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    • 专利摘要:
    METHOD FOR THE REAL-TIME DETECTION OF DEFECTS IN A FOOD PRODUCT AND IN ITS MANUFACTURING PROCESS, FIELD PROGRAMMABLE DOOR ARRANGEMENTS AND DEFECT MONITORING APPLIANCE IN A PROCESS FOR THE MANUFACTURING OF A FOOD PRODUCT. The present invention is a method for detecting defects in a process for manufacturing a food product using multivariate image analysis. In one aspect, an image of the food product in the visible spectrum is captured through on-line visualization equipment, then a multivariate image analysis is performed on the image through an algorithm programmed over a field-programmable gate array to determine the possible existence of a defect, then a signal is sent to the downstream sorting equipment, and said sorting equipment then rejects those food products that contain defects.
    公开号:BR112012004195B1
    申请号:R112012004195-9
    申请日:2010-08-20
    公开日:2021-05-04
    发明作者:Wilfred Marcellien Bourg Jr.；Enrique Michel
    申请人:Frito-Lay North America Inc.；
    IPC主号:
    专利说明:

    BACKGROUND OF THE INVENTION TECHNICAL SCOPE
    [001] The present invention is related to the use of multivariate image analysis to detect defects in a production line manufacturing a food product. DESCRIPTION OF TECHNICAL STATUS
    [002] The chemical compound acrylamide has long been used in its polymeric form in industrial applications for water treatment, optimized oil recovery, papermaking, flocculants, thickeners, ore processing and pass-free fabrics. Acrylamide precipitates as a white crystalline solid, is odorless and highly soluble in water (2155 g/L at 30°C). the list of synonyms for acrylamide includes 2-propenamide, ethylene carboxamide, acrylic acid amide, vinylamide and propenoic acid amide. Acrylamide has a molecular mass of 71.08, a melting point of 84.5°C and a boiling point of 125°C at 25 mmHg.
    [003] In recent times, a wide variety of foods have shown positive results for the test for the presence of acrylamide in monomer form. Acrylamide has been found especially in carbohydrate food products that have been heated or processed at elevated temperatures. Examples of foods that tested positive for acrylamide testing include coffee, cereals, cookies, potato snacks, crackers, French fries, breads and rolls, and fried breaded meats. It has not been established that acrylamide is harmful to humans, however its presence in food products, especially at high levels, is undesirable.
    [004] One way to reduce acrylamide formation is to thermally process food products to a higher moisture content. However, food products that contain an excess of moisture have poor organoleptic properties and are undesirable from the point of view of consumers. One of the objectives of the present invention is the detection of defects, particularly food products having a moisture content above a certain threshold, in a manufacturing process of a food product with a higher moisture content. SUMMARY OF THE INVENTION
    [005] One aspect of the present invention is directed to a method for real-time detection of defects in a food product comprising the steps of capturing an image of a food product in the visible spectrum, performing multivariate image analysis on the image to revealing a dataset, and determining whether a defect exists in the food product based on the dataset. In one aspect, the invention further comprises removing food products containing a defect prior to a packaging step. One aspect of the invention comprises adjusting a process variable to reduce the number of manufactured food products that are defective. One aspect of the present invention comprises the analysis and removal of food products due to the presence of acrylamide, considered a defect.
    [006] One aspect of the present invention is directed to a field-programmable gate array featuring an algorithm that transforms a color image of a food product into a data set such as a count space t1-t2 via multivariate image analysis , determines whether or not a defect exists based on the dataset, and sends a signal to a downstream sorting equipment to reject said defective item within about 0.002 seconds.
    [007] In one aspect, the present invention is directed to an apparatus for monitoring a manufacturing process of a food product for defects. In one aspect, the apparatus comprises an image capture device, a computational device capable of storing an algorithm, where said algorithm transforms a color image of a food product into a suitable expression of an image matrix via multivariate image analysis , and determines if there are any defects based on the resulting dataset.
