WO2023114974A1

WO2023114974A1 - Frozen finely textured beef (ftb) protein calibration for near infrared (nir) spectroscopy

Info

Publication number: WO2023114974A1
Application number: PCT/US2022/081758
Authority: WO
Inventors: Tina HOETMER; Jakob JONS; Andy LAFOLLETTE
Original assignee: Cargill, Incorporated
Priority date: 2021-12-17
Filing date: 2022-12-16
Publication date: 2023-06-22

Abstract

A characteristic of frozen finely textured beef may be evaluated using a spectrometer. For example, a method may include generating a set of near infrared (NIR) spectra points for a corresponding set of frozen FTB samples using a spectrometer, and correlating the set of NIR spectra points to respective protein values determined using a wet chemistry analysis. The method may include generating a calibration model for the spectrometer from the correlation, the calibration model configured to predict a protein value for a sample of frozen FTB.

Description

FROZEN FINELY TEXTURED BEEF (FTB) PROTEIN CALIBRATION FOR NEAR INFRARED (NIR) SPECTROSCOPY

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application No. 63/291,171, filed December 17, 2021, which is incorporated by reference herein in its entirety.

BACKGROUND

[0002] Scientists employ a variety of analytical tools to assist in quantitative evaluation of various characteristics of products, from raw materials to finished goods. Generally, analytical tools may rely upon careful control and preparation of a sample for evaluation, such as according to a standardized test or evaluation protocol in a “bench” setting. In this manner, traceable and repeatable results may be obtained. Such techniques may be applied to frozen beef. Use of analytical techniques to evaluate frozen beef helps to verify or maintain quality throughout the production and distribution process. For example, after processing, bench analytical techniques may be used to verify that amounts of protein, for example, are at, above, or below specified levels. Analytical techniques may also be used to assess beef for the presence of contaminants or adulterants.

BRIEF DESCRIPTION OF THE DRAWINGS

[0003] In the drawings, which are not necessarily drawn to scale, like numerals may describe similar components in different views. Like numerals having different letter suffixes may represent different instances of similar components. The drawings illustrate generally, by way of example, but not by way of limitation, various embodiments discussed in the present document. [0004] FIG. 1 illustrates generally an example showing a system that may include a spectrometer, such as for characterization of frozen beef.

[0005] FIG. 2 illustrates generally an example comprising a technique 200, such as an automated method, for calibration of a near infrared technique for evaluating a protein characteristic of a frozen FTB sample.

[0006] FIG. 3 illustrates a sample cup according to an example.

[0007] FIG. 4 illustrates generally an example comprising a technique, such as an automated method, for determining a quantitative indication of protein values in a prepared sample using a spectrometer.

[0008] FIG. 5 illustrates generally an example comprising a user input and display, such as a touch-screen user interface, such as may be used to receive inputs to control a spectrometer or to present results, such as a representation of a characteristic of frozen beef sample being assessed using the spectrometer.

[0009] FIG. 6 illustrates a block diagram of an example comprising a machine upon which any one or more of the techniques (e.g., methodologies) discussed herein may be performed.

DETAILED DESCRIPTION

[0010] The systems and methods described herein provide for calibration and use of spectroscopy to measure protein values in frozen finely textured beef (FTB). In Near-Infrared (NIR) Spectroscopy, pinholes and frozen products present difficulties in predicting accurate results. The systems and methods described herein provide a Frozen FTB protein calibration to accurately predict protein, for example using reference method AO AC 992.15 on a Foodscan 2 from Foss of Denmark.

[0011] The frozen pelletized Finely Textured Beef (FTB) may produce pinholes in the sample cup and may produce flatline spectra when averaged and may skew the protein predicted values. The systems and methods described herein include the use of an olive plastic bottom sample cup that holds additional sample to reduce pinholes from going completely through the sample scan. In an example, a Foss Foodscan 2 may be used with the larger sample such that outlier spectra are reduced out of the average predicted protein. The systems and methods described herein provide a new Frozen FTB calibration to predict protein with these limiting factors.

[0012] The plastic bottom olive cup is deeper than typical sample cups and may hold more sample frozen FTB than typical sample cups. By having more sample, this reduces the potential for spectra missing scans on at least some of the sample. Plastic may be used for the bottom of the olive cup to comply with regulations or safety requirements. The calibration systems and methods may be used to reduce flatline spectra out of average protein values to increase accuracy of the frozen FTB protein values being produced.

