Cognitive fluorescence sensing to monitor the storage conditions and locate adulterations of extra virgin olive oil

Food Control 103 (2019) 48–58

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Cognitive fluorescence sensing to monitor the storage conditions and locate adulterations of extra virgin olive oil

T

Miguel Lastra-Mejiasa, Manuel Izquierdoa, Albertina Torreblanca-Zancaa, Regina Aroca-Santosa, John C. Cancillab, Julia E. Sepulveda-Diazc, José S. Torrecillaa,∗ a

Departamento de Ingeniería Química y de Materiales, Universidad Complutense de Madrid, 28040, Madrid, Spain Scintillon Institute, San Diego, CA, USA c Elvesys - Microfluidic Innovation Center, 75011, Paris, France b

A R T I C LE I N FO

A B S T R A C T

Keywords: Extra virgin olive oil Storage Artificial neural networks Laser diode LED Fluorescence emission

In the present research, storage conditions of extra virgin olive oil (EVOO) have been monitored using costeffective fluorescence sensors integrated with intelligent algorithms. Three different Spanish pure EVOOs (Arbequina, Cornicabra, and Picual varieties), as well as samples adulterated with expired EVOO, were kept under different environmental conditions: presence or absence of light, bottle material (plastic or glass), and clearness of glass (uncolored or brown glass). To evaluate the effect of these conditions, 54 samples were prepared, and their emission spectra were measured 10 times during a 58-day period. The most statistically relevant information from these spectra was located by the relief-F feature selection method, which led to the design of machine learning-based models. With this aim, up to 158,250 artificial neural network-based models were designed and tested. It was possible to distinguish different storage conditions in terms of light exposure, bottle material, and clearness, as well as locate adulterations. All of the trained and optimized classifiers for all three EVOO varietals reached accuracies ranging between 91 and 100% after meticulous validations. The used technique is fast, portable, user-friendly, inexpensive, and non-destructive, and, therefore, it could be of great use for the olive oil sector as it enables real-time quality control and fraud detection.

1. Introduction It is well known that extra virgin olive oil (EVOO) is a basic pillar of the Mediterranean diet. However, due to its extraordinary organoleptic properties and renowned beneficial health effects, its recognition and presence has spread to other countries all over the world, such as The United States, Canada, or Japan (Visioli, Poli, & Gall, 2002). As an example of its influence on human health, EVOO has a positive impact on the cardiovascular system (Estruch et al., 2013) and helps maintaining a healthy cholesterol balance reflected in fewer amounts of lowdensity lipoproteins (Kris-Etherton et al., 2002). These beneficial effects are mainly due to the high amounts of antioxidants present in the EVOO (Covas et al., 2006; Carrasco-Pancorbo et al., 2006). It is also worth highlighting that these benefits are proportional to the quality of EVOO, meaning that its adulteration, becoming expired or degraded, or suffering other types of processes that hinder their quality, lead to diminished beneficial properties. From an economic point of view, several countries located in the Mediterranean basin, such as Spain, Italy, and Greece, heavily export

∗

EVOO, making it a fundamental commercial good (Gómez-Limón and Parras-Rosa, 2018; Mili, 2018). This represents another reason why maintaining its quality is crucial in order to meet the required organoleptic standards as well as other characteristics that the consumers expect or demand. Maintaining high-quality standards of EVOO facilitates establishing a sustainable and social market economy in the European Union (Gómez-Limón and Parras-Rosa, 2018). In this line, it should be noticed that the quality of a virgin olive oil not only depends on its geographic origin and its production in the olive mill. The distribution chain plays a vital role as well and has to be thoroughly defined and checked to assure that the quality of the EVOO that leaves the mill reaches the consumer as unaltered as possible. Some of the most important factors influencing the degradation of olive oil within the distribution chain are storage conditions. For example, high temperatures during distribution and/or long periods of time before consumption affect the composition of EVOO and lead to the degradation of the product, resulting in a decrease of its quality (Aroca-Santos, Lastra-Mejías, Cancilla, & Torrecilla, 2018). In addition, exposure to light promotes photo-oxidation of olive oil, which affects

Corresponding author. E-mail address: [email protected] (J.S. Torrecilla).

https://doi.org/10.1016/j.foodcont.2019.03.033 Received 20 January 2019; Received in revised form 4 March 2019; Accepted 27 March 2019 Available online 30 March 2019 0956-7135/ © 2019 Elsevier Ltd. All rights reserved.

