WO2023231903A1 - 适用于检测农产品中微量元素的光谱仪及其应用 - Google Patents

适用于检测农产品中微量元素的光谱仪及其应用 Download PDF

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Publication number
WO2023231903A1
WO2023231903A1 PCT/CN2023/096349 CN2023096349W WO2023231903A1 WO 2023231903 A1 WO2023231903 A1 WO 2023231903A1 CN 2023096349 W CN2023096349 W CN 2023096349W WO 2023231903 A1 WO2023231903 A1 WO 2023231903A1
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Prior art keywords
trace elements
light
agricultural products
detecting trace
spectrometer
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PCT/CN2023/096349
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English (en)
French (fr)
Inventor
谢丽娟
郑英杰
应义斌
李麟
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浙江大学
浙江开浦科技有限公司
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Application filed by 浙江大学, 浙江开浦科技有限公司 filed Critical 浙江大学
Publication of WO2023231903A1 publication Critical patent/WO2023231903A1/zh
Priority to US18/613,198 priority Critical patent/US20240230527A1/en

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/02—Food
    • G01N33/025—Fruits or vegetables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2201/00—Features of devices classified in G01N21/00
    • G01N2201/06—Illumination; Optics
    • G01N2201/061—Sources

Definitions

  • This application relates to the technical field of non-destructive testing of agricultural products, specifically, to a spectrometer suitable for detecting trace elements in agricultural products and its application.
  • the quality grading of agricultural products, especially fruits, has become an additional condition for cross-border trade and one of the important means to increase the added value of agricultural products.
  • the quality of agricultural products mainly includes external quality (size, shape, color, surface defects, etc.) and internal quality (sugar content, acidity, maturity, etc.).
  • Infrared spectroscopy is a powerful tool for determining molecular composition and structure.
  • the near-infrared light region is mainly the absorption band produced by the frequency doubling and combined frequency absorption of the stretching vibration of hydrogen-containing groups (such as O-H, N-H, C-H).
  • hydrogen-containing groups such as O-H, N-H, C-H.
  • infrared spectroscopy to detect trace elements (such as lycopene) that are beneficial to the human body in agricultural products.
  • trace elements such as lycopene
  • lycopene is widely present in tomatoes, tomato products, watermelon, grapefruit and other fruits. It is the main pigment in ripe tomatoes and one of the common carotenoids.
  • Studies have shown that lycopene can effectively reduce the risk of prostate cancer and other tumors and cardiovascular diseases. Therefore, it is of broad significance to classify the lycopene content of agricultural products containing lycopene.
  • some destructive detection methods are currently used, such as spectrophotometry. method, thin layer chromatography, high performance liquid chromatography, etc.
  • Some embodiments of the present application propose spectrometers suitable for detecting trace elements in agricultural products and their applications to solve the technical problems mentioned in the background art section above.
  • some embodiments of the present application provide a spectrometer suitable for detecting trace elements in agricultural products, including: a light source device for generating light required for spectral detection; a converging device for converging the light source The light generated by the device; the chopper device is used to modulate the frequency of the light collected through the lens device; the filter device is used to shield light of other wavelengths except the preset wavelength; the detection device is used to receive the agricultural products to be inspected and The light passes through the filter device and converts the optical signal into an electrical signal; the processing device is used to generate spectral analysis data according to the electrical signal output by the detection device and output the analysis result of the substance content according to the spectral analysis data; wherein, the pre-processing device of the filter device Let the wavelength range be from 400nm to 5000nm.
  • the light generated by the light source device includes infrared light. Further, the wavelength of the light generated by the light source device ranges from 400 nm to 5000 nm.
  • the light source device includes a halogen lamp.
  • the converging device includes at least one convex lens.
  • the chopper device includes: an optical chopper, including a rotating blade with an adjustable rotation frequency to periodically block the light collected through the lens device; a chopper driver, used to drive the rotating blade to rotate; a chopper controller, It is used to control the operation of the chopper driver to control the rotation frequency of the rotating blades; wherein the chopper driver and the rotating blades of the optical chopper form a mechanical connection, and the chopper driver and the chopper controller form an electrical connection.
  • an optical chopper including a rotating blade with an adjustable rotation frequency to periodically block the light collected through the lens device
  • a chopper driver used to drive the rotating blade to rotate
  • a chopper controller It is used to control the operation of the chopper driver to control the rotation frequency of the rotating blades
  • the chopper driver and the rotating blades of the optical chopper form a mechanical connection
  • the chopper driver and the chopper controller form an electrical connection.
  • the spectrometer suitable for detecting trace elements in agricultural products also includes: a phase locking device for improving the signal-to-noise ratio of the electrical signal output from the detection device to the processing device.
  • the phase locking device includes a lock-in amplifier; the lock-in amplifier is electrically connected to the detection device, the chopper controller and the processing device respectively.
  • the detection device includes a near-infrared light detector; wherein the near-infrared light detector includes at least one of a PbS detector array or an InGaAs detector array for detecting light signals of a preset wavelength.
  • the spectrometer suitable for detecting trace elements in agricultural products also includes: a signal amplifier, used to amplify the optical signal of a preset wavelength that passes through the filter device; wherein the signal amplifier uses a metamaterial with a micro-nano structure array .
  • some embodiments of the present application provide an application of the aforementioned spectrometer suitable for detecting trace elements in agricultural products, that is, the said spectrometer suitable for detecting trace elements in agricultural products is used to detect tomatoes in agricultural products. red pigment content.
  • the beneficial effect of this application is that it provides a spectrometer that can effectively detect the content of substances in agricultural products and is suitable for detecting trace elements in agricultural products and its application.