[0013] A sample of beef, such as frozen FTB may be evaluated using the systems and methods described herein. The sample may be prepared, for example by placing the frozen beef in a sample cup. The sample cup may include an olive cup, such as a cup deeper than a typical sample cup. The sample cup may have a plastic bottom in some examples.

[0014] A spectrometer may scan the prepared sample, for example with infrared spectroscopy. A processor (e.g., of a spectrometer device) may be used to determine a value corresponding to a characteristic for the prepared sample based on a result of the scan (e.g., based on raw data output by the spectrometer). The value corresponding to the characteristic may be output, for example displayed on a display device of the spectrometer, sent to a remote device (e.g., a mobile device such as a phone for display), or the like. The process may be repeated (e.g., two to four times), in some examples to generate a value indicative of a central tendency, such as an average or median value. This may help avoid inconsistencies.

[0015] FIG. 1 illustrates generally an example showing a system 100 that may include a spectrometer 110, such as for characterization of frozen FTB. Evaluation of a characteristic of the frozen FTB may be performed with the frozen FTB within a holding vessel in an example. The spectrometer 110 may include a user interface 130, such as including a user input or a display, as mentioned in relation to other examples described herein. In an example, the spectrometer 110 may be portable, such as sized and shaped to be manipulated by a user by hand. The spectrometer may be configured to emit light comprising a specified range of infrared wavelengths (e.g., near infra-red (NIR)), and to receive a reflection from frozen FTB. The spectrometer 110 may then establish reflectance data corresponding to the received reflection without requiring physical contact between the spectrometer 110 and the frozen FTB.

[0016] The spectrometer 110 may include a processor circuit configured to provide reflectance data comprising a series of values corresponding to discrete wavelength values spanning a specified range of wavelengths. As an illustrative example, the specified range may include wavelengths from about 400 nanometers to about 3000 nanometers or more specifically 780 nanometers to 2500 nanometers. The spectrometer 110 may include a housing and hardware configuration similar to the FOSS Foodscan 2 (available from Foss, Hilleroed, Denmark). The use of reflectance spectroscopy in the near-infrared range of wavelengths is illustrative, and other spectroscopic techniques may be used. The spectrometer 110 may be coupled via a wired or wireless communication channel 120 A or a wired communication channel 120B to another device, such as a device 104 (e.g., a mobile device such as a cellular handset, a tablet device, a laptop or desktop computer, or a base-station located in a facility, as illustrative examples). [0017] The wireless communication channel 120A may be established according to a wireless communication standard such as Bluetooth® (e.g., Bluetooth® Low Energy (BLE) as described in the Bluetooth Core Specification, v. 5.0, published December 6, 2016, by the Bluetooth® Special Interest Group, Kirkland, Washington) or according to one or more other standards (e.g., the Institute of Electrical and Electronics Engineers (IEEE) 802.11 family of standards known as Wi-Fi®, mobile communications standards such as relating to 4G / Long Term Evolution (LTE), or the IEEE 802.15.4 family of standards, as illustrative examples).

[0018] The device 104 may include one or more processor circuits coupled to one or more memory circuits. For example, the device 104 may be configured to transform received reflectance data provided by the spectrometer 110 such as using a model profile to generate a value of a characteristic being assessed. The device 104 may be coupled through another wireless communication channel 122A to a repository 106 such as a remotely -located server or a cloud-based (e.g., distributed) facility. For example, the wireless communication channel 122A may be established according to a wireless networking protocol mentioned above, or a digital cellular networking protocol, as illustrative examples. One or more criteria may be applied to the transformed reflectance data. For example, a value of a characteristic being assessed, such as a parameter relating to protein values may be determined from spectroscopy data for the frozen FTB. The result may be presented to a user. The result (which may include a color code, such as green or red corresponding to a threshold) may be presented to the user via the user interface 130 of the spectrometer or the device 104, or the like.

[0019] In another example, the device 104 serves as an intermediary device, and the repository 106 (or other facility such as a cloud-based resource) may perform the transformation of the reflectance data to establish a value of the characteristic being assessed. In yet another example, the spectrometer 110 includes one or more processor circuits coupled to one or more memory circuits, and the device 104 need not be used. For example, the spectrometer 110 may transmit reflectance data to the repository 106 for processing (e.g., transformation), or the spectrometer 110 may transform reflectance data.

