食品与发酵工业 >

2022 , Vol. 48 >Issue 2: 302 - 308

DOI: https://doi.org/10.13995/j.cnki.11-1802/ts.027746

综述与专题评论

木本粮油林果品质的近红外光谱及成像无损检测研究进展

  • 李兴鹏 ,
  • 姜洪喆 ,
  • 蒋雪松 ,
  • 顾海洋 ,
  • 周宏平
展开
  • (南京林业大学 机械电子工程学院,江苏 南京,210037)
硕士研究生(蒋雪松教授为通信作者,E-mail:xsjiang@126.com)

收稿日期: 2021-04-15

  修回日期: 2021-05-14

  网络出版日期: 2022-02-28

基金资助

江苏省农业科技自主创新资金(CX(20)3040)

收起

Advances on non-destructive quality detection of forest-fruit in the sort of woody grain and oil based on near infrared spectroscopy and hyperspectral imaging technology

  • LI Xingpeng ,
  • JIANG Hongzhe ,
  • JIANG Xuesong ,
  • GU Haiyang ,
  • ZHOU Hongping
Expand
  • (College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China)

Received date: 2021-04-15

  Revised date: 2021-05-14

  Online published: 2022-02-28

Fold

摘要

木本粮油林果的品质检测是产业发展的重要支撑,关乎国民健康安全的同时也有利于提升产品的国际竞争力。近红外光谱及高光谱成像检测技术相较于传统检测方法具有快速、无损、安全和便于实现在线检测等特点,在林果品质检测中具有优势。该文综述了近红外光谱和高光谱成像技术的原理,介绍了其在木本粮油林果营养组分预测、成熟度评估、产地溯源与品种鉴别、霉变与缺陷判别等方面的应用,指出了现阶段该领域研究的不足并提供了解决思路,以期为林果品质检测提供参考和借鉴。

关键词： 林果; 品质检测; 近红外; 高光谱; 霉变; 产地鉴别

本文引用格式

李兴鹏 , 姜洪喆 , 蒋雪松 , 顾海洋 , 周宏平 . 木本粮油林果品质的近红外光谱及成像无损检测研究进展[J]. 食品与发酵工业, 2022 , 48(2) : 302 -308 . DOI: 10.13995/j.cnki.11-1802/ts.027746

Abstract

The quality detection of the forest-fruit in woody grain and oil is significant support for the development of the industry since it is related to national health and safety. Meanwhile, it is also conducive to enhancing the international competitiveness of products. In contrast to the traditional detection methods, near-infrared spectroscopy and hyperspectral imaging detection technology have the following characteristics. It is high speedy, non-destructive, safe and online detection. This thesis reviewed the principle of near-infrared spectroscopy and hyperspectral imaging technology, introduced its application in many aspects of the forest-fruit (nutrient component prediction, maturity evaluation, origin and variety identification, mold and defect discrimination), and pointed out the inadequacy of the current research as well as provided many solutions. The results are expected to provide some reference and guidance for scholars of related research fields in the quality detection of the forest-fruit.

Key words： forest-fruit; quality detection; near-infrared; hyperspectral; mildew; origin identification