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Storage time and temperature of EVOO 2018

Sanaeifar, Jafari, and Golmakani (2018) Aroca-Santos et al. (2018) 100% correct storage time classification; R2 > 0.95 for prediction of quality indices 100% correct temperature identification; error rate of ∼6% for storage time Dielectric spectroscopy + computer vision + several machine learning approaches Visible absorption spectroscopy + artificial neural networks 2018

Error rate of 5.3% Best linear adjusted-R2 = 0.91 Linear equations and R2 provided (> 0.92)

Photodegradation time of EVOO Pigment contents and shelf-life/freshness of EVOO Oxidation changes in EVOO during storage in illuminated and dark conditions Storage time and quality indices of virgin olive oil 2015 2017 2018

Visible absorption spectroscopy + artificial neural networks Fluorescence spectroscopy + multiparametric linear models Right-angle fluorescence spectroscopy + linear models

Spyros, Philippidis, and Dais (2004) Torrecilla et al. (2015) Aparicio-Ruiz et al. (2017) Mishra et al. (2018) Not reported (“high accuracy”) 2004

31

Phosphorus NMR + differential equations

Gutiérrez and Fernández, 2002 Error of < 10% Physicochemical properties and composition + linear models

Time for EVOO to become VOO, in illuminated and dark conditions of EVOO Storage time in illuminated and dark conditions 2002

Reference Statistical performance Methods Estimated parameters Year

Table 1 Summary of the most recent work on monitoring storage conditions of olive oil.

quality as well (Torrecilla, Vidal, Aroca-Santos, Wang, & Cancilla, 2015). In 2010, work from Pristouri et al. revealed that packaging also plays an important role, as linear trends were found which correlated with the effect of the headspace of the packaging material, oxygen, light transmission, temperature, and storage time with quality-related traits of EVOO (Pristouri, Badeka, & Kontominas, 2010). One year later, Dabbou et al. concluded that nontransparent and oxygen impermeable bottles, such as stainless steel or glass, limit oxidative damage during storage (Dabbou et al., 2011). At the same time, different methods have been developed to monitor and estimate the influence of several conditions during the distribution chain (Table 1). In this field, prototypes using fluorescence spectroscopy have emerged as very promising tools to monitor quality, authentication, and adulteration of olive oil (Aparicio-Ruiz et al., 2017; Mishra, Lleó, Cuadrado, Ruiz-Altisent, & Hernández-Sánchez, 2018). They rely on the fact that minor components in olive oil, such as chlorophylls, pheophytins, polyphenols, etc., are fluorescent molecules that reflect its oxidation state. Thus, by monitoring the fluorescent profiles of olive oil it is possible to understand the changes that occur within this product in different conditions (Mishra et al., 2018). In addition, this technique is fast, non-destructive, relatively inexpensive, and allows the development of portable equipment for on-site and realtime analysis. In this work, fluorescence spectroscopy combined with powerful machine learning-based algorithms has been used to monitor and estimate different storage conditions of three monovarietal EVOOs (Arbequina, Cornicabra, and Picual), including: exposure to light, bottle material (plastic or glass), and clearness (uncolored or brown glass). In addition, adulteration of fresh EVOO with expired EVOO has also been studied following the same methodology.

2. Materials and methods In this section, the EVOOs used to prepare the samples, the analytical equipment employed, and the mathematical interpretations and models applied are described in detail.

2.1. Extra virgin olive oil samples and storage conditions Three monovarietal EVOOs (Arbequina, Cornicabra, and Picual) have been used in this work. Their commercial information is shown in Table 2. A total of 54 samples were prepared, 18 of each monovarietal EVOO, and they were all kept at a stable temperature (22 - 23 °C). Within each set of 18 samples, 6 were pure fresh EVOO, whereas the other 12 were mixed with expired EVOO at 1% (6 samples) and 5% (6 samples) in weight to prepare the adulterated samples. The different sets of samples were stored under specific conditions from mid-April to mid-June 2018 (58 days). For each monovarietal EVOO, half of the prepared samples (9) were exposed to sunlight, whereas the remaining were stored in the dark. Samples of each subclass (light or dark) were kept in one of three materials: one third of the 9 samples were stored in uncolored plastic (polyethylene terephthalate; PET), another third in uncolored glass, and the final third in brown glass (Fig. 1). Table 2 Commercial characteristics of the EVOOs used.

49

Brand

Varietal

Protected Designation of Origin

Best-Before Date

Marqués de Griñón Enclaves D. Oro Castillo de Tabernas EXPIRED EVOO

Arbequina Cornicabra Picual Coupage

– Montes de Toledo, Spain – –

December 2019 April 2019 December 2019 December 2015

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Fig. 1. Scheme of the distribution of the different samples (pure and adulterated) kept under unique storage conditions. This distribution includes all three fresh EVOOs (Arbequina, Cornicabra, and Picual). The numbers in brackets symbolize the number of samples in each group (54 total samples).

2.2. Equipment, fluorescence measurements, and databases

Table 3 Wavelengths that correspond to the maximum of the bands of each LED and laser diode employed, as well as their integration times and brands.

The fluorescence sensor is formed by a physical structure that contains the sample, a light source, and a photodetector (Fig. 2). To make it attractive to all users, it is important to reduce its manufacturing costs. In this line, firstly, the physical structure has been designed with a computer and produced by a 3D printer (Replicator Z18; MakerBot Industries, USA). In addition, two types of relatively inexpensive light sources were independently tested: light-emitting diodes (LEDs) and a laser diode. The system was designed to use the same equipment for both light sources. The physical structure of the fluorescence sensor has been designed with a gap to introduce a cuvette containing the samples to be measured (Fig. 2B.1). In this case, a transparent, uncolored, quartz cuvette with a 1 cm path length (Hellma Analytics, Germany) was employed. Each of the ten days that the samples were measured, the spectra were collected in triplicate, and the average of the three spectra was then used to build the databases to design and train the intelligent models. The samples were irradiated using five different LEDs (blue, green, pink, white, and UV), each with a 5 mm diameter and 20 mW of power, as well as with a UV laser diode with a 5 mm diameter and 500 mW of

