[1] 黄姗, 时文六, 白天,等.蛋白质组学技术在速冻食品中的应用研究进展[J].食品研究与开发, 2023, 44(22):180-185.
HUANG S, SHI W L, BAI T, et al.Recent progress in application of proteomics in quick-frozen food[J].Food Research and Development, 2023, 44(22):180-185.
[2] 钟婷婷. 食品微生物检测技术及其质量控制的重要性[J].食品安全导刊, 2023(30):166-168.
ZHONG T T.The importance of food microbial detection technology and its quality control[J].China Food Safety Magazine, 2023(30):166-168.
[3] 雷豆豆, 宋鹏悦, 徐青斌,等.色谱及联用技术在中药材农药残留检测中的应用进展[J].分析试验室,2024,43(10):1505-1502.
LEI D D, SONG P Y, XU Q B, et al.Progress in Chromatography and hyphenated techniques for pesticide residues detection in Chinese medicinal materials[J].Chinese Journal of Analysis Laboratory,2024,43(10):1505-1502.
[4] 王晓明, 章海亮, 罗微,等.近红外光谱检测梨果硬度研究[J].中国农机化学报, 2015, 36(6):120-123;142.
WANG X M, ZHANG H L, LUO W, et al.Study on pear firmness detection by using near infrared reflectance spectroscopy based on CARS[J].Journal of Chinese Agricultural Mechanization, 2015, 36(6):120-123;142.
[5] 袁伟东, 姜洪喆, 鞠皓, 等.木本油料产品掺假和品质评估的近红外光谱及成像检测研究进展[J].食品与发酵工业, 2023, 49(2):307-315.
YUAN W D, JIANG H Z, JU H, et al.Research progress of near-infrared spectroscopy and imaging detection for adulteration and quality evaluation of woody oil products[J].Food and Fermentation Industries, 2023, 49(2):307-315.
[6] WANG K Q, LI Z L, LI J J, et al.Raman spectroscopic techniques for nondestructive analysis of agri-foods:A state-of-the-art review[J].Trends in Food Science & Technology, 2021, 118:490-504.
[7] NASR-ESFAHANI S, MUTHUKUMAR V, REGENTOVA E, et al.Hyperspectral methods in microscopy image analysis:A Survey[C].Proceedings of the 18th International Conference on Signal Processing and Multimedia Applications, SCITEPRESS-Science and Technology Publications, 2021:111-119.
[8] WENG X, NEETHIRAJAN S.Aptamer-based fluorometric determination of norovirus using a paper-based microfluidic device[J].Microchimica Acta, 2017, 184(11):4545-4552.
[9] CAPORASO N, WHITWORTH M B, FOWLER M S, et al.Hyperspectral imaging for non-destructive prediction of fermentation index, polyphenol content and antioxidant activity in single cocoa beans.[J].Food Chemistry, 2018, 258:343-351.
[10] BURGER J, GELADI P.Hyperspectral NIR imaging for calibration and prediction:A comparison between image and spectrometer data for studying organic and biological samples[J].Analyst, 2006, 131(10):1152-1160.
[11] PU H B, LIN L, SUN D W.Principles of hyperspectral microscope imaging techniques and their applications in food quality and safety detection:A review[J].Comprehensive Reviews in Food Science and Food Safety, 2019, 18(4):853-866.
[12] ROTH G A, TAHILIANI S, NEU-BAKER N M, et al.Hyperspectral microscopy as an analytical tool for nanomaterials[J].Wiley Interdisciplinary Reviews:Nanomedicine and Nanobiotechnology, 2015, 7(4):565-579.
[13] XU Z P, JIANG Y M, HE S L.Multi-mode microscopic hyperspectral imager for the sensing of biological samples[J].Applied Sciences, 2020, 10(14):4876.
[14] 贺雨田, 杨颉, 隋海霞, 等.基于显微光谱法的双壳类海洋生物中微塑料的检测方法研究[J].分析测试学报, 2021, 40(7):1055-1061.
HE Y T, YANG J, SUI H X, et al.Research on detection methods for microplastics in bivalve marine organisms based on microspectroscopy[J].Journal of Instrumental Analysis, 2021, 40(7):1055-1061.