[0020] Data generated by the spectrometer may be used to generate a percentage or concentration of a characteristic in a sample. A calibration model may be generated for example based on an array of data created from the NIR spectra points. The generated calibration model may be used to evaluate the frozen FTB for a protein values.

[0021] A calibration model, for each product type and analyte, may be based on an array of data created from the NIR spectra points and the wet chemistry analysis values. The NIR spectra, including the signature of the samples, is correlated to the reference analysis values for specific analytes, usually a wet chemistry analysis method. This creates an algorithm or calibration model that may be used to predict the analyte values for similar products that fall within the parameter of the calibration model. The NIR spectra points may be generally collected at every 0.5nm, from 400 to 2500nm wavelength range or 800 to 1 lOOnm range, in various example.

[0022] To create the algorithm models, the step between points may be widened and only part of the wavelength range may be used. In an example, the math treatments selected are: 1st or 2nd derivatives, Gap of 4 to 24, 1st smoothing 4 to 24, 2nd smoothing 1 or 2

[0023] Scatter correction pre-processing may be done using standard normal variate and detrending. An algorithm may be created, for example using a Modified Partial Least Square (MPLS) method, for example based on a process initially defined by Shenk, J.S. and Westerhaus, M.O. (1991), Population Structuring of Near Infrared Spectra and Modified Partial Least Squares Regression. Crop Sciences 31, pp. 1548 - 1555.

[0024] MPLS involves a process of removing multivariate outliers & ‘inliers’ in a 2-step process. It involves the computation of Mahalanobis distances and in the 1st step data within the 3.0 boundary is selected and in a 2nd step, the data points further than 0.6 from each other are selected. The calibration models may be developed using dedicated software (e.g., WinISI from Foss Analytics of Denmark).

[0025] Similar results may be obtained different software & mathematics, such as with calibrator software or from the many machine learning algorithms or modelling framework available such as MATLAB, Unscrambler, R Earth, Python Py-Earth, Multivariate adaptive regression spline (MARS), or the like.

[0026] The calibrations may be developed by testing same side by side samples with an AO AC method listed and scanned on the Foss Foodscan 2 using the deep olive cup with a plastic bottom. The AO AC method reference data may be linked to the same sample spectra on the Foss Foodscan 2.

[0027] FIG. 2 illustrates generally an example comprising a technique 200, such as an automated method, for calibration of a near infrared technique for evaluating a protein characteristic of a frozen FTB sample. [0028] The technique 200 includes an operation 202 to generate a set of near infrared (NIR) spectra points for a corresponding set of frozen FTB samples using a spectrometer. The corresponding set of frozen FTB samples may be prepared in a deep olive cup (e.g., as described below with respect to FIG. 3). The deep olive cup may include a bottom surface made of plastic. The deep olive cup may be deeper than a typical sample cup. In an example, the near infrared spectra points are generated using infrared reflection spectroscopy. The set of near infrared spectra points may be generated using a wavelength within a range of frequencies between 400 nanometers and 2500 nanometers.

[0029] The technique 200 includes an operation 204 to use a wet chemistry analysis to determine respective protein values for each of the set of corresponding frozen FTB samples. The technique 200 includes an operation 206 to correlate the set of NIR spectra points to the respective protein values determined using the wet chemistry analysis. The technique 200 includes an operation 208 to generate a calibration model for the spectrometer from the correlation, the calibration model configured to predict a protein value for a sample of frozen FTB.

[0030] The technique 200 may further include using the calibration model to predict the protein value for the sample of frozen FTB. The protein value for the sample of frozen FTB may be output (e.g., displayed, stored, etc.). Outputting the protein value may include displaying the protein value on a display of the spectrometer or sending the protein value to a mobile device for display.