    [008] Other aspects, embodiments and features of the invention will be apparent from the following detailed description of the invention when considered in conjunction with the attached figures. The attached figures are schematic and are not intended to be drawn to scale. In said figures, each identical or substantially similar component that is illustrated in several figures is represented by a single numeral or notation. For the sake of clarity, not all components are numbered in in all figures. Nor are all components of each of the embodiments of the present invention illustrated where it is not necessary to illustrate them to enable those skilled in the art to understand the invention. BRIEF DESCRIPTION OF THE FIGURES
    [009] Those innovative aspects believed to be characteristic of the present invention are presented in the attached claim table. The invention itself, however, as well as a preferred mode of use, additional objectives and advantages thereof, will be best understood by reference to the following detailed description of illustrative embodiments when read in conjunction with the attached figures, where :
    [010] Figure 1 illustrates a general flowchart of a method for detecting defects in a process for manufacturing a food product according to an embodiment of the present invention;
    [011] Figure 2 illustrates predicted moisture content distributions of potato snacks;
    [012] Figure 3a illustrates a set of potato chips snacks, each snack featuring a desirable crunchy region and a defective soft central region;
    [013] Figure 3b is an illustration of the corrected image of the defective soft central region superimposed on the potato chip snacks illustrated in Figure 3a;
    [014] Figure 4 is the predicted presentation of the color images of two potato chips snacks transformed into a counting space t1-t2; and
    [015] Figure 5 illustrates a schematic representation of an embodiment of the present invention. DETAILED DESCRIPTION
    [016] The present invention, in an embodiment, comprises a method for real-time detection of defects in a process for manufacturing a food product. The present invention can be used to monitor a process for manufacturing a food product and detect food products that contain defects through the use of multivariate image analysis to differentiate between food product characteristics, some of which have defects and some of which do not. have defects, but appear to be similar when observed in the visible spectrum.
    [017] Referring now to Figure 1, an image is captured 100 of the food product in the visible spectrum, which covers the wavelength range from 400 nm to 700 nm, by means of online visualization equipment such as a camera digital as the product proceeds along the processing line. In an incorporation, the image is taken of the entire width of a conveyor belt, thus providing maximum inspection and analysis of the surface of the food product. In an incorporation, the food is in a monolayer configuration. Aggregate food products can be positioned in a monolayer configuration by transferring the aggregate food product from a first conveyor belt to a second conveyor belt that moves much faster. Multivariate image analysis (hereinafter referred to as the abbreviation “MIA”) is then performed on the image using an algorithm 110. In one embodiment, the algorithm can be programmed into a field-programmable array of gates (hereinafter referred to as the abbreviation FPGA), which is a semiconductor device, known in the state of the art, which can be programmed in the field. In one embodiment, an application-specific integrated circuit can be used to process the algorithm. The algorithm can be used to reveal a dataset, which illustrates the location of product characteristics within the t1-t2 count space or other suitable expression of the image matrix via multivariate image analysis.
    [018] Next, it is determined whether or not there are 120 defects based on the resulting dataset. In one embodiment, if a defect is found, a signal 130 can be sent to sorting equipment, such as a bank of independently selected air nozzles, located downstream of the display equipment, to reject the food product containing said defect. The sorting equipment then rejects those food products that contain defects by deflecting the defective food products away from the conveyor belt that is carrying the product with an air stream introduced through an air nozzle prior to a packaging step.
    [019] In an embodiment, the invention comprises the use of real-time measurement of defects to adjust a process variable in the food product manufacturing line to reduce the percentage of defects in food products.
    [020] An embodiment of the present invention can be explained by referring to a potato snack production line and "soft core" defects that occur in potato chips having a moisture content greater than about 2, 5% by mass. A soft core defect occurs when a thermally processed food such as a potato chip snack is not cooked to a moisture content that ensures a crunchy texture is achieved throughout the entire volume of the food product. In this way, the central region of the food product remains relatively soft. Soft cores are problematic because they adversely affect the shelf life of the product by increasing the percentage of moisture within the container that contains it and thus leading to earlier product rot. In addition, soft cores affect the texture of the potato snack, which results in reduced consumer satisfaction, and can cause multiple snacks to stick together, resulting in problems during further processing.