[15] 陈争. 基于红外-拉曼光谱的常见爆炸物快速识别分类研究[D].北京:中国人民公安大学, 2023.
CHEN Z.Research on rapid identification and classification of common explosives based on infrared and raman spectroscopy[D].Beijing: People’s Public Security University of China, 2023.
[16] 丘育辉, 陈伟权, 邓壬癸, 等. 聚甲基丙烯酸甲酯对二硫化钼拉伸应变控制及拉曼光谱研究[J]. 机电工程技术, 2023, 52(11):61-64.
QIU Y H, CHEN W Q, DENG R G, et al. Tensile strain engineering and Raman spectra of molybdenum disulfide on polymethyl methacrylate[J]. Mechanical & Electrical Engineering Technology, 2023, 52(11):61-64.
[17] TAO C L, DU J, TANG Y X, et al. A deep-learning based system for rapid genus identification of pathogens under hyperspectral microscopic images[J]. Cells, 2022, 11(14):2237.
[18] 何明霞, 田甜, 刘立媛, 等. 小鼠小梁细胞与肌成纤维细胞的同步辐射红外显微光谱[J]. 光谱学与光谱分析, 2019, 39(11):3346-3351.
HE M X, TIAN T, LIU L Y, et al. Synchrotron radiation infrared microscopy analysis of mouse trabecular meshwork cells and myofibroblasts[J]. Spectroscopy and Spectral Analysis, 2019, 39(11):3346-3351.
[19] TROVATELLO C, GENCO A, CRUCIANO C, et al. Hyperspectral microscopy of two-dimensional semiconductors[J]. Optical Materials: X, 2022, 14:100145.
[20] ZENATI T, FIGLIUZZI B, HAM S H. Surface oxides characterization based on hyperspectral observations[J]. Chemometrics and Intelligent Laboratory Systems, 2023, 240:104879.
[21] 褚小立. 化学计量学方法与分子光谱分析技术[M]. 北京: 化学工业出版社, 2011.
[22] 杨晨龙, 袁大林, 牟定荣, 等. 近红外显微成像技术及其应用进展[J]. 光谱实验室, 2012, 29(5):3198-3202.
YANG C L, YUAN D L, MOU D R, et al. Near infrared micro-imaging technology and its application progress[J]. Chinese Journal of Spectroscopy Laboratory, 2012, 29(5):3198-3202.
[23] 何勇. 光谱及成像技术在农业中的应用[M]. 北京: 科学出版社, 2016.
[24] GRIFFITHS P R, DE HASETH J A. Fourier Transform Infrared Spectrometry[M].Hoboken: Wiley, 2006.
[25] ROSTRON P, GERBER D. Raman spectroscopy—A review[J]. International Journal of Engineering and Technical Research, 2016, 6(1):50-64.
[26] HUANG H, LIU L, NGADI M O. Recent developments in hyperspectral imaging for assessment of food quality and safety[J]. Sensors, 2014, 14(4):7248-7276.
[27] ROBERTS J, POWER A, CHAPMAN J, et al. A short update on the advantages, applications and limitations of hyperspectral and chemical imaging in food authentication[J]. Applied Sciences, 2018, 8(4):505.
[28] GURRALA K, HARIGA M. Key food supply chain challenges: A review of the literature and research gaps[J]. Operations and Supply Chain Management, 2022:441-460.
[29] SU K, ZHU S Q, WEI L, et al. Classification of bee pollen grains using hyperspectral microscopy imaging and Fisher linear classifier[J]. Optical Engineering, 2016, 55(5):053102.
[30] LI S X, FAN X, MEI J Q, et al. Identification of antibiotic mycelia residues in cottonseed meal using Fourier transform near-infrared microspectroscopic imaging[J]. Food Chemistry, 2019, 293:204-212.
[31] LU Y, JIA B B, YOON S C, et al. Spatio-temporal patterns of Aspergillus flavus infection and aflatoxin B1 biosynthesis on maize kernels probed by SWIR hyperspectral imaging and synchrotron FTIR microspectroscopy[J]. Food Chemistry, 2022, 382:132340.
