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    • 专利摘要:
    Detection of adulteration and identification of the geographical origin of honey. The present invention describes a method for the detection of adulteration and identification of protected designation of origin of honey. The method is based on comparing emission intensities of chemical elements present in honey in plasma spectra obtained by irradiating honey samples with a laser focused on the surface. The comparison is made using a mathematical procedure that allows differentiating spectra with high precision, providing high sensitivity and specificity results quickly. The procedure is carried out by a portable equipment that includes a laser to irradiate the sample, a spectrometer-detector and a data processing device. (Machine-translation by Google Translate, not legally binding)
    公开号:ES2688968A1
    申请号:ES201800181
    申请日:2018-08-03
    公开日:2018-11-07
    发明作者:Jorge CACERES GIANNI；Vicente PEREZ ARRIBAS；David SANZ MANGAS；Sadia MANZOOR；Juan Daniel ROSALES MARTINEZ；Roberto IZQUIERDO HORNILLOS
    申请人:Universidad Complutense de Madrid；
    IPC主号:
    专利说明:

    DESCRIPTION

    Detection of adulteration and identification of the geographical origin of honey.

    Technical sector 5

    The invention is part of the field of quality control and food safety in the beekeeping industry. The invention generally relates to a method for the analysis of honey, the detection of adulteration and origin by means of a portable system based on the technique of laser-induced plasma spectroscopy (LIBS). In particular, it refers to a method of 10 honeys for detection of adulterations based on the intensity ratios of emission lines of elements present in honey, including pure honeys or mixtures of different honeys and identification of the origin, for example, for the fraud detection.

    Background of the invention

    The honey industry has grown significantly in the last decade, so honey characteristics, such as variety, origin of production and quality, must be preserved. Honey is the third most adulterated food in the world [European Parliament European Parliament Resolution of 1 March 2018 “Prospects and challenges 20 for the beekeeping sector of the Union” (2017/2115 (INI)). Fraud is therefore one of the most important aspects that must be controlled, which can appear as a result of the addition of sugar syrups, (glucose, corn, rice, etc.) cheaper than honey, or the elimination of pollen which it is carried out by filtration, which prevents the recognition of the origin of honey through a melisopalinological study, etc. 25

    As indicated in the European Parliament (previous quotation) there is no technique or instrumentation that allows to determine adulteration reliably and economically, this is mainly due to adulterants, (Fructose, glucose, syrups High fructose corn, rice syrups, etc.) are compounds that are already found in honey and are mainly sugars of lower price. As explained below, different methods have been used and are currently accredited to determine the adulteration of honey (isotopes or nuclear magnetic resonance). However, the high cost of the analysis and its limitations, in terms of the percentage of adulteration, which are capable of detecting or the difficulty of detecting some type of adulteration, for example, rice syrup by the determination of 13C isotopes . All this means that today there is no reliable, economical technique for any type of adulteration.

    Honey consists of two primary ingredients, sugar (~ 75%) and water (~ 20%). Other components in honey are vitamins (1-2%) organic acids (<0.5%), amino acids (<0.1%) 40 and essential elements (<1%) [Zhilin Gan et al. Journal of Food Engineering 178, 151-158 (2016)]. It is, therefore, a very complex matrix and very difficult to analyze. The organoleptic properties of honey are due to the presence of different aromatic compounds. The presence of mineral trace elements in honey is generally related to the type of honey, specifically honeydew honeys contain a greater proportion of these. However, they are also associated with soil composition, climate, and their origin. The differences between the amounts of the minerals present in them can be used to achieve a rapid identification of adulterations in honey. Sensory analyzes or standard analytical determinations are not adequate to specify the origin declared or the detection of fraud. fifty

