There is a contentious need for robust and rapid methodologies for maintaining the authenticity of foods. The aim of this study was to detect and quantify argan oil adulteration using Laser Induced Fluorescence (LIF) spectroscopy coupled with chemometric methods. Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR) were used to assess argan oil authenticity; PCA was used to classify samples according to their quality and the PLS model to determine the amount of adulterants in pure argan oil. The correlation coefficient of the obtained model was about 0.99, with Root Mean Square Error of Prediction (RMSEP) and Standard Error of Prediction (SEP) of 2%. This study demonstrated the feasibility of LIF spectroscopy combined with chemometric tools to identify adulterants in pure argan oil from a percentage of adulteration, of 0.35 % without the need to destruct samples.
Existe una necesidad de metodologías sólidas y rápidas para determinar la autenticidad de los alimentos. El objetivo de este estudio es detectar y cuantificar la adulteración del aceite de argán mediante espectroscopia de fluorescencia inducida por láser (LIF) junto con métodos quimiométricos. Se utilizaron el análisis de componentes principales (PCA) y la regresión de mínimos cuadrados parciales (PLSR) para evaluar la autenticidad del aceite de argán. Se utilizó PCA para clasificar las muestras según su calidad y el modelo PLS se aprovechó para determinar la cantidad de adulterantes en el aceite de argán puro. El coeficiente de correlación del modelo obtenido fue de alrededor de 0,99, el error cuadrático medio de la predicción (RMSEP) y el error estándar de predicción (SEP) del 2%. Este estudio demostró la viabilidad de la espectroscopia LIF combinada con herramientas quimiométricas que permiten identificar adulterantes en aceite de argán puro, sin necesidad de destruir muestras, a partir de un porcentaje de adulteración del 0,35 %.
Food fraud is when a food is not presented in its authentic form. It can present serious health risks if hazardous materials are added to food products. It can also have an economic impact on consumers for investing in products of inferior quality (
This oil is well known for its pharmacological properties and has been used in traditional medicine for centuries. Scientific evidence has inferred from experimental studies that the consumption of argan oil may reduce the risk of disease through a biological mechanism which acts on blood pressure, plasma lipids and antioxidant status (
The demand for high quality and safety in food production obviously calls for high standards of quality and process control (
Chemometric methods use multivariate statistic to extract information from complex analytical data (
In this paper, LIF has been used to determine argan oil adulteration with waste frying oil. A LIF system was assembled in the experiment. Using an Nd: YAG laser beam (532 nm), the fluorescence spectra of mixtures of pure and adulterated argan oil were measured. The identification of several important vegetable oils and the adulterated concentration were achieved by employing PCA (
In this work, argan oil, noted AO, was used to assess its adulteration by two commercial vegetable oils noted VO1 and VO2. VO1 is commercial sunflower oil and VO2 is commercial edible oil sold without any indication of its origin. Cheap vegetable oils (VO1 and VO2) were purchased from a market in Oujda (East of Morocco) and pure argan oil (handmade) was obtained from Agadir (south of Morocco).
The samples were divided into two groups. The first one contained samples of AO adulterated by VO1 (54 samples), and the second one contained AO adulterated by VO2 (55 samples). Cheap oils were heated at 200 °C for 30 minutes many times, until the oil had the same color as AO. VO1 and VO2 were heated for the reason that fraudsters use waste frying oil to adulterate argan, since this oil has the same color as argan oil and the detection of adulteration becomes more difficult. The process of adulteration was achieved as follows: AO was adulterated by VO1 from 0 to 31% and by VO2 from 0 to 32%. A total of 109 samples were prepared. The first sample in each group contained 100% AO and the second one contained 99.6% AO and 0.4% adulterant (VO1 or VO2). The procedure was repeated for all samples, increasing the number of drops of adulterant for each sample by one drop. Therefore, for each sample, a drop of pure AO oil was replaced by a drop of adulterant. The drops were added using a micropipette and masses were measured using a digital scale of very high sensitivity. Finally, the prepared samples were homogenized and stored in the dark at ambient temperature.
LIF is a physical phenomenon in which a molecule absorbs an amount of the energy from a laser beam. There is thus a transition from a ground state S_{0} to an excited state S_{1} with a change in the electron orbital. This excited state S_{1} has a very short lifetime (a few nanoseconds). Changes in conformation and interactions with surrounding molecules change the molecule from the excited state S1 to low vibrational levels of S_{1}: this is the internal conversion. In the case of fluorescent molecules, the transition from the excited state S_{1} to the ground state S_{0} takes place with the release of a photon of lower energy. This phenomenon is laser induced fluorescence, which occur at wavelengths greater than the incident excitation wavelength (
The obtained spectral data were converted into Microsoft Office Excel format for Matlab software analysis. The spectral range was reduced from 5001000 nm to 540750 nm. Then, to reduce noise and baseline shifts, the spectra were corrected using preprocessing, whose objectives were the attenuation of nonlinearity between variables, the elimination of interference and reduction of random noise (
Before multivariate data analysis, all LIF spectra were subjected to SavitzkyGolay smoothing (1 point, 2 orders) then the spectra obtained were subjected to a Multiplicative Scatter Correction (MSC) combined with a second derivative for PCA analysis and combined to Baseline for PLS modeling (
Estimation of the correction coefficients (additive and multiplicative contributions): Each spectrum
Correction of recorded spectrum:
represents the spectrum of the residues
corrected spectrum
are the correction coefficients that can be estimated by a least squares method.
The first and second derivatives are used for baseline variation reduction and separation of overlapping bands, so hidden bands are enhanced. With regards to baseline preprocessing, most correction methods make the supposition that the observed spectrum is the combination of a useful signal and a signal of uncontrolled variation. Therefore, the correction consists of subtracting the background from the obtained signal.
The wavelength range used for LIF analysis was reduced to 540  750 nm to keep only the part that contains relevant information and to eliminate noise. PCA was exploited to get main information from spectra and reduce the number of variables. Then PLS algorithm was applied on LIF spectra to establish a model that can predict the percentage of adulteration. PLS calibration gives optimum results compared to many other multivariate calibration methods. An important aspect of PLSR is that it collects the relevant spectral information in a few linear combinations of the spectral measurements. These combinations or components can be used to facilitate interpretation of the relationship between concentrations and spectra as well as the relationships among the spectral variables themselves.
The samples were divided into calibration/validation and prediction datasets. The optimum number of latent variables was obtained using the full cross validation method. The prediction performance of each model was evaluated throughout the root mean square error (which represents the standard deviation of the residuals), the prediction standard errors, and the coefficient of determination (R^{2}) of both calibration and validation data sets. In general, as low as the RMSEC/P and SEC/P values can be, and R^{2} as close as possible to 1, the better the model’s predictions will be. Equations corresponding to each parameter are:
= the predicted value of the i^{th} observation.
= the measured value of the i^{th} observation.
= number of observations in the calibration set.
= number of observations in the validation set.
After preprocessing the spectra using smoothing combined to MSC and the second derivative, PCA was performed to explore the similarities and differences in samples, to extract relevant information and reduce the number of variables.
After applying PCA to the pretreated spectra, samples were significantly classified. It can be seen from
PLS was exploited as a multivariate calibration technique. It constructs a mathematical model based on the features of PCA and multiple regression to find a linear mathematical relationship between two datasets, X (spectra) and Y (level of adulteration) (
The spectral data were arranged in a 2D matrix (X), the rows of this matrix represent the samples (109 samples) and the columns contain the number of variables. One column vector (Y) containing the concentration of each adulterant was added to this matrix and the data were then analyzed. To make sure that the obtained model was neither over nor underfit, a cross validation using the leaveoneout method was considered. The linearity of the regression model was evaluated by fitting the reference adulteration value against the predicted ones.
The above PCA model successfully identified the type of cheap edible oil added to AO. To further predict the contents of VO1 and VO2 in the blended oil samples, PLS regression was performed. This method has been successfully used in several studies to predict the percentage of adulterants in oils. PLS regression was applied on raw and pretreated spectra and the one that gave the best result was kept. In this work, LIF data were preprocessed by taking a smoothing (1 point) combined to MSC and Baseline. After removing the outliers, the set of samples was randomly divided into three sample sets samples, in which two sets were used as the experimental group, calibration/validation sets, containing almost 85% of the total set of samples (70% calibration, 15 % validation) and one set as the testing group containing the 15% remaining.
The efficiency of the calibration model can be determined generally from three statistical parameters: the correlation (R), the standard error of calibration (SEC) and the mean square error of calibration (RMSEC). a R value greater than 0.9 indicates a good response for the parameter studied and less than 0.7 indicates a poor response. When a new spectrum is inserted into the matrix, it is compared to the spectral basis, the closest spectrum is then used to make a PLS regression.
The graphic display of the calibration/validation and prediction produced using the PLS model with the best performances is shown in
Calibration  Prediction  