  • Figure 1 is a structural block diagram of a spectrometer according to an embodiment of the present application.
  • Figure 2 is a block diagram of the steps of a spectrum detection method according to an embodiment of the present application.
  • Figure 3 is a schematic architectural diagram of a spectrum detection system according to an embodiment of the present application.
  • Figure 4 is a schematic structural diagram of a spectrum detection device according to an embodiment of the present application.
  • Figure 5 is a step block diagram of a part of a spectrum detection method according to another embodiment of the present application.
  • Figure 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application;
  • 105 Detection device; 1051. Near-infrared light detector; 105a, PbS detector array; 105b, InGaAs detector array;
  • Phase-locking device 1071. Phase-locking amplifier;
  • a spectrometer 100 includes: a light source device 101 , a converging device 102 , a chopping device 103 , a filter device 104 , a detection device 105 and a processing device 106 .
  • the light source device 101 is used to generate the light required for spectrum detection; the converging device 102 is used to converge the light generated by the light source device 101; the chopper device 103 is used to modulate the frequency of the light collected through the lens device; and the filter device 104 is used to Shielding light of other wavelengths except the preset wavelength; the detection device 105 is used to receive the light that passes through the agricultural product 200 to be inspected and passes through the filter device 104 and converts the optical signal into an electrical signal; the processing device 106 is used to output according to the detection device 105 The electrical signal generates spectral analysis data and outputs the analysis results of the substance content based on the spectral analysis data.
  • the spectrometer 100 of the present application artificially selects the characteristic wavelengths of some trace substances or nutrients (such as the chemical reaction with lycopene) in the near-infrared light band. Component-related characteristic wavelengths), which greatly reduces the useless spectral information in the original spectrum, and has guiding significance for the detection of trace elements in agricultural products.
  • the preset wavelength of the filter device ranges from 400 nm to 5000 nm; according to the trace substances or nutrients that need to be detected, the preset wavelength of the filter device is set correspondingly to filter out light with non-characteristic wavelengths.
  • the preset wavelengths of the filter device 104 include at least one or more of 900nm, 1180nm, 1400nm, 1720nm and 2350nm, and these preset wavelengths are used for the detection of lycopene.
  • the filter device includes one or more filters.
  • the light generated by the light source device 101 includes infrared light, the wavelength range of which covers the characteristic wavelength required to detect trace substances or nutrients. More specifically, the light source device 101 includes a halogen lamp.
  • the wavelength of the light generated by the light source device 101 ranges from 400 nm to 5000 nm.
  • the converging device 102 includes at least one convex lens.
  • the convex lens is preferably a plano-convex lens, with its non-convex surface facing the light source position.
  • the chopper device 103 includes: an optical chopper 1031, a chopper driver 1032 and a chopper controller 1033;
  • the optical chopper 1031 includes a rotating blade with an adjustable rotation frequency, and the rotating blade periodically blocks the passing The light collected by the lens device;
  • the chopper driver 1032 is used to drive the rotating blade to rotate;
  • the chopper controller 1033 is used to control the operation of the chopper driver 1032 to control the rotation frequency of the rotating blade.
  • the relationship between the chopper driver 1032 and the optical chopper 1031 The rotating blades form a mechanical connection, and the chopper driver 1032 and the chopper controller 1033 form an electrical connection.
  • the chopper controller 1033 outputs a modulated frequency electrical signal, and then controls the modulation frequency of the optical chopper 1031 to modulate the light collected through the lens device into a high-frequency optical signal at a set frequency.
  • the detection device 105 includes a near-infrared light detector 1051.
  • the near-infrared light detector 1051 includes at least one of a PbS detector array 105a or an InGaAs detector array 105b to achieve detection of a preset wavelength.
  • the spectrometer 100 of an embodiment of the present application further includes: a phase locking device 107.
  • the phase locking device 107 is used to improve the signal-to-noise ratio of the electrical signal output by the detection device 105 to the processing device 106.
  • the lock-in device 107 includes a lock-in amplifier 1071; the lock-in amplifier 1071 is electrically connected to the detection device 105, the chopper controller 1033 and the processing device 106 respectively. Among them, the output signal interface of the detection device 105 is connected to the input signal interface of the lock-in amplifier 1071, and the frequency signal output interface of the chopper controller 1033 is connected to the reference signal interface of the lock-in amplifier 1071.
  • the lock-in amplifier 1071 is based on the frequency of the reference signal. Separate specific carrier frequency signals.
  • the spectrometer 100 also includes: a signal amplifier 108; the signal amplifier 108 is a metamaterial with a series of engraved micro-nano structure arrays on its surface, which can convert light of a specific wavelength.
  • the signal is amplified; specifically, in this application, the signal amplifier 108 amplifies the preset wavelength of the optical filter device 104, and the amplified optical signal is received by the detection device 105.
  • the signal amplifier 108 may be omitted.
  • the method is implemented by a spectrometer.
  • the spectrometer includes: a light source device, a convergence device, a chopping device, a filter device, and a detection device.
  • Device and processing device; the spectral detection method includes the following main steps:
  • the excitation light source device 101 generates light source light.
  • S2 Set a converging device 102 on the optical path of the light source to converge the light source into a converged ray.
  • S3 Control and drive the chopper device 103 to adjust the frequency of the post-collection light to obtain detection light with a modulated frequency for irradiating agricultural products.
  • S4 Set a filter device on the optical path of the detection light to block the light of other wavelengths except the preset wavelength in the detection light after passing through the agricultural products to obtain the characteristic light.