[0031] FIG. 3 illustrates a sample cup 300 according to an example. The sample cup 300 is an olive sample cup including a cup portion 302 and a bottom portion 304. In an example, the bottom portion 304 is removable. In other examples, it may be part of the cup portion 302 (e.g., forming a bottom of the cup portion 302). The bottom portion 304 may be made of plastic. The cup portion 302 may be made of metal or plastic, in various examples. The cup portion 302 may include a protruded wall portion 306 and a support portion 308. The bottom portion 304 may fit within the protruded wall portion 306 to rest on the support portion 308. The protruded wall portion 306 may have a height (e.g., as extended from the support portion 308). The height may be greater than a typical sample cup wall height. The greater height may be configured to hold more sample (e.g., more frozen FTB) than a typical sample cup. In the example shown in FIG. 3, the olive sample cup 300 is cylindrical, but in other examples it may be square, rectangular, or another shape. [0032] FIG. 4 illustrates generally an example comprising a technique 400, such as an automated method, for determining an indication of protein in a frozen FTB sample using a spectrometer. The technique 400 includes an operation 402 to receive a prepared sample of frozen FTB at a spectrometer. The sample may be prepared by placing in a cup such as a plastic bottom olive cup (e.g., with a specified minimum depth).

[0033] The technique 400 includes an operation 404 to scan, using the spectrometer, the prepared sample with infrared spectroscopy, such as according to a calibrated technique (e.g., developed as described in FIG. 2). The infrared spectroscopy may include infrared transmission spectroscopy or infrared reflection spectroscopy. A wavelength of the infrared spectroscopy may be within a near infrared electromagnetic spectrum, for example (e.g., 780 nm to 2500 nm). In an example, the wavelength may be within a range of frequencies between 400 nanometers and 2500 nanometers. The spectrometer may be a portable or mobile spectrometer.

[0034] The technique 400 includes an operation 406 to determine, for example using a processor (e.g., of the spectrometer), an indication of protein in the frozen FTB sample based on a result of the scan. The indication may include a quantitative indication, such as a relative indication, a ratio, a fraction such as a decimal fraction, or a percentage. Operation 406 may include converting raw spectrometer readings or data to a characteristic value using a formula.

[0035] The technique 400 includes an operation 408 to output the indication of protein in the frozen FTB sample. Operation 408 may include displaying the indication of protein in the frozen FTB sample on a display of the spectrometer or sending the indication of protein in the frozen FTB sample to a mobile device for display. In an example, operation 408 may include outputting an average or median of two or more iterations of the technique 400.

[0036] FIG. 5 illustrates generally an example 500 comprising a user input and display, such as a touch-screen user interface 530, such as may be used to receive inputs to control a spectrometer or to present results, such as a representation of a characteristic of frozen FTB being assessed using the spectrometer (such as the spectrometer 110 shown in FIG. 1), or a separate device in communication with the spectrometer, such as a mobile device or tablet. As an illustrative example, an input 510 may be used to receive an indication from the user that a particular characteristic is to be tested. Another input 515 may be used to receive an indication from the user that the spectrometer is to be calibrated. An input 520 may used to receive an indication from the user that a scan of a sample is to be initiated.

[0037] As mentioned in relation to various examples herein, data obtained using the spectrometer may be used to output a value of a characteristic being assessed, such as protein. The value itself may be presented on a display 550 of the touch-screen user interface 530 or a simplified representation may be presented (e.g., a pass/fail indication via a light or lights, for example based on a threshold). For example, the simplified representation may include a visual indication that the sample has a value for the characteristic over or below a threshold or within a range, such as via a “traffic light” (green/yellow/red, for example below a first threshold green, within a range between thresholds yellow, and above a second threshold red) style representation having three indicators 525 A, 525B, or 525C representing the threshold or range. Such states may be defined in a variety of manners, such as including a first state corresponding to “OK,” an second state such as “possibly unusable” or “try again,” or a third state indicative that the sample has a characteristic above a threshold for example “not ok.”

[0038] The interface of the example 500 of FIG. 5 shows user inputs unified with a display for presentation of results, but these elements may also be separate. For example, the inputs may be provided by soft-keys aligned with a display, or by a separate keypad or input (e.g., switches, knobs, etc.). The display may include a bit-field display or other display (e.g., an LED or liquidcrystal display having pre-defined display elements, such as a numerical indicator 540 having seven-segment digits or other arrangement or indicators 525 A, 525B, 525C comprising LED lamps). As an illustrative example, a unitless scale may be shown, such as a simplified numerical scale having values from one to five, or one to ten, such as having higher values to indicate relative concentration or percentage of the characteristic in the sample. Various aspects may be presented on the display 150, which may include a touchscreen display for receiving user input and displaying information.

