GUO Zhen,JIN Cheng-qian,LIU Peng,et al.Research Progress of Spectral Analysis and Spectral Imaging Technology in Soybean Quality Detection[J].Soybean Science,2022,41(01):99-106.[doi:10.11861/j.issn.1000-9841.2022.01.0099]
光谱分析和光谱成像技术检测大豆品质的研究进展
- Title:
- Research Progress of Spectral Analysis and Spectral Imaging Technology in Soybean Quality Detection
- Keywords:
- spectral analysis technology; spectral imaging technology; soybean; quality detection; quantitative detection; qualitative analysis
- 文献标志码:
- A
- 摘要:
- 光谱分析和光谱成像技术结合了化学计量学方法,可以用于检测物质中各成分的含量。随着我国大豆产业不断发展,光谱分析和光谱成像技术已应用到大豆品质检测技术的各个方面。本文介绍光谱分析和光谱成像技术的基本原理;归纳技术检测工艺,包括大豆检测状态、光谱预处理方法、光谱特征提取方法和常用建模分析方法;重点总结光谱分析和光谱成像技术在大豆品质检测领域定量分析和定性判别的应用成果。整理大豆水分、蛋白质、脂肪和其他功能性营养成分含量定量检测的研究进展;归纳大豆品种鉴定、转基因大豆鉴别,病虫害和大豆外观品质识别定性分析的研究进展。此外,还对光谱分析和光谱成像技术在大豆品质检测领域的研究重点和不足进行讨论,并从研究和应用两方面展望了今后的发展趋势。
- Abstract:
- The spectral analysis and spectral imaging technology combined with chemometrics methods can determine the content of different components in a substance. With the continuous development of soybean industry in China, spectral analysis and spectral imaging technology have been applied to many aspects of soybean quality detection technology. Firstly, this paper briefly introduced the principle of spectral analysis and spectral imaging technology. Secondly, we summarized the technical detection technology, which including soybean detection state, spectral preprocessing method, spectral feature extraction method and common model analysis method. Finally, this paper focused on the application results of spectral analysis and spectral imaging technology in quantitative analysis and qualitative discrimination in the field of soybean quality detection. We conclude the research progress on quantitative determination of moisture, protein, fat and other functional nutrients, summarized the research progress on qualitative inorganic analysis of soybean variety identification, transgenic soybean identification, plant diseases and insect pests identification and qualitative analysis of soybean appearance quality. In addition, we also discussed the research emphases and shortcomings of spectral analysis and spectral imaging technology in the field of soybean quality detection, and prospected the future development trend from two aspects of research and application.
参考文献/References:
[1]李福山, 常汝镇, 舒世珍, 等. 栽培、野生、半野生大豆蛋白质含量及氨基酸组成的初步分析[J]. 大豆科学, 1986, 5(1): 65-72. (LI F S, CHANG R Z, SHU S Z, et al. Preliminary analysis of protein content and amino acid composition of cultivated, wild and semi wild soybean[J]. Soybean Science, 1986, 5(1): 65-72.)[2]赵淑敏, 谭红梅, 葛红霞, 等. 大豆磷脂及其应用[J]. 大豆通报, 2006(1): 39-42. (ZHAO S M, TAN H M, GE H X, et al. Soybean phospholipid and its application[J]. Soybean Science & Technology, 2006(1): 39-42.)[3]刘志胜, 李里特, 辰巳英三. 大豆异黄酮及其生理功能研究进展[J]. 