    Some analytical methods based on the determination of 1H by nuclear magnetic resonance (NMR), [Spiteri, M., E. Jamin, et al. "Fast and global authenticity screening of honey
    using 1H-NMR profiling. "Food Chemistry 189: 60-66. (2015)] or authentication based on the proportion of stable isotopes of carbon-13 13C, 13C / 12C, have been used to determine adulteration in honey. However, these methods have limited applicability or require very expensive instrumentation and the procedure is imprecise. Analytical methods based on chromatographic techniques [Wang, S. et al. "Detection of honey 5 adulteration with starch syrup by high performance liquid chromatography." Food Chemistry 172: 669-674 (2015)] require prior sample preparation operations that include separation and extraction, leading to errors, great variability in results and therefore not being efficient in the detection of adulterants. based on DNA determination [Soares, S. et al. "Botanical authentication of lavender (Lavandula spp.) 10 honey by a novel DNA-barcoding approach coupled to high resolution melting analysis. ”Food Control 86: 367-373 (2018)] require genetic DNA / aRNA sequences and the use of expensive consumables such as primers (primers) for polymerase chain reaction (PCR). This technique also has a high cost both temporary and economic. Finally, other techniques such as the determination of the enzymatic activity of beta-fructofuranosidase and 15 beta / gamma-amylaza, dye detection (E150d), thermo-resistant enzyme detection, specific rice syrup markers (SM-R), markers traces of rice syrup (TM-R), or the detection of oligosaccharides other than honey, are a valuable help, but these techniques alone are inconclusive on the identification of adulterations, mainly related to the addition of different types of syrups twenty

    There are previous publications such as the international publication WO2010146199 that describe the use of LIBS technology in other fields, for example, for the search for pathogens or contaminants in food, or the publication: Zhilin Gan et al [Journal of Food Engineering 178, 151 -158 (2016)]. However, an accuracy greater than 25 87% is not achieved and none of these have resolved the specificities of the problem indicated above, that is, the determination of adulterations by the addition of sugary syrups that implies a different handling of the samples and data processing, to obtain a high level of certainty. On the other hand, a method for detecting honey adulterants that had a certainty index greater than 98% had not been developed to date. 30

    Therefore, in view of all of the above, there is still a need for a simple, economical and portable method capable of determining the presence of adulterants, the detection of honey fraud that overcomes the indicated disadvantages.
     35
    Explanation of the invention.

    The method described in the present invention consists in the analysis of the emission lines of carbon, hydrogen and oxygen (C, H and O) as well as other elements such as calcium, magnesium and sodium (Ca, Mg, Na) obtained with the technique of laser-induced plasma spectroscopy 40 (LIBS) combining a method of data processing and mathematical processing with a specific structure and architecture built expressly for this application.

    The method is based on the fact that the concentration and relative proportion of the components of honey depend on several factors such as the type of geographical area where the apiary is located and the physical processes, chemical reactions and biochemical pathways that participate in its formation giving place to a certain water content, presence of elements such as magnesium, calcium or potassium, and the content of different types of sugars. Therefore, the optical emission of the laser-induced plasma from a honey sample contains information on the honey and provides spectroscopic information useful for both quantitative and qualitative analysis of its composition.

    Therefore, the adulteration of honey by modification of its composition (for example, by the addition of syrups) can be determined from the relative proportion of the elements
    present in this and the emission intensity generated by the interaction of a laser beam on the surface of the honey and its subsequent mathematical analysis. The method developed underlies the instant identification of a honey sample using a characteristic of the laser ablation process that has the ability to generate a spectral “fingerprint” of the sample (ES 2 356 879). The comparison of the emission intensities and their relationships 5 allows the determination of the adulteration of original products by adding syrups.

    In the case of honey, the most important emission lines observed in the spectra and, therefore, are considered to compare and classify samples, correspond to the elements C, H, O, Mg, Ca and Na. The relationship between these elements allows to determine if the honey to which a given sample belongs is pure or has been adulterated; It also provides information on the specific geographical environment from which it comes.

    More specifically, the method of detection of adulteration and identification of protected designation of origin of honey comprises the following stages:

    a.- Irradiate a honey sample with a laser focused on its surface, obtaining a plasma of said sample;

    b.- Obtain a plasma spectrum of the sample using an optical analyzer. twenty

    c.- Select wavelength intervals for elements C, H, O, Mg, Na, Ca, K.

    d.- Determine the emission intensities for each of the elements.
     25
    e.- Calculate by means of conventional mathematical procedures of data analysis and from the emission intensities of the elements carbon, hydrogen and oxygen to determine if there has been adulteration due to sugar addiction. The variables for the calculation correspond to all the intensities at wavelengths that make up the band of the mentioned elements. A value greater than 1 in arbitrary units over 30 normalized spectra implies the detection of adulteration.

    f.- Calculate by means of conventional mathematical procedures of data analysis and from the emission intensities of the elements C, Mg, Na, Ca and K to determine and / or confirm the denomination of origin. The variables for the calculation, constitute all 35 intensities at the wavelengths that make up the band of the mentioned elements. This procedure is performed by spectral comparison.