[32] ONG L, PAX A P, ONG A, et al. The effect of pH on the fat and protein within cream cheese and their influence on textural and rheological properties[J]. Food Chemistry, 2020, 332:127327.
[33] 胡伟. 基于多尺度显微高光谱成像技术的鱼糜品质分析与识别机制[D]. 上海: 上海海洋大学, 2016.
HU W. Surimi quality analysis and recognition mechanism based on multi-scale microscopic hyperspectral imaging technology [D]. Shanghai: Shanghai Ocean University, 2016.
[34] CHEN J B, SUN S Q, ZHOU Q. Direct observation of bulk and surface chemical morphologies of Ginkgo biloba leaves by Fourier transform mid- and near-infrared microspectroscopic imaging[J]. Analytical and Bioanalytical Chemistry, 2013, 405(29):9385-9400.
[35] GHOSAL S, CHEN M, WAGNER J, et al. Molecular identification of polymers and anthropogenic particles extracted from oceanic water and fish stomach-A Raman micro-spectroscopy study[J]. Environmental Pollution, 2018, 233:1113-1124.
[36] ZHANG Y Y, GAO W J, CUI C J, et al. Development of a method to evaluate the tenderness of fresh tea leaves based on rapid, in situ Raman spectroscopy scanning for carotenoids[J]. Food Chemistry, 2020, 308:125648.
[37] FENG X, LIU N, ZHANG H H, et al. Chemical imaging of the microstructure of chickpea seed tissue within a cellular dimension using synchrotron infrared microspectroscopy: A preliminary study[J]. Journal of Agricultural and Food Chemistry, 2020, 68(41):11586-11593.
[38] WU L G, JIANG Q F, ZHANG Y, et al. Peroxidase activity in tomato leaf cells under salt stress based on micro-hyperspectral imaging technique[J]. Horticulturae, 2022, 8(9):813.
[39] 杜明华. 基于显微高光谱成像技术的番茄叶片抗氧化酶活性检测研究[D]. 银川: 宁夏大学, 2022.
DU M H. Detection of antioxidant enzyme activity in tomato leaves based on microscopic hyperspectral imaging technology[D]. Yinchuan: Ningxia University, 2022.
[40] 王福香. 基于光谱和图像信息融合的彩色马铃薯内部成分检测方法研究[D]. 呼和浩特: 内蒙古农业大学, 2022.
WANG F X. Research on detection method of colored potato internal components based on spectral and image information fusion [D]. Hohhot: Inner Mongolia Agricultural University, 2022.
[41] PARK B, SHIN T S, CHO J S, et al. Characterizing hyperspectral microscope imagery for classification of blueberry firmness with deep learning methods[J]. Agronomy, 2021, 12(1):85.
[42] PARK B, SHIN T, CHO J S, et al. Improving blueberry firmness classification with spectral and textural features of microstructures using hyperspectral microscope imaging and deep learning[J]. Postharvest Biology and Technology, 2023, 195:112154.
[43] 赵鹏, 唐艳慧, 李振宇. 支持向量机复合核函数的高光谱显微成像木材树种分类[J]. 光谱学与光谱分析, 2019, 39(12):3776-3782.
ZHAO P, TANG Y H, LI Z Y. Wood species recognition with microscopic hyper-spectral imaging and composite kernel SVM[J]. Spectroscopy and Spectral Analysis, 2019, 39(12):3776-3782.
[44] 唐艳慧. 基于光谱信息与纹理信息融合的显微高光谱木材分类[D]. 哈尔滨: 东北林业大学, 2020.
TANG Y H. Micro-spectral wood classification based on fusion of spectral information and texture information [D]. Harbin: Northeast Forestry University, 2020.
[45] 韩金城. 基于纹理和光谱融合的木材树种分类技术的研究[D]. 哈尔滨: 东北林业大学, 2021.
HAN J C. Study on classification technology of wood species based on fusion of texture and spectrum [D]. Harbin: Northeast Forestry University, 2021.
[46] PALLUA J D, UNTERBERGER S H, METZLER G, et al. Application of 3-D surface reconstruction by mid- and near-infrared microscopic imaging for anatomical studies on Hericium coralloides basidiomata[J]. Anal Methods, 2014, 6(4):1149-1157.