食品工业科技, 2000(1): 78-80. (LIU Z S, LI L T, CHENSI Y S. Research progress on soybean isoflavones and their physiological functions[J]. Science and Technology of Food Industry, 2000(1): 78-80.)[4]韩立德, 盖钧镒, 张文明. 大豆营养成分研究现状[J]. 种子, 2003(5): 58-60. (HAN L D, GAI J Y, ZHANG W M. Research status of soybean nutrition[J]. Seed, 2003(5): 58-60.)[5]杨树果. 产业链视角下的中国大豆产业经济研究[D]. 北京:中国农业大学, 2014.(YANG S G. Economics of soybean industry in China from industry chain perspective[D]. Beijing:China Agricultural University, 2014.)[6]卢俊玮, 田容才, 花宇辉, 等. 光谱技术在甘蓝型油菜品质检测中的研究进展[J].激光生物学报, 2020, 29(4): 289-294, 301. (LU J W, TIAN R C, HUA Y H, et al. Research progress of spectral technology in detection of quality in oilseed rape (Brassica napus L.)[J]. Acta Laser Biology Sinica, 2020, 29(4): 289-294, 301.)[7]CHEN H Z, QIAO H L, FENG Q X, et al. Rapid detection of pomelo fruit quality using near-infrared hyperspectral imaging combine with chemometric methods[J]. Frontiers in Bioengineering and Biotechnology, 2021, 8: 616943.[8]SYLVIE B, DANIEL C, CHRISTOPHER J C. Contributions of fourier-transform mid infrared (FT-MIR) spectroscopy to the study of fruit and vegetables:A review[J]. Postharvest Biology and Technology, 2019, 148: 1-14.[9]LU Y Z, WOUTER S, MOON K, et al. Hyperspectral imaging technology for quality and safety evaluation of horticultural products:A review and celebration of the past 20-year progress[J]. Postharvest Biology and Technology, 2020, 170: 111318.[10]HUANG Y F, DONG W T, ALIREZA S, et al. Development of simple identification models for four main catechins and caffeine in fresh green tea leaf based on visible and near-infrared spectroscopy[J]. Computers and Electronics in Agriculture, 2020, 173: 105388.[11]YUAN L, YAN P, HAN W Y, et al. Detection of anthracnose in tea plants based on hyperspectral imaging[J]. Computers and Electronics in Agriculture, 2019, 167: 105039.[12]MOHAMMED K, GAMAL E, SUN D W, et al. Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis[J]. Analytica Chimica Acta, 2011, 714: 57-67.[13]YAO X L, CAI F H, ZHU P Y, et al. Non-invasive and rapid pH monitoring for meat quality assessment using a low-cost portable hyperspectral scanner[J]. Meat Science, 2019, 152: 73-80.[14]李江波, 饶秀勤, 应义斌. 农产品外部品质无损检测中高光谱成像技术的应用研究进展[J]. 光谱学与光谱分析, 2011, 31(8): 2021-2026. (LI J B, RAO X Q, YING Y B. Research progress on hyperspectral imaging in nondestructive detection of agricultural products disease and pest[J]. Spectroscopy and Spectral Analysis, 2011, 31(8): 2021-2026.)[15]NICOLA C, MARTIN B W, IAN D F. Protein content prediction in single wheat kernels using hyperspectral imaging[J]. Food Chemistry, 2018, 240: 32-42.[16]BA T L. Application of deep learning and near infrared spectro-scopy in cereal analysis[J]. Vibrational Spectroscopy, 2020, 106: 103009.