    The portable device used for the identification and characterization of samples, shown in Figure 1, is composed of a pulsed laser (1); In this particular case, an Nd: 40 YAG laser has been used, although in practice any other type of solid or gaseous laser could be used (nitrogen laser, carbon dioxide laser eximer laser, OPO laser (Optical Parametric Oscilator), etc.) to obtain sufficient energy conditions to produce a plasma. For this reason lasers can be used both in the ultraviolet, visible or infrared. Four. Five

    The Nd: YAG laser used works at a frequency of 1 to 20 Hertz at a fixed wavelength of 1064 nm respectively. This wavelength is not limiting since this laser can also emit at other wavelengths such as 266, 355 or 532 nm (or other wavelengths produced by any other type of laser that allows obtaining sufficient energy conditions to produce a plasma). Said laser can provide up to 180 mJ / pulse of output energy. The pulse duration is 4 ns. Mirrors (2) and suitable lenses (3) have been used to focus the laser beam on the sample (4). In order to prevent black body radiation generated in the first moments of plasma, it was used
    as optimal time a delay of 1 ps between the laser pulse and obtaining the spectrum in the evolution of the plasma (5). The plasma emission is collected using an optical fiber (6) coupled to the spectrometer, which in turn is activated by the laser pulse (7). The detector is a CCD (Charge-Coupled Detector) optical sensor that provides 6991 spectral points in a range of 180 to 960 nm. The signal obtained from the detectors subsequently analyzed to assess the similarity of unknown spectra (determination of origin) and graphs of the relationship of intensities of different elements (determination of adulteration).

    The analysis method is based on an analysis of a single laser pulse that produces a process of vaporization and subsequent formation of a plasma from the surface of the sample, obtaining 10 the emission spectrum of this plasma in the order of a few microseconds and the subsequent spectral analysis of the intensity relations (adulteration) or the comparison with a dynamic database (determination of the protected geographical indication (PGI) and Protected Designation of Origin (PDO)).
     fifteen
    The results obtained show that, although there is no significant variation in the LIBS spectra of honey samples from which the origin or adulterations can be easily discriminated, there are, however, from a mathematical point of view, variations to from which each honey sample can be discriminated against or adulteration detected, based on your fingerprint. twenty

    The mathematical method consists in obtaining measurement metrics of the spectra obtained following a classification process, such as true positives, true negatives, false positives or false negatives. On the other hand, an area of the peak greater than 1 in arbitrary units, over the normalized spectra denotes falsification, being possible to quantify the degree of adulteration. In this way, the conjunction of the two methodologies (classification and carbon area) allow to determine the falsification by aggregate of syrups and the determination of geographical origin.

    Precision is the main characteristic of a recognition procedure as a resource for decision-making, which is why the metrics for evaluating detection processes are of significant importance and involve the relative frequency of the correct and incorrect acknowledgments made by a observer from the results obtained. The basic measures are the number of positive (P) and negative (N), (true and false, VP, VN, FP, FN), from which the sensitivity (S), specificity 35 (Es) and accuracy (E) of the detection processes.

    A true positive (VP) corresponds to the correct detection of a substance, compound or characteristic in a sample, when it really exists. A true negative (VN) corresponds to the negative detection of a substance, compound or characteristic in a sample when it does not exist. False detections (FP, FN) correspond to cases in which the detection does not correspond to the reality of the sample.

    S and Es are two performance metrics of a detection process that are constructed from the number of VP, FP, VN and FN in a validation sample. The sensitivity of a detection process 45 refers to the probability that a substance, compound or characteristic is detected when it actually exists. Sensitivity is specified as a fraction between 0 and 1.

