WO2020177423A1 - 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 - Google Patents

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 Download PDF

Info

Publication number
WO2020177423A1
WO2020177423A1 PCT/CN2019/123520 CN2019123520W WO2020177423A1 WO 2020177423 A1 WO2020177423 A1 WO 2020177423A1 CN 2019123520 W CN2019123520 W CN 2019123520W WO 2020177423 A1 WO2020177423 A1 WO 2020177423A1
Authority
WO
WIPO (PCT)
Prior art keywords
microwave
flavor
nuclear magnetic
vacuum
low
Prior art date
Application number
PCT/CN2019/123520
Other languages
English (en)
French (fr)
Inventor
张慜
孙亚男
陈慧芝
杨培强
Original Assignee
江南大学
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 江南大学 filed Critical 江南大学
Priority to CA3126171A priority Critical patent/CA3126171A1/en
Publication of WO2020177423A1 publication Critical patent/WO2020177423A1/zh

Links

Classifications

    • A—HUMAN NECESSITIES
    • A23—FOODS OR FOODSTUFFS; TREATMENT THEREOF, NOT COVERED BY OTHER CLASSES
    • A23B—PRESERVING, e.g. BY CANNING, MEAT, FISH, EGGS, FRUIT, VEGETABLES, EDIBLE SEEDS; CHEMICAL RIPENING OF FRUIT OR VEGETABLES; THE PRESERVED, RIPENED, OR CANNED PRODUCTS
    • A23B7/00—Preservation or 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 belongs to the technical field of intelligent identification of dry quality of spicy vegetables, and relates to a device and method for intelligently detecting the flavor of dried spicy vegetables based on low-field nuclear magnetism.
  • Spicy vegetables refer to vegetables that can emit an aromatic smell or have a pungent taste, and are generally used raw, as soup, seasoning or dish decorations. Such vegetables can increase appetite and also have many medicinal values.
  • the types of spicy vegetables commonly grown in our country include coriander, green onions, garlic, ginger, leaf fennel and so on.
  • Spiced vegetables are rich in nutrients.
  • the roots of ginger are rich in nutrients and contain ginger oil, gingerol, fatty oil, resin, starch, pentosan, cellulose, protein, pigment, wax and trace mineral elements.
  • the chemical composition of ginger is complex. There are more than 100 species that have been discovered, which can be divided into three categories: gingerol, terpenoid volatile oil and diphenylheptane.
  • the aroma and flavor of ginger are related to the volatile oil ginger essential oil.
  • the spicy flavor of ginger mainly depends on the gingerol in the non-volatile oil ginger oleoresin.
  • it also contains a variety of amino acids, vitamins, copper, iron, manganese, zinc, chromium, nickel, cobalt and other trace elements and a variety of functional ingredients. It has the effects of expelling wind and cold, anti-oxidation, anti-tumor, lowering cholesterol, lowering blood sugar, detoxification and sterilization, etc., so it has attracted wide attention from consumers and scholars at home and abroad.
  • Fresh garlic contains carbohydrates, protein, dietary fiber, fat, vitamins, minerals and rich amino acids.
  • the amino acid in vegetables is one of the important nutrients of vegetables, and its composition and content directly affect the nutritional value of vegetables such as white garlic, and are closely related to human taste.
  • Dehydration is an important technical means for long-term storage of agricultural products.
  • the drying method of spiced vegetables is hot air drying, which is simple in operation and low in investment, but it also has disadvantages such as long drying time, low efficiency and poor quality.
  • Microwave vacuum drying is an energy-saving, environment-friendly modern high-tech drying technology. The moisture content of spicy vegetables is high. Due to its unique advantages, the new drying technology of microwave vacuum drying has received extensive attention from domestic and foreign scholars in the field of fruit and vegetable dehydration in recent years.
  • Microwave vacuum drying can better retain the original color, fragrance, vitamins and other heat-sensitive nutrients or biologically active ingredients of the dried materials, and obtain better drying quality.
  • the processing of spicy vegetables into powder can be used directly as seasonings or solid beverages, as raw materials for medicinal materials and health foods, and as raw materials for food such as bread, candies, biscuits, etc. Therefore, the processing of spicy vegetable powder is important The application value and broad development prospects.
  • the unique flavor substances can not only cause people's appetite, but also promote the secretion of digestive juice, so that the human body can quickly digest and absorb nutrients.
  • the electronic nose is used to evaluate the flavor quality of spicy vegetables during the drying process, which has the advantages of less subjective factors, high reliability and repeatability.
  • the electronic nose can realize the qualitative and quantitative analysis of different sensitive types of substances through the metal sensor. It has been widely used in the fields of food quality detection, flavor evaluation, authenticity discrimination and characteristic aroma recognition.
  • LF-NMR Low-field nuclear magnetic resonance
  • Sun Qun and others (Patent Application Number: CN201711307888.3)
  • a low-field nuclear magnetic resonance non-destructive inspection line suitable for dried fruits with shells. Integrate the low-field nuclear magnetic resonance equipment with the transmission device of dried fruit in shell, perform principal component analysis on the existing sample data, divide the good and bad seeds area and calculate the boundary equation to obtain a suitable mathematical model.
  • low-field nuclear magnetic resonance technology is used to detect the transverse magnetization signal quantity of dried fruits with shells, and then by comparing with the constructed mathematical model, the good seeds, mold seeds and insects of dried fruits with shells are quickly obtained The accuracy of the quality of the moth can reach more than 85%.
  • Guo Tao et al. (Patent Application Number: CN201710984507.9) disclosed a fast identification method for grape seed oil adulteration based on low-field nuclear magnetism, which is suitable for the identification of grape seed oil and adulterated grape seed oil.
  • the low-field nuclear magnetic resonance analyzer is used as the main measurement tool, the difference between the relaxation profile data of grape seed oil and adulterated grape seed oil is the main identification basis, and the nuclear magnetic resonance signal is the main research object. Quick and accurate identification of seed oil and adulterated grape seed oil.