[47] OUYANG Q, YANG Y C, PARK B, et al. A novel hyperspectral microscope imaging technology for rapid evaluation of particle size distribution in matcha[J]. Journal of Food Engineering, 2020, 272:109782.
[48] OUYANG Q, WANG L, PARK B, et al. Assessment of matcha sensory quality using hyperspectral microscope imaging technology[J]. LWT, 2020, 125:109254.
[49] JIAO C W, XU Z P, BIAN Q W, et al. Machine learning classification of origins and varieties of Tetrastigma hemsleyanum using a dual-mode microscopic hyperspectral imager[J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2021, 261:120054.
[50] NICOLA B M, BEULLENS K, BOBELYN E, et al. Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review[J]. Postharvest Biology and Technology, 2007, 46(2):99-118.
[51] DE LA ROZA-DELGADO B, SOLDADO A, MARTÍNEZ-FERNÁNDEZ A, et al. Application of near-infrared microscopy (NIRM) for the detection of meat and bone meals in animal feeds: A tool for food and feed safety[J]. Food Chemistry, 2007, 105(3):1164-1170.
[52] 姜训鹏, 杨增玲, 韩鲁佳, 等. 精料补充料中肉骨粉的显微近红外成像识别[J]. 农业机械学报, 2011, 42(7):155-159.
JIANG X P, YANG Z L, HAN L J, et al. Discrimination of meat and bone meal in concentrate supplement by near-infrared microscopic imaging[J]. Transactions of the Chinese Society for Agricultural Machinery, 2011, 42(7):155-159.
[53] 姜训鹏, 杨增玲, 刘贤, 等. 肉骨粉显微近红外标准光谱库的快速构建方法[J]. 农业机械学报, 2012, 43(7):141-144.
JIANG X P, YANG Z L, LIU X, et al. Rapid construction of near-infrared microscopic spectra database for identification of meat and bone meal[J]. Transactions of the Chinese Society for Agricultural Machinery, 2012, 43(7):141-144.
[54] LYU C X, CHEN L J, YANG Z L, et al. Two-dimensional correlation spectroscopy (2D-COS) variable selection for near-infrared microscopy discrimination of meat and bone meal in compound feed[J]. Applied Spectroscopy, 2014, 68(8):844-851.
[55] TENA N, FERNÁNDEZ PIERNA J A, BOIX A, et al. Differentiation of meat and bone meal from fishmeal by near-infrared spectroscopy: Extension of scope to defatted samples[J]. Food Control, 2014, 43:155-162.
[56] 张聪, 袁伟东, 周禹, 等. 白纹肉与木质肉品质安全无损检测研究进展[J]. 食品与发酵工业,2024,50(8):365-373.
ZHANG C, YUAN W D, ZHOU Y, et al. Research progress on non-destructive detection of quality and safety of white striping and wooden breast[J/OL]. Food and Fermentation Industries,2024,50(8):365-373.
[57] SANDEN K W, BÖCKER U, OFSTAD R, et al. Characterization of collagen structure in normal, wooden breast and spaghetti meat chicken fillets by FTIR microspectroscopy and histology[J]. Foods, 2021, 10(3):548.
[58] XU Z P, JIANG Y M, JI J L, et al. Classification, identification, and growth stage estimation of microalgae based on transmission hyperspectral microscopic imaging and machine learning[J]. Optics Express, 2020, 28(21):30686-30700.
[59] 姚辛励. 面向海洋探测与食品检测的高光谱成像技术研究[D]. 杭州: 浙江大学, 2020.
YAO X L. Hyperspectral imaing technology for ocean exploration and food detection [D]. Hangzhou: Zhejiang University, 2020.
[60] LINTVEDT T A, ANDERSEN P V, AFSETH N K, et al. Raman spectroscopy and NIR hyperspectral imaging for in-line estimation of fatty acid features in salmon fillets[J]. Talanta, 2023, 254:124113.
[61] ZHANG N, PAN Y C, FENG H K, et al. Development of Fusarium head blight classification index using hyperspectral microscopy images of winter wheat spikelets[J]. Biosystems Engineering, 2019, 186:83-99.