    The sum of VP and FN corresponds to the total of positives in the detection process so S of a 50 detection system can be calculated as:

    S = VP / (VP + FN) (8)

    An S = 1 indicates that all substances, compounds or characteristics are detected. S is also called Positive True Fraction (FVP). The metric that complements the sensitivity is the specificity which measures the probability that a detection process correctly reports the non-existence of a substance, compound or characteristic when it does not exist. The sum of VN and FP corresponds to the total false in the detection process so E in a detection system can be calculated as:

    Es = VN / (VN + FP) (9)

    A value of Es = 1 indicates that the existence of a substance, compound or characteristic is never reported when it does not exist. The Fake Positive Fraction (FFP) is defined as (1-Es) and is the fraction of samples that are reported wrongly.

    To evaluate a detection process it is necessary to have the values of its two metrics: (S and Es), since a single metric cannot correctly evaluate the process. This is because S 15 can be forced if our detection system reports all cases as positive (which corresponds to Es = 0) and Es = 1 can also be forced if our system reports all cases as negative (this corresponds to S = 0).

    Accuracy (E) is the main recognition parameter in a decision-making process, and the reason why the metrics to evaluate the detection process are so impotent. This includes the relative frequency of correct and incorrect identification of the results obtained and can be calculated according to:

    E = VP + VN / (VP + VN + FP + FN) (10) 25

    The percentage accuracy (E x 100), defined in this patent and hereinafter as "SPECTRAL CORRELATION INDEX (ICE)" can therefore be used as an appropriate metric in the decision making of the classification obtained. This is the closer the ICE is to 100, the more similar the spectrum of the sample will be to the spectral base 30 of a given PDO, which ensures that the sample corresponds to that PDO. On the other hand, ICE also provides information about the adulteration of the honey sample.

    A value greater than or equal to 90% is used as a criterion to indicate the belonging or not of a given honey sample to a PDO. As shown in Table 5, the identification procedure allows the classification and therefore its identification of honey with an ICE greater than 97% in all cases evaluated. While Table 6 shows the results of the classification of national and international honeys and mixtures of honeys, with and without PDO. The new methodology allows the identification of honey samples without preparation of the sample, and identification is possible even with a single laser shot, in a time of less than 1 second.

    This method has the following advantages, such as: a) it does not require sample preparation, b) the analysis is performed on a small proportion or honey sample, c) the analysis is performed in a few seconds, d) the analysis It is carried out at atmospheric pressure and ambient temperature, e) provides a large amount of data that makes mathematical analysis suitable and f) can be performed on-site using the portable system developed for this purpose.

    Brief description of the drawings 50

    In the accompanying drawings, for illustrative and non-limiting purposes, the main features of the invention are shown.

    Figure 1.- Schematic view of the portable device comprising both laser equipment, mirrors, lenses, sample positioner, optical fiber, spectrometer-detector, and personal computer or Tablet.

    Figure 2.- Geographical map of Spain indicating the denominations of protected origin PDO 5 and PGI protected geographical indication of the different Spanish honeys.

    Figure 3.- Honey sample prepared for analysis. The method does not require sample preparation and can be analyzed under normal atmospheric conditions.
     10
    Figure 4.- Typical LIBS spectrum obtained from a honey sample. The figure shows the assignment of the emission lines of the most important elements present in honey and the wavelength ranges selected for data processing in a red box.
     fifteen
    The following is a list of the different elements represented in the figures that make up the invention:

    1 = laser equipment
     twenty
    2 = mirrors

    3 = lenses

    4 = positioner shows 25

    5 = fiber optic

    6 = spectrometer - detector
     30
    7 = personal computer or Tablet

    8 = PDO Honey from Liébana

    9 = PDO Honey from the Alcarria 35

    10 = PDO Pomegranate Honey

    11 = PDO Honey from Tenerife
     40
    12 = PDO Honey Villuercas-íberes

    13 = PGI Honey from Galicia

     Four. Five
    Preferred Embodiment of the Invention

    The present invention is further illustrated by the following example, which is not intended to be limiting of the scope thereof.
     fifty
    All honey samples used were purchased at local supermarkets. All jars containing honey were opened at the same time to subject all samples to the same environmental conditions.

