ZHANG Haifang, NA Ri, HAN Yumei, et al. Research Progress of Spectral Nondestructive Testing Technology in Traceability of Agricultural Products[J]. Science and Technology of Food Industry, 2023, 44(8): 17−25. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2022080091.
Citation: ZHANG Haifang, NA Ri, HAN Yumei, et al. Research Progress of Spectral Nondestructive Testing Technology in Traceability of Agricultural Products[J]. Science and Technology of Food Industry, 2023, 44(8): 17−25. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2022080091.

Research Progress of Spectral Nondestructive Testing Technology in Traceability of Agricultural Products

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  • Received Date: August 09, 2022
  • Available Online: February 19, 2023
  • Realizing nondestructive testing for traceability of the origin of agricultural products is an important way to establish a traceability system for the quality and safety of agricultural products, and an effective means to guarantee the quality of food safety and safeguard the legitimate rights and interests of consumers. Compared with traditional testing methods, nondestructive testing technology is widely used in the field of food traceability because it can achieve the advantages of obtaining internal and external effective information without damaging the inspected samples. This paper provides an overview of NIR spectroscopy, NIR testing, and NIR testing. In this paper, the principles of three spectral detection techniques, namely, near-infrared spectroscopy, hyperspectral imaging and Raman spectroscopy, and their latest applications in the origin traceability of different types of edible agricultural products are outlined, and the feasibility of each spectral detection technique in the origin identification of agricultural products is concluded. At the same time, the future research directions are prospected in order to provide theoretical references for the research of nondestructive testing technology system for origin traceability of agricultural products.
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  • [1]
    潘立刚, 张缙, 陆安祥, 等. 农产品质量无损检测技术研究进展与应用[J]. 农业工程学报,2008,24(S2):325−330. [PAN L G, ZHANG J, LU A X, et al. Research progress and application of nondestructive testing technology for agricultural product quality[J]. Transactions of the Chinese Society of Agricultural Engineering,2008,24(S2):325−330.
    [2]
    AFSAH HEJRI L, HAJEB P, ARA P, et al. A comprehensive review on food applications of terahertz spectroscopy and imaging[J]. Comprehensive Reviews in Food Science and Food Safety,2019,18(5):1563−1621. doi: 10.1111/1541-4337.12490
    [3]
    EISENSTECKEN D, STÜRZ B, ROBATSCHER P, et al. The potential of near infrared spectroscopy (NIRS) to trace apple origin: Study on different cultivars and orchard elevations[J]. Postharvest Biology and Technology,2019,147:123−131. doi: 10.1016/j.postharvbio.2018.08.019
    [4]
    张欣欣, 李尚科, 李跑, 等. 近红外光谱的不同产地柑橘无损鉴别方法[J]. 光谱学与光谱分析,2021,41(12):3695−3700. [ZHANG X X, LI S K, LI P, et al. Non-destructive identification methods of citrus of different origins in near-infrared spectroscopy[J]. Spectroscopy and Spectral Analysis,2021,41(12):3695−3700.
    [5]
    LEIVA-VALENZUELA G A, LU R, AGUILERA J M. Assessment of internal quality of blueberries using hyperspectral transmittance and reflectance images with whole spectra or selected wavelengths[J]. Innovative Food Science & Emerging Technologies,2014,24:2−13.
    [6]
    WANG L, LIU D, PU H, et al. Use of hyperspectral imaging to discriminate the variety and quality of rice[J]. Food Analytical Methods,2015,8(2):515−523. doi: 10.1007/s12161-014-9916-5
    [7]
    谭峰, 才巧玲, 马志欣, 等. 基于拉曼光谱分析寒地水稻叶片的有机结构[J]. 江苏农业科学,2016,44(4):358−361. [TAN F, CAI Q L, MA Z X, et al. Analysis of the organic structure of coldland rice leaves based on Raman spectroscopy[J]. Jiangsu Agricultural Sciences,2016,44(4):358−361. doi: 10.15889/j.issn.1002-1302.2016.04.102
    [8]
    李可, 闫路辉, 赵颖颖, 等. 拉曼光谱技术在肉品加工与品质控制中的研究进展[J]. 食品科学,2019,40(23):298−304. [LI K, YAN L H, ZHAO Y Y, et al. Research progress of Raman spectroscopy in meat processing and quality control[J]. Food Science,2019,40(23):298−304. doi: 10.7506/spkx1002-6630-20181130-358
    [9]
    LI H, CHEN Q, ZHAO J, et al. Nondestructive detection of total volatile basic nitrogen (TVB-N) content in pork meat by integrating hyperspectral imaging and colorimetric sensor combined with a nonlinear data fusion[J]. LWT-Food Science and Technology,2015,63(1):268−274. doi: 10.1016/j.lwt.2015.03.052
    [10]
    张丽华, 郝莉花, 李顺峰, 等. 基于支持向量机的近红外光谱技术快速鉴别掺假羊肉[J]. 食品工业科技,2015,36(23):289−293. [ZHAG L H, HAO L H, LI S F, et al. Near-infrared spectroscopy based on support vector machine to quickly identify adulterated lamb[J]. Science and Technology of Food Industry,2015,36(23):289−293.