[17]WANG Y, WANG C X, DONG F J, et al. Integrated spectral and textural features of hyperspectral imaging for prediction and visualization of stearic acid content in lamb meat[J]. Analytical methods: Advancing methods and applications, 2021, 13(36):4157-4168.[18]郭志明, 郭闯, 王明明, 等. 果蔬品质安全近红外光谱无损检测研究进展[J].食品安全质量检测学报, 2019, 10(24):8280-8288. (GUO Z M, GUO C, WANG M M, et al. Research advances in nondestructive detection of fruit and vegetable quality and safety by near infrared spectroscopy[J]. Journal of Food Safety & Quality, 2019, 10(24): 8280-8288.)[19]何勇, 彭继宇, 刘飞, 等. 基于光谱和成像技术的作物养分生理信息快速检测研究进展[J]. 农业工程学报, 2015, 31(3):174-189.(HE Y, PENG J Y, LIU F, et al. Critical review of fast detection of crop nutrient and physiological information wit spectral and imaging technology[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(3): 174-189.)[20]高迎旺, 耿金凤, 饶秀勤. 果蔬采后内部损伤无损检测研究进展[J]. 食品科学, 2017, 38(15): 277-287. (GAO Y W, GENG J F, YAO X Q. Non-invasive bruise detection in postharvest fruits and vegetables:A review[J]. Food Science, 2017, 38(15): 277-287.)[21]谭克竹. 基于高光谱图像和机器视觉技术的大豆品质检测研究[D]. 哈尔滨:东北农业大学, 2014. (TAN K Z. Research on quality of soybean using hyperspectral imaging and machine 〖JP4〗vision technique[D].Harbin:Northeast Agricultural University,2014.)[22]王丽萍, 陈文杰, 赵兴忠, 等. 基于近红外漫反射光谱法的大豆粗蛋白和粗脂肪含量的快速检测[J]. 大豆科学, 2019, 38(2): 280-285. (WANG L P, CHEN W J, ZHAO X Z, et al. Rapid determination of crude protein and crude oil content of soybean based on near infrared diffuse reflectance spectroscopy[J]. Soybean Science, 2019, 38(2): 280-285.)[23]柴玉华, 谭克竹. 基于近红外分析技术检测大豆脂肪酸含量的研究[J]. 农业工程学报, 2007(1): 238-241. (CHAI Y H, TAN K Z. Measurement of soybean fatty acid content by near infrared spectroscopy[J]. Transactions of the Chinese Society of Agricultural Engineering, 2007(1): 238-241.)[24]孙君明, 韩粉霞, 闫淑荣, 等. 傅里叶近红外反射光谱法快速测定大豆脂肪酸含量[J]. 光谱学与光谱分析, 2008(6): 1290-1295. (SUN J M, HAN F X, YAN S R, et al. Rapid determination of fatty acids in soybeans by FT-near-infrared reflectance spectroscopy[J]. Spectroscopy and Spectral Analysis, 2008(6): 1290-1295.)[25]王雪莲, 薛雅琳, 赵会义, 等. 近红外法测定大豆脂肪酸值方法的研究[J]. 中国粮油学报, 2009, 24(8): 152-154.(WANG X L, XUE Y L, ZHAO H Y, et al. Measuring soybean fatty acid value by near-infrared spectroscopy technique[J]. Journal of the Chinese Cereals and Oils Association, 2009, 24(8): 152-154.)[26]HAN S I, CHAE J H, KRISTIN B, et al. Non-destructive det-ermination of high oleic acid content in single soybean seeds by near infrared reflectance spectroscopy[J]. Journal of the American Oil Chemists’ Society, 2014, 91(2): 229-234.[27]MARK A B, MUKTI S, MICHAEL J B, et al. Quantitative NIR determination of isoflavone and saponin content of ground soybeans[J]. Food Chemistry, 2020, 317: 126373.[28]SHEN L Z, GAO M F, YAN J W, et al. Hyperspectral estimation of soil organic matter content using different spectral preprocessing techniques and PLSR method[J]. Remote Sensing, 2020, 12(7): 1206.