength and one fluorescence intensity at corresponding wavelength. The regression results proved that the model based on 375 and 475 nm could reach the best performance, with the highest correlation coefficient for prediction (rp) of 0.889 and 0.960 for PV and AV respectively. Finally, the explicit formulations for PV and AV were determined by nonlinear least squares fitting, and the rp could reach above 0.94 for two indicators.

Light-Emitting-Diode-Induced Fluorescence from Organic Dyes for Application in Excitation-Emission Fluorescence Spectroscopy for Food System Analysis

An experimental study is presented on the possibility of using the fluorescence from organic dyes as a broadband light source together with a monochromator for applications in excitation-emission matrix (EEM) fluorescence spectroscopy. A high-power single-chip light-emitting diode (LED) was chosen as an excitation source with a central output wavelength at 365 nm to excite a fluorescent solution of Coumarin 1 dye dissolved in ethanol. Two excitation configurations were investigated: direct excitation from the LED and excitation through an optical-fiber-coupled LED. A Czerny-Turner monochromator with a diffraction grating was used for the spectral tuning of the fluorescence. A simple method was investigated for increasing the efficiency of the excitation as well as the fluorescence signal collection by using a diffuse reflector composed of barium sulfate (BaSO4) and polyvinyl alcohol (PVA). As research objects, extra-virgin olive oil (EVOO), Coumarin 6 dye, and Perylene, a polycyclic aromatic hydrocarbon (PAH), were used. The results showed that the light-emitting-diode-induced fluorescence was sufficient to cover the losses on the optical path to the monochromator output, where a detectable signal could be obtained. The obtained results reveal the practical possibility of applying the fluorescence from dyes as a light source for food system analysis by EEM fluorescence spectroscopy.

Quantitative prediction of AFB1 in various types of edible oil based on absorption, scattering and fluorescence signals at dual wavelengths

This study aims to address the challenge of matrix interference of various types of edible oils on intrinsic fluorescence of aflatoxin B1 (AFB1) by developing a novel solution. Considering the fluorescence internal filtering effect, the absorption (μa) and reduced scattering (μ's) coefficients at dual wavelengths (excitation: 375 nm, emission: 450 nm) were obtained by using integrating sphere technique, and were used to improve the quantitative prediction results for AFB1 contents in six different kinds of edible oils. A research process of "Monte Carlo (MC) simulation - phantom verification - actual sample validation" was conducted. The MC simulation was used to determine interference rule and correction parameters for fluorescence, the results indicated that the escaped fluorescence flux nonlinearly decreased with the μa, μ's at emission wavelength (μa,em, μ's,em) and μa at excitation wavelength (μa,ex), however increased with the μ's at excitation wavelength (μ's,ex). And the required optical parameters to eliminate the interference of matrix on fluorescence intensity are: effective attenuation coefficients at excitation and emission wavelengths (μeff,ex, μeff,em) and μ's,ex. Phantom verification was conducted to explore the feasibility of fluorescence correction based on the identified parameters by MC simulation, and determine the optimal machine learning method. The modelling results showed that least squares support vector regression (LSSVR) model could reach the best performance. Three kinds of edible oil (peanut, rapeseed, corn), each with two brands were used to prepare oil samples with different AFB1 contamination. The LSSVR model for AFB1 based on μeff,ex, μeff,em, μ's,ex and fluorescence intensity at 450 nm was calibrated, both correlation coefficients for calibration (Rc) and the validation (Rv) sets could reach 1.000, root mean square errors for calibration (RMSEC) and the validation (RMSEV) sets were as low as 0.038 and 0.099 respectively. This study proposed a novel method which is based solely on the absorption, scattering, and fluorescence characteristics at excitation and emission wavelengths to achieve accurate prediction of AFB1 content in different types of vegetable oils.

Adulteration Detection and Quantification in Olive Oil Using Excitation-Emission Matrix Fluorescence Spectroscopy and Chemometrics

This research investigates the use of excitation-emission matrix fluorescence (EEMF) in conjunction with chemometric models to rapidly identify and quantify adulteration in olive oil, a critical concern where sample availability is limited. Adulteration is simulated by blending soybean, peanut, and linseed oils into olive oil, creating diverse adulterated samples. Principal component analysis (PCA) was applied to the EEMF spectral data as an initial exploratory measure to cluster and differentiate adulterated samples. Spatial clustering enabled vivid visualization of the variations and trends in the spectra. The novel application of parallel factor analysis (PARAFAC) for data decomposition in this paper focuses on unraveling correlations between the decomposed components and the actual adulterated components, which offers a novel perspective for accurately quantifying adulteration levels. Additionally, a comparative analysis was conducted between the PCA and PARAFAC methodologies. Our study not only unveils a new avenue for the quantitative analysis of adulterants in olive oil through spectral detection but also highlights the potential for applying these insights in practical, real-world scenarios, thereby enhancing detection capabilities for various edible oil samples. This promises to improve the detection of adulteration across a range of edible oil samples, offering significant contributions to food safety and quality assurance.