  • Li Dajing et al. (Patent Application No.: CN201510967968.6) disclosed a method for characterizing the drying end point of dried Agaricus bisporus based on moisture distribution.
  • the method selects fresh Agaricus bisporus slices as raw materials for far infrared drying and applies low-field nuclear magnetic resonance technology to scan drying.
  • the bisporus bisporus slices in the process obtain the inversion map of the moisture distribution, and the drying end point is determined according to the size of the free water relaxation area.
  • Tan Mingqian et al. (Patent Application No.: CN201610279790.0) disclosed a method for measuring soybean oil and water content using low-field nuclear magnetic resonance technology.
  • the CPMG sequence of low-field nuclear magnetic resonance technology was used to determine soybean samples to obtain relaxation spectrum data of each soybean sample.
  • Wang Xin et al. (Patent Application No.: CN201210435185.X) disclosed a low-field nuclear magnetic resonance detection method for the use limit of soybean frying.
  • the low-field nuclear magnetic resonance analyzer is the main measurement tool and the multi-component relaxation of soybean oil
  • TPC total polar compound
  • the nuclear magnetic resonance signal during the frying process of soybean is the main observation object
  • the multi-component lateral relaxation of soybean oil during the frying process The analysis of Yu map data is used to judge the use limit of soybean frying.
  • Tan Mingqian et al. (Patent Application Number: CN201610285372.2) disclosed a method for rapid and non-destructive testing of the moisture content of abalone during the drying and rehydration process.
  • the echo attenuation curve data of fresh and dried abalone samples were collected respectively. Collect the CPMG signal of the sample.
  • the dried abalone samples were rehydrated.
  • the CPMG signals of the samples were collected to determine the true value of the moisture content of each sample.
  • Corresponding to the true value of the moisture content establish the moisture content prediction model during the drying and rehydration process.
  • These inventions make use of the different content of hydrogen-containing proton components (water in vegetables, soybean oil, and water in abalone) in the sample matrix under different conditions, and their relaxation profile information in the low-field nuclear magnetic field is different. Quick and effective identification and prediction.
  • Cheng Xinfeng et al. studied the moisture diffusion characteristics of taro chips during the microwave vacuum drying process. They used a microwave vacuum drying oven to dry the taro chips at three microwave intensities of 1.5, 2.0 and 2.5 W/g, and measured it by low-field nuclear magnetic resonance technology. The migration and distribution of moisture during microwave vacuum drying of fragrant taro chips were reviewed. MRI detection showed that MVD taro loses water inside and outside at the same time, and the higher the microwave intensity, the faster the relaxation signal disappears. This study revealed the law of moisture diffusion in taro during microwave vacuum drying, that is, the higher the microwave intensity, the faster the moisture diffusion rate in the sample and the conversion between different components of moisture.
  • the change of flavor during the drying process of spiced vegetables is an important indicator to measure the drying quality.
  • Low-field nuclear magnetic resonance technology is widely used in intelligent detection of moisture content in the drying process of fruits and vegetables.
  • the moisture and flavor content of the material in the drying process are in a constantly changing process.
  • 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 correlation analysis between flavor characteristics and nuclear magnetic response parameters is carried out through the artificial neural network (BP-ANN) intelligent analysis system, so that the flavor changes of microwave vacuum drying fruits and vegetables can be reflected by the nuclear magnetic relaxation map information, achieving non-destructive, fast and intelligent Detection.
  • BP-ANN artificial neural network
  • the purpose of the present invention is to provide a microwave vacuum drying method for intelligently detecting the flavor changes of spicy vegetables, using nuclear magnetic resonance detection technology to ensure the shape and nutritional components of the spicy vegetables to the greatest extent, solving the original flavor detection of spicy vegetables Technically complex problems, to achieve lossless, convenient and intelligent detection.
  • a low-field nuclear magnetism-based microwave-dried spicy vegetable flavor intelligent detection device includes a microwave dryer, a computer 1, a temperature sensor 2, a movable sliding rod 5, a vacuum chamber 6, a raw material 7, a movable plate 8, and an NMR coil 9 , Vacuum controller 10, microwave controller 11, temperature controller 12, magnetron 13, NMR box 14, vacuum tube 15 and vacuum pump 16;
  • the said microwave dryer is provided with a vacuum chamber 6, the bottom of the vacuum chamber 6 is used to place the raw materials 7, the vacuum chamber 6 is provided with a moving slide bar 5 and a temperature sensor 2, the moving slide bar 5 is used to move the drying chamber, and the temperature sensor 2 It is used to measure the temperature in the microwave dryer in real time;
  • the vacuum chamber 6 is connected to the vacuum pump 16 outside the microwave dryer through a vacuum tube 15;
  • the microwave dryer is equipped with a vacuum controller 10, a microwave controller 11, a temperature controller 12 and a magnetron 13 ,
  • the vacuum controller 10 is used to control the vacuum pump 16 to adjust the degree of vacuum in the vacuum chamber 6;
  • the microwave controller 11 is used to control the microwave parameters of the microwave dryer;
  • the temperature controller 12 is used to adjust the temperature in the microwave dryer;
  • the control tube 13 is used to convert the energy obtained from a constant electric field into microwave energy;
  • the NMR box 14 is set under the microwave dryer through the moving plate 8.
  • the vacuum chamber 6 can move up and down between the microwave dryer and the NMR box 14 to ensure real-time sampling;
  • the NMR coil 9 is set in the NMR box 14 , Used for real-time monitoring of NMR parameters in the drying process;
  • the computer 1 is respectively connected to the temperature sensor 2, the microwave dryer, and the NMR box 14, and is used to transmit the detected data parameters to the computer 1.
  • the computer 1 contains a neural network model and inputs the detected data parameters into the neural network The model conducts data implementation analysis.