Light Source

Integration time (s)

Excitation Wavelength (nm)

Brand

LEDs

Blue LED Green LED Pink LED

1.000 1.000 1.000

461 500 and 570 449 and 642

White LED

1.000

459 and 545

0.500 0.025

< 400 405

Nichia, Japan Nichia, Japan RS Components, United Kingdom RS Components, United Kingdom VCC, USA SainSmart, USA

UV LED UV Laser Diode

power. The light bands produced by the LEDs and laser diode (excitation light) present maximum signal intensities at the wavelengths shown in Table 3. Once the light passes through the sample, the emitted fluorescence is detected at right angle with respect to the illumination source using a high-speed miniature fiber spectrometer (Quest X, B&W TEK, USA; Fig. 2A.3). In Table 3, the integration time values are shown Fig. 2. A) Experimental set-up to acquire fluorescence spectra; main components: A.1) UV laser diode; A.2) 3D-printed physical structure; A.3) fiber spectrometer; A.4) computer (data storage and treatment); B) Amplified image of A.2; B.1) location where the quartz cuvette and EVOO samples are introduced; B.2) location where LEDs are connected.

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neighbors, one per class (same and different), and adjusts the feature weighting vector to enable ranking variables according to their ability to discriminate neighbor samples from other classes. The function used to obtain the relief-F score is shown in equation (1).

(time that signal is collected while the samples are irradiated with light; it corresponds to the optimum time needed to measure the spectra between 349 and 1050 nm), as emission intensities depend on them (longer integration times lead to higher emission intensities). The output of the spectrometer (fluorescence emission spectra) was collected and analyzed in a computer. The fluorescence spectra of each of the 54 EVOO samples were measured 10 times during the mentioned 58-day period of the experiment. Different datasets were gathered according to the storage conditions (Fig. 1), enabling four unique binary classifications for specific EVOO varietal and light source combinations (vide infra). The final global database was formed by 3240 data points (18 samples (Fig. 1), 3 EVOO varietals, 6 light sources, and 10 measurements per sample; 18 × 3 × 6 × 10 = 3240). Moreover, the database contains 1912 independent variables (one for each fluorescence signal-wavelength pair in an emission spectrum; vide infra) and one qualitative or categorical dependent variable representing one of two conditions depending on the classification (represented by labels; 0 or 1; vide infra).

In equation (1), ft,i, fNM(xt),i, and fNH(xt),i respectively stand for the value of the sample analyzed (xt) of a specific feature (fi) and the values of the ith feature corresponding to the nearest neighbors of different (NM) and same (NH) classes. Finally, d(·) represents the function employed as a distance measurement between the sample and the nearest neighbors (Wu et al., 2013; Zhao et al., 2011). The application of this feature selection process was carried out with MATLAB 2018a. In all of the cases tested, the number of input variables selected ranged between 16 and 18 emission intensity points at different wavelengths in order to reach a suitable sample-to-variable ratio. In other words, the intelligent models designed employ this number of independent variables (vide infra).

2.3. Independent and dependent variable selection

2.4. Intelligent algorithms - optimization and validation

All fluorescence measurements (independent variables) were grouped into four categories to build four intelligent mathematical models. These groupings correspond to the conditions studied: (i) adulteration (presence or absence), (ii) storage environment (light or darkness), (iii) package material (plastic or glass), (iv) and glass color (uncolored or brown) (Fig. 3). A dichotomous dependent variable “yes” (1) or “no” (0) was defined for each category. Every fluorescence emission spectrum is composed of 1912 instances, and the wavelengths ranged between 349.69 and 1050.08 nm, signifying a spectral resolution of approximately 0.37 nm. Due to the quantity of samples, a reduction in the number of independent variables was necessary. In this study, the most relevant signals were selected statistically by implementing the relief-F feature selection method (Wu, Chen, Kechadi, & Sun, 2013). Relief-F feature selection method is based on an algorithm proposed by Kira and Rendell in 1992 (Kira & Rendell, 1992a; Kira & Rendell, 1992b). This algorithm is very efficient in the estimation of the quality of attributes or variables in relation to a further modeling phase. It is robust when assessing feature interactions, highly noise-tolerant, and can handle and interpret nonlinear variables well. This feature selection method operates by evaluating features and the extent of their ability to distinguish the values of instances or samples that are similar to each other. When analyzing a sample value, it seeks for the nearest