Rapid Authentication and Detection of Olive Oil Adulteration Using Laser-Induced Breakdown Spectroscopy

The adulteration of olive oil is a crucial matter for food safety authorities, global organizations, and consumers. To guarantee olive oil authenticity, the European Union (EU) has promoted the labeling of olive oils with the indices of Protected Designation of Origin (PDO) and Protected Geographical Identification (PGI), while food security agencies are also interested in newly emerging technologies capable of operating reliably, fast, and in real-time, either in situ or remotely, for quality control. Among the proposed methods, photonic technologies appear to be suitable and promising for dealing with this issue. In this regard, a laser-based technique, namely, Laser-Induced Breakdown Spectroscopy (LIBS), assisted via machine learning tools, is proposed for the real-time detection of olive oil adulteration with lower-quality oils (i.e., pomace, soybean, sunflower, and corn oils). The results of the present work demonstrate the high efficiency and potential of the LIBS technique for the rapid detection of olive oil adulteration and the detection of adulterants.

Post-heating Fluorescence-based Alteration and Adulteration Detection of Extra Virgin Olive Oil

Olive oils are more expensive compared with other vegetable oils. Therefore, adulterating such expensive oil is prevalent. The traditional methods for olive oil adulteration detection are complex and require pre-analysis sample preparation. Therefore, simple and precise alternative techniques are required. In the present study, the Laser-induced fluorescence (LIF) technique was implemented for detecting alteration and adulteration of olive oil mixed with sunflower or corn oil based on the post-heating emission characteristics. Diode-pumped solid-state laser (DPSS, λ = 405 nm) was employed for excitation and the fluorescence emission was detected via an optical fiber connected to a compact spectrometer. The obtained results revealed alterations in the recorded chlorophyll peak intensity due to olive oil heating and adulteration. The correlation of the experimental measurements was evaluated via partial least-squares regression (PLSR) with an R-squared value of 0.95. Moreover, the system performance was evaluated using receiver operating characteristics (ROC) with a maximum sensitivity of 93%.

Optical Characterization of Biological Tissues Based on Fluorescence, Absorption, and Scattering Properties

Optical diagnostics methods are significantly appealing in biological applications since they are non-destructive, safe, and minimally invasive. Laser-induced fluorescence is a promising optical spectrochemical analytical technique widely employed for tissue classification through molecular analysis of the studied samples after excitation with appropriate short-wavelength laser light. On the other hand, diffuse optics techniques are used for tissue monitoring and differentiation based on their absorption and scattering characteristics in the red to the near-infrared spectra. Therefore, it is strongly foreseen to obtain promising results by combining these techniques. In the present work, tissues under different conditions (hydrated/dry skin and native/boiled adipose fat) were distinguished according to their fluorescence emission, absorption, and scattering properties. The selected tissues' optical absorption and scattering parameters were determined via Kubelka-Munk mathematical model according to the experimental tissue reflectance and transmittance measurements. Such measurements were obtained using an optical configuration of integrating sphere and spectrometer at different laser wavelengths (808, 830, and 980 nm). Moreover, the diffusion equation was solved for the fluence rate at the sample surface using the finite element method. Furthermore, the accuracy of the obtained spectroscopic measurements was evaluated using partial least squares regression statistical analysis with 0.87 and 0.89 R-squared values for skin and adipose fat, respectively.

Olive oil classification with Laser-induced fluorescence (LIF) spectra using 1-dimensional convolutional neural network and dual convolution structure model

Laser-induced fluorescence (LIF) spectroscopy is widely used for the analysis and classification of olive oil. This paper proposes the classification of LIF data using a specific 1-dimensional convolutional neural network (1D-CNN) model, which does not require pre-processing steps such as normalisation or denoising and can be flexibly applied to massive data. However, by adding a dual convolution structure (Dual-conv) to the model, the features of the 1-dimensional spectra are more scattered within one convolution-pooling process; thus, the classification effects are improved. The models were validated through an olive oil classification experiment which contained a total of 72,000 sets of LIF spectra data, and the classification accuracy rate reached ∼99.69%. Additionally, a common classification approach, the support vector machine (SVM), was utilised for the comparison of the results. The results show that the neural networks perform better than the SVM. The Dual-conv model structure has a faster convergence speed and higher evaluation parameters than those of the 1D-CNN in the same period of iterations, without increasing the data dimension.

