WO2020177422A1 - 一种低场核磁智能检测微波真空干燥果蔬介电特性的装置及方法 - Google Patents

一种低场核磁智能检测微波真空干燥果蔬介电特性的装置及方法 Download PDF

Info

Publication number
WO2020177422A1
WO2020177422A1 PCT/CN2019/123519 CN2019123519W WO2020177422A1 WO 2020177422 A1 WO2020177422 A1 WO 2020177422A1 CN 2019123519 W CN2019123519 W CN 2019123519W WO 2020177422 A1 WO2020177422 A1 WO 2020177422A1
Authority
WO
WIPO (PCT)
Prior art keywords
nuclear magnetic
low
vacuum drying
field nuclear
microwave
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2019/123519
Other languages
English (en)
French (fr)
Inventor
张慜
李琳琳
王玉川
范东翠
杨培强
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Jiangnan University
Original Assignee
Jiangnan University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Jiangnan University filed Critical Jiangnan University
Priority to CA3125703A priority Critical patent/CA3125703C/en
Publication of WO2020177422A1 publication Critical patent/WO2020177422A1/zh
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • A—HUMAN NECESSITIES
    • A23—FOODS OR FOODSTUFFS; TREATMENT THEREOF, NOT COVERED BY OTHER CLASSES
    • A23B—PRESERVATION OF FOODS, FOODSTUFFS OR NON-ALCOHOLIC BEVERAGES; CHEMICAL RIPENING OF FRUIT OR VEGETABLES
    • A23B7/00—Preservation of fruit or vegetables; Chemical ripening of fruit or vegetables
    • A23B7/02—Dehydrating; Subsequent reconstitution
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N24/00—Investigating or analyzing materials by the use of nuclear magnetic resonance, electron paramagnetic resonance or other spin effects
    • G01N24/08—Investigating or analyzing materials by the use of nuclear magnetic resonance, electron paramagnetic resonance or other spin effects by using nuclear magnetic resonance