In order to estimate the storage conditions (vide supra), the fluorescence spectra of the EVOO samples have been analyzed with intelligent mathematical models based on artificial neural networks, particularly, supervised multilayer perceptrons (MLPs) (Demuth, Beale, & Hagan, 2007). MLPs have been designed to act as binary classifiers to distinguish the storage conditions in which the EVOO samples were kept (Fig. 4 shows the layered architecture of these algorithms, as well as the units that form it), and more information regarding these types of algorithms and their learning process can be found in the supplementary information section (subsection “Multilayer Perceptrons”). To reach accurate, reliable, and useful MLP models, a set of weighted parameters (weights), determined by the structure of the model (Fig. 4), have to be optimized during the training phase in such a way that avoids the creation of overfit algorithms (Rossi et al., 2014). This is carried out with proper database segmentation into different datasets (training, verification, and validation). Furthermore, several functions and parameters must be properly selected and/or optimized as well. These include the training and transfer functions, the hidden neuron number (HNN), and a set of learning coefficients (Lc, Lc-decrease (Lcd), and Lc-increase (Lci)) (Demuth et al., 2007; Torrecilla, Aragon, & Palancar, 2005). These processes are all covered and detailed in the supplementary information (subsection “Optimization of Multilayer Perceptrons”). Every calculation, design, and optimization process

RF (fi ) =

1 2

p

∑ d (ft,i

− fNM (xt ), i ) − d (ft , i − fNH (xt ), i )

t=1

Fig. 3. Scheme of the data treatment and classifications carried out. 51

(1)

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Fig. 4. Scheme of a binary classifying multilayer perceptron (topology: 4, 3, 1) containing four independent variables (inputs), three hidden neurons, and one categorical dependent variable (output or target).

Fig. 5. Fluorescence emission spectra of non-adulterated Cornicabra EVOO on day 1 for every light source used. (LEDs: blue, white, pink, green, and UV are blue, gray, pink, green, and purple lines, respectively; UV diode, black line). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

of the mathematical models were carried out with MATLAB 2018a. Once every MLP model is optimized, it is necessary to validate their generalizing capability and applicability with other interpolated cases not involved in the weight modification process. In this regard, a k-fold cross-validation and an internal validation have been applied. The k-fold cross-validation test consists on dividing the entire database into k arbitrary datasets (k = 6 in this case), which are all used as verification datasets in successive training runs. The aim of this is to test the entire database as part of the verification dataset and to evaluate the estimating ability of the network throughout the whole data range covered. For the internal validation, the database is divided into three different datasets: training, verification, and validation (or test), each one having ∼70%, ∼20%, and ∼10% of the original database, respectively. The validation data is left aside during the training and optimizing processes of the model, so this validation will let us know if the MLP is capable of accurately interpolating with data that has never been presented to the network.

progressively decreases from 400 nm towards 500 nm, signifying that there are more molecules in EVOO which absorb light at 400 nm than at 500 nm, which leads to the higher emission of EVOO after irradiating with the lower wavelength light (such as the UV LED and laser diode). It should be noted, that the molecules that absorb this light are pigments such as carotenoids, chlorophylls, and their derived molecules (Tarakowski et al., 2014; Torrecilla et al., 2015). In total, 1080 emission spectra have been reached per EVOO varietal (six groups of 180 (18 samples × 10 measurements); one per light source). As an example, the fluorescence measurements of different samples, adulterated with 1% of expired olive oil, using the blue LED, and carried out the 51st day of the experiment of Arbequina, Cornicabra, and Picual EVOOs are shown in Figs. 6–8, respectively. Concerning the Arbequina variety, when analyzing the fluorescence data, it can be seen that the incidence of sunlight has an influence on the EVOO emission development, as there is a clear difference in the spectra regarding the sample kept in the dark and the one exposed to light (Fig. 6a). In particular, the emission intensity of the main emission band covering from 650 to 750 nm of EVOO samples stored under illumination is significantly lower than that of the samples kept in the dark. As it is known that light harms the quality of EVOO (Torrecilla et al., 2015), it can be stated that higher fluorescence intensities after irradiating with the blue LED likely signify higher quality grades (this fact correlates well with the literature; Mishra et al., 2018). This effect is present in adulterated and pure EVOO samples and can be related to the amounts of pigments which absorb the blue light centered at 461 nm (Ferreiro-Gonzalez et al., 2017; Tarakowski et al., 2014). Afterwards, when these molecules return to their ground state, they emit light proportionally to the amount they absorbed, reason why the fluorescence is higher in the EVOO samples kept in the dark. Furthermore, as it has been demonstrated, unlike products like wine, the quality of EVOO can only deteriorate with time, never improve (ArocaSantos et al., 2018), and therefore, the quality of all samples diminished as the experiment progressed (reflected by the spectrum from day 1 in Fig. 6a, which reveals a higher emission intensity than the measurements taken on day 51). Nevertheless, the rate at which this quality decreased was clearly faster for the samples exposed to sunlight (Fig. 6a). On the other hand, the packaging material (PET or glass) has not shown to remarkably affect the fluorescence spectra of the EVOO (at least during the experimental period of two months) (Fig. 6b). However, under sunlight, PET and dark glass show an enhanced protective effect when compared to uncolored glass, as the emission intensities of these samples are slightly higher (Fig. 6c and d). The EVOO degradation depends mainly on the amount of energy or photons that