Quantitative Detection of Extra Virgin Olive Oil Adulteration, as Opposed to Peanut and Soybean Oil, Employing LED-Induced Fluorescence Spectroscopy

As it is high in value, extra virgin olive oil (EVOO) is frequently blended with inferior vegetable oils. This study presents an optical method for determining the adulteration level of EVOO with soybean oil as well as peanut oil using LED-induced fluorescence spectroscopy. Eight LEDs with central wavelengths from ultra-violet (UV) to blue are tested to induce the fluorescence spectra of EVOO, peanut oil, and soybean oil, and the UV LED of 372 nm is selected for further detection. Samples are prepared by mixing olive oil with different volume fractions of peanut or soybean oil, and their fluorescence spectra are collected. Different pre-processing and regression methods are utilized to build the prediction model, and good linearity is obtained between the predicted and actual adulteration concentration. This result, accompanied by the non-destruction and no pre-treatment characteristics, proves that it is feasible to use LED-induced fluorescence spectroscopy as a way to investigate the EVOO adulteration level, and paves the way for building a hand-hold device that can be applied to real market conditions in the future.

Improved KS-GMM algorithm applied in classification and recognition of honey based on laser-induced fluorescence spectra

The laser-induced fluorescence (LIF) technique, which has been widely used for food testing, can be combined with various algorithms to classify and recognize different kinds of honey. This paper proposes the Kolmogorov-Smirnov test-Gaussian mixture model (KS-GMM) algorithm, which is coupled with the LIF technique to realize accurate classification and recognition of different types of pure honey. The experiments are designed and carried out to obtain a set of LIF spectrum data from various honey and syrup samples. The proposed KS-GMM algorithm is applied for classification and recognition, with GMM, k-nearest neighbor (kNN), and decision tree algorithms as cross-validation methods. By comparing recognition results of training sets containing different amounts of data, it is found that the KS-GMM algorithm exhibits a maximum recognition accuracy of 96.52%. The research results prove that the KS-GMM algorithm outperforms, to the best of our knowledge, the other three algorithms in classifying and recognizing the honey types.

Two-Photon Fluorescence Study of Olive Oils at Different Excitation Wavelengths

Two-photon fluorescence (TPF) of olive oils is discovered and observed experimentally for the first time. Variations of the single-photon fluorescence (SPF) and TPF with the excitation wavelength are investigated for four different olive oils. The results show that fluorescence of the cosmetic olive oils (COO) is very weak and exhibits only one spectral peak around 490 nm. While for the ordinary edible oils (OEO) whether they are during their shelf life or not, their fluorescence spectra may exhibit multiple peak structures. The short-term natural expiration only slightly weakens TPF of OEO. Moreover, the excitation wavelength affects the OEO spectra considerably in terms of the spectral peak number, the spectral peak position, and spectral shapes. When the excitation wavelength decreases from 700 nm, the whole TPF of the OEO also decreases. Relatively, however, the short wave band will decrease and disappear more quickly. While for the SPF, the long wave band will decrease and disappear first. The optimal excitation wavelengths to make the TPF strongest are around 700 nm and 640 nm for OEOs and COO, respectively. And effects of temperature on SPF and TPF of extra virgin olive oil are also explored. This work may be of significance for its potential applications in TPF detection and two-photon laser.

A Review of Portable High-Performance Liquid Chromatography: the Future of the Field?

There is a growing need for chemical analyses to be performed in the field, at the point of need. Tools and techniques often found in analytical chemistry laboratories are necessary in performing these analyses, yet have, historically, been unable to do so owing to their size, cost and complexity. Technical advances in miniaturisation and liquid chromatography are enabling the translation of these techniques out of the laboratory, and into the field. Here we examine the advances that are enabling portable liquid chromatography (LC). We explore the evolution of portable instrumentation from its inception to the most recent advances, highlighting the trends in the field and discussing the necessary criteria for developing in-field solutions. While instrumentation is becoming more capable it has yet to find adoption outside of research.

