ES2684855A1 - INSPECTION EQUIPMENT FOR THE AUTOMATED CLASSIFICATION OR DISCRIMINATION OF ALMONDS BASED ON THE CONCENTRATION OF AMIGDALINE AND INSPECTION PROCEDURE (Machine-translation by Google Translate, not legally binding) - Google Patents

Abstract

Inspection equipment for the classification or automated discrimination of almonds according to their amygdalin concentration, and inspection procedure, which comprises at least one vision system for the detection of the almond in the equipment and at least one near infrared spectroscopy detector . Said equipment is preferably portable and allows the automation in the unit classification/discrimination of almonds in real time and in function of its level of bitterness that is determined by the concentration of amygdalin in the almond. The automated procedure allows the classification or discrimination of the almond without its destruction. (Machine-translation by Google Translate, not legally binding)

Description

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INSPECTION EQUIPMENT FOR AUTOMATED CLASSIFICATION OR DISCRIMINATION OF ALMONDS IN FUNCTION OF THE AMIGDALINE CONCENTRATION AND INSPECTION PROCEDURE DESCRIPTION

Technical field

The present invention relates to an inspection equipment for the automated classification or discrimination of almonds based on their concentration of tonsillin, and inspection procedure, which comprises at least one vision system for the detection of almonds in the equipment and the minus a near infrared spectroscopy detector. Said equipment is preferably portable and allows automation in the unit discrimination / classification of almonds in real time and depending on its level of bitterness, which is determined by the concentration of amygdalin in the almond. The automated procedure allows the classification or discrimination of the almond without its destruction.

The invention is applicable in the sector of classification of nuts and in particular almonds.

Background of the invention

It is known that the bitter taste of almonds is produced by amygdalin, which in contact with saliva produces the release of hydrocyanic acid and benzaldehyde. The latter is the cause of the unpleasant taste of bitter almonds, while hydrocyanic acid can be toxic if a significant amount of bitter almonds is consumed, and can even cause death due to a massive intake. It is estimated that the fatal dose is 20 bitter almonds for adults and 10 for children. Approximately 1% of the amount of almonds produced in Spain and in the rest of the producing countries of the Mediterranean area are bitter. The bitter character present in this type of almonds is genetically transmitted through a recessive gene. There are widely spread Spanish varieties, such as ‘Desmayo Largueta’, ‘Marcona’ or ‘Garrigues’, which despite producing sweet almonds, carry the bitter character as a recessive gene.

During the almond harvesting campaign, the almond processing companies receive tens of thousands of samples in their receiving warehouses. During the quality control carried out on each farmer who delivers the harvest at the receiving center, it is not possible to detect the presence of bitter almonds on-site, since there is no type of inspection system or measurement equipment that can detect and discriminate

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those almonds and that does not imply the destruction of the sample. At present, companies usually carry out a control for the detection of bitter almonds only during the product reception process. This control is performed randomly in some samples and through a cyanide presence test. The use of this test, apart from being very laborious since it involves the crushing of the sample, does not allow immediate results. Therefore, when assessing the quality of the production delivered, it is not possible to penalize the presence of bitter almonds and a high percentage is incorporated into the processing chain under the same conditions as sweet almonds. The presence of bitter almond trees in a farm, even if it is totally harmless to the farmer, is harmful to the commercial process of almonds.

It is very important the eradication of bitter almonds in the items issued, because the almonds of Spanish varieties and the rest of the Mediterranean countries have an important market niche in the US and Asia due to the following reasons:

- Better organoleptic characteristics than those produced in the US and Australia, and

- Greater ease of producing certified organic almonds. In the US, due to the type of intensive almond tree cultivation, this certification is more difficult.