Definitions

  • the invention relates to a low-field nuclear magnetic intelligent detection device and method for the dielectric characteristics of microwave vacuum drying fruits and vegetables, and belongs to the technical field of intelligent identification of the drying quality of fruits and vegetables.
  • fruits and vegetables have a high market share, and their rich vitamins, organic acids and active substances are beneficial to human health.
  • the high water content, perishable and strong seasonal characteristics make the post-harvest processing of fruits and vegetables particularly important.
  • the fruits and vegetables are made into finished products, which not only extends the shelf life, but also promotes the further development of the fruit and vegetable industry.
  • technologies that can be used for drying fruits and vegetables Traditional hot air drying is easy to operate and low in cost. However, the drying process is carried out under high temperature and aerobic conditions, which can degrade the nutrients such as vitamins and polyphenols in fruits and vegetables, reducing the nutritional value of the product. .
  • Microwave vacuum drying technology uses the internal heating characteristics of microwaves to quickly dry materials.
  • the vacuum environment can reduce the evaporation temperature of water to accelerate the drying process, and reduce the loss of nutrients in fruits and vegetables caused by oxidation reactions.
  • the dielectric constant ( ⁇ ′) indicates the ability of the material to couple microwaves and store microwaves
  • the dielectric loss factor ( ⁇ ′′) indicates the ability of the material to absorb microwaves.
  • the drying characteristics of the material are directly affected by its dielectric characteristics.
  • Low-field nuclear magnetic resonance (LF-NMR) technology is a fast and sensitive new type of non-destructive testing technology, which is widely used in the analysis of moisture mobility and distribution characteristics in food. There are complex interactions between components in food and water, and the determination of water status indirectly reflects the information of different components. Based on this theory, low-field nuclear magnetic resonance technology is widely used as a means of rapid detection of various components in the processing of food systems . Based on the sensitive response recognition of nuclear magnetic resonance technology to different varieties of the same species, this technology is used to distinguish different quality foods. Tan Mingqian et al. (Patent Publication No.
  • Tan Mingqian et al. Patent Publication No. CN105424739A
  • the CPMG sequence of sea cucumber samples was collected by a magnetic resonance analyzer and the data was inverted to obtain a transverse relaxation map. The degree of Yufeng shift to the left judges the change of free water content in sea cucumber.
  • Tan Mingqian et al. Patent Publication No. 106018453A also disclosed a rapid detection method for caviar quality based on low-field nuclear magnetic resonance technology to establish a database of water content, fat content and color difference values during the pickling process and storage of caviar. Evaluate the quality of unknown caviar through correlation analysis and database information comparison.
  • Li et al. (2014) used low-field nuclear magnetic resonance technology to detect changes in the moisture status of chicken breast during drying, and found the correlation between nuclear magnetic resonance parameters and chicken breast water content and shear force.
  • the accuracy of the above-mentioned methods for judging the changes in moisture status based on nuclear magnetic parameters and images and for quality identification through correlation needs to be improved.
  • the partial least squares regression model established by this patent improves the accuracy of prediction.
  • the relevant parameters were obtained by NMR transverse relaxation scanning of abalone samples through CPMG sequence, while using traditional Methods: Determine the water content and fat content of abalone, establish a regression model of abalone water and fat content based on low-field NMR technology. Based on this, use the same CPMG sequence to perform NMR transverse relaxation scanning on unknown abalone samples. The established regression model predicts its water content and fat content. Thybo et al. (2003) and Hansen et al. (2010) used a low-field nuclear magnetic resonance analyzer to determine the change of response parameters of potato samples, combined with the actual measured value of dry matter content, and adopted univariate linear regression and multivariate partial least squares. The regression model establishes the relationship between the two and realizes rapid detection.
  • the moisture content and state of the material during the drying process are constantly changing, and the dielectric properties that depend on it are also constantly changing.
  • the transverse relaxation time signal obtained by hydrogen protons in a magnetic field is pulsed.
  • the intensity of the relaxation signal is proportional to the number of nuclei with a fixed magnetic moment contained in the sample.
  • the composition and structure of the test substance are closely related and can provide valuable information such as the physical and chemical environment inside the nucleus.
  • the invention designs a set of intelligent detection device for the dielectric properties of microwave drying fruits and vegetables based on low-field nuclear magnetism, which collects the dielectric properties and relaxation characteristics of fruits and vegetables in the microwave drying process at multiple points, and uses the partial least squares method to obtain the data. Establish a regression model to realize the rapid detection of the dielectric properties of unknown samples, and give a detailed introduction to the supporting experimental equipment.
  • the purpose of the present invention is to provide a low-field nuclear magnetic intelligent detection device and method for the dielectric properties of microwave vacuum drying fruits and vegetables, using low-field nuclear magnetic resonance technology to perform non-destructive and intelligent detection of the dielectric properties of the materials during the drying process, and introduce the supporting equipment
  • the detection device contributes to the development of intelligent drying.
  • a device for intelligently detecting the dielectric properties of microwave vacuum drying of fruits and vegetables by low-field nuclear magnetic field includes a microwave vacuum drying oven 1, sample particles 2, a suction port 3, a microwave source 4, a take-out valve 5, a separator 6, and a gas release Valve 7, oil-sealed vacuum pump 8, water ring vacuum pump 9, sampling solenoid valve 10, vector network analyzer 11, industrial computer 12 and low-field nuclear magnetic resonance 13;
  • the said microwave vacuum drying oven 1 is provided with a multi-layer carrier plate, sample particles 2 are placed on the carrier plate, and a suction port 3 is arranged above the sample particles 2, and the suction port 3 is connected to the outside of the microwave vacuum drying oven 1 through a pipe
  • the separator 6 is equipped with a take-out valve 5 on the pipe between the suction port 3 and the separator 6 to control the amount of sample particles 2 entering the separator 6; the lower end of the separator 6 is connected to a low-field nuclear magnetic resonance instrument 13 ,
  • a sample injection solenoid valve 10 is set between the two to control the amount of sample particles 2 entering the low-field nuclear magnetic resonance instrument 13; the low-field nuclear magnetic resonance instrument 13 is connected to the industrial computer 12 and the vector network analyzer 11 in turn; a separator