    [021] As foods are thermally processed to reach higher moisture contents to reduce the levels of acrylamide present in the food, soft core defects become more prevalent. For example, potato snacks are typically cooked by frying until reaching a predicted moisture content distribution illustrated by curve 200 in Figure 2. As illustrated in Figure 2, when potato snacks are fried to achieve a moisture content of about 1.4% by mass, very few of the potato chips have moisture contents above 2% by mass. However, thermal processing of foods until higher moisture contents such as an objective moisture content of about 1.8% by mass are reached, in order to reduce acrylamide formation may result in an unintended consequence of production of a greater number of soft cores, which need to be removed from the product's production chain before packaging. Curve 220 in Figure 2 represents the predicted moisture content distribution predicted moisture content distribution of a thermally processed potato chip snack to reach a target moisture content of about 1.8%. As illustrated in Figure 2, increasing the target moisture content of potato snacks results in a much higher percentage of snacks having a moisture content of more than about 2.0%. Also evidenced in Figure 2 is the fact that the predicted distribution of moisture content 220 becomes wider as the target moisture content increases. The reason for the increased moisture distribution 220 is that the lower end of the distribution is further away from the “aggregate” moisture content restriction of the finished potato snack. Consequently, an even higher than expected level of soft core defects occurs when increasing the target moisture content.
    [022] Existing screening equipment in the production of potato snacks based on the visible spectrum identifies defective snacks based on the degree of color clarity (eg black, brown, green), and the size of the defect observed in the snack. However, detecting soft core defects using existing equipment is difficult because soft cores reflect light differently than other defects because soft core defects emit a wavelength pattern of white or reflective light/ brillant. For example, staining is occasionally described in an HSI staining space (h2ue, s2aturation, i2intensity - hue, saturation, intensity). It is difficult to use the HSI color space to accurately detect soft nuclei due to the fact that the reflective or bright component, which for the most part is unrelated to the actual saturation and intensity properties of the object, is necessarily measured using the HSI technology. A further complication of the problem is that oil-soaked defective snacks, which are not considered defective, also emit a white or reflective wavelength pattern and can be mistakenly rejected along with the soft cores.
    [023] Defective oil-soaked snacks are actually fried food products where the oil has not been bound to the starch. Various regions of defective fried snacks may present as soaked in oil. In some embodiments, as defective snacks are analyzed for defects within a relatively short period of time after leaving the fryer, the oil may still be on the surface of the fried food if the oil has not yet been embedded in the food product. Defective snacks soaked in oil are not considered defective. Consequently, there is a need for an apparatus and method which allows to monitor a production line of thermally processed food products for identification of soft cores, followed by selective rejection of the soft cores without however rejecting the oil-soaked defective snacks.
    [024] Although thermally processed fried food products are typically processed to a moisture content of less than 2.5% by weight of the food product, and more preferably less than about 2.0% by weight of the food product , baked items such as biscuits can be thermally processed to higher moisture contents and still preserve their shelf life. Consequently, as used herein, the term thermally processed food product is defined as a food product having a moisture content of less than about 5% by weight, and more preferably less than about 3.5% by weight. pasta. As used herein, the term defective snacks and thermally processed food product are synonymous and can be used in place of one another.
    [025] An embodiment of the present invention allows soft core defects and oil-soaked defective snacks to be differentiated by performing a multivariate image analysis on an image taken in the visible spectrum of the thermally processed food product to build an algorithm that can be used to identify characteristics such as soft core defects and oil soaked areas in the food product.
    [026] A color image captured in the visible spectrum is a multivariate image composed of three variables - red, green and blue channels. The color of each of the pixels in the image has varying intensities of red, green, and blue colors and is characterized by the numerical values (usually integers from 0 to 255) of its red, green, and blue channels. A color image can be expressed in the form of a 3-dimensional matrix. Two dimensions represent the xy spatial coordinates and the third dimension is the color channel. Without considering the spatial coordinates of the pixels, the image matrix can be unfolded and expressed in the form of a two-dimensional matrix.