[62] LI M, ZHANG L, JIANG L L, et al. Label-free Raman microspectroscopic imaging with chemometrics for cellular investigation of apple ring rot and nondestructive early recognition using near-infrared reflection spectroscopy with machine learning[J]. Talanta, 2024, 267:125212.
[63] EADY M, PARK B, CHOI S. Rapid and early detection of Salmonella serotypes with hyperspectral microscopy and multivariate data analysis[J]. Journal of Food Protection, 2015, 78(4):668-674.
[64] EADY M, PARK B. Classification of Salmonella enterica serotypes with selective bands using visible/NIR hyperspectral microscope images[J]. Journal of Microscopy, 2016, 263(1):10-19.
[65] EADY M, SETIA G, PARK B. Detection of Salmonella from chicken rinsate with visible/near-infrared hyperspectral microscope imaging compared against RT-PCR[J]. Talanta, 2019, 195:313-319.
[66] 康睿. 基于显微高光谱成像和深度学习技术的食源性致病菌快速检测识别研究[D]. 南京: 南京农业大学, 2020.
KANG R. The detection and classification of foodborne pathogens using hyperspectral microscope imaging technology coupled with deep learning frameworks[D]. Nanjing: Nanjing Agricultural University, 2020.
[67] KANG R, PARK B, EADY M, et al. Classification of foodborne bacteria using hyperspectral microscope imaging technology coupled with convolutional neural networks [J]. Applied Microbiology and Biotechnology, 2020, 104(7):3157-3166.
[68] KANG R, PARK B, EADY M, et al. Single-cell classification of foodborne pathogens using hyperspectral microscope imaging coupled with deep learning frameworks[J]. Sensors and Actuators B: Chemical, 2020, 309:127789.
[69] 康睿, 程雅雯, 周玲莉, 等. 基于显微高光谱成像技术判别食源性致病菌种类的方法研究[J]. 光谱学与光谱分析, 2024, 44(2):392-397.
KANG R, CHENG Y W, ZHOU L L, et al. A novel classification method of foodborne bacterial species based on hyperspectral microscopy imaging technology[J]. Spectroscopy and Spectral Analysis, 2024, 44(2):392-397.
[70] PARK B, YOON S C, LEE S, et al. Acousto-optic tunable filter hyperspectral microscope imaging method for characterizing spectra from foodborne pathogens[J]. Transactions of the ASABE, 2012, 55(5):1997-2006.
[71] PARK B, SHIN T, KANG R, et al. Automated segmentation of foodborne bacteria from chicken rinse with hyperspectral microscope imaging and deep learning methods[J]. Computers and Electronics in Agriculture, 2023, 208:107802.
[72] 黎静. 大豆源蛋白饲料原料中三聚氰胺/三聚氰酸的近红外显微成像分析方法研究[D]. 北京: 中国农业大学, 2014.
LI J. NIRM imaging analysis o melamine/cyanuric acid in soy source protein feed materials [D]. Beijing: China Agricultural University, 2014.
[73] SHEN G H, FAN X, YANG Z L, et al. A feasibility study of non-targeted adulterant screening based on NIRM spectral library of soybean meal to guarantee quality: The example of non-protein nitrogen[J]. Food Chemistry, 2016, 210:35-42.
[74] 石冬冬, 康雪, 李庆波, 等. 近红外显微成像光谱法快速定性鉴别蛋鸭饲料中的苏丹红[J]. 饲料研究, 2017, 40(21):36-41.
SHI D D, KANG X, LI Q B, et al. Rapid qualitative identification of Sudan red in feed for egg-laying ducks by near-infrared microimaging spectroscopy [J]. Feed Research, 2017, 40(21):36-41.
[75] DALE L M, THEWIS A, BOUDRY C, et al. Hyperspectral imaging applications in agriculture and agro-food product quality and safety control: A review[J]. Applied Spectroscopy Reviews, 2013, 48(2):142-159.
[76] MAGWAZA L S, OPARA U L, NIEUWOUDT H, et al. NIR spectroscopy applications for internal and external quality analysis of Citrus fruit—A review[J]. Food and Bioprocess Technology, 2012, 5(2):425-444.