  • S5 Control the detection device 105 to receive the characteristic light and convert the optical signal of the characteristic light into an electrical signal.
  • the control processing device 106 generates spectral analysis data according to the electrical signal output by the detection device 105 and outputs the analysis result of the substance content according to the spectral analysis data.
  • the preset wavelength range of the filter device 104 is 400 nm to 5000 nm.
  • the preset wavelength of the filter device is limited as follows:
  • the preset wavelength value range of the filter device at least includes 900 nm to 1200 nm.
  • the preset wavelength value range of the filter device at least includes 1300nm to 1500nm.
  • the preset wavelength value range of the filter device at least includes 1600nm to 1800nm.
  • the preset wavelength value range of the filter device includes 2200nm to 2400nm.
  • the preset wavelength of the filter device includes at least one or more of 900nm, 1180nm, 1400nm, 1720nm and 2350nm.
  • the spectral detection method includes steps performed by the spectrometer:
  • the excitation light source device 101 generates light source light.
  • S2 Set a converging device 102 on the optical path of the light source to converge the light source into a converged ray.
  • S3 Control and drive the chopper device 103 to adjust the frequency of the post-collection light to obtain detection light with a modulated frequency for irradiating agricultural products.
  • S4 Set a filter device on the optical path of the detection light to block the light of other wavelengths except the preset wavelength in the detection light after passing through the agricultural products to obtain the characteristic light.
  • S5 Control the detection device 105 to receive the characteristic light and convert the optical signal of the characteristic light into an electrical signal.
  • the control processing device 106 generates spectral analysis data according to the electrical signal output by the detection device 105 and outputs the analysis result of the substance content according to the spectral analysis data.
  • the preset wavelength range of the filter device is 400nm to 5000nm.
  • the spectrum detection method includes steps performed by the server 300:
  • St1 In response to the analysis results output by a spectrometer processing device, query the modulation frequency and analysis results in the historical data of the spectrometer.
  • St2 Select the modulation frequency and analysis results corresponding to the reciprocal N groups in the historical data and input them into a modulation frequency analysis model so that the modulation frequency analysis model outputs the modulation frequency prediction value and the corresponding confidence level.
  • St3 Determine whether the confidence is greater than or equal to the preset confidence threshold. If so, the predicted modulation frequency value will be accepted and fed back to the spectrometer; if not, the predicted modulation frequency value will not be accepted.
  • the modulation frequency analysis model is trained by using modulation frequencies and analysis results in historical data of multiple spectrometers as training data.
  • the spectrum detection device of the present application includes: a query module, used to query the modulation frequency and analysis results in the historical data of a spectrometer in response to the analysis results output by the spectrometer processing device; an output module, used to Select the modulation frequency and analysis results corresponding to the last N groups in the historical data and input them into a modulation frequency analysis model so that the modulation frequency analysis model outputs the modulation frequency prediction value and the corresponding confidence level; the judgment module is used to judge whether the confidence level is greater than or equal to the predicted value. Set the confidence threshold. If yes, the modulation frequency prediction value will be accepted and fed back to the spectrometer; if not, the modulation frequency prediction value will not be accepted.
  • the modulation frequency analysis model consists of the modulation frequency and the modulation frequency in the historical data of multiple spectrometers. The analysis results are used as training data for training.
  • the N value is a positive integer and can be considered to be set and adjusted.
  • the advantage of using the above solution is that when applied to the fruit production line, the spectral frequency band can be quickly selected and adjusted according to the different trace substances or nutrients that need to be detected in a certain batch of fruits. And the model based on system training can reduce training costs and improve prediction accuracy.
  • the modulation frequency analysis model is a convolutional neural network model, and its input data is a number of matrices converted into several standard size spectrograms and corresponding modulation frequencies, which are used to be trained to achieve the prediction function.
  • the specific model architecture and The training method is well known to those skilled in the art and will not be described in detail again.
  • the electronic device 800 may include a processing device (eg, central processing unit, graphics processor, etc.) 801 , which may be loaded into a random access device according to a program stored in a read-only memory (ROM) 802 or from a storage device 808 .
  • the program in the memory (RAM) 803 executes various appropriate actions and processes.
  • various programs and data required for the operation of the electronic device 800 are also stored.
  • the processing device 801, ROM 802 and RAM 803 are connected to each other via a bus 804.
  • An input/output (I/O) interface 805 is also connected to bus 804.
  • the following devices may be connected to the I/O interface 805: Input devices including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc. 806: including, for example, a liquid crystal display (LCD), speakers, vibration An output device 807 such as a computer; a storage device 808 including a magnetic tape, a hard disk, etc.; and a communication device 809.
  • the communication device 809 may allow the electronic device 800 to communicate wirelessly or wiredly with other devices to exchange data.
  • FIG. 6 illustrates electronic device 800 with various means, it should be understood that implementation or availability of all illustrated means is not required. More or fewer means may alternatively be implemented or provided. Each block shown in Figure 6 may represent one device, or may represent multiple devices as needed.
  • the processes described above with reference to the flowcharts may be implemented as a computer software program.
  • some embodiments of the present disclosure include a computer program product including a computer program carried on a computer-readable medium, the computer program containing program code for performing the method illustrated in the flowchart.
  • the computer program may be downloaded and installed from the network via communication device 809, or from storage device 808, or from ROM 802.
  • the processing device 801 the above-described functions defined in the methods of some embodiments of the present disclosure are performed.
  • the computer-readable medium mentioned above in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the above two.