    [027] I is a three-dimensional image matrix with an image size of NlinhaxNcol. I is the unfolded two-dimensional image matrix. N is the number of pixels in the image, N= NlinexNcol, ci,r, ci,g, ci,b (i=1,...,N) correspond to the intensity values of the red, green and blue channels for the pixel i. ci (i=1,...,N) is the vector of the i-th row of I, which represents the color values of pixel i. It is possible to use different regression methods known in the prior art, such as Principal Component Analysis (P2rinciple C2omponent A2nalysis or PCA) or Partial Least Squares (P2artial L2east S2quares or PLS) on the two-dimensional matrix I to obtain a count space t1 -t2.
    onde A é o número de componentes principais, os ta’s são vetores de contagem e os pa’s correspondentes são vetores de carregamento.[028] For example, a multi-way Principal Component Analysis can be performed on a multivariate color image to generate a count space t1-t2. Performing a multi-way Principal Component Analysis is equivalent to performing a Principal Component Analysis on the unfolded two-dimensional image matrix I. where A is the number of principal components, the ta's are count vectors and the corresponding pa's are load vectors.
    e então a decomposição de valores singulares (s2ingular v2alue d2ecomposition - SVD) é efetuada nesta matriz de dimensão notavelmente baixa (3x3 para uma imagem a cores) com o objetivo de gerar os vetores de carregamento pa (a=1,...,A).[029] Because the row dimension of the two-dimensional image matrix I is too large (equal to 307,200 for a 480x640 image space) and because the column dimension is much smaller (equal to 3 for an RGB-red image , green and blue - in color), it is possible to use a kernel algorithm to compute the load and count vectors. In this algorithm, the kernel matrix (ITI) is initially formed (for a set of images, the matrix is calculated as m and then the singular value decomposition (s2ingular v2alue d2ecomposition - SVD) is performed on this remarkably low dimension matrix (3x3 for a color image) in order to generate the loading vectors pa (a=1,...,A ).
    [030] After obtaining the load vectors, the corresponding count vectors ta are computed ta=Ipa. Since the first two components usually explain most of the variance, rather than working in the original three-dimensional RGB space, the option to work in the t1-t2 orthogonal two-dimensional counting space allows the interpretation of the images to be remarkably facilitated .
    [031] Figure 3a illustrates a set of potato chips snacks, each of the defective snacks showing a desirable non-defective crunchy region 302 and a soft central region 304. The lightly hatched region illustrated by the number 304 necessarily represents a more color dark in this drawing of what would be indicative of a soft core in a true color image, and is illustrated to indicate the predicted soft core region 304. Figure 4 is a predicted representation of the color images of two potato chips transformed into the count space t1-t2. The computer programs necessary to carry out the transformation of an image into a counting space t1-t2 are already known from the state of the art.
    [032] To develop the algorithm used to perform multivariate image analysis that correlates the color image of a potato chip snack to determine whether the defective snacks are indeed defective, a multi-way Principal Component Analysis is performed on two of the images in Figure 3a to convert the count space t1-t2 of each potato snack 410 411 illustrated in Figure 4.
    [033] It is possible to make modifications to existing equipment to allow the user to search for white/reflective areas, such as changing the belt material from white to a darker coloration such as blue to allow for differentiation between the background image / color of the conveyor belt and the defect itself, thus allowing a more accurate detection of soft cores. Consequently, in an embedding, the background image coloring, for example the color of the conveyor belt, is removed from the image in Figure 3a before converting the image of each potato chip into a t1-t2 counting space. After removing the background image, the red-green-blue potato snack image illustrated in Figure 3a can then be converted to a transformed image 410 411 as illustrated in Figure 4. Those skilled in the art will understand that different food products should produce different counting space t1-t2. For example the t1-t2 count space for defective tortilla snacks will be different from the t1-t2 count space for a potato snack. It should be noted that there are other ways to unfold and express the image matrix without resorting to the t1-t2 counting space and such expression is offered for the purpose of illustration and not for limiting the scope of the present invention.
    [034] Next, a mask is created by highlighting a defect identified in the RGB space and observing where the defect falls within the t1-t2 space. A mask 402 is created that highlights the area in the t1-t2 space that is characteristic of the defect, which corresponds to the soft central region identified by the number 304 in Figure 3a. In one embodiment, mask 402 occurs in the same t1-t2 space even though the counting space of each potato chip 410 411 can span different areas in the t1-t2 space.