However, Spanish producers, when exporting organic almonds, must ensure that their product is not contaminated with bitter almonds. Both almond varieties grown in California and Australia are sweet and do not have the bitter character of the recessive gene; This, coupled with the toxic nature of tonsillin, causes them to see the presence of bitter almonds as a poison and demand a total absence of bitter almonds in their imports. As mentioned above, Spanish almond production has a non-traceable proportion of bitter almonds of up to 1%, which has a significant negative impact on the aforementioned strategic markets such as the United States and other emerging markets such as Asian countries, in those that the commercialization of almond begins to be very important. On many occasions, these countries do not risk importing Spanish almonds with the risk of finding a bitter one if they have 100% sweet American almonds.

Therefore, the existence of bitter almonds in exported items can not only cause the loss of customers, but even the veto of certain countries at the entrance of Spanish almonds.

The search for technological solutions for identification and possible separation

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Bitter almonds in the process and production lines have shown that there are no commercial solutions focused on solving this problem. According to the manufacturers themselves, traditional systems (artificial vision) and the most advanced (hyper spectral cameras) are not prepared to face the type of detection demanded by the Mediterranean almond industry.

In the state of the art there are methods, based on liquid chromatography (HPLC, UHPLC) with visible ultraviolet detection (UV-Vis) or mass spectrometry (MS), which have been applied to the detection and quantification of cyanogenic glycosides such as the tonsil (1). However, these methodologies are complex, slow, destructive and, in short, impossible to apply in a production line.

The solution proposed in this patent is based on spectroscopic techniques called vibrational, more specifically Near InfraRed spectroscopy (Near InfraRed, NIR). These techniques provide non-specific information about the molecular structure and allow non-destructive measurements to be carried out quickly and easily, since no sample treatment is required. Many of the preliminary studies conducted with almonds have been based on Raman spectroscopy (2), as it provides very intense bands in the spectrum, although the technique is more expensive than, for example, NIR spectroscopy. In line with the above, for the discrimination or non-destructive classification of bitter almonds an inspection method based on NIR spectroscopy can be used together with a multivariate method of discrimination / classification offering, percentages of correctness in the detection and discrimination / classification close to 98% (3).

NIR spectroscopy, in combination with the new instrumental accessories and chemometric methods of classification or multivariate discrimination, has become an indispensable analytical tool for the industry and has been successfully introduced in different fields, such as petrochemical, pharmaceutical, biomedical and, of course, agrifood. The main advantages of NIR spectroscopy over other instrumental analytical techniques are the following (4):

- It is a non-destructive measurement technique, which allows its implementation for the realization of in-line measurements,

- It is much faster than other analytical techniques such as HPLC, UHPLC or GC,

- It can operate smoothly in hostile work environments,

- Because it does not require solvents, it can be defined as a non-polluting and, therefore, sustainable and

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- The technique is very profitable due mainly to savings in labor, although this should not be misunderstood in terms of no need for maintenance.

On the other hand, its non-specificity and the intrinsic dependence of the method of measurement of the irregularities of the sample, make the measurements by NIR spectroscopy not the best alternative for detections of substances in concentrations less than 1% -3% of the sample .

However, tonsillin in bitter almonds is present in a concentration range between 3-5% (1), and very localized on the surface of the almond, which favors its inspection by spectroscopic techniques such as NIR spectroscopy.

Therefore, although in the state of the art it is known to extract almond tonsillin by destroying the sample as well as the use of NIR spectroscopy for real-time monitoring of characteristics or properties of laboratory food, it is not known no equipment that allows automation in discrimination or unitary classification in real time of almonds depending on their concentration of tonsillin from the use of NIR spectroscopy outside a laboratory.

References:

(1) J. Lee, G. Zhang, E. Wood, C. Rogel Castillo, A. E. Mitchell. Quantification of Amygdalin in Nonbitter, Semibitter, and Bitter Almonds (Prunus dulcis) by UHPLC- (ESI) QqQ MS / MS. J. Agric. Food Chem. 61 (2013) 7754-7759.

(2) E. Micklander, L. Brimer, S.B. Engelsen Noninvasive Assay for Cyanogenic Constituents in Plants by Raman Spectroscopy: Content and Distribution of Amygdalin in Bitter Almond (Prunus amygdalus). Applied Spectroscopy 56 (2002) 1139-1146.