  • the side end of 6 is connected to an oil-sealed vacuum pump 8, and a vent valve 7 is set between the two; the bottom of the microwave vacuum drying box 1 is connected to the water ring vacuum pump 9 through a pipe, and the
  • the installed power of the microwave vacuum drying oven 1 is 5 kW, and the vacuum degree adjustment range is 15-101 kPa.
  • the vector network analyzer 11 includes an E5062A vector network analyzer and a matching measurement probe.
  • the low-field nuclear magnetic resonance analyzer 13 is a PQ001 low-field nuclear magnetic resonance analyzer.
  • the steps of using the device are as follows: First, turn on the microwave vacuum drying oven 1, the vector network analyzer 11, the industrial computer 12 and the low-field nuclear magnetic resonance instrument 13. Place the sample particles 2 on the tray in the microwave vacuum drying oven 1, set the corresponding parameters of the microwave vacuum drying oven 1, keep the intake valve 5, the air release valve 7, and the sample injection solenoid valve 10 closed, and open the water ring
  • the vacuum pump 9 turns on the microwave source 4 when the vacuum reaches the set value, and the drying starts.
  • the water ring vacuum pump 9 turn on the oil-sealed vacuum pump 8, and vacuum the separator 6.
  • the vacuum degree is lower than the vacuum degree set in the microwave vacuum drying oven 1, turn off the oil-sealed vacuum pump 8.
  • the sample enters the low-field nuclear magnetic resonance instrument 13 for nuclear magnetic data collection, while the vector network analyzer 11 collects the dielectric properties of the sample through the industrial computer 12; finally, after the above process is completed, close the gas release valve 7, the injection solenoid valve 10. Turn on the water ring vacuum pump 9, when the vacuum reaches the set value, turn on the microwave source 4, and the drying continues.
  • Microwave vacuum drying Place the cut fruit and vegetable samples in the tray, set the microwave loading power, perform microwave vacuum drying at an absolute vacuum of 15 kPa, and perform phased sampling during the entire microwave vacuum drying process;
  • the vector network analyzer 11 is used to determine the dielectric properties of the collected sample particles 2; the frequency sweep method is used to obtain a dielectric spectrum in the range of 100-3000 MHz, and two of 915 and 2450 MHz are selected. The dielectric constant and loss factor values of the points are taken as the result of the dielectric properties of the sample.
  • Low-field nuclear magnetic resonance analysis Perform low-field nuclear magnetic resonance analysis on the collected sample particles 2 to obtain the relaxation time T 2 curve of the sample particles 2 and various response signal parameters; the various response signal parameters include transverse Relaxation time and peak area.
  • the lateral relaxation time includes three types: bound water relaxation time T 21 , non-flowing water relaxation time T 22 , and free water relaxation time T 23 ; the peak area includes bound water peak There are 4 types of area A 21 , non-flowing water peak area A 22 , free water peak area A 23 and total water peak area A ;
  • the step (3) low-field nuclear magnetic resonance analysis adopts the Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence to collect the proton decay signal to obtain the CPMG attenuation curve.
  • the peak area needs to be normalized by mass.
  • step (4) when the relaxation time and peak area of the different components of the water in the sample nuclear magnetic response signal parameters are established with the dielectric properties of the regression model, it is necessary to ensure the consistency of the sample to be tested.
  • the present invention uses low-field nuclear magnetic resonance detection technology to solve the original special requirements for the shape and thickness of materials in the detection of dielectric properties of fruits and vegetables, realizes non-destructive, convenient and intelligent detection, and improves detection efficiency and product integrity.
  • the present invention has convenient operation, simple process, good model prediction performance, high accuracy of detection results, short time-consuming, and can realize effective monitoring of changes in dielectric properties of materials during drying.
  • the method proposed by the present invention can accurately and effectively judge the change of dielectric properties during the drying process of fruits and vegetables, and provide a strong basis for the regulation of microwave power in intelligent drying.
  • Figure 1 is a schematic diagram of the experimental device.
  • Figure 3 shows the relationship between the model predicted value and the measured value of an unknown yam sample.
  • (a) is the dielectric constant ⁇ ′ and (b) is the loss factor ⁇ ′′.
  • Figure 4 shows the T 2 curve of the apple microwave vacuum drying process.
  • Figure 5 shows the relationship between the model predicted value and the measured value of an unknown apple product.
  • (a) is the dielectric constant ⁇ ′ and (b) is the loss factor ⁇ ′′.
  • Example 1 Intelligent detection of dielectric properties of microwave dried yam based on low-field nuclear magnetism. Take 100 g of cleaned, peeled, and sliced yam, and dry it in a microwave vacuum drying oven. The microwave power is set to 400 W and the vacuum is 15 kPa. During the drying process, samples are taken every 10 minutes for low-field NMR analysis.
  • the peaks appearing on the coordinate axis from left to right in the figure They respectively represent the combined water peak, the non-flowing water peak and the free water peak.
  • a vector network analyzer was used to measure the dielectric properties of the corresponding samples at 2450 MHz. After repeated experiments, a large number of samples’ dielectric properties and corresponding nuclear magnetic response parameters were obtained.
  • Partial least squares method was used to establish the regression model.
  • the regression model R 2 and RMSE of the dielectric constant of yam ⁇ ′ were 0.978 and 2.782 respectively, and the loss factor
  • the regression model R 2 and RMSE of ⁇ " are 0.918 and 1.093 respectively, which have good model performance.
  • the unknown yam sample is placed in the drying chamber of the device shown in Figure 1 for drying.
  • Example 2 Intelligent detection of the dielectric properties of microwave-dried apples based on low-field nuclear magnetism. Take 100 g of cleaned, peeled, and sliced apples, and dry them in a microwave vacuum drying oven. The microwave power is set to 600 W and the vacuum is 15 kPa. During the drying process, sampling is performed every 5 minutes for low-field NMR analysis.
  • a vector network analyzer was used to measure the dielectric properties of the corresponding samples at 915 MHz. After many repeated experiments, a large number of samples’ dielectric properties and their corresponding nuclear magnetic response parameters were obtained. Partial least squares method was used to establish a regression model.
  • the regression model R 2 and RMSE of Apple’s dielectric constant ⁇ ′ were 0.971 and 2.499, respectively. Loss factor
  • the regression model R 2 and RMSE of ⁇ " are 0.936 and 1.271, respectively, showing good model performance.
  • the unknown apple sample is placed in the drying chamber of the device shown in Figure 1 for drying.
  • the drying is carried out to any selected time point, turn off the microwave, open the take-out valve so that the material is sucked into the lofting tank of the nuclear magnetic inspection equipment, the moisture status of the sample is checked online, and the above-established model is used for the dielectric characteristics of the apple
  • the prediction of the NMR signal is repeated at multiple points and the dielectric properties are predicted.
  • the relationship between the predicted value of ⁇ ′ and ⁇ ′′ and the measured value is shown in Figure 5, which has good prediction performance.