    [035] The area comprising mask 402 in space t1-t2 is selected and a corrected image is projected back into the RGB space in the potato snack illustrated in Figure 3b. Mask areas around the defect region 304 illustrated in Figure 3a are, in one embodiment, selected through trial and error until the corrected image mapped back into RGB space is substantially superimposed over the defect area 314 of the defective tidbits illustrated in Figure 3b. In one embodiment, the mask areas around the defective region 304 illustrated in Figure 3a can be selected through an automation algorithm that can optimize the mask generation task.
    [036] The above process can be repeated to define masks that are correlated with other properties of the food product including, but not limited to, other defects. For example, it has been found that potato slices exhibiting defects are also linked to higher levels of acrylamide when fried in hot oil (eg, fried in an oil having an oil temperature greater than about 280°F). than slices of potato that had no defects. A potato slice without blemishes is a slice showing a regular golden color over its entire surface area after frying. Potato defects are already well known to those skilled in the art and such defects include, but are not limited to zebra defects, dry rot, crust, hollow core, greening, bruises, budding, stains, leaf roll and leaf defects. sugar. Further details on those defects found in potatoes, including a listing of such defects, can be found in Information Bulletin 205 entitled 'Detecting Potato Tuber Defects and Disease' published by Cornell University's Department of Botanical Pathologies on its website http: / /vegetablemdonline.ppath.cornell.edu /factsheets/potato Detection. htm. This newsletter is incorporated herein by reference.
    [037] Several potato chips showing various defects were fried to a moisture content below 2% by mass in hot oil and analyzed for their level of acrylamide. The results are shown in the table below.
    [038] Sugar defects are typically not removed from production chains before packaging. Interestingly, defective snacks exhibiting the highest levels of acrylamide due to sugar defects have not historically been consumer defects because these defects are predominantly coppery to slightly brown in color and therefore are not considered unacceptable. In contrast, defects such as rotting, black spots and budding that are predominantly black or very dark in color are the types of potato defect most likely to be removed prior to packaging.
    [039] As exemplified by the above data, removing defective potato chips from the packaging process can help to substantially reduce the average level of acrylamide in a portion of a food product. Accordingly, in an embodiment of the present invention, a food product having an acrylamide defect known to be characteristic of high levels of acrylamide is removed prior to packaging the food products. As used herein, a food product has an acrylamide defect known to be characteristic of a high level of acrylamide if the concentration of acrylamide due to said defect is more than twice the level of a thermally processed potato slice without defect under the same conditions. Therefore, a slice showing a defect in sugar is one such that due to the higher than normal sugar content will result in a finished potato slice having more than double the level of acrylamide compared to a potato slice having a normal sugar content (e.g., snack potatoes typically have less than 0.05% reducing sugar by mass of a fresh potato) that is thermally processed under the same conditions.
    [040] In an incorporation, a mask is created by highlighting a non-defective portion of a defective snack, such as a region soaked in oil and it is observed where the defect falls in the RGB space. Masked areas can again be selected through trial and error or through an automated algorithm until the oil soaked area produces a corrected image that adequately covers the non-defective area of the defective snacks. In this way, one can differentiate between an area of light coloring in the potato snack that is caused by a defective soft core in contrast to an area of light coloring in a potato snack that corresponds to non-defective, oil-soaked snacks. It is possible to use software such as Proportion, from Prosensus, Inc. To develop the algorithm in the manner discussed above in order to consummate the multivariate image analysis that can be used to create the corrected image.
    [041] This algorithm can then be programmed into an FPGA to determine, based on the captured image and the corresponding dataset calculated from said image, the number, type and degree of pixels detected within the defective snacks, and establish which ones of the defective snacks are in fact defective. FPGA's are known from the prior art and can, for example, be purchased from Hunt Engineering def Brent Knoll Village, Somerset, England.
    [042] Advantageously, the present invention, unlike the state of the art, allows one or more defective areas within the defective snacks to be aggregated. In one embodiment, the defects most associated with acrylamide can be weighed in such a way that acrylamide defects require a smaller defect-removal area than other defects, such as soft cores, which have relatively lower levels of acrylamide. Whether or not a particular defective tidbit is classified as defective can be determined using one or more predetermined variables. In an embedding, a defect exists when the dataset or corrected image reveals that at least about 10% of the food that generated the image comprises a soft core.