3. Results and discussion In this section, a description and discussion of all the results obtained in every part of this work are presented. 3.1. Fluorescence spectroscopy Every EVOO sample was measured 10 times using the fluorescence equipment during a period of two months. In every case, five LEDs (blue, white, pink, UV, and green LED) and a UV laser diode were used as light sources (vide supra), and as an example, spectra obtained from each light source on a given sample (pure Cornicabra on day 1; results are comparable for the EVOOs from other olive varietals) are shown in Fig. 5. As can be seen, regardless of the light source, the same main fluorescent band appears (from 650 to 750 nm; main peak centered at ∼670 nm and shoulder at ∼735 nm), only differing in intensity. This means that the same molecule types (pigments: chlorophylls and pheophytins (Mishra et al., 2018)) absorb the light from every light source used and lead to the same emission band. The difference relies on the number of molecules that become excited from each light source, which ranked from higher to lower are UV diode followed by UV LED, pink LED, green LED, blue LED, and white LED, for the established integration times (vide supra) (Fig. 5). In addition, when the absorption spectra of an EVOO is analyzed (data not shown; can be seen in publications such as Tarakowski, Malanowski, Kościesza, and Siegoczyński (2014) or Torrecilla et al. (2015)), it can be seen that the intensity of the main absorption band 52

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Fig. 6. Fluorescence emission spectra of adulterated (1%) Arbequina EVOO during day 51 of the experiment using the blue LED. (a) Stored in dark and under sunlight (orange and blue lines, respectively), and spectra from the same sample taken on day 1 included for comparison (green line); inset of the 400–600 nm region is depicted for a better view; (b) Stored in dark, packaging material plastic or uncolored glass (orange and blue, respectively); (c) Stored exposed to sunlight, packaging material plastic or uncolored glass (orange and blue, respectively); (d) Stored exposed to sunlight, contained in brown or uncolored glass (orange and blue, respectively). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 7. Fluorescence emission spectra of adulterated (1%) Cornicabra EVOO during day 51 of the experiment using the blue LED. (a) Stored in dark and under sunlight (orange and blue lines, respectively), and spectra from the same sample taken on day 1 included for comparison (green line); (b) Stored in dark, packaging material plastic or uncolored glass (orange and blue, respectively); (c) Stored exposed to sunlight, packaging material plastic or uncolored glass (orange and blue, respectively); (d) Stored exposed to sunlight, contained in brown or uncolored glass (orange and blue, respectively). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.) 53

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Fig. 8. Fluorescence emission spectra of adulterated (1%) Picual EVOO during day 51 of the experiment using the blue LED. (a) Stored in dark and under sunlight (orange and blue lines, respectively), and spectra from the same sample taken on day 1 included for comparison (green line); (b) Stored in dark, packaging material plastic or uncolored glass (orange and blue, respectively); (c) Stored exposed to sunlight, packaging material plastic or uncolored glass (orange and blue, respectively); (d) Stored exposed to sunlight, contained in brown or uncolored glass (orange and blue, respectively). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

quality (Torrecilla et al., 2015). Also, it has been seen that when exposed to light, dark glass and PET reveal a stronger protective effect on EVOO when compared to clear glass (Figs. 6–8). Finally, in Fig. 9, a representation of different spectra obtained for every EVOO type with the UV LED regarding the adulteration state of the samples are shown. As can be seen, notable differences can be observed regarding the four analyzed conditions (adulteration, sunlight exposure, type of container, and type of glass). Therefore, it is meaningful to design mathematical models to interpret the relations between the fluorescence data and the storage conditions of EVOO to enable the estimation of these conditions and determine how the product has been handled.

the container allows through, and the attained results seem to be aligned with the literature, as dark material protects the quality of the product better than transparent vessels (Torrecilla, 2010). As a final note regarding the fluorescence spectra, it is worth mentioning that the molecules responsible for generating an emission band from 485 to 540 nm are a series of primary and secondary products that are a consequence of the oxidation of EVOO (mainly pheophytins derived from chlorophylls) (Mishra et al., 2018). Although differences are small, the conditions that negatively affect EVOO (time, sunlight, uncolored glass) lead to higher emissions in that region (see inset in Fig. 6a). Regarding the Cornicabra EVOO samples, the variation of their quality is similar to the Arbequina variety (comparable trends have been observed) (Fig. 7). Nevertheless, given the intensity emitted, the degradation of this type of EVOO is visually higher as time passes (Fig. 7a). In addition, in this case, a small emission band can be observed around 628 nm (not seen in Arbequina) for all the Cornicabra samples that were kept in the dark or contained in brown glass. This band may appear due to the presence of a specific molecule or pigment that is related to this Cornicabra olive varietal, and is highly susceptible of being degraded by sunlight, as it appears protected in the dark or by brown glass. Finally, in relation with the Picual variety, the results are shown in Fig. 8, and they highly resemble the trends observed for the Arbequina EVOO (Fig. 6). From this information, it can be deduced that there are trends within the fluorescence spectra that can be linked to the differences between every EVOO and their storage conditions including exposure to light, material of the containers, and their clearness. Moreover, in concurrence with the literature, these fluorescence results would suggest that EVOO should be stored in the dark, avoiding exposure to light as much as possible, in order to evade a faster degradation or loss of