  • the microwave dryer and the NMR box 14 are connected to the computer through the microwave dryer data line 3 and the NMR data line 4, respectively.
  • the moving plate 8 is opened, and the moving sliding rod 5 is operated to send the vacuum chamber 6 and the material 7 into the NMR box 14.
  • the NMR coil 9 is used for sampling for nuclear magnetic parameter collection. After the collection, the sliding rod is moved. 5 Pull up the drying chamber, close the moving plate 8, and continue drying.
  • the neural network model is built in the computer 1, and the detected data parameters are input into the neural network model for data implementation analysis.
  • a method for intelligent detection of the flavor of dried spiced vegetables by microwave based on low-field nuclear magnetism the steps are as follows:
  • Microwave vacuum drying process Put the spicy vegetable raw materials into the vacuum chamber 6 of the microwave vacuum machine, turn on the vacuum pump 16, when the vacuum reaches 10MPa, adjust the microwave controller and enter the drying stage. During the microwave vacuum drying process, Take periodic sampling.
  • Low-field nuclear magnetic resonance analysis of dry materials perform low-field nuclear magnetic resonance analysis to obtain various nuclear magnetic response signal parameters of the sample; the nuclear magnetic response signal parameters include the transverse relaxation time and the peak area; the transverse relaxation time includes There are three types of combined water relaxation time T 21 , non-flowing water relaxation time T 22 , and free water relaxation time T 23 ; the peak areas include combined water peak area A 21 , non-flowing water peak area A 22 , and free water a 23 peak area and peak area a total water a total of four kinds.
  • Flavor detection of dry materials The electronic nose is used to measure the changes of different types of flavor substances in the same kind of spicy vegetables, and the response value of the electronic nose flavor characteristic sensor is obtained.
  • the microwave power is 150 W, and samples are taken every 10 min until the dry basis moisture content of the spicy vegetable raw material is less than 10%.
  • the low-field nuclear magnetic resonance analysis uses a CPMG (carr-purcell-meiboom-gill) pulse sequence for signal acquisition.
  • the collected signals are passed through the nuclear magnetic resonance T 2 inversion software to obtain the T 2 inversion spectrum and the corresponding nuclear magnetic parameters.
  • the electronic nose is used to determine the changes of different types of flavor substances in the same spiced vegetables, and the sample (2.0 g dry basis) is placed in a sealed vial (20 mL) and left to stand for 60 min.
  • the acquisition time is 150 s.
  • the peak area needs to be mass normalized.
  • the spicy vegetables include, but are not limited to, ginger, garlic, green onions, and peppers.
  • the present invention uses nuclear magnetic resonance detection technology to solve the original technical problem of flavor change detection during vegetable drying process on the basis of ensuring the shape of spicy vegetable materials to the greatest extent, realizes non-destructive, convenient and intelligent detection, improves detection efficiency and Product integrity and effective monitoring.
  • the present invention has convenient operation, simple process, high accuracy of detection results, short time-consuming, no damage to the sample, and can effectively monitor the change of flavor during the drying process in real time.
  • the method proposed in the present invention can accurately and effectively determine the changes of different types of flavor substances in the drying process of spicy vegetables, which is of great help to the adjustment and control of the drying process.
  • Figure 1 shows the BP-ANN prediction model of ginger flavor during microwave vacuum drying.
  • (a) is the training set
  • (b) is the verification set
  • (c) is the test set
  • (d) is the comprehensive set
  • Figure 2 shows the BP-ANN prediction model of garlic flavor during microwave vacuum drying.
  • (a) is the training set
  • (b) is the verification set
  • (c) is the test set
  • (d) is the comprehensive set
  • Figure 3 is a simplified diagram of the device integration.
  • Figure 4 is a diagram of the sample detection state, where (a) is when the microwave is dried, (b) is when the moving plate is removed, (c) is when the low-field NMR measurement is performed, and (d) is when returning to the microwave drying.
  • Example 1 Microwave vacuum-dried ginger flavor intelligent detection method and device based on low-field nuclear magnetism
  • Model establishment and intelligent control After repeated experiments, a large number of sample flavor characteristic sensor response values and their corresponding nuclear magnetic response signal parameter database are obtained, and the lateral relaxation time and peak area data of ginger during the drying process are measured with the characteristic sensor.
  • the BP-ANN input parameters are the nuclear magnetic signals (T 21 , T 22 , T 23 , A 21 , A 22 , A 23 and A total ), and the characteristic sensor of the electronic nose is the output parameter, randomly selected 70% of the sample size is used as a training set to establish a flavor-related prediction model ( Figure 1). It can be seen from the figure that there is a good correlation between the predicted value of the ginger sample's flavor and the chemical value obtained by the BP-ANN method, and the R of the training set is greater than 0.9.
  • Embodiment 2 Microwave vacuum drying garlic flavor intelligent detection method and device based on low-field nuclear magnetism
  • Model establishment and intelligent control After repeated experiments, a large number of sample flavor characteristic sensor response values and their corresponding nuclear magnetic response signal parameter database are obtained, and the horizontal relaxation time and peak area data of garlic during the drying process are measured with the characteristic sensor.
  • the BP-ANN input parameters are the nuclear magnetic signals (T 21 , T 22 , T 23 , A 21 , A 22 , A 23 and A total ), and the characteristic sensor of the electronic nose is the output parameter, randomly selected 70% of the sample size is used as a training set to establish a flavor-related prediction model ( Figure 2). It can be seen from the figure that the predicted value and chemical value of different garlic flavors obtained by the BP-ANN method have a good correlation, and the R of the training set is greater than 0.9.