[29]CHENG J H, QU J H, SUN D W, et al. Visible/near-infrared hyperspectral imaging prediction of textural firmness of grass carp (Ctenopharyngodon idella) as affected by frozen storage[J]. Food Research International, 2014, 56: 190-198.[30]曹毅, 崔国华. 大豆安全储藏技术综述[J]. 粮食储藏, 2005(3): 17-23. (CAO Y, CUI G H. Review about safe storage technology of soybean[J]. Grain Storage, 2005(3): 17-23.)[31]薛雅琳, 王雪莲, 赵会义, 等. 利用近红外分析技术测定大豆水分含量方法的研究[J]. 中国油脂, 2009, 34(7): 69-71. (XUE Y L, WANG X L, ZHAO H Y, et al. Measurement of moisture content in soybean by near-infrared spectroscopy technique[J]. China Oils and Fats, 2009, 34(7): 69-71.)[32]DANIELA S F, JULIANA A L P, RONEI J P. Fourier transform near-infrared spectroscopy (FT-NIRS) application to estimate Brazilian soybean [Glycine max(L.)Merril] composition[J]. Food Research International, 2013, 51(1): 53-58.[33]郭东升, 张志勇, 武志明, 等. 基于近红外光谱的大豆水分和粗脂肪含量的快速检测[J]. 食品安全质量检测学报, 2020, 11(20):7378-7384. (GUO D S, ZHANG Z Y, WU Z M, et al. Rapid detection of moisture and crude fat content in soybean based on near infrared spectroscopy[J]. Journal of Food Safety & Quality, 2020, 11(20): 7378-7384.)[34]陈智慧, 史梅, 王秋香, 等. 用凯氏定氮法测定食品中的蛋白质含量[J]. 新疆畜牧业, 2008(5): 22-24. (CHEN Z H, SHI M, WANG Q X, et al. Determination of protein content in food by Kjeldahl method[J]. XINJIANG XUMUYE, 2008(5): 22-24.)[35]姚虹. 索氏提取法测定脂肪含量方法改进[J]. 中州大学学报, 1996(4): 64-65. (YAO H. Improvement of Soxhlet extraction method for determination of fat content[J]. Journal of Zhongzhou University, 1996(4): 64-65.)[36]朱丽伟, 马文广, 胡晋, 等. 近红外光谱技术检测种子质量的应用研究进展[J]. 光谱学与光谱分析, 2015, 35(2): 346-349. (ZHU L W, MA W G, HU J, et al. Advances of NIR spectroscopy technology applied in seed quality detection[J]. Spectroscopy and Spectral Analysis, 2015, 35(2): 346-349.)[37]李琳琳, 金华丽, 崔彬彬, 等. 基于近红外透射光谱的大豆蛋白质和粗脂肪含量快速检测[J]. 粮食与油脂, 2014, 27(12): 57-60. (LI L L, JIN H L, CUI B B, et al. Rapid determination of soybean protein and crude fat content by near-infrared transmittance spectroscopy[J]. Cereals & Oils, 2014, 27(12): 57-60.)[38]王燕, 鞠涛, 刘晓兰, 等. 近红外光谱法预测大豆营养成分含量模型的建立和应用[J]. 中国畜牧杂志, 2014, 50(7): 62-65. (WANG Y, JU T, LIU X L, et al. Building and application of soybean nutrients content model predicted by near infrared spectral method[J]. Chinese Journal of Animal Science, 2014, 50(7): 62-65.)[39]FERREIRA D S, GALO O F, PALLONE J A L, et al. Comp-arison and application of near-infrared (NIR) and mid-infrared (MIR) spectroscopy for determination of quality parameters in soybean samples[J]. Food Control, 2014, 35(1): 227-232.[40]王翠秀, 曹见飞, 顾振飞, 等. 基于近红外光谱大豆蛋白质、脂肪快速无损检测模型的优化构建[J]. 大豆科学, 2019, 38(6): 968-976. (WANG C X, CAO J F, GU Z F, et al. Rapid nondestructive test of soybean protein and fat by near infrared spectroscopy combined with different model methods[J]. Soybean Science, 2019, 38(6): 968-976.)