    Example: Determination of adulteration and geographical origin of honey samples

    As an example, in order to determine the adulteration of a honey sample, 6 different honeys are used. One from the Zamora region, another from Portugal with a 15 km separation and a honey from Madrid. Other appellations of origin include Honeys from Galicia, Alcarria and 5 Granada and different commercial samples shown in table 3. The adulterated samples of each honey were prepared at 5% adulteration, adding separately fructose syrups (FRU), glucose (GLU) , rice (ARR), agave (AGA), apple (MAN) and coconut (COC), respectively. A typical LIBS spectrum of one of these honeys is shown in Figure 4. A total of 100 laser pulses of each sample are stored as “fingerprints” of the 10 samples. As shown in Table 4 all honeys are correctly classified as honeys with PDO. The assignment does not produce any false positive (honeys classified as honeys without appellation of origin), nor false negatives (honeys with denomination of origin classified as honeys without PDO), all assignments being correct as true positives or true negatives. fifteen

    Table 1: Emission wavelength of the main atomic and molecular lines elements identified in honey

     Element  Wavelength (nm)
     C (I)  247.87
     Mg (II)  280.27
     Ca (I)  422.67; 445.48; 551.30; 560.13
     Ca (II)  393.37; 396.85
     Na (l)  588.99; 589.59
     O (l)  777.37; 844.62
     He has  656.39
     twenty
    Table 2: Selection of length intervals for data processing

     Wavelength Range (nm)  Element
     247.4-248.5  C (l)
     640 - 675  He has
     764-782  O (I)
     27-291  Mg (II)
     390 - 429  Ca (II)
     581-598  Na (I)
     760-783  K (I)

    Table 3: Honey samples included in the analyzes.
     25
     Geographical Denomination  Sample identifier Trademark Type of honey Year of production
     Galician honey  MG1 Pazo de Lusio Mountain flowers 2,018
     MG2  From our land Flores 2,018
     MG3  Mel da anta Mountain flowers 2,018
     Honey of the Alcarria  MA1 La Orza de Valdemoro Flores 2018
     MA2  The Colmenar of Grandfather Romero 2018
     MA3  Guadalhor Romero and Espliego and Multufloral 2018
     Pomegranate Honey  MGr1 Gardens of the Alhambra Castaño 2.018
     MGr2  Arana honey Thousand flowers 2,018
     MGr3  Honey the Almijara Thyme 2,018
     Zamora honey  MZ1 The workers of eniste Bosque 2.018
     Portugal honey  MP1 Forest 2,018
     Madrid honey  MM1 MM3 MM3 Apismel Flores 2018

    Table 5: Results of classification of different honey analyzed.

        Prediction of membership in a PDO
     Sample Adulterated ICE% Pure ICE% Origin
     Pure Galicia honey  MG1 0 99 Galicia
     MG2  0 98 Galicia
     MG3  0 100 Galicia
     Adulterated with Syrups  MG1-FRU 99 1 unknown
     MG1-GLU        unknown
     MG1-COC        unknown
     MG1-MAN        unknown
     MG1-AGA        unknown
     MG1-ARR        unknown
     Pure Alcarria honey  MA1 0 99 Galicia
     MA2  0 98 Galicia
     MA3  0 100 Galicia
     Adulterated with Syrups  MA1-FRU 99 1 unknown
     MG1-GLU        unknown
     MG1-COC        unknown
     MG1-MAN        unknown
     MG1-AGA       unknown
     MG1-ARR        unknown
     Pure Pomegranate Honey  MGr1 0 99 Galicia
     MGr2  0 98 Galicia
     MGr3  0 100 Galicia
     Adulterated with Syrups  MGr1-FRU 99 1 unknown
     MGr1-GLU        unknown
     MGr1-COC        unknown
     MGr1-MAN        unknown
     MGr1-AGA        unknown
     MGr1-ARR        unknown
     Pure Zamora Honey  MZ1 0 99 Galicia
     Adulterated with Syrups  MZ1-FRU 99 1 unknown
     MZ1-GLU        unknown
     MZ1-COC        unknown
     MZ1-MAN        unknown
     MZ1-AGA        unknown
     MZ1-ARR        unknown
     Pure Portugal Honey  MP1 0 99 Galicia
     Adulterated with Syrups  MP1-FRU 99 1 unknown
     MP1-GLU        unknown
     MP1-COC        unknown
     MP1-MAN        unknown
     MP1-AGA        unknown
     MP1-ARR        unknown
     Pure Madrid honey  MM1 0 99 Galicia
     Adulterated with Syrups  MM1-FRU 99 1 unknown
     MM1-GLU        unknown
     MM1-COC        unknown
     MM1-MAN        unknown
     MM1-AGA        unknown
     MM1-ARR        Unknown