  • the computer-readable storage medium may be, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any combination thereof. More specific examples of computer readable storage media may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard drive, random access memory (RAM), read only memory (ROM), removable Programmd read-only memory (EPROM or flash memory), fiber optics, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.
  • a computer-readable storage medium may be any tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device.
  • the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code therein. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above.
  • a computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device .
  • Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wire, optical cable, RF (radio frequency), etc., or any suitable combination of the above.
  • the client and server can communicate using any currently known or future developed network protocol such as HTTP (HyperTextTransferProtocol, Hypertext Transfer Protocol), and can communicate with digital data in any form or medium ( For example, communication network) interconnection.
  • HTTP HyperTextTransferProtocol, Hypertext Transfer Protocol
  • communications networks include local area networks (“LAN”), wide area networks (“WAN”), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or developed in the future network.
  • the above-mentioned computer-readable medium may be included in the above-mentioned electronic device; it may also exist alone without being assembled into the electronic device.
  • the computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: In response to the analysis results output by a spectrometer processing device, query the historical data of the spectrometer.
  • modulation frequency and analysis results select the modulation frequency and analysis results corresponding to the reciprocal N groups in the historical data and input them into a modulation frequency analysis model so that the modulation frequency analysis model outputs the modulation frequency prediction value and the corresponding confidence level; determine whether the confidence level is greater than Equal to the preset confidence threshold, if yes, the modulation frequency prediction value is accepted and fed back to the spectrometer; if not, the modulation frequency prediction value is not accepted; where, the modulation frequency analysis model is composed of the modulation frequency in the historical data of multiple spectrometers. Frequencies and analysis results are trained as training data.
  • Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages, including object-oriented programming languages such as Java, Smalltalk, C++, or a combination thereof, Also included are conventional procedural programming languages: such as "C" or similar programming languages.
  • the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