    [043] In an incorporation, the defective snacks are destined for removal. If the removal of a defective snack has been planned, the FPGA can calculate the intended removal area, translate the intended area to the specific reject nozzles in the downstream air nozzle bank, calculate the necessary schedule, and communicate the sequence of trigger to the ejector controller. Screening equipment such as a high-capacity Manta classifier available from Key Technologies of Walla Walla, Washington can be used.
    [044] Figure 5 illustrates a schematic representation of an embodiment of the present invention. In one embodiment, the bank of independently fired air nozzles 508, located along the entire width of the conveyor belt 502, is located a short distance away (eg, less than about 5 feet and more preferably less than about 3 ft) downstream of an image capture equipment 504. Therefore, in such an embodiment, if the food product 502 is moving along the conveyor belt at speeds above 500 ft/min., multivariate image analysis and the determination of whether a defective tidbit is indeed defective needs to happen very quickly.
    [045] In order to enable this, the algorithm can be programmed in the processor 506 that is connected to the visualization equipment 504 and the sorting equipment 508. The color image of a potato snack 502 can be taken by the visualization equipment 504 and sent to processing unit 506. Processing unit 506 may comprise an FPGA.
    [046] Processor 506 applies the algorithm that was developed through the methods discussed above to the image, which transforms the color image into a t1-t2 count space or other suitable expression of the image matrix via multivariate image analysis and determines whether there is in fact a defect based on the resulting dataset. In an embedding, the resulting dataset is used to superimpose a corrected image in RGB space onto the food substrate.
    [047] In an incorporation, if in fact there is a defect, a signal is sent to the downstream sorting equipment 508 to reject the defective snacks. The use of FPGA and/or 506 high speed processor array technology allows the process to occur in less than about 0.002 seconds and more preferably in less than about 0.001 seconds to allow actuation of air solenoid valves. high speed connected to 508 air nozzles that are selected to remove identified defects from the product production chain. Defective snacks are routed to a defectives chain 510 while the non-defective snacks chain 512 is routed to the seasoning addition and packaging steps.
    [048] In an incorporation, if a defect does exist, it is possible to use a signal to adjust processing variables in order to adjust defect levels in a finished food product. For example, the exposure time and temperature of a food product inside the fryer can be optimized in order to reduce, lower and/or minimize defect levels in the finished food product. For example, the paddle wheel speed can be reduced to allow for a longer residence time inside the fryer and/or the hot oil temperature can be increased to fry the soft cores. Other processing levels that can be adjusted include, but are not limited to, the oil flow rate into the fryer, the oil level inside the fryer, the submerger speed, the take-up conveyor speed, the temperature of the inlet oil and the product feed rate.
    [049] In an incorporation, an evaluation of the defect chain 510 and/or non-defective chain 512 takes place to provide further fine-tuning of the process. For example, in a development, defective chain 510 is measured to establish the level of non-defective snacks in defective chain 510. In a development, non-defective chain 512 is measured to establish the level of defective snacks within of the non-defective chain 512. This information is collected, along with statistics of incoming defects by type and degree calculated from the processor 506 and used to tune the algorithm. This fine-tuning can be achieved in an incorporation by observing the mask shape in the t1-t2 image and magnification (causing a greater number of pixels to fit within the definition of a specified defect class) or reduction ( causing a smaller number of pixels to fit the definition of a specified class of defects) of the radial distance measured from the centroid of the mask, 402 illustrated in Figure 4.
    [050] In an embedding, the number, type and degree of defective pixels within each of the defective tidbits within the defective chain 510 and/or the non-defective chain 512 are counted for statistical analysis purposes 514. In In an embodiment, these statistics can be combined with the level of bad tidbits in the 512 non-defective chain to assess the 516 performance of the system. Using the information from the performance of the 516 system, and the level of non-defective food products in the 510 defective chain, it is possible to make calculations to adjust the degree of aggressiveness 518 of the adjustment as it applies to each of the classes of individual defect. For example, as it applies to each of the individual defect classes if a large number of defects are being passed through the system, the tuning action would be to gradually increase the sensitivity of each defect, by class, until if it reached an acceptable degree of defect rejection. On the other hand, if the number of defects in the non-defective chain 512 is within acceptable performance limits, and the number of "good" defective snacks within the reject chain 510 is unacceptably high (meaning that part of the production is being abandoned), then the system could be adjusted by reducing the degree of sensitivity or aggressiveness 518 for certain defect classes (those that are less egregious in terms of acrylamide) in order to reduce the number of "good" defective snacks occurring in the rejection chain 510.