    * Percentage of adulteration: 5%
    权利要求:
    Claims (10)
    [1]

    1. Method of detection of adulteration and identification of denomination of protected origin of honey comprising the following stages:
     5
    a.- Irradiate a honey sample with a laser focused on the surface of the same obtaining a plasma of said sample
    b.- Obtain a plasma spectrum of the sample using an optical analyzer.
     10
    c.- Select wavelength intervals for elements C, H, O, Mg, Na, Ca, K.
    d.- Determine the emission intensities for each of the previous elements.
    e.- Compare the emission intensities of the C, H and O elements of the sample with the 15 intensities corresponding to an unadulterated sample by means of conventional mathematical data analysis procedures to determine if there has been adulteration due to sugar addiction. A value greater than 1 in arbitrary units over normalized spectra implies detection of adulteration.
     twenty
    f.- Compare the emission intensities of the elements C, Mg, Na, Ca and K of the sample with the intensities corresponding to a sample of known origin to determine and / or confirm the denomination of origin.

    [2]
    2. Method of detection of adulteration and identification of protected designation of origin of honey, according to claim 1, wherein the honey samples are light, amber or dark samples, pure honeys or mixtures of honey from different countries and / or adulterated.

    [3]
    3. Method of detection of adulteration and identification of protected designation of origin of honey, according to claim 1, characterized in that it uses a conventional method of data analysis as "principal component analysis" (PCA) to determine adulteration and / or The origin of honey

    [4]
    4. Method of detection of adulteration and identification of protected designation of origin of honey, according to claim 1, wherein the focused laser radiating the honey sample is a solid, liquid or gaseous state laser that emits visible or ultraviolet electromagnetic radiation. infrared and produces a plasma of the ablated material.

    [5]
    5. Method of detection of adulteration and identification of protected designation of origin of honey, according to claim 4, wherein the laser is an Nd: YAG laser that operates at a frequency of 1 to 20 Hertz at a fixed wavelength of 1064 nm

    [6]
    6. Detection method according to claims 1, characterized in that the detection of the radiation produced by the chemical elements of the plasma to obtain the spectrum is carried out through a CCD optical sensor that provides 6991 spectral points in a range of 180 to 960 nm .

    [7]
    7. Equipment for the detection of adulteration and identification of protected designation of origin of honey following the claimed method comprising a pulsed laser, mirrors and lenses to focus the laser beam on the sample and cause a plasma whose emission is collected using optical fiber coupled to a spectrometer which in turn is activated by the laser pulse.

    [8]
    8. Portable equipment for the detection of adulteration and identification of the protected designation of origin of honey, according to claim 7, wherein the focused laser that radiates the honey sample is a solid, liquid or gaseous state laser that emits visible ultraviolet electromagnetic radiation or infrared and produces a plasma of the ablated material.
     5
    [9]
    9. Portable equipment for the detection of adulteration and identification of protected designation of origin of honey, according to claim 7, wherein the laser is a Nd: YAG laser that operates at a frequency of 1 to 20 Hertz at a fixed wavelength 1064 nm.

    [10]
    10. Portable equipment for the detection of adulteration and identification of appellation of origin protected from honey, according to claim 7, characterized in that the detection of the radiation produced by the chemical elements of the plasma to obtain the spectrum is carried out through a sensor optical CCD that provides 6991 spectral points in a range of 180 to 960 nm.
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    ES2688968B2|2020-03-19|
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    ES2356879A1|2009-06-18|2011-04-14|Universidad Complutense De Madrid|Instantaneous identification and characterisation of samples using the dynamic combination of laser ablation and mathematical algorithms|
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