Landscapes

  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • High Energy & Nuclear Physics (AREA)
  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • General Chemical & Material Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Biochemistry (AREA)
  • Health & Medical Sciences (AREA)
  • Wood Science & Technology (AREA)
  • Zoology (AREA)
  • Food Science & Technology (AREA)
  • Polymers & Plastics (AREA)
  • Investigating Or Analysing Materials By Optical Means (AREA)

Abstract

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，属于果蔬干燥品质智能化识别技术领域，将香辛蔬菜样品进行微波真空干燥直至干燥结束，在微波真空干燥过程中阶段性取样进行低场核磁共振分析，用电子鼻对物料的风味变化进行测定，建立香辛蔬菜在干燥过程中低场核磁驰豫时间信号及峰面积信号与电子鼻特征传感器的关系，通过人工神经网络智能分析系统进行分析，预测微波真空干燥过程中香辛蔬菜风味品质变化。利用低场核磁共振检测技术，在最大程度保证香辛蔬菜形状的基础上，解决原有干燥过程中风味变化检测的技术难题，实现无损、快捷、智能检测，提高了检测工作效率及产品完整性，并有效监控干燥过程风味变化。

Description

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 技术领域
本发明属于香辛蔬菜干燥品质智能化识别技术领域，涉及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。
 背景技术
香辛蔬菜指能散发芳香气味或具有辛辣味的蔬菜，一般用生食、作汤、调料或菜肴装饰品。这类蔬菜能增进食欲，同时还具有许多药用价值。我国普遍种植的香辛蔬菜品种有香菜、大葱、蒜、姜、叶用茴香等。香辛蔬菜均含有丰富的营养元素，例如生姜的根茎营养丰富，含有姜油、姜辣素、脂肪油、树脂、淀粉、戊聚糖、纤维素、蛋白质、色素、蜡和微量矿物元素等。生姜的化学成分复杂，已发现的有100多种，可分为姜辣素、萜类挥发油和二苯基庚烷3大类，其中，姜的香气和风味与其含有的挥发性油姜精油有关，而姜的辛辣风味主要取决于非挥发性油姜油树脂中的姜辣素。另外还含有多种氨基酸、维生素、铜、铁、锰、锌、铬、镍、钴等多种微量元素及多种功能性成分。具有驱风散寒、抗氧化、抗肿瘤、降低胆固醇、降低血糖及解毒杀菌等作用，因此受到国内外消费者和学者的广泛关注。新鲜的大蒜中含有碳水化合物、蛋白质、膳食纤维、脂肪、维生素、矿物质及丰富的氨基酸。其中蔬菜中的氨基酸是蔬菜重要营养成分之一，其组成及含量直接影响白蒜等蔬菜营养价值，并与人类味觉密切相关。脱水干燥是农产品长期储藏的一项重要技术手段。通常香辛蔬菜的干燥方法是以热风干燥为主，其操作简单、投资少，但也存在干燥时间长、效率低和品质差等缺点。微波真空干燥是一种能源节约型、环境友好型的现代高新干燥技术。香辛蔬菜的水分含量高，微波真空干燥这一干燥新技术由于其独特的优势，近年来在果蔬脱水方面倍受国内外学者的广泛关注。微波真空干燥能较好地保留被干燥物料原有的色香味、维生素等热敏性营养成分或生物活性成分，得到较好的干燥品质。将香辛蔬菜加工成粉，既可直接作调味料或固体饮料食用，也可以作为药材和保健食品的原料，还可以作为面包、糖果、饼干等食品的原辅料，因此香辛蔬菜粉的加工具有重要的应用价值和广阔的发展前景。
在脱水过程中，对于香辛蔬菜最重要的就是风味的变化。香辛蔬菜风味物质种类繁多，主要包括醇类、醛类、酯类、酸类、烷烃类、酸类及含硫化合物等挥发性风味物质和可溶性糖、有机酸、游离氨基酸等非挥发性风味物质，前者决定了食品的特征滋味，并为后者合成提供前体物质，后者则宏观表现为食品的气味，这些物质含量极微，气味各异，共同作用形成了食品的风味体系。香辛蔬菜独特的香气对风味的贡献与其含量及其阈值大小有关，独特的风味物质不仅能引起人们的食欲，而且能促进消化液的分泌，从而使人体迅速消化吸收营养成分。用电子鼻对干燥过程香辛蔬菜风味品质进行了评价，具有结果受主观因素影响小、可信度和重复性高的优点。电子鼻通过金属传感器可以实现对不同敏感类型物质的定性和定量分析，目前已在食品品质检测、风味评价、真伪甄别和特征性香气识别领域有着广泛的应用。
低场核磁共振(low-field nuclear magnetic resonance，LF-NMR)是利用氢原子核在磁场中的自旋弛豫特性，通过弛豫时间的变化从微观的角度解释样品中水分的分布变化和迁移情况，具有快速、准确、无损、无侵入等优点，近年来在食品科学领域得到广泛的应用。
孙群等(专利申请号:CN201711307888.3) 公开了一种适用于干制带壳水果的低场核磁共振无损检测线。将低场核磁共振设备与干制带壳水果的传输装置整合，对已有样品数据进行主成分分析，进行好坏籽区域划分和边界方程计算，得到合适的数学模型。检测过程中，利用低场核磁共振技术检测干制带壳水果的横向磁化强度信号量，再通过与构建的数学模型进行比对的方式，快速获得干制带壳水果好籽、霉籽和虫蛀的质量情况，其准确率可达到85%以上。
郭涛等(专利申请号:CN201710984507.9)公开了一种基于低场核磁的葡萄籽油掺伪快速鉴别方法，适用于葡萄籽油和掺伪葡萄籽油的鉴别。以低场核磁共振分析仪为主要测定工具，以葡萄籽油和掺伪葡萄籽油的弛豫图谱数据的区别为主要鉴别依据，以核磁共振信号为主要研究对象，运用数模分析方法实现葡萄籽油和掺伪葡萄籽油的快速准确鉴别。