A Brief History of Whiskey Adulteration and the Role of Spectroscopy Combined with Chemometrics in the Detection of Modern Whiskey Fraud

Food fraud and adulteration is a major concern in terms of economic and public health. Multivariate methods combined with spectroscopic techniques have shown promise as a novel analytical strategy for addressing issues related to food fraud that cannot be solved by the analysis of one variable, particularly in complex matrices such distilled beverages. This review describes and discusses different aspects of whisky production, and recent developments of laboratory, in field and high throughput analysis. In particular, recent applications detailing the use of vibrational spectroscopy techniques combined with data analytical methods used to not only distinguish between brand and origin of whisky but to also detect adulteration are presented.

Fluorescence Spectroscopy Based Detection of Adulteration in Desi Ghee

Desi ghee, obtained by buffalo and cow milk, is highly expensive because it contains valuable vitamins and conjugated linoleic acid (CLA). Its high demand and cost result in to its adulteration with inferior banaspati ghee. In this study, Fluorescence spectroscopy along with multivariate analysis has been utilised for the detection and quantification of adulteration. Spectroscopic analysis showed that buffalo ghee contains more vitamins and CLA than cow, whereas cow ghee is enriched with beta-carotene. For multivariate analysis, principle component analysis (PCA) and partial least square regression (PLSR) have been applied on the spectral data for the determination of adulteration. PLSR model was authenticated by predicting 23 unknown samples including 3 commercial brands of desi ghee. The root mean square error in prediction (RMSEP) of unknown samples was found to be 1.7 which is a reasonable value for quantitative prediction. Due to non-destructive and requiring no sample pre-treatment, this method can effectively be employed as on line characterization tool for the food safety assurance.

Excitation wavelength analysis of a laser-induced fluorescence technique for quantification of extra virgin olive oil adulteration

The performance of the laser-induced fluorescence (LIF) technique is greatly affected by the excitation wavelength (EW). This study aims to find an appropriate EW that can be used for analyzing extra virgin olive oil (EVOO) adulteration quantification by comparing the effect of different EWs. The EWs of 405 nm, 450 nm, and 532 nm were selected to perform the comparative experiments. By using the three EWs in the experiments, the LIF spectra of EVOO samples adulterated with peanut oil (PO) or soybean oil (SO) in different proportions, as well as the prediction models established through different multivariate analysis algorithms were analyzed. The linear discriminant analysis (LDA) was applied for qualitative analysis, while the partial least squares regression (PLSR), backpropagation neural network, and k-nearest neighbor were employed for quantitative analysis. The results show that the performance of 450 nm EW is always superior to that of 405 and 532 nm EWs in any model, with a smaller root mean square error (RMSE). Using the LDA-PLSR model, the RMSE is 1.35% for SO adulterants and 1.36% for PO adulterants, respectively.

Improved continuous locality preserving projection for quantification of extra virgin olive oil adulteration by using laser-induced fluorescence

An optimized dimensionality reduction technique is proposed as the improved continuous locality preserving projection (ICLPP), which was developed by modifying and optimizing the weighting functions and weighting factors of the continuous locality preserving projection (CLPP) algorithm. With only one adjustable parameter, this optimized technique not only enhances CLPP's capability of maintaining the continuity of the massive data, but also results in better simplicity and adaptability of the algorithm. In this paper, the performance of ICLPP is validated through quantification analysis of the adulteration of extra virgin olive oil (EVOO) with low-cost oils based on laser-induced fluorescence spectroscopy. Through cross validation and comparative studies, ICLPP, combined with the regression algorithm, is employed to predict and screen adulteration in EVOO, and is found to generally outperform other state-of-the-art dimensionality reduction algorithms, especially for prediction of adulterants at low level (<10%). It is evidenced that the ICLPP-based framework is superior in detecting adulteration by using spectral data.

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.

Establishment of a spectral database for classification of edible oils using matrix-assisted laser desorption/ionization mass spectrometry

In this study, we aim to establish a comprehensive spectral database for analysis of edible oils using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). More than 900 edible oil samples, including 30 types of edible oils, were analyzed and compared, and the characteristic peaks and spectral features of each edible oil were obtained. Edible oils were divided into eight groups based on their characteristic spectral patterns and principal component analysis results. An overall correct rate of 97.2% (98.1% for testing set) was obtained for classification of 435 edible oil products using partial least square-discriminant analysis, with nearly 100% correct rate for commonly used edible oils. Differentiation of counterfeit edible oils, repeatedly cooked edible oils and gutter oils from normal edible oils could also be achieved based on the MALDI-MS spectra. The establishment of this spectral database provides reference spectra for spectral comparison and allows rapid classification of edible oils by MALDI-MS.