3.2. Data analysis for input selection As a first step, every dataset was checked for statistical outliers using their interquartile range. Data points outside of this range have been removed from the database to avoid modeling incorrect mathematical trends. After this, a feature selection process via the relief-F algorithm was used to reduce the number of independent variables from 1912 to 18 or less (vide supra). Given that the database is composed of all fluorescent spectra, the 3 created initial databases have a dimension of 180 (18 samples × 10 measurements) × 1912. Selecting the most relevant information from the database to be used as inputs (independent variables) has a huge impact on the applicability of the MLP-based models. This is why the form and size of the database which is presented will considerably condition the accuracy of the final results as well as avoid a pointless computational load. In this work, the common concept of selecting the maximum of the spectroscopic peaks of every spectrum as variables has been changed for choosing the most representative signal-wavelength pairs. In other 54

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designing the MLP models. In this case, for every EVOO variety and source of light combination, an MLP model was designed for each of the four pursued classifications (vide supra). These four classifications were done by combining fluorescence data and MLPs: (i) to determine which EVOO samples are adulterated; (ii) to determine if a sample has been exposed to light or not during its storage; (iii) to identify the material of the container in which the EVOO was kept (plastic or glass); (iv) to indicate the color of this glass (uncolored or brown). In total, 72 MLP models were designed (3 × 4 × 6; three EVOO varieties, four classifications, and six light sources). 3.3.1. Optimized parameters of the MLPs For all of the MLPs, the Levenberg-Marquardt (TrainLM) algorithm was chosen as the training function, while the sigmoid function was selected as the transfer function (see supplementary information). On the other hand, the topology of every MLP model tested contains from 16 to 18 input nodes (vide supra) and only one output neuron (a single dependant variable). The number of hidden neurons is optimized, as well as other MLP parameters (vide infra). As described in the supplementary information section, HNN, Lc, Lcd, and Lci were optimized heuristically, leading to the design of 2198 MLP models with different parameter combinations. Every MLP model was repeated ten times using different initial random weights to reach the optimal weight matrix. These parameters were optimized for all four classifications (adulteration content, sunlight exposure, material of container, type of glass) carried out according to the best performing model, symbolized by the lowest number of misclassifications. The final values of these optimized parameters can be found in tables S1 through S4 of the supplementary information section. Once the optimal parameters were located, all MLP models became ready to perform properly with data from new samples. 3.3.2. Statistical performance of the MLPs Now that the models are optimized and set to carry out their classifications, it is time to validate them to prove that the final MLPs are able to accurately estimate the storage conditions of the EVOO samples. In this validation process, the percentage of correct classifications and their average are evaluated. Fig. 9. Fluorescence spectra of Arbequina (a), Cornicabra (b), and Picual (c) at different adulterant concentrations using UV LED light during day 1 (nonadulterated sample in gray, 1% adulterated in black, and 5% adulterated in blue) and day 51 (non-adulterated sample in purple, 1% adulterated in green, and 5% adulterated in pink), stored in uncolored glass and exposed to sunlight. On the left, the major emission peak centered at 675 nm is enlarged for a better view. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

3.3.2.1. Is the EVOO adulterated?. In Table 4, the results of the MLP trained to detect adulterated EVOO samples are shown. The best results are marked in bold, and in the light of them, the best ways to determine the adulteration of the EVOOs Arbequina, Cornicabra, and Picual, using MLPs, are creating models with the fluorescence spectra given by UV LED, UV laser diode, and white LED, respectively. The classification accuracy attained for the best models considering the internal validation results went from 92 to 100%.

words, the intensity values of the most significant wavelengths from the emitted radiation contained in the fluorescence spectra have been selected. In this way, the initial dimension of the databases has been reduced to at least 180 × 18, which enables an adequate sample-tovariable ratio which helps dissipate potential over-fit systems (always at least 10 times more samples than variables). In particular, 18 variables were selected in the four classifications done with the Arbequina EVOO for all six light sources. In the case of Cornicabra, 18 input variables were selected for all classifications except from the spectra obtained with the white LED and the UV laser diode, where 17 variables were used. Finally, the four classifications regarding the Picual EVOO samples used 16 input variables except for the blue LED, where 17 were used.

3.3.2.2. Is the EVOO stored in the dark?. The statistical results of the MLPs obtained after classifying the samples according to their illumination state are shown in Table 5. According to these results, the best approach to determine whether an EVOO sample has been kept exposed to sunlight or stored in the dark is to use the information from the fluorescence spectra collected using a blue LED for the Arbequina EVOO varietal, a blue LED, a UV LED, or a UV laser diode for the Cornicabra EVOO varietal, and a UV LED for the Picual varietal. The best models provided classification accuracies between 97 and 100% during internal validations. 3.3.2.3. Is the EVOO stored in glass or plastic?. In Table 6, the performance of the MLP models to determine the EVOO packaging material or container is shown (samples in brown glass were left out of this classification). In particular, the light sources which lead to the most accurate models to classify EVOO samples into either being contained in uncolored glass or plastic vessels are the UV LED for the Arbequina varietal and white LED for Cornicabra and Picual varietals.

3.3. Multilayer perceptrons Once the quality of the database was statistically verified and the independent variables were selected, everything was set to start 55

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Table 4 Performances of every MLP model developed to classify adulterated (Adul.) samples by means of an internal validation (IV) and a k-fold cross validation (k = 6). Results are shown in percentages of correct classification. Best cases for each EVOO varietal are marked in bold. Arbequina

LEDs

Blue Green Pink White UV

UV Laser Diode

K-fold IV K-fold IV K-fold IV K-fold IV K-fold IV K-fold IV

Cornicabra

Picual

Mean

Pure

Adul.