李大婧等(专利申请号:CN201510967968.6)公开了一种基于水分分布表征远红外干燥双孢菇干燥终点的方法，该方法选取新鲜双孢菇片为原料，进行远红外干燥，应用低场核磁共振技术扫描干燥过程中的双孢菇片，得到水分分布反演图，根据自由水弛豫面积的大小确定干燥终点。
谭明乾等(专利申请号:CN201610279790.0)公开了一种利用低场核磁共振技术测定大豆含油含水量的方法，采用低场核磁共振技术CPMG序列测定大豆样品，获得各大豆样品的弛豫谱数据，将各大豆样品的实际含油含水量与弛豫谱数据相对应，利用化学计量学方法进行拟合，得到大豆含油含水量的预测模型。
王欣等(专利申请号:CN201210435185.X)公开了一种大豆油煎炸使用极限的低场核磁共振检测法，以低场核磁共振分析仪为主要测量工具，以大豆油的多组分弛豫图谱数据与总极性化合物(TPC)数据间建立的数学模型为基础，以大豆油煎炸过程中的核磁共振信号为主要观察对象，通过对煎炸过程中大豆油的多组分横向弛豫图谱数据的分析进行大豆油煎炸使用极限的判断。
谭明乾等(专利申请号:CN201610285372.2)公开了一种快速无损检测鲍鱼在干制及复水过程中水分含量的方法，分别采集新鲜的、干制后的鲍鱼样品的回波衰减曲线数据，采集样品的CPMG信号。将干鲍鱼样品复水，复水过程中，采集样品的CPMG信号，测定各样品水分含量真实值。对应水分含量真实值,建立干制及复水过程中水分含量预测模型。这些发明利用样品基质在不同状态下含氢质子成分(如果蔬中的水、大豆油中的水及鲍鱼中的水)含量不同，其在低场核磁场中的弛豫图谱信息不同，从而进行快速有效的鉴别及预测。
程新峰等研究香芋片微波真空干燥过程中水分扩散特性，采用微波真空干燥箱在1.5、2.0和2.5 W/g 3个微波强度下对香芋片进行了干燥试验，利用低场核磁共振技术测定了香芋片微波真空干燥过程中水分迁移及分布情况。MRI检测表明，MVD香芋内外同时失水，且微波强度越高，弛豫信号消失的越快。该研究揭示了微波真空干燥中香芋内水分扩散规律，即微波强度越高，样品内水分扩散速率及不同组分水分之间的转化越迅速。
香辛蔬菜干燥过程中风味的变化是衡量干燥品质的一个重要指标，目前还没有一种快速检测香辛蔬菜干燥过程中风味变化的装置。低场核磁共振技术被广泛的应用于果蔬干燥过程中水分含量的智能检测。本发明中，干燥加工中物料水分及风味物质含量是处于不断变化的过程。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。将风味特性与核磁响应参数进行相关性分析，通过人工神经网络(BP-ANN)智能分析系统进行分析，从而微波真空干燥果蔬的风味变化可由核磁弛豫图谱信息反映出来，实现无损、快速、智能检测。
 技术问题
本发明的目的是提供一种微波真空干燥香辛蔬菜风味变化智能检测的方法，利用核磁共振的检测技术，在最大程度上保证香辛蔬菜物料形状和营养成分的基础上，解决原有香辛蔬菜风味检测技术复杂的问题，实现无损、便捷、智能检测。
 技术解决方案
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，该装置包括微波干燥机、电脑1、温度传感器2、移动滑杆5、真空室6、原料7、移动板8、NMR线圈9、真空控制器10、微波控制器11、温控仪12、磁控管13、NMR盒子14、真空管15和真空泵16；
所述的微波干燥机中设置真空室6，真空室6的底部用于放置原料7，真空室6中设置移动滑杆5和温度传感器2，移动滑杆5用于移动干燥仓，温度传感器2用于实时测量微波干燥机中的温度；真空室6通过真空管15与微波干燥机外部的真空泵16相连；微波干燥机上设置真空控制器10、微波控制器11、温控仪12和磁控管13，真空控制器10用于控制真空泵16，以调控真空室6中的真空度；微波控制器11用于控制微波干燥机的微波参数；温控仪12用于调控微波干燥机中的温度；磁控管13用于把从恒定电场中获得能量转变成微波能量；
所述的NMR盒子14通过移动板8设置于微波干燥机的下方，真空室6能在微波干燥机与NMR盒子14中上下移动，确保进行实时采样；所述的NMR线圈9设置于NMR盒子14中，用于实时监测干燥过程中的NMR参数；
所述的电脑1分别与温度传感器2、微波干燥机、NMR盒子14相连，用于将检测的数据参数传输给电脑1；电脑1中内载神经网络模型，将检测到的数据参数输入神经网络模型进行数据的实施分析。
所述的微波干燥机和NMR盒子14分别通过微波干燥机数据线3和 NMR数据线4与电脑相连。
装置操作说明：
首先将物料7置于微波干燥机的真空室中6，设置真空控制器10、微波控制器11、温控仪12、移动板8、NMR线圈9上对应的物理参数，开启电脑1及其分析软件，开启真空泵16，待到达相应的真空度后开启磁控管13(微波发生器)，干燥开始。
其次，干燥到达设定时间后，移动板8被打开，操作移动滑杆5将真空室6及物料7送入NMR盒子14中，通过NMR线圈9进行取样进行核磁参数采集，采集结束移动滑杆5将干燥仓拉上，移动板8闭合，干燥继续。
最后，电脑1中内载神经网络模型，将检测到的数据参数输入神经网络模型进行数据的实施分析。
一种基于低场核磁的微波干燥香辛蔬菜风味智能检测的方法，步骤如下：
(1) 干燥前香辛蔬菜预处理：将香辛蔬菜原料清洗干净后，切成1×1×1cm的立方体，将其放置于干燥的托盘上。
(2) 微波真空干燥过程：将香辛蔬菜原料放入微波真空机的真空室6内，开启真空泵16，当真空度达到10MPa时，调控微波控制器，进入干燥阶段，在微波真空干燥过程中，进行阶段性取样。
(3) 干燥物料的低场核磁共振分析：进行低场核磁共振分析，得到样品各项核磁响应信号参数；所述核磁响应信号参数包含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 总共4种。