Landscapes

  • Chemical & Material Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • High Energy & Nuclear Physics (AREA)
  • Engineering & Computer Science (AREA)
  • Wood Science & Technology (AREA)
  • Analytical Chemistry (AREA)
  • Food Science & Technology (AREA)
  • Polymers & Plastics (AREA)
  • General Chemical & Material Sciences (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Health & Medical Sciences (AREA)
  • Zoology (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Drying Of Solid Materials (AREA)
  • Investigating Or Analyzing Materials By The Use Of Electric Means (AREA)
  • Investigating Or Analyzing Materials By The Use Of Magnetic Means (AREA)

Abstract

一种低场核磁智能检测微波真空干燥果蔬介电特性的装置及方法，采用微波真空干燥技术进行果蔬干燥，干燥过程中阶段性取样，对样品进行低场核磁共振分析，同样的样品采用矢量网络分析仪（11）测定介电特性。对得到的样品介电特性数据和核磁结果参数进行分析，采用偏最小二乘回归法建立表达两者关系的回归模型，依据此模型进行未知样品的介电特性预测。利用低场核磁共振技术，解决原有果蔬介电特性检测中对物料形状和厚度的特殊要求，实现果蔬干燥过程中介电特性变化的无损、便捷、智能检测，为智能干燥中微波功率的调控提供有力依据。