(3) E. Borrás, J.M. Friend, F. van den Berg, R. Boqué, O. Bust. Fast and robust discrimination of almonds with respect to their bitterness by using near infrared and partial least squares-discriminant analysis, Food Chemistry, 153 (2014) 15-19.

(4) H.W. Siesler, Application to industrial process control, in: H.W. Siesler, Y. Ozaki, S. Kawata, H.M. Heise (Eds.). Near-Infrared spectroscopy: Principles, Instruments and Applications. Wiley-VCH Verlag GMbH, Weinheim, 2002, pp. 247-249

Description of the invention

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The present invention proposes, in accordance with a first object of the invention, an inspection equipment for the classification or discrimination of almonds according to the concentration of tonsillin according to claim 1.

The equipment, preferably portable, is especially useful during the process of receiving almond shipments, in order to be able to determine in an automated way the percentage of bitter almonds existing in the cargo, which contributes to determine the price of the almond and even to reject the cargo if the presence of bitter almonds is disproportionate as it could be harmful to health and not suitable for export.

Specifically, said equipment includes:

- A measuring cell where at least one almond is located,

- A vision system located above the measuring cell for detecting the presence of the almond in said measuring region and calculating the position of said almond in said measuring region, which preferably corresponds to the center of the almond,

- An NIR detector / sensor located below the measuring cell to inspect the almond, and

- A computer that controls and coordinates the different components of the equipment.

The computer comprises a processor or CPU, and different ports and cards, and controls, in a coordinated way, the operation of the vision system and its components, as well as the NIR detector / sensor, processes the almond spectrum and produces a binary signal of classification of the almond as "bitter" or "non-bitter" according to a multivariate classification or discrimination model. The equipment presents a display or screen for a user to handle.

Likewise, the equipment includes a ramp for the entrance of the almond into the measuring cell and two ramps for the exit of the almond from the measuring cell, one for non-bitter almonds and one for bitter almonds. The supply or dosage of the almond to the entrance ramp, and therefore to the measuring cell, can be done automatically by means of a hopper that supplies almonds in a unitary manner to said cell.

Thus, after the entry of the almond into the measuring cell, it is possible, and probable, that the almond is not placed in the optimum position for the measurement by the NIR spectroscopic detector / sensor, so it is necessary set up a system

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NIR detector / sensor positioning to move it in a horizontal plane (x, y axes) to the coordinates identified by the vision system.

After the positioning of the NIR detector / sensor, the spectrum of the sample that will be used to classify the almond is measured according to its bitterness in "bitter" or "non-bitter" almond.

Once the almond is classified, it is removed from the measuring cell using an almond removal system. Preferably, the same almond removal system is also used as a cleaning system of the measuring cell after discrimination or classification, and removal of the almond, although it is possible to use other cleaning systems. Preferably said almond removal and cleaning system of the measuring cell comprises pressurized air means that push the almond by means of a jet of air, moving it from the measuring cell to one of the exit ramps. At the same time, the air jet drives the dirt in the cell, cleaning it and leaving the measuring cell ready for the classification of another almond.

A second object of the invention is an inspection procedure for the classification or discrimination of almonds according to the concentration of tonsillin according to claim 7.

In particular, the process essentially comprises the following steps:

- Detection with a vision system of at least one almond located in a measuring cell of an almond inspection team,

- Calculation with the vision system of the position of the almond in the measurement area,

- Almond spectroscopic characterization by means of a NIR detector / sensor, and

- Almond discrimination or classification according to its spectrum.

The stage of discrimination or classification of almonds includes the following stages:

- Processing of the NIR spectrum of the almond,

- Application of a multivariate classification or discrimination model developed during the equipment calibration phase, and

- Classification of the almond as non-bitter or bitter as a result of applying the multivariate model.