(4) 干燥物料的风味检测：采用电子鼻测定同种香辛蔬菜中不同类别风味物质变化的变化，得到电子鼻风味特征传感器响应值。
(5) 基于低场核磁的微波干燥香辛蔬菜风味预测模型的建立：通过单次干燥实验阶段性取样及重复干燥实验得到各种样品电子鼻风味特征传感器响应值与其对应的核磁响应信号参数数据库，并采用BP-ANN建立关系，得到微波干燥香辛蔬菜风味预测模型。
(6) 微波干燥过程中香辛蔬菜风味变化的智能检测：干燥中的香辛蔬菜样品经取样进行低场核磁共振分析，由步骤(5)中得到的微波干燥香辛蔬菜风味预测模型预测当前风味物质变化。
进一步的，所述步骤(2)中，所述的微波功率是150 W，每间隔10 min取样，直至香辛蔬菜原料的干基含水率小于10%。
进一步的，所述步骤(3)中，低场核磁共振分析利用CPMG (carr-purcell-meiboom-gill) 脉冲序列进行信号采集。CPMG 序列采用的参数为：采样点数 TD＝784794，谱宽100 kHz，回波个数18000，重复扫描次数 NS＝4，采样重复时间 TW＝4000 ms。将采集的信号通过核磁共振 T 2反演软件得到T 2反演谱及对应的核磁参数。
进一步的，所述步骤(4)中，同种香辛蔬菜中不同类别风味物质变化的测定利用电子鼻，将样品(2.0g干基)置于密封小瓶(20mL)中，静置60min。采集时间为150 s。
进一步的，所述步骤(5)中，所述样品核磁响应信号参数中的不同组分水的峰面积与电子鼻传感器响应值建立关系方程时，需要对峰面积作质量归一化处理。
所述的香辛蔬菜包括但不限于姜、大蒜、大葱、辣椒。
 有益效果
1、本发明利用核磁共振的检测技术，在最大程度保证香辛蔬菜物料形状的基础上，解决原有蔬菜干燥过程风味变化检测的技术难题，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，并有效监控。
2、本发明操作方便、流程简单，检测结果准确性高、耗时短、对样品没有损坏，并且可以实时有效监测干燥过程风味的变化。
3、本发明提出的方法可以精确有效判断出香辛蔬菜干燥过程中不同类别风味物质的变化，对于干燥过程的调节控制具有很大帮助。
 附图说明
图1为微波真空干燥过程中姜风味BP-ANN预测模型。其中，(a)为训练集，(b)验证集，(c)为测试集，(d)为综合集
图2为微波真空干燥过程中大蒜风味BP-ANN预测模型。其中，(a)为训练集，(b)验证集，(c)为测试集，(d)为综合集
图3为装置集成简图。
图4为样本检测状态图，其中，(a)为微波干燥时，(b)为移动板移除时，(c)为低场核磁共振测量时，(d)为返回微波干燥时。
图中：1电脑；2温度传感器；3微波干燥机数据线；4NMR数据线；5移动滑杆；6真空室；7原料；8移动板；9NMR线圈；10真空控制器；11微波控制器；12温控仪；13磁控管；14NMR盒子；15真空管；16真空泵。
 本发明的实施方式
下面将结合具体实施例和附图对本发明的技术方案进行进一步的说明。
实施例1：基于低场核磁的微波真空干燥姜风味智能检测方法及装置
1.姜经清水清洗干净后，去皮切成1×1×1cm的立方体，将其放置于微波真空干燥的托盘上。开启真空泵，当真空度达到10 kPa时，启动微波加热系统，微波功率被设置为150W。阶段性取样进行低场核磁共振分析，得到样品的横向弛豫时间T 2曲线及各响应信号参数，并采用电子鼻测定该样品的风味变化，确定电子鼻的特征传感器。
2. 模型的建立及智能调控：经多次重复实验得到大量样品风味特征传感器响应值与其对应的核磁响应信号参数数据库，将干燥过程中姜的横向驰豫时间及峰面积数据与特征传感器通过计量学软件进行拟合，以核磁信号(T 21、T 22、T 23、A 21、A 22、A 23及A 总)为BP-ANN的输入参数，电子鼻的特征传感器为输出参数，随机取70%的样本量作为训练集，建立风味相关预测模型(如图1)。从图可见，采用BP－ANN方法获得的姜样品的风味的预测值与化学值之间均具有良好的相关性，训练集的R均大于0.9。为验证预测模型的准确性与稳定性，将15%的样品作为验证集，15%的样品作为测试集，结果显示验证集的R均大于0.9，表明模型的预测能力很好，综合R为0.97798这说明预测模型的稳定性较好。随机抽取干燥中20组样品经自动取样系统进行低场核磁共振分析，经建立的姜风味BP-ANN分析模型，预测当前的风味情况。模型验证集的相关系数R为 0.9688，说明低场核磁共振结合BP-ANN模型可以准确的预测姜干燥过程中的风味变化。
实施例2：基于低场核磁的微波真空干燥大蒜风味智能检测方法及装置
1.大蒜经去皮清洗干净后，切成0.4 cm的薄片，将其放置于微波真空干燥的托盘上。开启真空泵，当真空度达到10 kPa时，启动微波加热系统，微波功率被设置为150W。阶段性取样进行低场核磁共振分析，得到样品的横向弛豫时间T 2曲线及各响应信号参数，并采用电子鼻测定该样品的风味变化，确定电子鼻的特征传感器为S4。
2. 模型的建立及智能调控：经多次重复实验得到大量样品风味特征传感器响应值与其对应的核磁响应信号参数数据库，将干燥过程中大蒜的横向驰豫时间及峰面积数据与特征传感器通过计量学软件进行拟合，以核磁信号(T 21、T 22、T 23、A 21、A 22、A 23及A 总)为BP-ANN的输入参数，电子鼻的特征传感器为输出参数，随机取70%的样本量作为训练集，建立风味相关预测模型(如图2)。从图可见，采用BP－ANN方法获得的不同大蒜的风味的预测值与化学值之间均具有良好的相关性，训练集的R均大于0.9。为验证预测模型的准确性与稳定性，将15%的样品作为验证集，15%的样品作为测试集，结果显示验证集的R均大于0.9，表明模型的预测能力很好，综合R为0.97581这说明预测模型的稳定性较好。随机抽取干燥中20组样品经自动取样系统进行低场核磁共振分析，经建立的大蒜风味BP-ANN分析模型，预测当前的风味情况。模型验证集的相关系数R为 0.9589，说明低场核磁共振结合BP-ANN模型可以准确的预测大蒜干燥过程中的风味变化。