  • the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (such as an Internet service provider). connected via the Internet).
  • LAN local area network
  • WAN wide area network
  • Internet service provider such as an Internet service provider
  • each block in the flowchart or block diagram may represent a module, segment, or portion of code that contains one or more logic functions that implement the specified executable instructions.
  • each block of the block diagram and/or flowchart illustration, and combinations of blocks in the block diagram and/or flowchart illustration can be implemented by special purpose hardware-based systems that perform the specified functions or operations. , or can be implemented using a combination of specialized hardware and computer instructions.
  • the units described in some embodiments of the present disclosure may be implemented in software or hardware.
  • the described units may also be provided in the processor, and the names of these units do not constitute limitations on the units themselves under certain circumstances.
  • FPGAs Field Programmable Gate Arrays
  • ASICs Application Specific Integrated Circuits
  • ASSPs Application Specific Standard Products
  • SOCs Systems on Chips
  • CPLD Complex Programmable Logical device

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Abstract

一种适用于检测农产品中微量元素的光谱仪（100）及其应用，光谱仪（100）包括：光源装置（101），用于产生光谱检测所需的光线；汇聚装置（102），用于汇聚光源装置（101）产生的光线；斩波装置（103），用于调制经过透镜装置汇聚的光线的频率；滤光装置（104），用于屏蔽除预设波长以外的其他波长的光线；探测装置（105），用于接收经过待检农产品并通过滤光装置（104）的光线并将光信号转化为电信号；处理装置（106），用于根据探测装置（105）输出的电信号生成光谱分析数据并根据光谱分析数据输出物质含量的分析结果；滤光装置（104）的预设波长取值范围为400nm至5000nm。提供了一种能够有效检出农产品中微量元素的适用于检测农产品中微量元素的光谱仪（100）。

Description

适用于检测农产品中微量元素的光谱仪及其应用 技术领域
本申请涉及农产品的无损检测技术领域，具体而言，涉及一种适用于检测农产品中微量元素的光谱仪及其应用。
 背景技术
农产品特别是水果的品质分级已经成为其跨境贸易的一项附加条件，是作为提升农产品附加价值的重要手段之一。农产品的品质主要包括外部品质(尺寸、形状、颜色、表面缺陷等)和内部品质(糖度、酸度、成熟度等)。
红外光谱是确定分子组成和结构的有力工具，其中近红外光区主要是含氢基团(如O‑H、N‑H、C‑H)伸缩振动的倍频及组合频吸收产生的吸收带，得益于近红外光谱技术的发展，农产品的内部品质的无损检测也已经成为了可能，如今已经能够实现一些高效益的农产品的内部品质检测，比如水果的内部糖度、酸度的检测，并以此作为分级依据进行内部品质分级，来提高其经济效益。
利用红外光谱检测农产品中对人体有益的微量元素(例如番茄红素)，具有重要意义。其中，番茄红素是一种广泛存在于番茄、番茄制品及西瓜、葡萄柚等水果中，是成熟番茄中的主要色素，也是常见的类胡萝卜素之一。研究表明，番茄红素可以有效降低前列腺癌等多种肿瘤和心血管疾病等发生的风险。因此，对含有番茄红素的农产品进行番茄红素含量的分级具有广泛的意义，但是由于其在农产品内部的含量远低于其他成分，目前采用的都是一些有损的检测方法，比如分光光度法、薄层色谱法、高效液相色谱法等，这些方法明显不符合对农产品快速分级的需求。然而采用传统的近红外光谱仪获得的光谱数据，其包含的与番茄红素含量相关的光学信息又较少，因此目前还很难实现农产品中番茄红素含量的快速无损检测。
 发明内容
本申请的内容部分用于以简要的形式介绍构思，这些构思将在后面的具体实施方式部分被详细描述。本申请的内容部分并不旨在标识要求保护的技术方案的关键特征或必要特征，也不旨在用于限制所要求的保护的技术方案的范围。
本申请的一些实施例提出了适用于检测农产品中微量元素的光谱仪及其应用，来解决以上背景技术部分提到的技术问题。
作为本申请的第一方面，本申请的一些实施例提供了一种适用于检测农产品中微量元素的光谱仪，包括：光源装置，用于产生光谱检测所需的光线；汇聚装置，用于汇聚光源装置产生的光线；斩波装置，用于调制经过透镜装置汇聚的光线的频率；滤光装置，用于屏蔽除预设波长以外的其他波长的光线；探测装置，用于接收经过待检农产品并通过滤光装置的光线并将光信号转化为电信号；处理装置，用于根据探测装置输出的电信号生成光谱分析数据并根据光谱分析数据输出物质含量的分析结果；其中，滤光装置的预设波长取值范围为400nm至5000nm。
进一步地，光源装置产生的光线包括红外光线。进一步地，光源装置产生的光线的波长的取值范围为400nm至5000nm。
进一步地，光源装置包括一个卤素灯。
进一步地，汇聚装置至少包括一个凸透镜。
进一步地，斩波装置包括：光学斩波器，包含一个旋转频率可调的旋转叶片以周期性的遮挡经过透镜装置汇聚的光线；斩波驱动器，用于驱动旋转叶片转动；斩波控制器，用于控制斩波驱动器的运行以控制旋转叶片的旋转频率；其中，斩波驱动器与光学斩波器的旋转叶片构成机械连接，斩波驱动器与斩波控制器构成电性连接。