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Also, in the event that the almond is not positioned correctly in the measuring cell, it will be necessary to move the coordinates indicated by the vision system to the NIR detector / sensor by means of a displacement system. Said position preferably corresponds to the center of the almond to ensure that the beam of light strikes the almond since, in the event that the beam of light is positioned at one end of the almond, there is a risk that the beam of Light does not completely affect the surface of the almond.

Once the measure is taken or inspected and the almond is classified as "non-bitter" or "bitter", the almond is removed from the measuring cell using the almond removal system described.

Brief description of the drawings

The foregoing and other advantages and features will be more fully understood from the following detailed description of the embodiments, with reference to the attached figures, which should be considered in an illustrative and non-limiting manner, in which:

Figure 1 shows a perspective view of an inspection equipment for the classification or discrimination of almonds according to the present invention in which the protective housing has been removed.

Figure 2 shows another perspective view of the equipment object of the present invention.

Figure 3 shows a front view of the equipment.

Figure 4 shows a perspective view of the equipment frame in which the main elements thereof are observed.

Figure 5 shows another perspective view of the frame.

Figure 6 shows a perspective view of the equipment partially covered by the protective housing.

Figure 7 shows a perspective view of the equipment completely covered by the housing.

Detailed description of the preferred embodiments

Next, a preferred embodiment of the invention will be described with reference to the figures accompanying the present description.

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Figure 1 shows a portable inspection equipment 100 for the classification or discrimination of almonds in an automated manner according to the concentration of tonsillin according to the invention. It comprises a frame 10 on which the main components of the equipment 100 are arranged, a computer with a processor or CPU 22 and the rest of the automatisms necessary for the operation of the equipment, such as motors, solenoid valves, electrical components 24 and electronic components , such as a data acquisition card 23. The device 100 presents a display or screen 25 to be controlled by a user. Likewise, all the components of the equipment are protected thanks to a protective housing 26 as seen in Figures 6 and 7. In Figure 7 the accesses in the housing 26 for the entrance ramp 16 and for one of the exit ramps 21.

Said equipment is preferably portable when presenting maximum measures of approximately 500x375x384.5 mm, since one of its characteristics is to be transported to the reception plants of the almonds to sample the loads of almonds to be transported and to determine the percentage of almonds Bitter and not bitter.

On said frame 10 at least one vision system 11, a near infrared spectroscopy detector (NIR) 12 and a measuring cell 13 where an almond is arranged are installed. The vision system 11, comprising a camera 11a and a light source 11b, is disposed above the measuring cell 13 while the NIR detector / sensor 12 is located below the measuring cell 13, the cell being 13 composed of a translucent optical window.

The vision system 11 also includes the camera 11a, a diffused light emitter 11b and a light filter to illuminate the measuring cell where the almond to be inspected is arranged. The vision system 11 allows to calculate the position of the almond in the measuring cell 13, at coordinates in the xy plane, by identifying the center of the almond, by processing the image captured by the camera 11a. The almond enters the measuring cell 13 through an input ramp 16 preferably from a hopper that feeds the almond measuring cell 13 one at a time automatically.

The almond detection procedure carried out by the vision system 11, consists of capturing the real image of the almond with a camera 11a and after binarizing the image and processing it with the CPU 22 of the equipment 100 the center of the almond.

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The NIR detector / sensor 12, located below the measuring cell 13, performs the spectroscopic characterization of the almond in the range between 1550 and 1950 nm. The NIR detector / sensor is a complete instrument consisting of: (i) radiation source emitting a beam of light, (ii) a radiation filter that separates the reflected light from the almond at its wavelengths, (iii) and an NIR detector. The detector / sensor also allows to detect the concentration of water in the almond working in the same range.

The almond is not always positioned in the same way in the measuring cell 13, so it will be necessary to position the NIR detector / sensor 12 at the precise coordinates determined by the vision system 11, that is, the detector / sensor 12 must be positioned under the center of the almond identified by the vision system 11. To this end, said detector / sensor 12 has a positioning system, preferably formed by two electronic tables 14, 15 that move said detector 12 to the position x and previously calculated by the vision system 11.