Mean

Pure

Adul.

Mean

Pure

Adul.

75 83.3 77.8 91.7 86.1 86.1 77.8 88.9 86.1 92.3 83.3 86.1

65.8 100 60 86.7 73.3 93.3 81.3 100 88.9 88.9 50 87.5

80 70 90.5 94.2 95.2 80.1 75 80 85.2 94.4 92.9 85.7

80.6 91.7 77.8 86.1 80.6 94.4 69.4 88.9 80.5 88.9 86.1 97.2

58.3 91.7 60 93.3 61.5 84.6 75 91.7 45.5 81.8 77.8 88.9

91.7 91.7 90.5 80.9 91.3 100 66.7 87.5 96 92 88.9 100

83.3 86.1 75.8 90.9 81.8 90.9 90.9 100 81.8 90.9 78.8 90.9

66.7 91.6 83.3 83.3 78.6 92.8 83.3 100 71.4 78.6 85.71 92.8

91.7 83.3 71.4 95.2 84.2 89.5 92.6 100 89.5 100 73.7 89.5

Table 5 Performances of every MLP model developed to determine if the EVOOs were stored in the dark or under sunlight by means of an internal validation (IV) and a k-fold cross validation (k = 6). Results are shown in percentages of correct classification. Best cases for each EVOO varietal are marked in bold. Arbequina

LEDs

Blue Green Pink White UV

UV Laser Diode

K-fold IV K-fold IV K-fold IV K-fold IV K-fold IV K-fold IV

Cornicabra

Picual

Mean

Light

Dark

Mean

Light

Dark

Mean

Light

Dark

97.2 100 80.6 83.3 72.2 86.1 80.6 83.3 77.8 94.4 75 86.1

93.8 100 83.3 83.3 75 95 63.2 73.7 70.6 88.2 76.5 94.1

100 100 77.8 83.3 68.8 75 100 94.1 84.2 100 73.7 78.9

97.2 100 83.3 88.9 88.9 94.4 94.4 94.4 97.2 100 94.4 100

100 100 84.2 89.5 88.9 94.4 93.3 100 93.8 100 94.4 100

94.7 100 82.3 88.2 88.9 94.4 95.2 90.5 100 100 94.4 100

80.6 86.1 78.8 87.9 75.8 90.9 81.8 90.9 84.8 96.9 81.8 90.9

75 93.8 88.2 88.2 84.2 94.7 82.4 88.2 94.7 100 78.9 84.2

85 80 68.8 87.5 64.3 85.7 81.3 93.8 71.4 92.9 85.7 100

Table 6 Performances of every MLP model developed to identify the container (uncolored glass or PET) in which EVOO was stored by means of an internal validation (IV) and a k-fold cross validation k = 6. Results are shown in percentages of correct classification. Best cases for each EVOO varietal are marked in bold. Arbequina

LEDs

Blue Green Pink White UV

UV Laser Diode

K-fold IV K-fold IV K-fold IV K-fold IV K-fold IV K-fold IV

Cornicabra

Picual

Mean

Plastic

Glass

Mean

Plastic

Glass

Mean

Plastic

Glass

86.1 88.9 80.6 88.9 75 86.1 80.6 91.7 88.9 94.4 77.8 88.9

78.3 91.3 82.6 82.6 73.9 91.3 72.7 95.5 95.8 100 78.3 91.3

100 84.6 76.9 100 76.9 76.9 92.9 85.7 75 83.3 76.9 84.6

69.4 86.1 83.3 94.4 77.8 97.2 83.3 94.4 63.9 86.1 83.3 80.6

76.2 90.5 78.3 91.3 69.6 100 83.3 95.8 63 85.2 96 76.9

60 80 92.3 100 92.3 92.3 83.3 91.7 66.7 88.9 54.5 90.9

83.3 91.7 84.4 87.9 81.8 93.9 84.8 90.9 78.8 93.9 81.8 93.9

88.5 92.3 95.4 90.9 83.3 95.8 90 90 86.4 100 78.3 91.3

70 90 63.6 81.8 77.8 88.9 76.9 92.3 63.3 81.8 90 100

of these best models during internal validations covered from 94 to 100% accuracy. Once seeing the results and statistical performance of the MLP models, it can be said that the approach based on combining fluorescence data, feature selection algorithms, and intelligent non-linear models is suitable to determine the storage conditions (light exposure and container type) in which EVOO has been kept as well as locate potential fraudulently adulterated samples. Focusing on the light sources employed, just one of the six tested has not led to any of the best classifications (green LED). In contrast, the UV and white LEDs, as well as the UV laser diode, have consistently provided the strongest models

Classification accuracies of the internal validations ranged from 91 to 94%. 3.3.2.4. Is the EVOO stored in uncolored or brown glass?. And finally, the MLP-based models for the determination of the color of the glass container (uncolored or brown) were also estimated and shown in Table 7 (samples in plastic were left out of this classification). As can be seen, the best results for the Arbequina, Cornicabra, and Picual EVOO varietal databases have been reached by using the information provided by the UV laser diode, the pink LED, and the UV LED, respectively, as light sources for the fluorescence analysis. The statistical performances 56