[0039] FIG. 6 illustrates a block diagram of an example comprising a machine 600 upon which any one or more of the techniques (e.g., methodologies) discussed herein may be performed. The machine 600 may be included as a portion of elements shown in the system 100 of FIG. 1. In various examples, the machine 600 may operate as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine 600 may operate in the capacity of a server machine, a client machine, or both in server-client network environments. In an example, the machine 600 may act as a peer machine in peer-to-peer (P2P) (or other distributed) network environment. The machine 600 may be a personal computer (PC), a tablet device, a personal digital assistant (PDA), a mobile telephone, a web appliance, a network router, switch or bridge, a portable (e.g., hand-held) spectrometer such as including a microprocessor or microcontroller, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein, such as cloud computing, software as a service (SaaS), other computer cluster configurations.

[0040] Examples, as described herein, may include, or may operate by, logic or a number of components, or mechanisms. “Circuitry” refers generally a collection of circuits implemented in tangible entities that include hardware (e.g., simple circuits, gates, logic elements, etc.).

Circuitry membership may be flexible over time and underlying hardware variability. Circuitries include members that may, alone or in combination, perform specified operations when operating. In an example, hardware of the circuitry may be immutably designed to carry out a specific operation (e.g., hardwired). In an example, the hardware comprising the circuitry may include variably connected physical components (e.g., execution units, transistors, simple circuits, etc.) including a computer readable medium physically modified (e.g., magnetically, electrically, such as via a change in physical state or transformation of another physical characteristic, etc.) to encode instructions of the specific operation.

[0041] In connecting the physical components, the underlying electrical properties of a hardware constituent may be changed, for example, from an insulating characteristic to a conductive characteristic or vice versa. The instructions enable embedded hardware (e.g., the execution units or a loading mechanism) to create members of the circuitry in hardware via the variable connections to carry out portions of the specific operation when in operation. Accordingly, the computer readable medium is communicatively coupled to the other components of the circuitry when the device is operating. In an example, any of the physical components may be used in more than one member of more than one circuitry. For example, under operation, execution units may be used in a first circuit of a first circuitry at one point in time and reused by a second circuit in the first circuitry, or by a third circuit in a second circuitry at a different time.

[0042] Machine (e.g., computer system) 600 may include a hardware processor 600 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory 600 and a static memory 606, some or all of which may communicate with each other via an interlink (e.g., bus) 608. The machine 600 may further include a display unit 610, an alphanumeric input device 612 (e.g., a keyboard), and a user interface (UI) navigation device 614 (e.g., a mouse). In an example, the display unit 610, input device 612 and UI navigation device 614 may be a touch screen display. The machine 600 may additionally include a storage device (e.g., drive unit) 616, a signal generation device 618 (e.g., a speaker), a network interface device 620, and one or more sensors 621, such as a global positioning system (GPS) sensor, compass, accelerometer, or other sensor. The machine 60 may include an output controller 628, such as a serial (e.g., universal serial bus (USB), parallel, or other wired or wireless (e.g., infrared (IR), near field communication (NFC), etc.) connection to communicate or control one or more peripheral devices (e.g., a printer, card reader, etc.).

[0043] The storage device 616 may include a machine readable medium 622 on which is stored one or more sets of data structures or instructions 624 (e.g., software) embodying or utilized by any one or more of the techniques or functions described herein. The instructions 624 may also reside, completely or at least partially, within the main memory 604, within static memory 606, or within the hardware processor 602 during execution thereof by the machine 600. In an example, one or any combination of the hardware processor 602, the main memory 604, the static memory 606, or the storage device 616 may constitute machine readable media.

[0044] While the machine readable medium 622 is illustrated as a single medium, the term "machine readable medium" may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) configured to store the one or more instructions 624.