    [051] This information can be used alone or in conjunction with manual data entry by an operator to adjust the overall 520 sensitivity of the system. In such an embodiment, an operator would have access to a manual data entry device such as a slider bar or up/down arrows, or a "tuning" tuning/numeric entry based on any desired scale (eg 0100, +/-10, etc.) which would be used to adjust the system's overall sensitivity to defects. For example, if the operator wants to increase the tolerable defects in the “good” or non-defective 512 chain to increase or decrease by a certain percentage, such as from 5% to 4%, the operator would be able to make this adjustment manually . In one embodiment, manual adjustment by an operator would be unavailable to adjust the sensitivity of certain defect classes, specifically those resulting in increased levels of acrylamide, in order to ensure that rejection of said defects could not be manually disabled by an operator. Forecast example
    [052] Potato slices are cooked in a continuous fryer, for example at a temperature of about 340°F to about 370°F for a time period of approximately 3 minutes. The cooking step generally reduces the moisture content of defective snacks to less than 2% by mass. For example, a typical potato chip snack comes out of the fryer with approximately 1.5% moisture by mass.
    [053] The baked potato snacks exit the fryer and proceed along a conveyor belt at a speed of approximately 8 feet per second. A digital camera, positioned above the conveyor belt, captures a color image of the defective snacks as they proceed forward on the conveyor belt. The image is sent to the processing unit containing the FPGA or processor arrangement with the programmed algorithm. The FPGA or processor array applies the algorithm to transform the color image into a t1-t2 count space. The algorithm then determines whether the potato snack is defective based on where the characteristics of the defective snack in the count space t1-t2 are located. A mask is created that highlights the area in the t1-t2 count space that is characteristic of that defect. This is done first by highlighting an identified defect in the RGB space and observing where the defect falls in the t1-t2 space. An area around the point in count space t1-t2 is selected and projected back to RGB space. Mask areas around the defective region would have been previously identified by trial and error until the area mapped back to RGB space adequately covers the defective area of the defective tidbits. The FPGA signals to sorting equipment, which in a development comprises one or more air nozzles, that a defective tidbit is approaching in 3 feet or 0.006 seconds. The sorting equipment then rejects the defective tidbit by striking the defective tidbit with a blast of air as the defective tidbit is launched through an opening approximately 12 inches wide located between the conveyor belt to a receiving/brake conduit . The air jet deflects the defective item off the conveyor belt and into a discard chain.
    [054] An advantage of having a short distance between the detection zone and the reject nozzles is that defective snacks moving at high speeds, meaning these speeds greater than about 500 feet per minute, present a good aerodynamic profile. and can move relative to the predicted information being transmitted to the reject air nozzles. Any relative positional movement of the defective tidbits could result in either a misfire or possibly the improper rejection of adjacent non-defective tidbits. An advantage of placing the display units as close as possible to the reject nozzles is that the theoretical probability of missing defective snacks or false rejection is reduced. In an incorporation, an image is captured during the "flight" of the defective snacks between the conveyor belt and the receiving/brake conduit. In these cases, the distance is probably on the order of less than a foot between the image acquisition system and the ejection nozzles.
    [055] Although the present invention has been described with reference to a potato snack production line and soft core defects in potato snacks, it should be understood that the present invention is in fact applicable to other defects familiar to the food industry. potato processing, and also to other thermally processed food products, such as defective baked or fried corn snacks, defective tortilla snacks, crackers, etc. The examples and explanations presented herein are not intended to limit the scope of the present invention.