[77] 马天兰. 基于显微高光谱成像技术的滩羊肉品质检测研究[D]. 银川: 宁夏大学, 2017.
MA T L. Research about the quality detection of tan lamb based on microscopic hyperspectral imaging [D]. Yinchuan: Ningxia University, 2017.
[78] XU Y, CHEN Q S, LIU Y, et al. A novel hyperspectral microscopic imaging system for evaluating fresh degree of pork[J]. Korean Journal for Food Science of Animal Resources, 2018, 38(2):362-375.
[79] 黄琪评. 基于光谱成像技术的猪肉品质检测研究[D]. 镇江: 江苏大学, 2016.
HUANG Q P. Study of pork quality based on spectral imaging technology [D]. Zhenjiang: Jiangsu University, 2016.
[80] CARTON I, BÖCKER U, OFSTAD R, et al. Monitoring secondary structural changes in salted and smoked salmon muscle myofiber proteins by FT-IR microspectroscopy[J]. Journal of Agricultural and Food Chemistry, 2009, 57(9):3563-3570.
[81] PERISIC N, AFSETH N K, OFSTAD R, et al. Monitoring protein structural changes and hydration in bovine meat tissue due to salt substitutes by Fourier transform infrared (FTIR) microspectroscopy[J]. Journal of Agricultural and Food Chemistry, 2011, 59(18):10052-10061.
[82] 徐霞, 成芳, 应义斌. 近红外光谱技术在肉品检测中的应用和研究进展[J]. 光谱学与光谱分析, 2009, 29(7):1876-1880.
XU X, CHENG F, YING Y B. Application and recent development of research on near-infrared spectroscopy for meat quality evaluation[J]. Spectroscopy and Spectral Analysis, 2009, 29(7):1876-1880.
[83] SANAEIFAR A, LI X L, HE Y, et al. A data fusion approach on confocal Raman microspectroscopy and electronic nose for quantitative evaluation of pesticide residue in tea[J]. Biosystems Engineering, 2021, 210:206-222.
[84] 李盼, 杨玉菲, 薛振, 等. 微纳米塑料对陆生哺乳动物毒性效应的研究进展[J]. 生态毒理学报, 2023, 18(6):168-176.
LI P, YANG Y F, XUE Z, et al. Research progress of toxic effects of micro-nano plastics on terrestrial mammals[J]. Asian Journal of Ecotoxicology, 2023, 18(6):168-176.
[85] MISERLI K, LYKOS C, KALAMPOUNIAS A G, et al. Screening of microplastics in aquaculture systems (fish, mussel, and water samples) by FTIR, scanning electron microscopy-energy dispersive spectroscopy and micro-Raman spectroscopies[J]. Applied Sciences, 2023, 13(17):9705.
[86] RAGUSA A, NOTARSTEFANO V, SVELATO A, et al. Raman microspectroscopy detection and characterisation of microplastics in human breastmilk[J]. Polymers, 2022, 14(13):2700.
[87] KATSARA K, KENANAKIS G, ALISSANDRAKIS E, et al. Low-density polyethylene migration from food packaging on cured meat products detected by micro-Raman spectroscopy[J]. Microplastics, 2022, 1(3):428-439.
[88] SHI Y Z, YI L, DU G R, et al. Visual characterization of microplastics in corn flour by near field molecular spectral imaging and data mining[J]. Science of the Total Environment, 2023, 862:160714.
[89] LIU S D, SHANG E X, LIU J N, et al. What have we known so far for fluorescence staining and quantification of microplastics: A tutorial review[J]. Frontiers of Environmental Science & Engineering, 2021, 16(1):8.
[90] 樊许娜, 陈永艳, 邢方潇, 等. 显微拉曼光谱法和显微红外光谱法检测饮用水中微塑料的初步比较研究[J]. 环境卫生学杂志, 2023, 13(1):54-59.
FAN X N, CHEN Y Y, XING F X, et al. Preliminary comparison of microplastic determination in drinking water by micro-Raman spectroscopy and micro-infrared spectroscopy[J]. Journal of Environmental Hygiene, 2023, 13(1):54-59.