参考文献

[1] 伊博. 2019年全国林业产业总产值7.56万亿元, 2020年目标8.1万亿元[J].中国人造板, 2020, 27(2):44.
YI B.The total output value of China's forestry industry will reach 7.56 trillion yuan in 2019 and 8.1 trillion yuan in 2020[J].China Wood-Based Panels.2020, 27(2):44.
[2] 于宏威, 王强, 刘丽, 等.粮油品质安全高光谱成像检测技术的研究进展[J].光谱学与光谱分析, 2016, 36(11):3 643-3 650.
YU H W, WANG Q, LIU L, et al.Research progress on hyperspectral imaging detection technology for the quality and scfety of grain and oil[J].Spectroscopy and Spectral Analysis, 2016, 36(11):3 643-3 650.
[3] 严衍禄, 陈斌, 朱大洲, 等.近红外光谱分析的原理、技术与应用[M].北京:中国轻工业出版社, 2013.
YAN Y L, CHEN B, ZHU D Z, et al.Principle, Technology and Application of Near Infrared Spectroscopy[M].Beijing:China Light Industry Press, 2013.
[4] 褚小立, 陈瀑, 李敬岩, 等.近红外光谱分析技术的最新进展与展望[J].分析测试学报, 2020, 39(10):1 181-1 188.
CHU X L, CHEN P, LI J Y, et al.Progresses and perspectives of near infrared spectroscopy analytical technology[J].Journal of Instrumental Analysis, 2020, 39(10):1 181-1 188.
[5] 童庆禧, 张兵, 郑兰芬.高光谱遥感——原理、技术与应用[M].北京:高等教育出版社, 2006.
TONG Q X, ZHANG B, ZHENG L F.Hyperspectral Remote Sensing:Principle, Technology and Application[M].Beijing:Higher Education Press, 2006.
[6] 刘阳, 王虹虹, 刘英, 等.林果机械采收与分选研究进展[J].世界林业研究, 2020, 33(3):20-25.
LIU Y, WANG H H, LIU Y, et al.Research progress of forest-fruit mechanized picking and sorting[J].World Forestry Research, 2020, 33(3):20-25.
[7] 唐敏, 刘英, 费叶琦, 等.图像处理技术在现代林果采摘中的应用[J].林业机械与木工设备, 2020, 48(4):4-7.
TANG M, LIU Y, FEI Y Q, et al.Application of image processing technology in modern forest fruit picking[J].Forestry Machinery & Woodworking Equipment, 2020, 48(4):4-7.
[8] COSTA C, ANTONUCCI F, PALLOTTINO F, et al.Shape analysis of agricultural products:A review of recent research advances and potential application to computer vision[J].Food and Bioprocess Technology, 2011, 4(5):673-692.
[9] 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.
[10] 彭彦昆, 张雷蕾.农畜产品品质安全高光谱无损检测技术进展和趋势[J].农业机械学报, 2013, 44(4):137-145.
PENG Y K, ZHANG L L.Advancement and trend of hyperspectral imaging technique for nondestructive detection of agro-product quality and safety[J].Transactions of the Chinese Society for Agricultural Machinery, 2013, 44(4):137-145.
[11] YI J H, SUN Y F, ZHU Z B, et al.Near-infrared reflectance spectroscopy for the prediction of chemical composition in walnut kernel[J].International Journal of Food Properties, 2017, 20(7):1 633-1 642.
[12] RITTHIRUANGDEJ P, RITTHIRON R, SHINZAWA H, et al.Non-destructive and rapid analysis of chemical compositions in Thai steamed pork sausages by near-infrared spectroscopy[J].Food Chemistry, 2011, 129(2):684-692.
[13] MALLEY D F, MCCLURE C, MARTIN P D, et al.Compositional analysis of cattle manure during composting using a field portable near infrared spectrometer[J].Communications in Soil Science & Plant Analysis, 2005, 36(4-6):455-475.
[14] 蒋大鹏, 张冬妍, 李丹丹, 等.基于近红外的松子蛋白质品质分类处理[J].计算技术与自动化, 2018, 37(3):180-184.
JIANG D P, ZHANG D Y, LI D D, et al.Classification of pine nut protein quality based on near infrared[J].Computing Technology and Automation, 2018, 37(3):180-184.
[15] 仇逊超. 机器视觉和近红外光谱对红松籽品质检测方法的研究[D].哈尔滨:东北林业大学, 2017.