Description

一种低场核磁智能检测微波真空干燥果蔬介电特性的装置及方法 技术领域
本发明涉及一种低场核磁智能检测微波真空干燥果蔬介电特性的装置及方法，属于果蔬干燥品质智能化识别技术领域。
 背景技术
果蔬作为人类食物的重要组成部分，具有很高的市场占有率，其丰富的维生素，有机酸及活性物质等对人体健康大有益处。然而含水量高，易腐烂以及季节性强的特点，使得果蔬采后的加工显得尤为重要。通过干燥的方式，将果蔬制成成品，不但延长了货架期，对果蔬的深加工又可促进果蔬产业的进一步发展。可用于果蔬干燥的技术很多，传统的热风干燥操作简便，费用低，但干燥过程在高温有氧条件下进行，对果蔬中的维生素、多酚等营养成分具有降解作用，降低了产品的营养价值。相反，冷冻干燥可最大程度的保留果蔬的营养成分，但干燥时间长，费用高。微波真空干燥技术利用微波的内部加热特性对物料进行快速干燥，真空环境可降低水分蒸发温度加速干燥进程，并且减少由氧化反应引起的果蔬营养成分的损失。提到微波干燥，不得不提的一个概念便是物料的介电特性，它是影响微波干燥的重要参数。其中介电常数(ɛ′)表示物料耦合微波，储存微波的能力；介电损耗因子(ɛ″)表示物料吸收微波的能力。物料的干燥特性受其介电特性的直接影响。
当前干燥行业蓬勃发展，人们已不满足于基础的干燥方式，对干燥过程自动化、智能化检测与控制的要求越来越高，以传感器、智能检测方法、实时反馈智能控制系统为核心的智能一体化控制装备的应用是干燥设备发展的必然趋势。
低场核磁共振（LF-NMR）技术是一种快速、灵敏的新型无损检测技术，被广泛用于食品中水分流动性和分布特征的分析。食品中组分与水存在复杂的相互作用，通过水分状态的测定间接反映不同成分的信息，基于此理论，低场核磁共振技术被广泛用作食品体系加工处理过程中各组分快速检测的手段。依据核磁共振技术对同一物种不同品种差别的灵敏响应识别，该技术被用作不同品质食品的区分。谭明乾等（专利公开号：106018452A）公开了一种基于核磁共振技术的花生品种无损检测方法，采用主成分分析法对各花生样品的CPMG序列反演数据进行处理，得到各花生样品的主成分得分散点图；采用相同的CPMG序列对未知品种花生样本进行核磁共振横向弛豫扫描，对获得的CPMG序列反演数据进行主成分分析，对照已建立的主成分得分散点图确定未知品种花生样本的品种。谭明乾等（专利公开号：CN105806872A）采用类似的分析方法公开了利用低场核磁技术鉴别不同鳃鱼卵的方法。不同于上述两者从宏观上对花生和鳃鱼卵品种的鉴别，本专利是对果蔬特定品质——介电特性的准确预测。
食品加工处理过程常伴随理化性质的变化，常规的物理化学分析手段耗时且具有破坏性，低场核磁共振技术的快速无损检测在加工过程品质的监控中表现出突出优势。谭明乾等（专利公开号：CN105424739A）公开了一种基于核磁共振技术无损检测海参自溶的方法，采用磁共振分析仪采集海参样品的CPMG序列并进行数据反演得出横向弛豫图谱，根据弛豫峰左移的程度判断海参体内自由水含量的变化。并利用核磁共振分析仪的成像功能获取海参样品的 T 1和 T 2加权成像，从图像直观判断结合水和自由水在储藏过程中的变化。谭明乾等（专利公开号：106018453A）还公开了一种基于低场核磁共振技术的鱼子酱品质快速检测方法，建立鱼子酱在腌渍加工和贮藏过程中水分含量、脂肪含量和色差值信息数据库，通过相关性分析和数据库信息比对评价未知鱼子酱的品质。Li等（2014）利用低场核磁共振技术检测鸡胸肉干燥过程水分状态的变化，发现核磁共振参数与鸡胸肉含水量及剪切力之间的相关性。上述依据核磁参数和图像判断水分状态的变化及通过相关性进行品质鉴定的方法准确性有待提高，本专利建立的偏最小二乘回归模型提高了预测的准确性。
低场核磁共振技术不仅可以宏观判断食品加工过程品质变化的大体趋势，大量研究证明其在准确预测化学成分含量及物理特性方面也有突出表现，且该类研究一般需要结合模型分析手段。Zhang等（2017）建立了低场核磁共振参数与发酵香肠水分含量之间的线性方程，关系方程在特定含油量的样品间表现出很好的预测性能。谭明乾等（专利公开号：CN105606637A）公开了一种利用低场核磁共振技术检测鲍鱼中水分和脂肪含量的方法，通过CPMG序列对鲍鱼样品分别进行核磁共振横向弛豫扫描得到相关参数，同时采用传统方法测定鲍鱼含水量和脂肪含量，建立基于低场核磁共振技术的鲍鱼水分和脂肪含量的回归模型，以此为基础，对未知鲍鱼样品采用相同的CPMG序列进行核磁共振横向弛豫扫描，通过已建立的回归模型预测其含水量和脂肪含量。Thybo等（2003）及Hansen等（2010）采用低场核磁共振分析仪对土豆样品的响应参数变化规律进行测定，并结合干物质含量的实测值，采用单变量线性回归和多变量偏最小二乘回归模型建立两者之间的关系，实现快速检测。这些研究以低场核磁共振技术为基础，结合了一定的数学分析手段，如线性回归、多变量偏最小二乘回归等建立核磁响应参数与研究对象品质指标的关系，从而实现快速检测。本专利与其相比突出特点在于，自主设计了一套基于低场核磁的微波干燥果蔬介电特性智能检测的装置，可实现干燥过程果蔬介电特性的智能检测。
本发明中，干燥加工中物料水分含量、状态处于不断变化的过程，对其有依赖性的介电特性也处于不断的变化中。利用核磁共振技术的检测原理，氢质子在磁场中受到脉冲激发得到的横向弛豫时间信号，弛豫信号强度与被测样品中所含具有固定磁矩的原子核数目成正比，信号衰减过程与被测物质的成分结构密切相关，可以提供核内部的物理化学环境等有价值的信息。
本发明设计了一套基于低场核磁的微波干燥果蔬介电特性智能检测的装置，对微波干燥过程中果蔬的介电特性和驰豫特征进行多点采集，将获取的数据采用偏最小二乘法建立回归模型，以实现对未知样品介电特性的快速检测，并对配套实验装置做出详细介绍。
 技术问题
本发明的目的是提供一种低场核磁智能检测微波真空干燥果蔬介电特性的装置及方法，利用低场核磁共振技术，对干燥过程中物料的介电特性进行无损、智能检测，并介绍配套检测装置，为智能化干燥的发展贡献力量。
 技术解决方案
一种低场核磁智能检测微波真空干燥果蔬介电特性的装置，该装置包括微波真空干燥箱1、样品颗粒2、吸料口3、微波源4、取料阀5、分离器6、放气阀7、油封真空泵8、水环真空泵9、进样电磁阀10、矢量网络分析仪11、工控机12和低场核磁共振仪13；
所述的微波真空干燥箱1中设置多层载物盘，载物盘上铺放样品颗粒2，样品颗粒2的上方设置吸料口3，吸料口3通过管道与微波真空干燥箱1外部的分离器6，吸料口3与分离器6之间的管道上设置取料阀5，用于控制进入分离器6的样品颗粒2的量；分离器6的下端连接低场核磁共振仪13，二者之间设置进样电磁阀10，用于控制进入低场核磁共振仪13的样品颗粒2的量；低场核磁共振仪13依次与工控机12、矢量网络分析仪11相连；分离器6的侧端连接油封真空泵8，二者之间设置放气阀7；微波真空干燥箱1的底部通过管道与水环真空泵9相连，微波真空干燥箱1顶部设置微波源4，用于为微波真空干燥箱1提供微波能量。
进一步的，所述的微波真空干燥箱1的装机功率为5 kW，真空度调节范围为15-101 kPa。
进一步的，所述的矢量网络分析仪11包括E5062A型矢量网络分析仪及配套测定探头。