进一步地，适用于检测农产品中微量元素的光谱仪还包括：锁相装置，用于提高探测装置输出到处理装置的电信号的信噪比。
进一步地，锁相装置包括一个锁相放大器；锁相放大器分别与探测装置、斩波控制器和处理装置构成电性连接。
进一步地，探测装置包含近红外光探测器；其中，近红外光探测器包含用于检测预设波长的光信号的PbS检测器阵列或InGaAs检测器阵列中的至少一种。
进一步地，适用于检测农产品中微量元素的光谱仪还包括：信号放大器，用于对通过滤光装置的预设波长的光信号进行放大；其中，信号放大器采用一种具有微纳结构阵列的超材料。
作为本申请的第二方面，本申请的一些实施例提供了一种前述适用于检测农产品中微量元素的光谱仪的应用，即将所述适用于检测农产品中微量元素的光谱仪用于检测农产品中的番茄红素的含量。
本申请的有益效果在于：提供了一种能够有效检出农产品中物质含量的适用于检测农产品中微量元素的光谱仪及其应用。
 附图说明
构成本申请的一部分的附图用来提供对本申请的进一步理解，使得本申请的其它特征、目的和优点变得更明显。本申请的示意性实施例附图及其说明用于解释本申请，并不构成对本申请的不当限定。
另外，贯穿附图中，相同或相似的附图标记表示相同或相似的元素。应当理解附图是示意性的，元件和元素不一定按照比例绘制。
在附图中：
图1是根据本申请一种实施例的光谱仪的结构框图；
图2是根据本申请一种实施例的光谱检测方法的步骤框图；
图3是根据本申请一种实施例的光谱检测系统的架构示意图；
图4是根据本申请一种实施例的光谱检测装置的架构示意图；
图5是根据本申请另一种实施例的光谱检测方法的一部分的步骤框图；图6是根据本申请一种实施例的电子设备的结构示意图；
附图标记的含义为：
100、光谱仪；
101、光源装置；102、汇聚装置；
103、斩波装置；1031、光学斩波器；1032、斩波驱动器；1033、斩波控制器；
104、滤光装置；
105、探测装置；1051、近红外光探测器；105a、PbS检测器阵列；105b、InGaAs检测器阵列；
106、处理装置；
107、锁相装置；1071、锁相放大器；
108、信号放大器；
200、待检农产品；
300、服务器。
 具体实施方式
下面将参照附图更详细地描述本公开的实施例。虽然附图中显示了本公开的某些实施例，然而应当理解的是，本公开可以通过各种形式来实现，而且不应该被解释为限于这里阐述的实施例。相反，提供这些实施例是为了更加透彻和完整地理解本公开。应当理解的是，本公开的附图及实施例仅用于示例性作用，并非用于限制本公开的保护范围。
另外还需要说明的是，为了便于描述，附图中仅示出了与有关发明相关的部分。在不冲突的情况下，本公开中的实施例及实施例中的特征可以相互组合。
需要注意，本公开中提及的“第一”、“第二”等概念仅用于对不同的装置、模块或单元进行区分，并非用于限定这些装置、模块或单元所执行的功能的顺序或者相互依存关系。
需要注意，本公开中提及的“一个”、“多个”的修饰是示意性而非限制性的，本领域技术人员应当理解，除非在上下文另有明确指出，否则应该理解为“一个或多个”。
本公开实施方式中的多个装置之间所交互的消息或者信息的名称仅用于说明性的目的，而并不是用于对这些消息或信息的范围进行限制。
下面将参考附图并结合实施例来详细说明本公开。
如图1所示，本申请一种实施例的光谱仪100包括：光源装置101、汇聚装置102、斩波装置103、滤光装置104、探测装置105和处理装置106。
其中，光源装置101用于产生光谱检测所需的光线；汇聚装置102用于汇聚光源装置101产生的光线；斩波装置103用于调制经过透镜装置汇聚的光线的频率；滤光装置104用于屏蔽除预设波长以外的其他波长的光线；探测装置105用于接收经过待检农产品200并通过滤光装置104的光线并将光信号转化为电信号；处理装置106用于根据探测装置105输出的电信号生成光谱分析数据并根据光谱分析数据输出物质含量的分析结果。
采用以上方案，由于农产品中的微量物质或营养物质含量过低，本申请的光谱仪100通过在近红外光波段中人为地挑选出一些微量物质或营养物质的特征波长(如与番茄红素的化学成分相关的特征波长)，大大减少了原始光谱中无用的光谱信息，对实现农产品中的微量元素检测具有指导意义。
具体的，滤光装置的预设波长取值范围为400nm至5000nm；根据需要检测的微量物质或营养物质，对应设定滤光装置的预设波长以滤除非特征波长的光线。
更具体而言，滤光装置104的预设波长至少包含900nm、1180nm、1400nm、1720nm和2350nm中的一个或几个，这些预设波长用于番茄红素的检测。
作为一种优选方案，滤光装置包含一个或多个滤光片。
具体而言，光源装置101产生的光线包括红外光线，其波长范围覆盖所需检测微量物质或营养物质的特征波长。更具体的，光源装置101包括一个卤素灯。
更具体而言，光源装置101产生的光线的波长的取值范围为400nm至5000nm。
作为具体的方案，汇聚装置102至少包括一个凸透镜。凸透镜优选采用平凸透镜，其非凸面朝向光源位置。
作为具体的方案，斩波装置103包括：光学斩波器1031、斩波驱动器1032及斩波控制器1033；光学斩波器1031包含一个旋转频率可调的旋转叶片，旋转叶片周期性的遮挡经过透镜装置汇聚的光线；斩波驱动器1032用于驱动旋转叶片转动；斩波控制器1033用于控制斩波驱动器1032的运行以控制旋转叶片的旋转频率，斩波驱动器1032与光学斩波器1031的旋转叶片构成机械连接，斩波驱动器1032与斩波控制器1033构成电性连接。
采用这样的方案，斩波控制器1033输出调制的频率电信号，进而控制光学斩波器1031的调制频率，将经过透镜装置汇聚的光线调制成设定频率下的高频光信号。
作为具体的方案，探测装置105包含近红外光探测器1051，近红外光探测器1051包含PbS检测器阵列105a或InGaAs检测器阵列105b中的至少一种，实现预设波长的检测。[0039]作为优选的方案，本申请一种实施例的光谱仪100还包括：锁相装置107，锁相装置107用于提高探测装置105输出到处理装置106的电信号的信噪比。具体的，锁相装置107包括一个锁相放大器1071；锁相放大器1071分别与探测装置105、斩波控制器1033和处理装置106构成电性连接。其中，探测装置105的输出信号接口与锁相放大器1071的输入信号接口相连，斩波控制器1033的频率信号输出接口和锁相放大器1071的参考信号接口相连，锁相放大器1071依据参考信号的频率分离出特定载波频率信号。
作为优选的方案，本申请一种实施例的光谱仪100还包括：信号放大器108；信号放大器108为一种超材料，其表面具有一系列雕刻而成的微纳结构阵列，能够将特定波长的光信号进行放大；具体的，本申请中信号放大器108对滤光装置104的预设波长进行放大，且放大后的光信号被探测装置105接收。在其中一些技术方案中，可以省略信号放大器108。
如图2所示，作为优选的方案，本申请一种实施例的光谱检测方法，该方法由一个光谱仪所实现，所述光谱仪包括：光源装置、汇聚装置、斩波装置、滤光装置、探测装置和处理装置；光谱检测方法包括以下主要步骤：