[0045] The term “machine readable medium” may include any medium that is capable of storing, encoding, or carrying instructions for execution by the machine 600 and that cause the machine 600 to perform any one or more of the techniques of the present disclosure, or that is capable of storing, encoding or carrying data structures used by or associated with such instructions. Non-limiting machine readable medium examples may include solid-state memories, and optical and magnetic media. Accordingly, machine-readable media are not transitory propagating signals. Specific examples of massed machine readable media may include: non-volatile memory, such as semiconductor memory devices (e.g., Electrically Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM)) and flash memory devices; magnetic or other phase-change or statechange memory circuits; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

[0046] The instructions 624 may further be transmitted or received over a communications network 626 using a transmission medium via the network interface device 620 utilizing any one of a number of transfer protocols (e.g., frame relay, internet protocol (IP), transmission control protocol (TCP), user datagram protocol (UDP), hypertext transfer protocol (HTTP), etc.). Example communication networks may include a local area network (LAN), a wide area network (WAN), a packet data network (e.g., the Internet), mobile telephone networks (e.g., cellular networks such as conforming to one or more standards such as a 4G standard or Long Term Evolution (LTE)), Plain Old Telephone (POTS) networks, and wireless data networks (e.g., Institute of Electrical and Electronics Engineers (IEEE) 802.11 family of standards known as Wi-Fi®, IEEE 802.15.4 family of standards, peer-to-peer (P2P) networks, among others. In an example, the network interface device 620 may include one or more physical jacks (e.g., Ethernet, coaxial, or phonejacks) or one or more antennas to connect to the communications network 626. In an example, the network interface device 620 may include a plurality of antennas to wirelessly communicate using at least one of single-input multiple-output (SIMO), multiple-input multiple-output (MIMO), or multiple-input single-output (MISO) techniques. The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine 600, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software.

[0047] Each of the non-limiting examples below can stand on its own, or can be combined in various permutations or combinations with one or more of the other aspects or other subject matter described in this document.

[0048] Example 1 is a method for calibration of a near infrared technique for evaluating a protein characteristic of a frozen finely textured beef (FTB) sample, the method comprising: generating a set of near infrared (NIR) spectra points for a corresponding set of frozen FTB samples using a spectrometer; using a wet chemistry analysis to determine respective protein values for each of the set of corresponding frozen FTB samples; correlating the set of NIR spectra points to the respective protein values determined using the wet chemistry analysis; and generating a calibration model for the spectrometer from the correlation, the calibration model configured to predict a protein value for a sample of frozen FTB.

[0049] In Example 2, the subject matter of Example 1 includes, wherein the corresponding set of frozen FTB samples are prepared in a deep olive cup.

[0050] In Example 3, the subject matter of Example 2 includes, wherein the deep olive cup includes a bottom surface made of plastic.

[0051] In Example 4, the subject matter of Examples 1-3 includes, wherein the near infrared spectra points are generated using infrared reflection spectroscopy.

[0052] In Example 5, the subject matter of Examples 1-4 includes, wherein the set of near infrared (NIR) spectra points are generated using a wavelength within a range of frequencies between 400 nanometers and 2500 nanometers.

[0053] In Example 6, the subject matter of Examples 1-5 includes, using the calibration model to predict the protein value for the sample of frozen FTB.

[0054] In Example 7, the subject matter of Example 6 includes, outputting the protein value. [0055] In Example 8, the subject matter of Example 7 includes, wherein outputting the protein value includes displaying the protein value on a display of the spectrometer or sending the protein value to a mobile device for display.

[0056] Example 9 is a system for calibration of a near infrared technique for evaluating a protein characteristic of a frozen finely textured beef (FTB) sample, the system comprising: a spectrometer configured to: emit light comprising a specified range of near infrared wavelengths; receive a set of reflections from a set of frozen FTB samples; and generate, based on the set of reflections, a set of near infrared (NIR) spectra points corresponding to the set of frozen FTB samples; and processing circuitry; memory, including instructions, which when executed by the processing circuitry, cause the processing circuitry to: correlate the set of NIR spectra points to respective protein values determined using a wet chemistry analysis; and generate a calibration model for the spectrometer from the correlation, the calibration model configured to predict a protein value for a sample of frozen FTB.

[0057] In Example 10, the subject matter of Example 9 includes, wherein the corresponding set of frozen FTB samples are prepared in a deep olive cup.

[0058] In Example 11, the subject matter of Example 10 includes, wherein the deep olive cup includes a bottom surface made of plastic.

[0059] In Example 12, the subject matter of Examples 9-11 includes, wherein the near infrared spectra points are generated by the spectrometer using infrared reflection spectroscopy.