进一步的，所述的低场核磁共振仪13为PQ001型低场核磁共振分析仪。
装置使用步骤如下：首先，开启微波真空干燥箱1、矢量网络分析仪11、工控机12及低场核磁共振仪13。在微波真空干燥箱1中的载物盘上铺放样品颗粒2，设置微波真空干燥箱1相应的参数，取料阀5、放气阀7、进样电磁阀10保持关闭状态，开启水环真空泵9，待真空度到达设定值时，开启微波源4，干燥开始。其次，干燥到达设定时间后，关闭微波源4、水环真空泵9，开启油封真空泵8，将分离器6抽真空，当其真空度低于微波真空干燥箱1设置的真空度，关闭油封真空泵8，调节取料阀5，样品颗粒2部分通过上方吸料口3进入分离器6，关闭取料阀5，开启放气阀7，样品颗粒2进入分离器6的底部，开启进样电磁阀10，样品进入低场核磁共振仪13进行核磁数据采集，同时矢量网络分析仪11通过工控机12采集样品介电特性；最后，上述过程完成后，关闭放气阀7、进样电磁阀10，开启水环真空泵9，待真空度到达设定值，开启微波源4，干燥继续。
一种低场核磁智能检测微波真空干燥果蔬介电特性的方法，主要包括以下步骤：
（1）微波真空干燥：将切割处理后的果蔬样品置于载物盘中，设置微波加载功率，在绝对真空度15 kPa下进行微波真空干燥，在整个微波真空干燥过程中进行阶段性取样；
（2）介电特性的测定：采用矢量网络分析仪11对采集的样品颗粒2进行介电特性的测定；采用频率扫描的方法获得100-3000 MHz范围的介电图谱，选取915和2450 MHz两点的介电常数和损耗因子值作为样品介电特性的结果。
（3）低场核磁共振分析：对采集的样品颗粒2进行低场核磁共振分析，得到样品颗粒2的弛豫时间 T 2曲线和各项响应信号参数；所述的各项响应信号参数包括横向弛豫时间和峰面积，所述横向弛豫时间包括结合水弛豫时间 T 21、不易流动水弛豫时间 T 22、自由水弛豫时间 T 23共3种；所述峰面积包括结合水峰面积 A 21、不易流动水峰面积 A 22、自由水峰面积 A 23和全部水的峰面积 A共4种；
（4）基于低场核磁的微波真空干燥果蔬介电特性预测模型的建立与评价：通过单次干燥实验阶段性取样及重复干燥实验得到大量样品介电特性与其对应的核磁响应信号参数数据库，采用偏最小二乘法建立回归模型，并依据回归模型预测值与实际测定值的相关系数 R 2和均方根误差RMSE进行评价；
（5）果蔬介电特性的智能检测：对未知的样品进行低场核磁共振分析，调用步骤（4）中得到回归模型预测当前样品的介电特性。
所述步骤（3）低场核磁共振分析采用Carr-Purcell-Meiboom-Gill (CPMG)脉冲序列采集质子衰变信号，获得CPMG衰减曲线，具体采集参数为：TW (等待时间)设置范围为1000-10000 ms，TE (回波时间) = 0-1 ms，NECH (回波数) = 1000-10000，NS (扫描次数) =4、8、16、32，将CPMG衰减曲线使用SRIT算法进行多指数拟合，获得样品的横向弛豫时间 T 2曲线及对应的核磁响应信号参数。
所述步骤（4）中所述样品介电特性与其对应的核磁响应信号参数建立关系方程时，需要对峰面积作质量归一化处理。
所述步骤（4）中所述样品核磁响应信号参数中不同组分水的驰豫时间及峰面积与介电特性建立回归模型时，需要保证待测试样的一致性。
 有益效果
（1）本发明利用低场核磁共振的检测技术，解决原有果蔬介电特性检测中对物料形状和厚度的特殊要求，实现无损、便捷、智能检测，提高了检测工作效率及产品完整性。（2）本发明操作方便、流程简单，模型预测性能好，检测结果准确性高、耗时短，可以实现干燥过程物料介电特性变化的有效监测。（3）本发明提出的方法可以精确有效判断果蔬干燥过程中介电特性变化，为智能干燥中微波功率的调控提供有力依据。
 附图说明
图1为实验装置示意图。
图2山药微波真空干过程 T 2曲线。
图3为未知山药样品的模型预测值与测定值的关系。其中，（a）为介电常数ɛ′，（b）为损耗因子ɛ″。
图4为苹果微波真空干过程 T 2曲线。
图5为未知苹果品的模型预测值与测定值的关系。其中，（a）为介电常数ɛ′，（b）为损耗因子ɛ″。
图中：1微波真空干燥箱；2样品颗粒；3吸料口；4微波源；5取料阀；6分离器；7放气阀；8油封真空泵；9水环真空泵；10进样电磁阀；11矢量网络分析仪；12工控机；13低场核磁共振仪。
 本发明的实施方式
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。
实施例1：基于低场核磁的微波干燥山药介电特性智能检测取经清洗、去皮、切片的山药100 g，于微波真空干燥箱内开展干燥，微波功率设置为400 W，真空度15 kPa，干燥过程中每10 min取样一次进行低场核磁共振分析，具体采集参数为：TW (等待时间) = 3000 ms，TE (回波时间) = 0.6 ms，NECH (回波数) = 5000，NS (扫描次数) = 16，将CPMG衰减曲线使用SRIT算法进行多指数拟合，得到样品的横向弛豫时间 T 2曲线（如图2）及各响应信号参数，图中坐标轴自左向右出现的峰分别代表结合水峰、不易流动水峰和自由水峰。并采用矢量网络分析仪测定对应样品在2450 MHz下的介电特性。经多次重复实验得到大量样品介电特性与其对应的核磁响应参数的数据库，采用偏最小二乘法建立回归模型，山药介电常数ɛ′的回归模型 R 2和RMSE分别为0.978和2.782，损耗因子ɛ″的回归模型 R 2和RMSE分别为0.918和1.093，具有很好的模型性能。在介电特性智能检测中，将未知山药样品置于图1所示装置的干燥仓内进行干燥，当干燥进行至选定的任意时间点时，关闭微波，打开取料阀使物料吸取至核磁检测设备放样槽中，对样品的水分状态进行在线检测，将上述已建立的模型用于介电特性的预测，重复多点采集核磁信号并预测其介电特性，得到ɛ′和ɛ″预测值与测定值的关系如图3，具有很好的预测性能。
实施例2：基于低场核磁的微波干燥苹果介电特性智能检测取经清洗、去皮、切片的苹果100 g，于微波真空干燥箱内开展干燥，微波功率设置为600 W，真空度15 kPa，干燥过程中每5 min取样一次进行低场核磁共振分析，具体采集参数为：TW (等待时间) = 5000 ms，TE (回波时间) = 0.8 ms，NECH (回波数) = 5000，NS (扫描次数) = 32，将CPMG衰减曲线使用SRIT算法进行多指数拟合，得到样品的横向弛豫时间 T 2曲线（如图4）及各响应信号参数。并采用矢量网络分析仪测定对应样品在915 MHz下的介电特性。经多次重复实验得到大量样品介电特性与其对应的核磁响应参数的数据库，采用偏最小二乘法建立回归模型，苹果介电常数ɛ′的回归模型 R 2和RMSE分别为0.971和2.499，损耗因子ɛ″的回归模型 R 2和RMSE分别为0.936和1.271，表现出很好的模型性能。在介电特性智能检测中，将未知苹果样品置于图1所示装置的干燥仓内进行干燥，当干燥进行至选定的任意时间点时，关闭微波，打开取料阀使物料吸取至核磁检测设备放样槽中，对样品的水分状态进行在线检测，将上述已建立的模型用于苹果介电特性的预测，重复多点采集核磁信号并预测其介电特性，得到ɛ′和ɛ″预测值与测定值的关系如图5，具有很好的预测性能。