[41]XU R X, HU W, ZHOU Y C, et al. Use of near-infrared spe-ctroscopy for the rapid evaluation of soybean [Glycine max(L.) Merri.] water soluble protein content[J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2020, 224: 117400.[42]邹涛, 兰树明, 阎巍, 等. 基于便携式近红外光谱仪的大豆蛋白波长优选[J]. 分析仪器, 2019(3): 94-99. (ZOU T, LAN S M, YAN W, et al. Soybean protein wavelength optimization based on portable NIR spectrometer[J]. Analytical Instrumentation, 2019(3): 94-99.)[43]胡明祥, 梁歧, 孟祥勋. 我国大豆品种脂肪酸组成的分析研究[J]. 吉林农业科学, 1986(1): 12-17. (HU M X, LIANG Q, MENG X X. Analysis of fatty acid composition of soybean varieties in China[J]. Journal of Northeast Agricultural Sciences, 1986(1): 12-17.)[44]李丹华, 朱圣陶. 气相色谱法测定常见植物油中脂肪酸[J]. 粮食与油脂, 2006(8): 46-48. (LI D H, ZHU S T. Determination of fatty acids in vegetable oils by gas chromatography[J]. Cereals & Oils, 2006(8): 46-48.)[45]PATIL A G, OAK M D, TAWARE S P, et al. Nondestructive estimation of fatty acid composition in soybean [Glycine max(L.) Merrill] seeds using near-infrared transmittance spectroscopy[J]. Food Chemistry, 2009, 120(4): 1210-1217.[46]ROBERTS C A, REN C, BEUSELINCK P R, et al. Fatty acid profiling of soybean cotyledons by near-infrared spectroscopy[J]. Applied Spectroscopy, 2006, 60(11): 1328.[47]王力立, 段灿星, 双少敏, 等. 大豆中异黄酮含量的测定及其近红外分析[J]. 食品科技, 2011, 36(1): 242-246. (WANG L L, DUAN C X, SHUANG S M, et al. Determine the isoflavones content in soybean and analysis by near-infrared reflectance spectroscopy(NIRS)[J]. Food Science and Technology, 2011, 36(1): 242-246.)[48]李楠, 许韵华, 宋雯雯, 等. 利用近红外光谱技术快速检测大豆氨基酸含量[J]. 植物遗传资源学报, 2012, 13(6): 1037-1044. (LI N, XU Y H, SONG W W, et al. A rapid method for detecting amino acid compositions in soybean by using near-infrared spectroscopy[J]. Journal of Plant Genetic Resources, 2012, 13(6): 1037-1044.)[49]WU J G, SHI C H, ZHANG X M. Estimating the amino acid composition in milled rice by near-infrared reflectance spectroscopy[J]. Field Crops Research, 2002, 75(1): 1-7.[50]DANIELA S F, RONEI J P, JULIANA AL P. Evaluation of dietary fiber of Brazilian soybean (Glycine max) using near-infrared spectroscopy and chemometrics[J]. Journal of Cereal Science, 2015, 64: 43-47.[51]马莉, 孙日飞, 刘超群. 近红外光谱法快速检测大豆磷脂[J]. 现代食品, 2018(16): 93-96,99. (MA L, SUN R F, LIU C Q. Rapid detection of soybean phospholipids by near infrared spectroscopy[J]. Modern Food, 2018(16): 93-96, 99.)[52]HANIM Z A, RAHUL J, RUDIATI E M, et al. Nondestructive measurement of anthocyanin in intact soybean seed using Fourier Transform Near-Infrared (FT-NIR) and Fourier Transform Infrared (FT-IR) spectroscopy[J]. Infrared Physics and Technology, 2020, 111: 103477.[53]杨冬风, 朱洪德. 基于近红外透射光谱分析和反向传播神经网络的大豆品种识别[J]. 大豆科学, 2013, 32(2): 249-253. (YANG D F, ZHU H D. Recognition of soybean varieties based on near infrared transmittance spectroscopy and BP neural network[J]. Soybean Science, 2013, 32(2): 249-253.)