Once in the detection position, the detector / sensor 12 together with the processor 22, or CPU, which stores the processing algorithm of the NIR spectrum of the almond proceeds to the classification or discrimination thereof in bitter or non-bitter, in NIR spectrum function obtained. The discrimination or classification procedure includes:

- Processing of the NIR spectrum of the almond,

- Application of a multivariate classification or discrimination model developed during the equipment calibration phase, and

- Classification of the almond as non-bitter or bitter as a result of applying the multivariate model.

Said automated classification or discrimination process implies the application of a chemometric algorithm that is responsible for processing the near infrared spectrum (NIR) and the subsequent classification or discrimination of almond into bitter or non-bitter. As an example of a multivariate discrimination model, the one obtained using the PLS-DA method (Partial Least-Squares Discriminant Analysis - Discriminant analysis of partial least squares) can be used. PLS-DA is a statistical method based on the PLS regression (partial least squares regression) that constructs a multivariate model between the near infrared (NIR) spectra of almonds (matrix X) and a vector of classes (vector y), where These classes are represented by coded variables, zeros for non-bitter almonds and ones for bitter almonds. He

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Use of PLS-DA is possible, but not necessary. As an alternative to PLS-DA, other discrimination methods such as Support Vector Machines (SVM), or classification as SIMCA (class analysis by independent soft modeling) that perform the same function (provide a "bitter" binary output) - "not bitter" from the NIR spectrum of the almond) and which can provide percentages of discrimination or correct classification similar to those obtained by PLS-DA

Once the almond has been discriminated against or classified as bitter or non-bitter, it is necessary to remove it from measuring cell 13 to continue with the classification or discrimination of other almonds. For this, the equipment 100 comprises an almond removal system comprising pressurized air means 17, 18 that propel the almond up to exit ramps 20, 21 depending on its classification.

Specifically, the measuring cell 13 is preferably square or rectangular, and has on one side, entrance side, the almond entrance ramp 16, and on each side adjacent to the input side, exit sides, a exit ramp 20, 21. Likewise, on each of the exit sides there are pressurized air means 17, 18 and doors 28, 29 automatically actuated by the same mechanism that give access to the exit ramp and the ramp input 20, 21 from the measurement cell 13.

Based on the above components, and after discrimination or classification of the almond into bitter or non-bitter, the outlet doors 28, 29 are opened so that the pressurized air means 17, 18 located behind the doors 28, 29 have access to the measuring cell 13. Depending on the discrimination or classification of the almond, one or the other of the pressurized air means 17, 18 will be activated and the almond will be pushed up to the exit ramp 20, 21 opposite to the medium air pressure 17.18 activated. Also, the air jet is used for cleaning the measuring cell 13.

For example, and according to the figures, on one side of the measuring cell 13, the exit ramp 21 is arranged, which is the ramp for the exit of non-bitter almonds, and also has on that exit side, the exit door 29 of non-bitter almonds and the pressurized air means 17 for pushing the bitter almonds out of the measuring cell 13. Therefore, on the exit side of the opposite measuring cell, the ramp is arranged output 20 of bitter almonds, the exit door 28 of bitter almonds and the pressurized air means 18 to push the non-bitter almonds out of the measuring cell 13. Therefore, in the event that an almond is detected not bitter, the two exit doors 28, 29 operated by the same mechanism and the pressurized air means 18 located on the opposite side of the sweet almond exit ramp 21 will open

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it will be activated, pushing the almond to the exit ramp 21 that will fall into a container located under said ramp 21. In the event that the almond is classified as bitter, the process for its withdrawal will be the opposite.