Claims (10)

  1. 一种低场核磁智能检测微波真空干燥果蔬介电特性的装置，其特征在于，该装置包括微波真空干燥箱（1）、样品颗粒（2）、吸料口（3）、微波源（4）、取料阀（5）、分离器（6）、放气阀（7）、油封真空泵（8）、水环真空泵（9）、进样电磁阀（10）、矢量网络分析仪（11）、工控机（12）和低场核磁共振仪（13）；
    所述的微波真空干燥箱（1）中设置多层载物盘，载物盘上铺放样品颗粒（2），样品颗粒（2）的上方设置吸料口（3），吸料口（3）通过管道与微波真空干燥箱（1）外部的分离器（6）相连，吸料口（3）与分离器（6）之间的管道上设置取料阀（5），用于控制进入分离器（6）的样品颗粒（2）的量；分离器（6）的下端连接低场核磁共振仪（13），二者之间设置进样电磁阀（10），用于控制进入低场核磁共振仪（13）的样品颗粒（2）的量；低场核磁共振仪（13）依次与工控机（12）、矢量网络分析仪（11）相连；分离器（6）的侧端连接油封真空泵（8），二者之间设置放气阀（7）；微波真空干燥箱（1）的底部通过管道与水环真空泵（9）相连，微波真空干燥箱（1）顶部设置微波源（4），用于为微波真空干燥箱（1）提供微波能量。
  2. 根据权利要求1所述的装置，其特征在于，所述的微波真空干燥箱（1）的装机功率为5 kW，真空度调节范围为15-101 kPa。
  3. 根据权利要求1或2所述的装置，其特征在于，所述的矢量网络分析仪（11）包括E5062A型矢量网络分析仪及配套测定探头。
  4. 根据权利要求1或2所述的装置，其特征在于，所述的低场核磁共振仪（13）为PQ001型低场核磁共振分析仪。
  5. 根据权利要求3所述的装置，其特征在于，所述的低场核磁共振仪（13）为PQ001型低场核磁共振分析仪。
  6. 采用权利要求1-5任一所述的装置的一种低场核磁智能检测微波真空干燥果蔬介电特性的方法，其特征在于，包括以下步骤：
    （1）微波真空干燥：将切割处理后的果蔬样品置于载物盘中，设置微波加载功率，在绝对真空度15 kPa下进行微波真空干燥，在整个微波真空干燥过程中进行阶段性取样；
    （2）介电特性的测定：采用矢量网络分析仪（11）对采集的样品颗粒（2）进行介电特性的测定；采用频率扫描的方法获得100-3000 MHz范围的介电图谱，选取915和2450 MHz两点的介电常数和损耗因子值作为样品介电特性的结果；
    （3）低场核磁共振分析：对采集的样品颗粒（2）进行低场核磁共振分析，得到样品颗粒（2）的弛豫时间 T 2曲线和各项响应信号参数；所述的各项响应信号参数包括横向弛豫时间和峰面积，所述横向弛豫时间包括结合水弛豫时间 T 21、不易流动水弛豫时间 T 22、自由水弛豫时间 T 23共3种；所述峰面积包括结合水峰面积 A 21、不易流动水峰面积 A 22、自由水峰面积 A 23和全部水的峰面积 A共4种；
    （4）基于低场核磁的微波真空干燥果蔬介电特性预测模型的建立与评价：通过单次干燥实验阶段性取样及重复干燥实验得到大量样品介电特性与其对应的核磁响应信号参数数据库，采用偏最小二乘法建立回归模型，并依据回归模型预测值与实际测定值的相关系数 R 2和均方根误差RMSE进行评价；
    （5）果蔬介电特性的智能检测：对未知的样品进行低场核磁共振分析，调用步骤（4）中得到回归模型预测当前样品的介电特性。
  7. 根据权利要求6所述的方法，其特征在于，所述步骤（3）低场核磁共振分析采用Carr-Purcell-Meiboom-Gill脉冲序列采集质子衰变信号，获得CPMG衰减曲线，具体采集参数为：等待时间TW设置范围为1000-10000 ms，回波时间TE= 0-1 ms，回波数NECH = 1000-10000，扫描次数NS=4、8、16、32，将CPMG衰减曲线使用SRIT算法进行多指数拟合，获得样品的横向弛豫时间 T 2曲线及对应的核磁响应信号参数。
  8. 根据权利要求6所述的方法，其特征在于，所述步骤（4）中所述样品介电特性与其对应的核磁响应信号参数建立关系方程时，需要对峰面积作质量归一化处理。
  9. 根据权利要求7所述的方法，其特征在于，所述步骤（4）中所述样品介电特性与其对应的核磁响应信号参数建立关系方程时，需要对峰面积作质量归一化处理。
  10. 根据权利要求7、8或9所述的方法，其特征在于，所述步骤（4）中所述样品核磁响应信号参数中不同组分水的驰豫时间及峰面积与介电特性建立回归模型时，需要保证待测试样的一致性。
PCT/CN2019/123519 2019-03-01 2019-12-06 一种低场核磁智能检测微波真空干燥果蔬介电特性的装置及方法 Ceased WO2020177422A1 (zh)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CA3125703A CA3125703C (en) 2019-03-01 2019-12-06 Apparatus and method for intelligently detecting dielectric property of fruits and vegetables during microwave vacuum drying based on low-field nuclear magnetic resonance

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201910156449.X 2019-03-01
CN201910156449.XA CN109799255A (zh) 2019-03-01 2019-03-01 一种低场核磁智能检测微波真空干燥果蔬介电特性的装置及方法

Publications (1)

Publication Number Publication Date
WO2020177422A1 true WO2020177422A1 (zh) 2020-09-10

Family

ID=66561502

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2019/123519 Ceased WO2020177422A1 (zh) 2019-03-01 2019-12-06 一种低场核磁智能检测微波真空干燥果蔬介电特性的装置及方法

Country Status (3)

Country Link
CN (1) CN109799255A (zh)
CA (1) CA3125703C (zh)
WO (1) WO2020177422A1 (zh)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112730497A (zh) * 2020-12-21 2021-04-30 贺州学院 一种基于低场核磁共振的真空油炸杏鲍菇条水分油脂快速检测技术
CN116879340A (zh) * 2023-07-06 2023-10-13 安徽中烟工业有限责任公司 一种烟草颗粒动态吸湿过程中结合水吸附量的定量表征方法
CN120720829A (zh) * 2025-08-28 2025-09-30 烟台开达威尔能源装备科技有限公司 一种节能烘干设备及烘干方法

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109799255A (zh) * 2019-03-01 2019-05-24 江南大学 一种低场核磁智能检测微波真空干燥果蔬介电特性的装置及方法
CN111406792B (zh) * 2020-04-21 2022-08-05 中国农业大学 一种微波灭酶和低场核磁协同干制果蔬的方法
CN119985590A (zh) * 2021-06-22 2025-05-13 江南大学 一种智能判定果蔬冻干升华/解析转化点的方法
CN114002254B (zh) * 2021-10-08 2024-07-02 华中农业大学 基于低场核磁共振的谷物中γ-氨基丁酸含量无损检测方法
CN115542862B (zh) * 2022-11-04 2023-03-28 日照鼎立钢构股份有限公司 一种提高果蔬冻干品质的干燥方案决策方法及系统