QIU X C.Study on quality detection method of Korean pine seed using machine vision and near infrared spectrum[D].Harbin:Northeast Forestry University, 2017.
[16] PARK S H, LIM K T, LEE H, et al.Prediction of soluble solids content of chestnut using VIS/NIR spectroscopy[J].Journal of Bio systems Engineering, 2013, 38(3):185-191.
[17] 刘洁, 李小昱, 李培武, 等.基于近红外光谱的板栗水分检测方法[J].农业工程学报, 2010, 26(2):338-341.
LIU J, LI X Y, LI P W, et al.Determination of moisture in chestnuts using near infrared spectroscopy[J].Transactions of the Chinese Society of Aqriculture Engineering, 2010, 26(2):338-341.
[18] 傅谊, 张拥军, 陈华才, 等.基于偏最小二乘法的板栗近红外光谱分析模型的建立[J].食品科技, 2012,37(5):42-45;51.
FU Y, ZHANG Y J, CHEN H C, et al.Establishment of NIR models for components determination in fresh chestnut[J].Food Science and Technology, 2012,37(5):42-45;51.
[19] SAMAMAD N T I, RIBEIRO L P D, DE ALMEIDA LOPES M M, et al.Near infrared spectroscopy, a suitable tool for fast phenotyping-the case of cashew genetic improvement[J].Scientia Horticulturae, 2018, 238:363-368.
[20] RIBEIRO L P D, ANA M D S, ALINY A D L, et al.Non-destructive determination of quality traits of cashew apples (Anacardium occidentale, L.) using a portable near infrared spectrophotometer[J].Journal of Near Infrared Spectroscopy, 2016, 24(1):77.
[21] 李水芳, 李一帆, 付红军, 等.油桐籽含油率近红外光谱检测模型的构建[J].林业工程学报, 2017, 2(6):45-49.
LI S F, LI Y F, FU H J, et al.Modeling on determination of oil content of Vernicia fordii seeds by near-infrared spectroscopy[J].Journal of Forestry Engineering, 2017, 2(6):45-49.
[22] 奚如春, 钟燕梅, 邓小梅, 等.基于近红外光谱的油茶种子含油量定标模型构建[J].林业科学, 2013, 49(4):1-6.
XI R C, ZHONG Y M, DENG X M, et al.Models for determining oil contents in Camellia oleifera seeds by using near infrared spectroscopy[J].Scientia Silvae Sinicae, 2013, 49(4):1-6.
[23] 严守雷. 板栗贮藏与加工[J].中国农村科技, 2005(9):15-16.
YAN S L.Storage and processing of chestnut[J].China Rural Science & Technology, 2005(9):15-16.
[24] 康明丽, 牟德华.板栗加工技术[J].保鲜与加工, 2002, 2(3):24-25.
KANG M L, MOU D H.Technology of chestnut processing[J].Storage & Process, 2002, 2(3):24-25.
[25] 周轩明. 板栗成熟度指标的筛选及近红外检测方法初探[D].北京:中国农业大学, 2007.
ZHOU X M.Screening of maturity index of chestnut and preliminary study on near infrared detection method[D].Beijing:China Agricultural University, 2007.
[26] 章林忠, 丁玲玲, 蔡雪珍, 等.基于近红外高光谱图像技术的栗果品质无损检测[J].安徽农业大学学报, 2019, 46(1):160-166.
ZHANG L Z, DING L L, CAI X Z, et al.Non-destructive detection of Chinese chestnut (Castanea mollissima) nut qualities based on near-infrared hyperspectral imaging techniques[J].Journal of Anhui Agricultural University, 2019, 46(1):160-166.
[27] 丁玲玲. 基于近红外高光谱图像技术对板栗果实的无损检测与品质鉴定[D].合肥:安徽农业大学, 2016.
DING L L.Nondestructive detection and quality evaluation of Chinese chestnut fruits using near infrared hyper spectral image technique[D].Hefei:Anhui Agricultural University, 2016.
[28] 周宏平, 胡逸磊, 姜洪喆, 等.基于高光谱成像的油茶籽含油率检测方法研究[J/OL].农业机械学报,2021.http://kns.cnki.net/kcms/detail/11.1964.S.20210205.0943.006.html.
ZHOU H P, HU Y L, JIANG H Z, et al.Study on detection method of oil content of camellia oleifera seed based on hyperspectral imaging[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2021.http://kns.cnki.net/kcms/detail/11.1964.S.20210205.0943.006.html.