Therefore, the inspection procedure for the classification or automated discrimination of almonds, preferably performed on a portable device such as the one described above, comprises at least the following steps:

- Detection with a vision system 11 of at least one almond located in a measuring cell 13, preferably arranged in an almond inspection equipment 100,

- Calculation with the vision system 11 of the position of the almond in the measuring cell 13, and preferably calculation of the center of the almond at the xy coordinates in a horizontal plane,

- Inspection or measurement with a detector / sensor of near infrared spectroscopy (NIR) 12 of the existence of tonsillin in the almond by obtaining the NIR spectrum of the almond, that is, spectroscopic characterization of the almond by said detector / sensor NIR, and

- Discrimination or classification, according to the multivariate analysis method used, of the almond according to its spectrum.

This stage of discrimination or classification includes the stages mentioned.

previously:

- Processing of the NIR spectrum of the almond in the processor 22, or CPU, of the computer 22,

- Application of a multivariate classification or discrimination model developed during the equipment calibration phase, and

- Classification of the almond as non-bitter or bitter as a result of applying the multivariate model.

Also, before the inspection of the almond, the NIR detector / sensor 12 can be moved to the position calculated by the vision system 11 by means of a positioning system 14, 15. Subsequently, and after the classification of the almond, the the removal of the almond from the measuring cell 13.

In view of the foregoing, preferably, the complete automated procedure carried out by the equipment 100, object of the present invention, comprises the following steps:

- Unit dosage of an almond in measuring cell 13,

- Detection of the almond located in the measuring cell 13 by means of a vision system 11,

- Calculation of the center of the almond at the xy coordinates using the system of

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- Positioning in the xy coordinates identified by the vision system 11 of the NIR detector / sensor 12, by means of a positioning system composed of two electronic tables 14, 15,

- Obtaining an NIR spectrum of the almond by means of the NIR detector / sensor 12.

10 - NIR spectroscopic signal processing measured by the NIR detector / sensor 12.

- Almond classification, bitter or non-bitter, depending on the result obtained when applying the multivariate discrimination or classification model.

- Removal of the almond by means of the removal and cleaning system of the measuring cell 13.

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The above examples are only some of the constructive possibilities of the invention object of the present application and should not be considered as limiting.

Claims (10)

5 10 fifteen twenty 25 30 1. Inspection equipment for the discrimination or classification of almonds according to the concentration of tonsillin characterized in that it comprises: - A measuring cell where at least one almond is located, - A vision system located above the measuring cell for the detection of the presence of the almond in said measuring region and calculation of the position of said almond in said measuring region, - A near infrared spectroscopy (NIR) detector / sensor located below the measuring cell to inspect the almond, and - A computer or CPU that controls and coordinates the equipment components. 2. Equipment according to claim 1, characterized in that it comprises a positioning system of the NIR detector / sensor to move it in a horizontal plane. 3. Equipment, according to previous claims, characterized in that it comprises an almond removal system from the measuring cell. 4. Equipment, according to previous claims, characterized in that it comprises a measuring cell cleaning system. 5. Equipment, according to previous claims, characterized in that it comprises an almond entrance ramp in the measuring cell and two almond exit ramps from the measuring cell, one for sweet almonds and one for bitter almonds. 6. Equipment according to previous claims, characterized in that the almond removal system from the measuring cell to the exit ramps comprises pressurized air means which in turn constitute the cleaning system of the measuring cell. 7. Inspection procedure for discrimination or automated classification of almonds depending on the concentration of tonsillin characterized by comprising at least the stages of: - Detection with a vision system of at least one almond located in a measuring cell of an almond inspection team, - Calculation with the vision system of the position of the at least one almond in the measurement area, - Almond spectroscopic characterization by means of a NIR detector / sensor, and - Almond discrimination or classification according to its spectrum. Method according to claim 7, characterized in that the spectroscopy detector is moved to the position calculated by the vision system before inspection of the almond. 9. Method according to claims 7 to 8, characterized in that after the classification of 5 the almond is removed from the measuring cell. 10. Method according to claims 7 to 9, characterized in that the classification step of the almond comprises: - Processing of the NIR spectrum of the almond. - Application of a multivariate classification or discrimination model developed 10 during the equipment calibration phase. - Classification of the almond as non-bitter or bitter as a result of applying the multivariate model.