[0060] In Example 13, the subject matter of Examples 9-12 includes, wherein the set of near infrared (NIR) spectra points are generated using a wavelength within a range of frequencies between 400 nanometers and 2500 nanometers.

[0061] In Example 14, the subject matter of Examples 9-13 includes, wherein the instructions are further configured to cause the processing circuitry to use the calibration model to predict the protein value for the sample of frozen FTB.

[0062] In Example 15, the subject matter of Example 14 includes, wherein the instructions are further configured to cause the processing circuitry to output the protein value.

[0063] In Example 16, the subject matter of Example 15 includes, wherein to output the protein value, the instructions are further configured to cause the processing circuitry to display the protein value on a display of the spectrometer or send the protein value to a mobile device for display.

[0064] Example 17 is a spectrometer for calibration of a near infrared technique for evaluating a protein characteristic of a frozen finely textured beef (FTB) sample, the spectrometer comprising: a light configured to emit light comprising a specified range of near infrared wavelengths; a detector to receive a set of reflections from a set of frozen FTB samples; processing circuitry; and memory, including instructions, which when executed by the processing circuitry, cause the processing circuitry to: generate, based on the set of reflections, a set of near infrared (NIR) spectra points corresponding to the set of frozen FTB samples; correlate the set of NIR spectra points to respective protein values determined using a wet chemistry analysis; and generate a calibration model for the spectrometer from the correlation, the calibration model configured to predict a protein value for a sample of frozen FTB.

[0065] In Example 18, the subject matter of Example 17 includes, wherein the corresponding set of frozen FTB samples are prepared in a deep olive cup.

[0066] In Example 19, the subject matter of Example 18 includes, wherein the deep olive cup includes a bottom surface made of plastic.

[0067] In Example 20, the subject matter of Examples 17-19 includes, wherein the near infrared spectra points are generated by the spectrometer using infrared reflection spectroscopy.

[0068] In Example 21, the subject matter of Examples 17-20 includes, wherein the set of near infrared (NIR) spectra points are generated using a wavelength within a range of frequencies between 400 nanometers and 2500 nanometers.

[0069] In Example 22, the subject matter of Examples 17-21 includes, wherein the instructions are further configured to cause the processing circuitry to use the calibration model to predict the protein value for the sample of frozen FTB.

[0070] In Example 23, the subject matter of Example 22 includes, wherein the instructions are further configured to cause the processing circuitry to output the protein value.

[0071] In Example 24, the subject matter of Example 23 includes, wherein to output the protein value, the instructions are further configured to cause the processing circuitry to display the protein value on a display of the spectrometer or send the protein value to a mobile device for display.

[0072] Example 25 is at least one machine-readable medium including instructions that, when executed by processing circuitry, cause the processing circuitry to perform operations to implement of any of Examples 1-24.

[0073] Example 26 is an apparatus comprising means to implement of any of Examples 1-24. [0074] Example 27 is a system to implement of any of Examples 1-24.

[0075] Example 28 is a method to implement of any of Examples 1-24.

[0076] Method examples described herein can be machine or computer-implemented at least in part. Some examples can include a computer-readable medium or machine-readable medium encoded with instructions operable to configure an electronic device to perform methods as described in the above examples. An implementation of such methods can include code, such as microcode, assembly language code, a higher-level language code, or the like. Such code can include computer readable instructions for performing various methods. The code may form portions of computer program products. Further, in an example, the code can be tangibly stored on one or more volatile, non-transitory, or non-volatile tangible computer-readable media, such as during execution or at other times. Examples of these tangible computer-readable media can include, but are not limited to, hard disks, removable magnetic disks, removable optical disks (e.g., compact disks and digital video disks), magnetic cassettes, memory cards or sticks, random access memories (RAMs), read only memories (ROMs), and the like.

Claims

THE CLAIMED INVENTION IS:

1. A method for calibration of a near infrared technique for evaluating a protein characteristic of a frozen finely textured beef (FTB) sample, the method comprising: generating a set of near infrared (NIR) spectra points for a corresponding set of frozen FTB samples using a spectrometer; using a wet chemistry analysis to determine respective protein values for each of the set of corresponding frozen FTB samples; correlating the set of NIR spectra points to the respective protein values determined using the wet chemistry analysis; and generating a calibration model for the spectrometer from the correlation, the calibration model configured to predict a protein value for a sample of frozen FTB.