[29] 邵园园, 王永贤, 玄冠涛, 等.基于高光谱成像的肥城桃品质可视化分析与成熟度检测[J].农业机械学报, 2020, 51(8):344-350.
SHAO Y Y, WANG Y X, XUAN G T, et al.Visual detection of SSC and firmness and maturity prediction for Feicheng peach by using hyperspectral imaging[J].Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(8):344-350.
[30] 李伟. 基于偏振高光谱成像的南疆红枣水分快速检测方法研究[D].阿拉尔:塔里木大学, 2020.
LI W.Research on the rapid detection method of jujube moisture in southern Xinjiang based on polarized hyperspectral imaging[D].Aral:Tarim University, 2020.
[31] 王朝辉, 赵层, 赵倩, 等.基于高光谱成像的大米中蛋白质含量的可视化研究[J].食品研究与开发, 2020, 41(6):124-129.
WANG Z H, ZHAO C, ZHAO Q, et al.Visualization of protein content in rice based on hyper-spectral imaging[J].Food Research and Development, 2020, 41(6):124-129.
[32] 张政权, 黄冬梅, 孟宪菁, 等.同位素比率质谱法在农产品产地溯源中的研究进展[J].农产品质量与安全, 2019(2):13-19.
ZHANG Z Q, HUANG D M, MENG X Q, et al.Research progress of isotope ratio mass spectrometry in traceability of agricultural products[J].Quality and Safety of Agro-Products, 2019(2):13-19.
[33] 马冲先, 刘洁, 刘巍.电感耦合等离子体质谱分析应用的新进展[J].分析试验室, 2019, 38(6):732-760.
MA C X, LIU J, LIU W.Recent advances and applications of inductively coupled plasma mass spectrometry analysis[J].Chinese Journal of Analysis Laboratory, 2019, 38(6):732-760.
[34] MANFREDI M, ROBOTTI E, QUASSO F, et al.Fast classification of hazelnut cultivars through portable infrared spectroscopy and chemometrics[J].Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2018, 189:427-435.
[35] MOSCETTI R, RADICETTI E, MONARCA D, et al.Near infrared spectroscopy is suitable for the classification of hazelnuts according to protected designation of origin[J].Journal of the Science of Food and Agriculture, 2015, 95(13):2 619-2 625.
[36] CARVALHO L C, MORAIS C L M, LIMA K M G, et al.Using intact nuts and near infrared spectroscopy to classify Macadamia cultivars[J].Food Analytical Methods, 2018, 11(7):1 857-1 866.
[37] ARNDT M, DREES A, AHLERS C, et al.Determination of the geographical origin of walnuts (Juglans regia L.) using near-infrared spectroscopy and chemometrics[J].Foods, 2020, 9(12):1 860.
[38] ARNDT M, RURIK M, DREES A, et al.Comparison of different sample preparation techniques for NIR screening and their influence on the geographical origin determination of almonds (Prunus dulcis Mill.)[J].Food Control, 2020, 115:1-32.
[39] 卢军党, 刘东琴, 田智辉.机器视觉技术在核桃分级检测中的应用[J].农产品加工, 2020(20):106-107;110.
LU J D, LIU D Q, TIAN Z H.Application of machine vision technology in walnut grading detection[J].Farm Products Processing, 2020(20):106-107;110.
[40] 刘军, 郭俊先, 史建新, 等.基于机器视觉与支持向量机的核桃外部缺陷判别分析方法[J].食品科学, 2015, 36(20):211-217.
LIU J, GUO J X, SHI J X, et al.Discrimination of walnut external defects based on machine vision and support vector machine[J].Food Science, 2015, 36(20):211 - 217.
[41] 李成吉, 张淑娟, 孙海霞, 等.基于计算机视觉的核桃外观缺陷检测[J].现代食品科技, 2019, 35(8):247-253;246.
LI C J, ZHANG S J, SUN H X, et al.Walnut appearance defect detection based on computer vision[J].Modern Food Science and Technology, 2019, 35(8):247-253;246.