    [056] Having thus described several aspects of at least one embodiment of the present invention, it should be appreciated the fact that several changes, modifications and improvements will readily occur to those skilled in the art. It is intended that such changes, modifications and improvements are understood as an integral part of this descriptive report, as they are within the spirit and scope of the present invention. Consequently, the description and drawings given above are by way of example only.
    权利要求:
    Claims (6)
    [0001]
    1. METHOD FOR DEFECT DETECTION TO MONITORING A PROCESS FOR THE MANUFACTURING OF A FOOD PRODUCT, characterized in that it comprises the following steps: a) capture (100) of a color image of said food product (502) in a visible spectrum, b ) transformation (110) of said color image into a t1-t2 score space through multivariate image analysis to reveal a dataset, c) determination (120) of the existence or not of a defect based on said dataset , wherein said defect occurs when said dataset reveals that at least 10% of an image area of said reflected food product comprises a soft center (304); and d) rejection (130, 140) of said food product presenting said defect within 0.002 seconds after said determination step by sending a signal to downstream sorting equipment comprising air solenoid valves connected to air nozzles ( 508), in which said multivariate image analysis occurs by an algorithm programmed in an array of field-programmable gates.
    [0002]
    2. METHOD, according to claim 1, characterized in that it further comprises the step of adjusting a process variable to provide a reduced number of said defects.
    [0003]
    3. METHOD, according to claim 1, characterized in that it additionally comprises a step of counting the food products that comprise said defect.
    [0004]
    The method of claim 1, wherein said defect further comprises an acrylamide defect, optionally wherein said acrylamide defect further comprises a sugar defect.
    [0005]
    5. APPARATUS ADAPTED TO DETECT DEFECTS FOR MONITORING A PROCESS FOR THE MANUFACTURING OF A FOOD PRODUCT, characterized in that it comprises: an image capture device (504) for capturing a color image of said food product (502) in a visible spectrum; a computational device (506) for storing an algorithm, wherein said algorithm transforms a color image of a food product into a counting space t1-t2 via multivariate image analysis to reveal a set of data; and determines whether there is a defect based on a dataset, wherein said defect occurs when said dataset reveals that at least 10% of an imaged area of said reflected food product comprises a soft center; and downstream screening equipment comprising air solenoid valves connected to air nozzles that reject said food product with said defect after having received a signal from said computing device less than 0.002 seconds after said computing device has determined whether a defect exists, in which said multivariate image analysis occurs by an algorithm programmed in an array of field-programmable gates.
    [0006]
    Apparatus according to claim 5, characterized in that said computational device comprises a plurality of computational processing arrangements that segments said colored image.
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    同族专利:
    公开号 | 公开日
    US20110050880A1|2011-03-03|
    AU2010289863B2|2013-08-29|
    RU2012111126A|2013-10-10|
    ZA201201957B|2012-11-28|
    WO2011028447A1|2011-03-10|
    US8284248B2|2012-10-09|
    RU2509356C2|2014-03-10|
    EP2471024A1|2012-07-04|
    AU2010289863A1|2012-03-15|
    CA2771868C|2014-10-14|
    CN102598025A|2012-07-18|
    CA2771868A1|2011-03-10|
    MX2012002431A|2012-06-27|
    CL2012000510A1|2012-07-13|
    EP2471024A4|2015-10-14|
    EP2471024B1|2018-10-03|
    BR112012004195A2|2020-12-15|
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    法律状态:
    2020-12-22| B06U| Preliminary requirement: requests with searches performed by other patent offices: procedure suspended [chapter 6.21 patent gazette]|
    2021-04-13| B09A| Decision: intention to grant [chapter 9.1 patent gazette]|
    2021-05-04| B16A| Patent or certificate of addition of invention granted [chapter 16.1 patent gazette]|Free format text: PRAZO DE VALIDADE: 10 (DEZ) ANOS CONTADOS A PARTIR DE 04/05/2021, OBSERVADAS AS CONDICOES LEGAIS. |
    优先权:
    申请号 | 申请日 | 专利标题
    US12/547,075|2009-08-25|
    US12/547,075|US8284248B2|2009-08-25|2009-08-25|Method for real time detection of defects in a food product|
    PCT/US2010/046171|WO2011028447A1|2009-08-25|2010-08-20|Method for real time detection of defects in a food product|
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