S1:激发光源装置101产生光源光线。
S2:在光源光线的光路上设置汇聚装置102以将光源光线汇聚为汇后光线。
S3:控制并驱动斩波装置103以使调整汇后光线的频率以获取用于照射农产品的具有调制频率的检测光线。
S4:在检测光线的光路上设置滤光装置以屏蔽穿过农产品后的检测光线中的除预设波长以外的其他波长的光线后获得特征光线。
S5:控制探测装置105接收特征光线并将特征光线的光信号转化为电信号。
S6:控制处理装置106根据探测装置105输出的电信号生成光谱分析数据并根据光谱分析数据输出物质含量的分析结果。
具体而言，对于农产品中的微量物质或营养物质，滤光装置104的预设波长取值范围为400nm至5000nm。
更具体的，对于农产品中番茄红素的检测，以下对滤光装置的预设波长进行限定：
作为优选的方案，滤光装置的预设波长取值范围至少包括900nm至1200nm。
作为优选的方案，滤光装置的预设波长取值范围至少包括1300nm至1500nm。
作为优选的方案，滤光装置的预设波长取值范围至少包括1600nm至1800nm。
作为优选的方案，滤光装置的预设波长取值范围包括2200nm至2400nm。
更优选的，滤光装置的预设波长至少包含900nm、1180nm、1400nm、1720nm和2350nm中的一个或几个。
如图3所示，作为优选的方案，本申请另一种实施例的光谱检测方法，该方法由若干光谱仪100和一个服务器300所实现。
如图2所示，光谱检测方法包括光谱仪执行的步骤：
S1:激发光源装置101产生光源光线。
S2:在光源光线的光路上设置汇聚装置102以将光源光线汇聚为汇后光线。
S3:控制并驱动斩波装置103以使调整汇后光线的频率以获取用于照射农产品的具有调制频率的检测光线。
S4:在检测光线的光路上设置滤光装置以屏蔽穿过农产品后的检测光线中的除预设波长以外的其他波长的光线后获得特征光线。
S5:控制探测装置105接收特征光线并将特征光线的光信号转化为电信号。
S6:控制处理装置106根据探测装置105输出的电信号生成光谱分析数据并根据光谱分析数据输出物质含量的分析结果。
其中，滤光装置的预设波长取值范围为400nm至5000nm。
如图4所示，光谱检测方法包括服务器300执行的步骤：
St1:响应于一个光谱仪处理装置输出的分析结果，查询该光谱仪的历史数据中的调制频率和分析结果。
St2:选取历史数据中倒数N组对应的制频率和分析结果输入至一个调制频率分析模型以使调制频率分析模型输出调制频率预测值以及对应的置信度。
St3:判断置信度是否大于等于预设的置信度阈值，如果是，则采信调制频率预测值并反馈至该光谱仪；如果否，则不采信调制频率预测值。
具体而言，调制频率分析模型由多个光谱仪的历史数据中的调制频率和分析结果作为训练数据训练而成。
如图5所示，本申请的光谱检测装置，包括：查询模块，用于响应于一个光谱仪处理装置输出的分析结果，查询该光谱仪的历史数据中的调制频率和分析结果；输出模块，用于选取历史数据中倒数N组对应的制频率和分析结果输入至一个调制频率分析模型以使调制频率分析模型输出调制频率预测值以及对应的置信度；判断模块，用于判断置信度是否大于等于预设的置信度阈值，如果是，则采信调制频率预测值并反馈至该光谱仪；如果否，则不采信调制频率预测值；其中，调制频率分析模型由多个光谱仪的历史数据中的调制频率和分析结果作为训练数据训练而成。
作为优选方案，N值为正整数且可以认为设置和调整。
采用以上方案的好处在于，在于应用于水果生产线时，可以根据某一批次水果需检测的微量物质或营养物质的不同，快速实现光谱频段的选择和调整。并且基于系统训练的模型可以降低训练成本同时提高预测精度。
作为优选方案，调制频率分析模型为一个卷积神经网络模型，且其输入数据为若干标准大小光谱图转化成的若干矩阵以及对应调制频率，其用于被训练以实现预测功能，具体模型架构以及训练方法为本领域技术人员所熟知的手段，再次不加赘述。
如图6所示，电子设备800可以包括处理装置(例如中央处理器、图形处理器等)801，其可以根据存储在只读存储器(ROM)802中的程序或者从存储装置808加载到随机访问存储器(RAM)803中的程序而执行各种适当的动作和处理。在RAM803中，还存储有电子设备800操作所需的各种程序和数据。处理装置801、ROM802以及RAM803通过总线804彼此相连。输入/输出(I/O)接口805也连接至总线804。
通常，以下装置可以连接至I/O接口805：包括例如触摸屏、触摸板、键盘、鼠标、摄像头、麦克风、加速度计、陀螺仪等的输入装置806：包括例如液晶显示器(LCD)、扬声器、振动器等的输出装置807；包括例如磁带、硬盘等的存储装置808：以及通信装置809。通信装置809可以允许电子设备800与其他设备进行无线或有线通信以交换数据。虽然图6示出了具有各种装置的电子设备800，但是应理解的是，并不要求实施或具备所有示出的装置。可以替代地实施或具备更多或更少的装置。图6中示出的每个方框可以代表一个装置，也可以根据需要代表多个装置。
特别地，根据本公开的一些实施例，上文参考流程图描述的过程可以被实现为计算机软件程序。例如，本公开的一些实施例包括一种计算机程序产品，其包括承载在计算机可读介质上的计算机程序，该计算机程序包含用于执行流程图所示的方法的程序代码。在这样的一些实施例中，该计算机程序可以通过通信装置809从网络上被下载和安装，或者从存储装置808被安装，或者从ROM802被安装。在该计算机程序被处理装置801执行时，执行本公开的一些实施例的方法中限定的上述功能。
需要说明的是，本公开的一些实施例上述的计算机可读介质可以是计算机可读信号介质或者计算机可读存储介质或者是上述两者的任意组合。计算机可读存储介质例如可以是——但不限于——电、磁、光、电磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。计算机可读存储介质的更具体的例子可以包括但不限于：具有一个或多个导线的电连接、便携式计算机磁盘、硬盘、随机访问存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、光纤、便携式紧凑磁盘只读存储器(CD‑ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。
在本公开的一些实施例中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。而在本公开的一些实施例中，计算机可读信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了计算机可读的程序代码。这种传播的数据信号可以采用多种形式，包括但不限于电磁信号、光信号或上述的任意合适的组合。计算机可读信号介质还可以是计算机可读存储介质以外的任何计算机可读介质，该计算机可读信号介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程序。计算机可读介质上包含的程序代码可以用任何适当的介质传输，包括但不限于：电线、光缆、RF(射频)等等，或者上述的任意合适的组合。