[42] 展慧, 李小昱, 王为, 等.基于机器视觉的板栗分级检测方法[J].农业工程学报, 2010, 26(4):327-331.
ZHAN H, LI X Y, WANG W, et al.Determination of chestnuts grading based on machine vision[J].Transactions of the Chinese Society of Aqiculture Enyineering, 2010, 26(4):327-331.
[43] 蒋大鹏. 基于改进近红外漫反射技术与流形学习的东北松子霉变分类研究[D].哈尔滨:东北林业大学, 2019.
JIANG D P.Study on the classification of molds of Pinus koraiensis nuts based on improved near-infrared diffuse reflection technique and manifold learning[D].Harbin:Northeast Forestry University, 2019.
[44] 马晓晨. 近红外光谱无损检测霉变板栗的研究[D].北京:北京林业大学, 2016.
MA X C.Non-destructive detection of mold-damaged chestnuts based on near-infrared spectroscopy[D].Beijing:Beijing Forestry University, 2016.
[45] HU J Q, MA X C, LIU L L, et al.Rapid evaluation of the quality of chestnuts using near-infrared reflectance spectroscopy[J].Food Chemistry, 2017, 231:141-147.
[46] 周竹, 李小昱, 李培武, 等.基于GA-LSSVM和近红外傅里叶变换的霉变板栗识别[J].农业工程学报, 2011, 27(3):331-335.
ZHOU Z, LI X Y, LI P W, et al.Near-infrared spectral detection of moldy chestnut based on GA-LSSVM and FFT[J].Transactions of the Chinese Society of Aqriculture Engineering, 2011, 27(3):331-335.
[47] FENG L, ZHU S, LIN F, et al.Detection of oil chestnuts infected by blue mold using near infrared hyperspectral imaging combined with artificial neural networks[J].Sensors, 2018, 18(6):1 944.
[48] 袁康培, 苏珍珠, 冯雷, 等.基于高光谱成像技术的霉菌侵染板栗的检测方法:中国, CN108801971A[P].2018-11-13.
YUAN K P, SU Z Z, FENG L, et al.Detection method of mold infection in chestnut based on hyperspectral imaging technology:China, CN108801971A[P].2018-11-13.
[49] KALKAN H, BERIAT P, YARDIMCI Y, et al.Detection of contaminated hazelnuts and ground red chili pepper flakes by multispectral imaging[J].Computers and Electronics in Agriculture, 2011, 77(1):28-34.
[50] MOSCETTI R, HAFF R P, SARANWONG S, et al.Nondestructive detection of insect infested chestnuts based on NIR spectroscopy[J].Postharvest Biology & Technology, 2014, 87:88-94.
[51] 丛莎. 生咖啡豆贮藏过程中脂质氧化对其风味品质劣变的影响研究[D].大庆:黑龙江八一农垦大学, 2020.
CONG S.Effect of lipid oxidation on flavor quality deterioration of raw coffee beans during storage[D].Daqing:Heilongjiang Bayi Agricultural University, 2020.
[52] PANNICO A, SCHOUTEN R E, BASILE B, et al.Non-destructive detection of flawed hazelnut kernels and lipid oxidation assessment using NIR spectroscopy[J].Journal of Food Engineering, 2015, 160:42-48.
[53] CANNEDDU G, JU'NIOR L C C, TEIXEIRA G H.Quality evaluation of shelled and unshelled Macadamia nuts by means of near-infrared spectroscopy (NIR)[J].Journal of Food Science, 2016, 81(7):1 613-1 621.
[54] 何鸿举, 王魏, 李波, 等.近红外高光谱快速无接触评估冷鲜猪肉脂质氧化[J].食品与机械, 2020, 36(8):117-122.
HE H J, WANG W, LI B, et al.Rapid and non-contact evaluation of lipid oxidation in fresh chilled pork by near-infrared hyperspectral imaging[J].Food & Machinery, 2020, 36(8):117-122.
[55] 张青青, 沈晓芳, 马晶晶, 等.近红外光谱法快速分析马铃薯煎炸油的品质[J].中国油脂, 2019, 44(1):132 - 136.
ZHANG Q Q, SHEN X F, MA J J, et al.Rapid analysis of oil quality in potato frying system by near-infrared spectroscopy[J].China Oils and Fats, 2019, 44(1):132-136.
Options
文章导航

/

〈 〉

技术支持：北京玛格泰克科技发展有限公司