[54]TAN K Z, WANG R T, LI M Y, et al. Discriminating soybean seed varieties using hyperspectral imaging and machine learning[J]. Journal of Computational Methods in Sciences and Engineering, 2019, 19(4): 1001-1015.[55]柴玉华, 毕文佳, 谭克竹, 等. 基于高光谱图像技术的大豆品种无损鉴别[J]. 东北农业大学学报, 2016, 47(3): 86-93. (CHAI Y H, BI W J, TAN K Z, et al. Nondestructive identification of soybean seed varieties based on hyperspectral image technology[J]. Journal of Northeast Agricultural University, 2016, 47(3): 86-93.)[56]刘瑶, 谭克竹, 陈月华, 等. 基于分段主成分分析和高光谱技术的大豆品种识别[J]. 大豆科学, 2016, 35(4): 672-678. (LIU Y, TAN K Z, CHEN Y H, et al. Variety recognition of soybeans using segmented principal component analysis and hyperspectral technology[J]. Soybean Science, 2016, 35(4): 672-678.)[57]朱大洲, 王坤, 周光华, 等. 单粒大豆的近红外光谱特征及品种鉴别研究[J]. 光谱学与光谱分析, 2010, 30(12): 3217-3221. (ZHU D Z, WANG K, ZHOU G H, et al. The NIR spectra based variety discrimination for single soybean seed[J]. Spectroscopy and Spectral Analysis, 2010, 30(12): 3217-3221.)[58]ZHU S L, CHAO M N, ZHANG J Y, et al. Identification of soybean seed varieties based on hyperspectral imaging technology[J]. Sensors, 2019, 19(23): 5225.[59]吴江, 黄富荣, 黄才欢, 等. 近红外光谱结合主成分分析和反向传播神经网络的转基因大豆无损鉴别研究[J]. 光谱学与光谱分析, 2013, 33(6): 1537-1541. (WU J, HUANG F R, HUANG C H, et al. Study on near infrared spectroscopy of transgenic soybean identification based on principal component analysis and neural network[J]. Spectroscopy and Spectral Analysis, 2013, 33(6): 1537-1541.)[60]方慧, 张昭, 王海龙, 等. 基于中红外光谱技术鉴别转基因大豆的方法研究[J]. 光谱学与光谱分析, 2017, 37(3): 760-765. (FANG H, ZHANG Z, WANG H L, et al. Identification of transgenic soybean varieties using mid-infrared spectroscopy[J]. Spectroscopy and Spectral Analysis, 2017, 37(3): 760-765.)[61]王海龙, 杨向东, 张初, 等. 近红外高光谱成像技术用于转基因大豆快速无损鉴别研究[J]. 光谱学与光谱分析, 2016, 36(6): 1843-1847. (WANG H L, YANG X D, ZHANG C, et al. Fast identification of transgenic soybean varieties based near infrared hyperspectral imaging technology[J]. Spectroscopy and Spectral Analysis, 2016, 36(6): 1843-1847.)[62]CHELLADURAI V, KARUPPIAH K, JAYAS D S, et al. Detection of Callosobruchus maculatus(F.) infestation in soybean using soft X-ray and NIR hyperspectral imaging techniques[J]. Journal of Stored Products Research, 2014, 57: 43-48.[63]黄敏, 万相梅, 朱启兵, 等. 基于高光谱图像技术的菜用大豆厚度检测[J]. 食品与生物技术学报, 2012, 31(11): 1142-1147. (HUANG M, WAN X M, ZHU Q B, et al. Thickness measurement of green soybean using hyperspectral imaging technology[J]. Journal of Food Science and Biotechnology, 2012, 31(11): 1142-1147.)[64]WANG L S, HUANG Z L, WANG R J. Discrimination of cracked soybean seeds by near-infrared spectroscopy and random forest variable selection[J]. Infrared Physics & Technology, 2021,115: 103731).[65]OLESEN M H, NIKNESSHAN P, SHRESTHA S, et al. Viability prediction of Ricinus cummunis L. seeds using multispectral imaging[J]. Sensors, 2015, 15(2): 4592-4604.
备注/Memo
收稿日期:2021-06-30