在一些实施方式中，客户端、服务器可以利用诸如HTTP(HyperTextTransferProtocol，超文本传输协议)之类的任何当前已知或未来研发的网络协议进行通信，并且可以与任意形式或介质的数字数据通信(例如，通信网络)互连。通信网络的示例包括局域网(“LAN”)，广域网(“WAN”)，网际网(例如，互联网)以及端对端网络(例如，adhoc端对端网络)，以及任何当前已知或未来研发的网络。
上述计算机可读介质可以是上述电子设备中所包含的：也可以是单独存在，而未装配入该电子设备中。上述计算机可读介质承载有一个或者多个程序，当上述一个或者多个程序被该电子设备执行时，使得该电子设备：响应于一个光谱仪处理装置输出的分析结果，查询该光谱仪的历史数据中的调制频率和分析结果；选取历史数据中倒数N组对应的制频率和分析结果输入至一个调制频率分析模型以使调制频率分析模型输出调制频率预测值以及对应的置信度；判断置信度是否大于等于预设的置信度阈值，如果是，则采信调制频率预测值并反馈至该光谱仪；如果否，则不采信调制频率预测值；其中，调制频率分析模型由多个光谱仪的历史数据中的调制频率和分析结果作为训练数据训练而成。
可以以一种或多种程序设计语言或其组合来编写用于执行本公开的一些实施例的操作的计算机程序代码，上述程序设计语言包括面向对象的程序设计语言―诸如Java、Smalltalk、C++，还包括常规的过程式程序设计语言：诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络——包括局域网(LAN)或广域网(WAN)——连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。
附图中的流程图和框图，图示了按照本公开各种实施例的系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段、或代码的一部分，该模块、程序段、或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。
也应当注意，在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。
例如，两个接连地表示的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或操作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
描述于本公开的一些实施例中的单元可以通过软件的方式实现，也可以通过硬件的方式来实现。所描述的单元也可以设置在处理器中，这些单元的名称在某种情况下并不构成对该单元本身的限定。
本文中以上描述的功能可以至少部分地由一个或多个硬件逻辑部件来执行。例如，非限制性地，可以使用的示范类型的硬件逻辑部件包括：现场可编程门阵列(FPGA)、专用集成电路(ASIC)、专用标准产品(ASSP)、片上系统(SOC)、复杂可编程逻辑设备(CPLD)等等。
以上描述仅为本公开的一些较佳实施例以及对所运用技术原理的说明。本领域技术人员应当理解，本公开的实施例中所涉及的发明范围，并不限于上述技术特征的特定组合而成的技术方案，同时也应涵盖在不脱离上述发明构思的情况下，由上述技术特征或其等同特征进行任意组合而形成的其它技术方案。例如上述特征与本公开的实施例中公开的(但不限于)具有类似功能的技术特征进行互相替换而形成的技术方案。

Claims (11)

  1. 一种适用于检测农产品中微量元素的光谱仪，包括：
    光源装置，用于产生光谱检测所需的光线；
    汇聚装置，用于汇聚所述光源装置产生的光线；
    斩波装置，用于调制经过所述透镜装置汇聚的光线的频率；
    其特征在于：
    所述光谱仪还包括：
    滤光装置，用于屏蔽除预设波长以外的其他波长的光线；
    探测装置，用于接收经过待检农产品并通过所述滤光装置的光线并将光信号转化为电信号；
    处理装置，用于根据所述探测装置输出的电信号生成光谱分析数据并根据所述光谱分析数据输出物质含量的分析结果；
    其中，所述滤光装置的预设波长取值范围为400nm至5000nm。
  2. 根据权利要求1所述的适用于检测农产品中微量元素的光谱仪，其特征在于：
    所述光源装置产生的光线包括红外光线。
  3. 根据权利要求1所述的适用于检测农产品中微量元素的光谱仪，其特征在于：
    所述光源装置产生的光线的波长的取值范围为400nm至5000nm。
  4. 根据权利要求1所述的适用于检测农产品中微量元素的光谱仪，其特征在于：
    所述光源装置包括一个卤素灯。
  5. 根据权利要求1所述的适用于检测农产品中微量元素的光谱仪，其特征在于：
    所述汇聚装置至少包括一个凸透镜。
  6. 根据权利要求1所述的适用于检测农产品中微量元素的光谱仪，其特征在于：
    所述斩波装置包括：
    光学斩波器，包含一个旋转频率可调的旋转叶片以周期性的遮挡经过所述透镜装置汇聚的光线；
    斩波驱动器，用于驱动所述旋转叶片转动；
    斩波控制器，用于控制所述斩波驱动器的运行以控制旋转叶片的旋转频率；
    其中，所述斩波驱动器与所述光学斩波器的旋转叶片构成机械连接，所述斩波驱动器与所述斩波控制器构成电性连接。
  7. 根据权利要求6所述的适用于检测农产品中微量元素的光谱仪，其特征在于：
    所述适用于检测农产品中微量元素的光谱仪还包括：
    锁相装置，用于提高所述探测装置输出到所述处理装置的电信号的信噪比。
  8. 根据权利要求7所述的适用于检测农产品中微量元素的光谱仪，其特征在于：
    所述锁相装置包括一个锁相放大器；所述锁相放大器分别与所述探测装置、所述斩波控制器和所述处理装置构成电性连接。
  9. 根据权利要求1所述的适用于检测农产品中微量元素的光谱仪，其特征在于：
    所述探测装置包含近红外光探测器；其中，所述近红外光探测器包含用于检测预设波长的光信号的PbS检测器阵列或InGaAs检测器阵列中的至少一种。
  10. 根据权利要求1所述的适用于检测农产品中微量元素的光谱仪，其特征在于：
    所述适用于检测农产品中微量元素的光谱仪还包括：
    信号放大器，用于对通过所述滤光装置的预设波长的光信号进行放大；
    其中，所述信号放大器采用一种具有微纳结构阵列的超材料。
  11. 一种根据权利要求1至10任意一项所述适用于检测农产品中微量元素的光谱仪的应用，其特征在于：将所述适用于检测农产品中微量元素的光谱仪用于检测农产品中的番茄红素的含量。
PCT/CN2023/096349 2022-06-15 2023-05-25 适用于检测农产品中微量元素的光谱仪及其应用 WO2023231903A1 (zh)

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