Correlation  SEC  RMSEC  Correlation  SEP  RMSEP 
0.99  2.25  2.23  0.92  2.40  2.38 
SEC: Standard Error of Calibration, RMSEC: Root Mean Squares Error of Calibration, SEP: Standard Error of Prediction, RMSEP: Root Mean Squares Error of Prediction. Each value in the table represents the result of three repeated measurements.
Laser Induced Fluorescence  Near Infrared Spectroscopy  


0.99  0.92 

2.25  3.22 

2.23  3.24 
SEC: Standard Error of Calibration, RMSEC: Root Mean Squares Error of Calibration.
To prove the efficiency of these models, they were tested on two nonsynthetic samples and the results are shown in
Currently, spectroscopy techniques for food authentication are important in the food industry. In this work, it has been demonstrated that LIF spectroscopy in combination with chemometric methods (PCA and PLS) can be used as fast and nondestructive methods for the rapid detection of AO adulteration with different concentrations of cheap vegetable oil, VO1 and VO2. 54 samples were prepared containing AO adulterated by VO1 and 55 samples of AO adulterated by VO2.
PCA was applied to show the existence of spectral differences and discriminate spectral data in relation with the adulteration of argan oil with cheap vegetable oils. Samples were divided into two wellseparated groups, easily allowing for the determination of the type of adulterant. It was important to use the combination of smoothing, MSC and second derivative as adequate spectral preprocessing to eliminate noise and any information that could skew the results. Then PLSR model was used to predict the amount of adulterant in argan oil. Before applying PLSR, the spectra were corrected using smoothing combined with MSC and baseline. Analyses were made on 109 samples where 70% were randomly chosen to make the calibration model, 15% for the validation and the remaining 15% for prediction sets. The calibration results produced excellent models with a correlation of 0.99, RMSEC and SEC of about 2% and for the prediction model, the correlation obtained greater than 0.92 RMSEP and about 2% SEP. The obtained models were tested using nonsynthetic samples and the results were satisfactory. New samples were successfully classified according to the type of adulterant.
This study provided valuable results which could be applied to consumer protection, because the demand for high quality and safety in food production obviously calls for high standards of quality and process control. Less than 1 second LIF analysis by this model can detect the amount of adulterant in argan oil from 0.35%.