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102062525A (zh) * 2010-12-05 2011-05-18 江南大学 一种负压微波均匀化喷动干燥装置及应用
CN204166340U (zh) * 2014-09-10 2015-02-18 湖北三江航天机电设备有限责任公司 基于微波水分在线检测的颗粒饲料烘干控制装置
CN106841268A (zh) * 2017-01-12 2017-06-13 大连工业大学 实时在线无损监测真空干燥过程中水分变化方法及装置
US20180080887A1 (en) * 2016-09-20 2018-03-22 Frito-Lay North America, Inc. Nmr based non-invasive and quantitative food attribute measurement apparatus and method
CN108519398A (zh) * 2018-04-04 2018-09-11 江南大学 微波喷动冻干智能检测高糖分水果含水率及质构的方法
CN108895767A (zh) * 2018-08-14 2018-11-27 江南大学 一种负压脉冲协同常压射频喷动床干燥装置及其应用
CN109799255A (zh) * 2019-03-01 2019-05-24 江南大学 一种低场核磁智能检测微波真空干燥果蔬介电特性的装置及方法

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6216025B1 (en) * 1999-02-02 2001-04-10 Optosonics, Inc. Thermoacoustic computed tomography scanner
CN102661662A (zh) * 2012-05-28 2012-09-12 上海东富龙制药设备制造有限公司 一种用于真空冷冻干燥机的真空吸料系统及真空吸料方法
CN104330432A (zh) * 2014-10-27 2015-02-04 江南大学 一种基于水分分布表征油炸果蔬脆片贮藏过程品质变化的方法
CN107727683A (zh) * 2017-10-31 2018-02-23 合肥华凌股份有限公司 以食品未冻结水分判断冻结点的方法、控制器和制冷设备

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102062525A (zh) * 2010-12-05 2011-05-18 江南大学 一种负压微波均匀化喷动干燥装置及应用
CN204166340U (zh) * 2014-09-10 2015-02-18 湖北三江航天机电设备有限责任公司 基于微波水分在线检测的颗粒饲料烘干控制装置
US20180080887A1 (en) * 2016-09-20 2018-03-22 Frito-Lay North America, Inc. Nmr based non-invasive and quantitative food attribute measurement apparatus and method
CN106841268A (zh) * 2017-01-12 2017-06-13 大连工业大学 实时在线无损监测真空干燥过程中水分变化方法及装置
CN108519398A (zh) * 2018-04-04 2018-09-11 江南大学 微波喷动冻干智能检测高糖分水果含水率及质构的方法
CN108895767A (zh) * 2018-08-14 2018-11-27 江南大学 一种负压脉冲协同常压射频喷动床干燥装置及其应用
CN109799255A (zh) * 2019-03-01 2019-05-24 江南大学 一种低场核磁智能检测微波真空干燥果蔬介电特性的装置及方法

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112730497A (zh) * 2020-12-21 2021-04-30 贺州学院 一种基于低场核磁共振的真空油炸杏鲍菇条水分油脂快速检测技术
CN116879340A (zh) * 2023-07-06 2023-10-13 安徽中烟工业有限责任公司 一种烟草颗粒动态吸湿过程中结合水吸附量的定量表征方法
CN120720829A (zh) * 2025-08-28 2025-09-30 烟台开达威尔能源装备科技有限公司 一种节能烘干设备及烘干方法

Also Published As

Publication number Publication date
CA3125703A1 (en) 2020-09-10
CN109799255A (zh) 2019-05-24
CA3125703C (en) 2023-08-29

Similar Documents

Publication Publication Date Title
CA3125703C (en) Apparatus and method for intelligently detecting dielectric property of fruits and vegetables during microwave vacuum drying based on low-field nuclear magnetic resonance
Egelandsdal et al. Detectability of the degree of freeze damage in meat depends on analytic-tool selection
Mirzaee-Ghaleh et al. Identification of fresh-chilled and frozen-thawed chicken meat and estimation of their shelf life using an E-nose machine coupled fuzzy KNN
Jha et al. Measurement techniques and application of electrical properties for nondestructive quality evaluation of foods—A review
Lv et al. Online measurement of moisture content, moisture distribution, and state of water in corn kernels during microwave vacuum drying using novel smart NMR/MRI detection system
CN109799256A (zh) 一种基于低场核磁的微波干燥香辛蔬菜风味智能检测的装置及方法
WO2019192088A1 (zh) 微波喷动冻干智能检测高糖分水果含水率及质构的方法
US6548303B2 (en) Method and apparatus for rapid fat content determination
CN112730499B (zh) 一种低场核磁共振检测鲜肉水分质量和/或水分含量的方法及其标准样品制备方法和应用
CN105758884B (zh) 一种鉴别海参干燥方式及监控干燥过程品质变化的方法
CN109769922A (zh) 一种基于低场核磁的高淀粉水果干燥终点智能调控的装置及方法
CN105548234A (zh) 一种黄花鱼水分和脂肪含量的无损测定方法
WO1999040176A1 (en) Inspection method for microorganisms and the like, and unit therefor
Xia et al. Simultaneous, rapid and nondestructive determination of moisture, fat content and storage time in leisure dried tofu using LF-NMR
CN106556680A (zh) 一种基于电子鼻技术快速预测肉及其制品脂肪氧化程度的方法
CN106841268A (zh) 实时在线无损监测真空干燥过程中水分变化方法及装置
Di Caro et al. Quality assessment of the inshell hazelnuts based on TD-NMR analysis
Aboonajmi et al. Non-destructive quality assessment of table eggs for online sorting
Di Caro et al. Quality control of hazelnuts by means of NMR measurements
Ding et al. Detection of fruits in warehouse using Electronic nose
CN115791902B (zh) 巴氏杀菌牛奶、高温灭菌牛奶和配方奶粉的快速鉴别方法
CN109709132A (zh) 一种低场核磁智能检测红外干燥果蔬收缩特性的装置及方法
US7220591B2 (en) Method and apparatus for rapid fat content determination
CN112485308A (zh) 一种用于冷藏水产品品质的快速检测方法
TANG et al. Determination of fat content in pork by low-field nuclear magnetic resonance

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 19917659

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 3125703

Country of ref document: CA

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 19917659

Country of ref document: EP

Kind code of ref document: A1