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Table 7 Performances of every MLP model developed to identify the color of the glass container (uncolored or brown) in which EVOO was stored by means of an internal validation (IV) and a k-fold cross validation (k = 6). Results are shown in percentages of correct classification. Best cases for each EVOO varietal are marked in bold. Arbequina

LEDs

Blue Green Pink White UV

UV Laser Diode

K-fold IV K-fold IV K-fold IV K-fold IV K-fold IV K-fold IV

Cornicabra

Picual

Mean

Brown

Uncolored

Mean

Brown

Uncolored

Mean

Brown

Uncolored

80.6 94.4 75 88.9 80.6 88.9 66.7 83.3 86.1 91.7 88.9 94.4

80.8 96.2 76.9 88.5 91.3 86.9 70.8 83.3 95.2 90.5 92.6 92.6

80 90 70 90 61.6 92.3 58.3 83.3 73.3 93.3 77.8 100

83.3 94.4 77.8 88.9 86.1 100 80.6 88.9 75 88.9 77.8 88.9

81.5 96.3 73.9 91.3 95.2 100 76.2 85.7 80.9 85.7 95.2 90.5

88.9 88.9 84.6 84.6 73.3 100 86.7 93.3 66.7 93.3 53.3 86.7

75 88.9 75.8 90.9 87.9 93.9 75.8 90.9 90.9 97 78.8 90.9

70.4 88.9 70 95 90 95 86.4 95.5 91.7 100 79.2 91.7

88.9 88.9 84.6 84.6 84.2 92.3 54.5 81.8 88.9 88.9 77.8 88.9

Table 8 Top performing light sources which lead to the most accurate MLP models. Classifications: (i) adulteration detection; (ii) light exposure; (iii) type of container material; (iv) type of glass. Arbequina Classification LED

i Blue Green Pink White UV

UV Laser Diode Best Light Source per Variety

Cornicabra

Picual

ii

iii

iv

i

ii

– X – – – – – – X – – – UV LED

– – – – X –

– – – – – X

– X – – – – – – – X X X UV Laser Diode

Total (best options)

iii

iv

i

ii

iii

iv

– – – X – –

– – X – – –

– – – – – – – – – X – X – X – – – – White and UV LEDs

– – – – X –

2 0 1 3 5 3

at 1 and 5% in weight, with correct classification rates ranging from 92 to 100%, in an attempt to continue the pursue of safe and lawful food products by the design of tools that fight against fraud. Additionally, the statistical performance of the models has allowed to identify the light sources that lead to better models for every EVOO varietal and classification combination, enabling a somewhat optimized set-up selection. All this clearly showcases the great potential of this chemometric tool for its successful real-time and in situ implementation in the olive oil sector to monitor storage (and distribution) conditions, as the analytical device can fit in a regular suitcase. Further research could be done to automatize and program the mathematical models into the system so that it could it be used directly by producers or even consumers, without the need of high qualified personnel, which would further increase the practicality of the approach. Finally, in addition to these valuable traits, these intelligent algorithms can be updated in parallel with the application, allowing continuous improvement as new data is generated. In general, these characteristics make this tool valuable for quality control purposes and can open doors to other applications and foods.

in most of the classifications. In Table 8, the number of times that each light source has performed as the best option are shown for every EVOO variety analyzed. From these results, it can be deduced that the fluorescence spectra obtained from by EVOOs combined with MLP-based models can rightfully estimate or identify the storage conditions that EVOO samples have undergone, and also locate adulterated products. All the trained binary classifiers perform well for all three varietals, reaching accuracies between 91 and 100% for thorough validation tests. Therefore, this chemometric approach can be very helpful to reach tools to monitor the conditions of the distribution chain and shelf-life, thus facilitating the preservation of the quality of EVOO from its departure at the olive mill to the final consumer, as well as fighting against fraudulent activities such as adulteration. By implementing this methodology, some currently relevant problems during EVOO exportation between, for instance, the USA and European countries like Spain, can be prevented.

4. Conclusion In this work, the advantages of fluorescence spectroscopy as a rapid, non-destructive, inexpensive, and portable technique to monitor storage conditions of EVOO, have been combined with the power of artificial intelligence to create mathematical models based on neural networks capable of estimating different conditions with high accuracy. Specifically, the prototype presented has successfully differentiated EVOO samples kept under direct sunlight, from the ones stored in the dark, EVOO samples bottled in glass from samples kept in plastic, and EVOO samples bottled in brown glass from others stored in uncolored glass, with accuracies ranging between 91 and 100% after meticulous validation tests. On the other hand, it has also been possible to distinguish fresh EVOO from adulterated samples containing expired EVOO

Acknowledgment 3D printing and mechanization of the LED sensor were performed at CAI – Talleres, Asistencia a la Investigación of the Universidad Complutense de Madrid, with the collaboration of their staff. This work has been partially funded by the FEI program of the Complutense University of Madrid under the project reference FEI-EU-17-03, FEI 16/ 123 and bTB-Test under H2020-MSCA-RISE-2017 project, grant agreement number: 777832.

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Appendix A. Supplementary data

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