Claims (10)

  1. 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，其特征在于，该装置包括微波干燥机、电脑(1)、温度传感器(2)、移动滑杆(5)、真空室(6)、原料(7)、移动板(8)、NMR线圈(9)、真空控制器(10)、微波控制器(11)、温控仪(12)、磁控管(13)、NMR盒子(14)、真空管(15)和真空泵(16)；
    所述的微波干燥机中设置真空室(6)，真空室(6)的底部用于放置原料(7)，真空室(6)中设置移动滑杆(5)和温度传感器(2)，移动滑杆(5)用于移动干燥仓，温度传感器(2)用于实时测量微波干燥机中的温度；真空室(6)通过真空管(15)与微波干燥机外部的真空泵(16)相连；微波干燥机上设置真空控制器(10)、微波控制器(11)、温控仪(12)和磁控管(13)，真空控制器(10)用于控制真空泵(16)，以调控真空室(6)中的真空度；微波控制器(11)用于控制微波干燥机的微波参数；温控仪(12)用于调控微波干燥机中的温度；磁控管(13)用于把从恒定电场中获得能量转变成微波能量；
    所述的NMR盒子(14)通过移动板(8)设置于微波干燥机的下方，真空室(6)能在微波干燥机与NMR盒子(14)中上下移动，确保进行实时采样；所述的NMR线圈(9)设置于NMR盒子(14)中，用于实时监测干燥过程中物料的NMR参数；
    所述的电脑(1)分别与温度传感器(2)、微波干燥机、NMR盒子(14)相连，用于将检测的数据参数传输给电脑(1)；电脑(1)中内载神经网络模型，将检测到的数据参数输入神经网络模型进行数据的实施分析。
  2. 根据权利要求1所述的装置，其特征在于，所述的微波干燥机和NMR盒子(14)分别通过微波干燥机数据线(3)和NMR数据线(4)与电脑相连。
  3. 采用权利要求1或2任一所述的装置的一种基于低场核磁的微波干燥香辛蔬菜风味智能检测的方法，其特征在于，步骤如下：
    (1) 干燥前香辛蔬菜预处理：将香辛蔬菜原料清洗干净后，切成1×1×1cm的立方体，将其放置于干燥的托盘上；
    (2) 微波真空干燥过程：将香辛蔬菜原料放入微波真空机的真空室(6)内，开启真空泵(16)，当真空度达到10MPa时，调控微波控制器(11)，进入干燥阶段，在微波真空干燥过程中，进行阶段性取样；
    (3) 干燥物料的低场核磁共振分析：进行低场核磁共振分析，得到样品各项核磁响应信号参数；所述核磁响应信号参数包含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 总共4种；
    (4) 干燥物料的风味检测：采用电子鼻测定同种香辛蔬菜中不同类别风味物质的变化，得到电子鼻风味特征传感器响应值；
    (5) 基于低场核磁的微波干燥香辛蔬菜风味预测模型的建立：通过单次干燥实验阶段性取样及重复干燥实验得到各种样品电子鼻风味特征传感器响应值与其对应的核磁响应信号参数数据库，并采用BP-ANN建立关系，得到微波干燥香辛蔬菜风味预测模型；
    (6) 微波干燥过程中香辛蔬菜风味变化的智能检测：干燥中的香辛蔬菜样品经取样进行低场核磁共振分析，由步骤(5)中得到的微波干燥香辛蔬菜风味预测模型预测当前风味物质变化。
  4. 根据权利要求3所述的方法，其特征在于，所述步骤(2)中，所述的微波功率是150 W，每间隔10 min取样，直至香辛蔬菜原料的干基含水率小于10%。
  5. 根据权利要求3所述的方法，其特征在于，所述步骤(3)中，低场核磁共振分析利用CPMG脉冲序列进行信号采集；CPMG 序列采用的参数为：采样点数 TD＝784794，谱宽100 kHz，回波个数18000，重复扫描次数 NS＝4，采样重复时间 TW＝4000 ms；将采集的信号通过核磁共振 T 2反演软件得到T 2反演谱及对应的核磁参数。
  6. 根据权利要求4所述的方法，其特征在于，所述步骤(3)中，低场核磁共振分析利用CPMG脉冲序列进行信号采集；CPMG 序列采用的参数为：采样点数 TD＝784794，谱宽100 kHz，回波个数18000，重复扫描次数 NS＝4，采样重复时间 TW＝4000 ms；将采集的信号通过核磁共振 T 2反演软件得到T 2反演谱及对应的核磁参数。
  7. 根据权利要求3所述的方法，其特征在于，所述步骤(4)中，同种香辛蔬菜中不同类别风味物质变化的测定利用电子鼻，将样品置于密封小瓶中，静置60min；采集时间为150 s。
  8. 根据权利要求4、5或6所述的方法，其特征在于，所述步骤(4)中，同种香辛蔬菜中不同类别风味物质变化的测定利用电子鼻，将样品置于密封小瓶中，静置60min；采集时间为150 s。
  9. 根据权利要求3所述的方法，其特征在于，所述步骤(5)中，所述样品核磁响应信号参数中的不同组分水的峰面积与电子鼻传感器响应值建立关系方程时，需要对峰面积作质量归一化处理。
  10. 根据权利要求4、5、6或7所述的方法，其特征在于，所述步骤(5)中，所述样品核磁响应信号参数中的不同组分水的峰面积与电子鼻传感器响应值建立关系方程时，需要对峰面积作质量归一化处理。
PCT/CN2019/123520 2019-03-01 2019-12-06 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 WO2020177423A1 (zh)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CA3126171A CA3126171A1 (en) 2019-03-01 2019-12-06 Low-field nuclear magnetic resonance-based device and method for intelligent detection of microwave-dried spicy vegetable flavor

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201910157019.XA CN109799256A (zh) 2019-03-01 2019-03-01 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
CN201910157019.X 2019-03-01

Publications (1)

Publication Number Publication Date
WO2020177423A1 true WO2020177423A1 (zh) 2020-09-10