2. The method of claim 1, wherein the corresponding set of frozen FTB samples are prepared in a deep sided olive cup for generating the set of NIR spectra points.

3. The method of claim 2, wherein the deep olive cup includes a bottom surface comprising plastic.

4. The method of any of claims 1-3, wherein the near infrared spectra points are generated using infrared reflection spectroscopy.

5. The method of any of claims 1-4, wherein the set of near infrared (NIR) spectra points are generated using a wavelength within a range of frequencies between 400 nanometers and 2500 nanometers.

6. The method of any of claims 1-5, further comprising using the calibration model to predict the protein value for the sample of frozen FTB.

7. The method of any of claims 1-6, further comprising outputting the protein value.

8. The method of any of claims 1-7, wherein outputting the protein value includes displaying the protein value on a display of the spectrometer or sending the protein value to a mobile device for display.

9. A system for calibration of a near infrared technique for evaluating a protein characteristic of a frozen finely textured beef (FTB) sample, the system comprising: a spectrometer configured to: emit light comprising a specified range of near infrared wavelengths; receive a set of reflections from a set of frozen FTB samples; and generate, based on the set of reflections, a set of near infrared (NIR) spectra points corresponding to the set of frozen FTB samples; and processing circuitry; memory, including instructions, which when executed by the processing circuitry, cause the processing circuitry to: correlate the set of NIR spectra points to respective protein values determined using a wet chemistry analysis; and generate a calibration model for the spectrometer from the correlation, the calibration model configured to predict a protein value for a sample of frozen FTB.

10. The system of claim 9, wherein the corresponding set of frozen FTB samples are prepared in a deep sided olive cup for generating the set of NIR spectra points.

11. The system of claim 10, wherein the deep olive cup includes a bottom surface comprising plastic.

12. The system of any of claims 9-11, wherein the near infrared spectra points are generated by the spectrometer using infrared reflection spectroscopy.

13. The system of any of claims 9-12, wherein the set of near infrared (NIR) spectra points are generated using a wavelength within a range of frequencies between 400 nanometers and 2500 nanometers.

14. The system of any of claims 9-13, wherein the instructions are further configured to cause the processing circuitry to use the calibration model to predict the protein value for the sample of frozen FTB.

15. The system of any of claims 9-14, wherein the instructions are further configured to cause the processing circuitry to output the protein value.

16. The system of any of claims 9-15, wherein to output the protein value, the instructions are further configured to cause the processing circuitry to display the protein value on a display of the spectrometer or send the protein value to a mobile device for display.

17. A spectrometer for calibration of a near infrared technique for evaluating a protein characteristic of a frozen finely textured beef (FTB) sample, the spectrometer comprising: a light configured to emit light comprising a specified range of near infrared wavelengths; a detector to receive a set of reflections from a set of frozen FTB samples; processing circuitry; and memory, including instructions, which when executed by the processing circuitry, cause the processing circuitry to: generate, based on the set of reflections, a set of near infrared (NIR) spectra points corresponding to the set of frozen FTB samples; correlate the set of NIR spectra points to respective protein values determined using a wet chemistry analysis; and generate a calibration model for the spectrometer from the correlation, the calibration model configured to predict a protein value for a sample of frozen FTB.

18. The spectrometer of claim 17, wherein the corresponding set of frozen FTB samples are prepared in a deep sided olive cup for generating the set of NIR spectra points.

19. The spectrometer of claim 18, wherein the deep olive cup includes a bottom surface comprising plastic.

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20. The spectrometer of any of claims 17-19, wherein the near infrared spectra points are generated by the spectrometer using infrared reflection spectroscopy.

21. The spectrometer of any of claims 17-20, wherein the set of near infrared (NIR) spectra points are generated using a wavelength within a range of frequencies between 400 nanometers and 2500 nanometers.

22. The spectrometer of any of claims 17-21, wherein the instructions are further configured to cause the processing circuitry to use the calibration model to predict the protein value for the sample of frozen FTB.

23. The spectrometer of any of claims 17-22, wherein the instructions are further configured to cause the processing circuitry to output the protein value.

24. The spectrometer of any of claims 17-23, wherein to output the protein value, the instructions are further configured to cause the processing circuitry to display the protein value on a display of the spectrometer or send the protein value to a mobile device for display.

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