Family

ID=66561517

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2019/123520 WO2020177423A1 (zh) 2019-03-01 2019-12-06 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

Country Status (3)

Country Link
CN (1) CN109799256A (zh)
CA (1) CA3126171A1 (zh)
WO (1) WO2020177423A1 (zh)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112285144A (zh) * 2020-10-15 2021-01-29 青岛农业大学 一种利用低场核磁共振检测白羽肉鸡鸡胸肉肌病的方法
CN114354670A (zh) * 2022-01-06 2022-04-15 南京海关动植物与食品检测中心 一种基于mlp神经网络的注水注胶肉检测方法
CN114441583A (zh) * 2022-02-07 2022-05-06 南京海关动植物与食品检测中心 基于低场核磁共振数据的冻融肉鉴别模型及鉴别方法

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109799256A (zh) * 2019-03-01 2019-05-24 江南大学 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
CN110432527B (zh) * 2019-07-25 2021-04-27 中国农业科学院农产品加工研究所 膨化果蔬产品体积膨胀度的控制方法及应用
CN111208241B (zh) * 2020-03-03 2021-10-26 江南大学 一种基于电子鼻结合人工神经网络预测煎炸油品质的方法
CN112525942A (zh) * 2020-11-10 2021-03-19 中国农业科学院茶叶研究所 一种基于低场核磁共振技术快速无损检测干燥过程中绿茶含水率及干燥程度的方法
CN112834546A (zh) * 2020-12-01 2021-05-25 上海纽迈电子科技有限公司 一种植物籽粒中含水率和含油率的测试方法及其应用

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2002112729A (ja) * 2000-10-10 2002-04-16 Toyo Shinyaku:Kk ケール乾燥粉末の製造方法
CN106324011A (zh) * 2016-08-25 2017-01-11 江南大学 一种确定调理水产品低温货架期的鲜度联合检测方法
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
CN107091852A (zh) * 2017-07-06 2017-08-25 山东省分析测试中心 一种丹参干燥过程水分分布的无损检测方法
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
CN109042870A (zh) * 2018-07-16 2018-12-21 浙江工业大学 一种高品质稻谷的干燥方法
CN109799256A (zh) * 2019-03-01 2019-05-24 江南大学 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

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2002112729A (ja) * 2000-10-10 2002-04-16 Toyo Shinyaku:Kk ケール乾燥粉末の製造方法
CN106324011A (zh) * 2016-08-25 2017-01-11 江南大学 一种确定调理水产品低温货架期的鲜度联合检测方法
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
CN107091852A (zh) * 2017-07-06 2017-08-25 山东省分析测试中心 一种丹参干燥过程水分分布的无损检测方法
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
CN109042870A (zh) * 2018-07-16 2018-12-21 浙江工业大学 一种高品质稻谷的干燥方法
CN109799256A (zh) * 2019-03-01 2019-05-24 江南大学 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

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112285144A (zh) * 2020-10-15 2021-01-29 青岛农业大学 一种利用低场核磁共振检测白羽肉鸡鸡胸肉肌病的方法
CN112285144B (zh) * 2020-10-15 2022-12-27 青岛农业大学 一种利用低场核磁共振检测白羽肉鸡鸡胸肉肌病的方法
CN114354670A (zh) * 2022-01-06 2022-04-15 南京海关动植物与食品检测中心 一种基于mlp神经网络的注水注胶肉检测方法
CN114441583A (zh) * 2022-02-07 2022-05-06 南京海关动植物与食品检测中心 基于低场核磁共振数据的冻融肉鉴别模型及鉴别方法

Also Published As

Publication number Publication date
CA3126171A1 (en) 2020-09-10
CN109799256A (zh) 2019-05-24

Similar Documents

Publication Publication Date Title
WO2020177423A1 (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
Wang et al. Pulsed vacuum drying of Thompson seedless grape: Effects of berry ripeness on physicochemical properties and drying characteristic
Sun et al. Pulse-spouted microwave freeze drying of raspberry: Control of moisture using ANN model aided by LF-NMR
CN108519398A (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
Al Faruq et al. A novel vacuum frying technology of apple slices combined with ultrasound and microwave
Park Moisture and Ash Contents of Food
Milczarek et al. Assessment of tomato pericarp mechanical damage using multivariate analysis of magnetic resonance images
Tang et al. Magnetic resonance applications in food analysis
CN104020129A (zh) 一种基于近红外光谱结合氨基酸分析技术的工夫红茶发酵质量的判别方法
CN106841083A (zh) 基于近红外光谱技术的芝麻油品质检测方法
CN103955626A (zh) 一种干制毛豆品质评价模型及构建方法
Hnin et al. Comparison of quality aspects and energy consumption of restructured taro and potato chips under three drying methods
WO2020177422A1 (zh) 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
Mariette et al. Quantitative MRI in food science & food engineering
CN113588703A (zh) 一种智能判定果蔬冻干升华/解析转化点的方法
Lu et al. Characterization of moisture transformation and distribution in mushroom (Lentinus edodes) during hot‐air drying
Zhu et al. Moisture variation analysis of the green plum during the drying process based on low‐field nuclear magnetic resonance
Younas et al. Multispectral imaging for predicting the water status in mushroom during hot‐air dehydration
CN103487446A (zh) 一种基于介电特性的油炸食品中明矾添加剂的检测方法
Song et al. Rapid and non-destructive detection method for water status and water distribution of rice seeds with different vigor
Xue et al. Study of Malus Asiatica Nakai’s firmness during different shelf lives based on visible/near-infrared spectroscopy
CN108593596A (zh) 基于近红外光谱技术快速检测椰汁中原汁含量的方法
Cao et al. Real-time monitoring system for quality monitoring of jujube slice during drying process
Chen et al. Nondestructive Detection Technologies for Real-Time Monitoring Food Quality During Processing
Divya et al. Magnetic resonance imaging technology for process control and quality maintenance in food quality operation

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: 19918000

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 3126171

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: 19918000

Country of ref document: EP

Kind code of ref document: A1