    [11]
    刘爽, 柴春祥. 近红外光谱技术在水产品检测中的应用进展[J]. 食品安全质量检测学报,2021,12(21):8590−8596. [LIU S, CHAI C X. Application progress of near infrared spectroscopy in aquatic product detection[J]. Journal of Food Safety & Quality,2021,12(21):8590−8596. doi: 10.3969/j.issn.2095-0381.2021.21.spaqzljcjs202121041
    [12]
    马冬红, 王锡昌, 刘利平, 等. 近红外光谱技术在食品产地溯源中的研究进展[J]. 光谱学与光谱分析,2011,31(4):877−881. [MA D H, WANG X C, LIU L P, et al. Research progress of near-infrared spectroscopy in food origin traceability[J]. Spectroscopy and Spectral Analysis,2011,31(4):877−881. doi: 10.3964/j.issn.1000-0593(2011)04-0877-05
    [13]
    马佳佳, 王克强. 水果品质光学无损检测技术研究进展[J]. 食品工业科技,2021,42(23):427−437. [MA J J, WANG K Q. Research progress of optical nondestructive testing for fruit quality[J]. Science and Technology of Food Industry,2021,42(23):427−437. doi: 10.13386/j.issn1002-0306.2020110235
    [14]
    ZAREEF M, ARSLAN M, HASSAN M M, et al. Recent advances in assessing qualitative and quantitative aspects of cereals using nondestructive techniques: A review[J]. Trends in Food Science & Technology,2021,116:815−828.
    [15]
    张保华, 李江波, 樊书祥, 等. 高光谱成像技术在果蔬品质与安全无损检测中的原理及应用[J]. 光谱学与光谱分析,2014,34(10):2743−2751. [ZHANG B H, LI J B, FAN S X, et al. Principle and application of hyperspectral imaging technology in nondestructive testing of quality and safety of fruits and vegetables[J]. Spectroscopy and Spectral Analysis,2014,34(10):2743−2751. doi: 10.3964/j.issn.1000-0593(2014)10-2743-09
    [16]
    梁慧芳, 张惠芳, 从明芳, 等. 高光谱成像技术在纺织检测领域的研究进展[J]. 现代纺织技术,2022,30(6):211−218, 260. [LIANG H F, ZHANG H F, CONG M F, et al. Research progress of hyperspectral imaging in textile inspection[J]. Advanced Textile Technology,2022,30(6):211−218, 260.
    [17]
    QIN J, CHAO K, KIM M S, et al. Hyperspectral and multispectral imaging for evaluating food safety and quality[J]. Journal of Food Engineering,2013,118(2):157−171. doi: 10.1016/j.jfoodeng.2013.04.001
    [18]
    刘燕德, 靳昙昙. 拉曼光谱技术在农产品质量安全检测中的应用[J]. 光谱学与光谱分析,2015,35(9):2567−2572. [LIU Y D, JIN T T. Application of Raman spectroscopy in quality and safety detection of agricultural products[J]. Spectroscopy and Spectral Analysis,2015,35(9):2567−2572. doi: 10.3964/j.issn.1000-0593(2015)09-2567-06
    [19]
    高振, 赵春江, 杨桂燕, 等. 典型拉曼光谱技术及其在农业检测中应用研究进展[J]. 智慧农业(中英文),2022,4(2):121−134. [GAO Z, ZHAO C J, YANG G Y, et al. Research progress on typical Raman spectroscopy and its application in agricultural detection[J]. Smart Agriculture,2022,4(2):121−134.
    [20]
    GUO Z, CHEN P, YOSRI N, et al. Detection of heavy metals in food and agricultural products by surface-enhanced raman spectroscopy[J]. Food Reviews International,2021(4):1−22.
    [21]
    XU Y, ZHONG P, JIANG A, et al. Raman spectroscopy coupled with chemometrics for food authentication: A review[J]. TrAC Trends in Analytical Chemistry,2020,131:116017. doi: 10.1016/j.trac.2020.116017
    [22]
    马雪亭, 罗华平, 高峰, 等. 近红外光谱技术在苹果检测方面的研究与应用[J]. 食品安全质量检测学报,2022,13(13):4219−4227. [MA X T, LUO H P, GAO F, et al. Research and application of near-infrared spectroscopy in apple detection[J]. Journal of Food Safety & Quality,2022,13(13):4219−4227. doi: 10.3969/j.issn.2095-0381.2022.13.spaqzljcjs202213019
    [23]
    苏学素, 张晓焱, 焦必宁, 等. 基于近红外光谱的脐橙产地溯源研究[J]. 农业工程学报,2012,28(15):240−245. [SU X S, ZHANG X Y, JIAO B N, et al. Study on the origin tracing of navel orange based on near infrared spectroscopy[J]. Transactions of the Chinese Society of Agricultural Engineering,2012,28(15):240−245.
    [24]
    马永杰, 郭俊先, 郭志明, 等. 基于近红外透射光谱及多种数据降维方法的红富士苹果产地溯源[J]. 现代食品科技,2020,36(6):303−309. [MA Y J, GUO J X, GUO Z M, et al. Origin traceability of red Fuji apple based on near-infrared transmission spectroscopy and multiple data dimensionality reduction methods[J]. Modern Food Science and Technology,2020,36(6):303−309.
    [25]
    孙晓明, 陈小龙, 余向阳, 等. 基于近红外光谱分析技术的水蜜桃产地溯源[J]. 江苏农业学报,2020,36(2):507−512. [SUN X M, CHEN X L, YU X Y, et al. Origin traceability of peaches based on near-infrared spectroscopy[J]. Jiangsu Journal of Agricultural Sciences,2020,36(2):507−512. doi: 10.3969/j.issn.1000-4440.2020.02.035
    [26]
    FU X, YING Y, ZHOU Y, et al. Application of probabilistic neural networks in qualitative analysis of near infrared spectra: Determination of producing area and variety of loquats[J]. Analytica Chimica Acta,2007,598(1):27−33. doi: 10.1016/j.aca.2007.07.032
    [27]
    陈璐, 谷晓红, 王文博, 等. 近红外光谱技术识别沾化和陕西冬枣产地的研究[J]. 山东农业科学,2016,48(3):133−136. [CHEN L, GU X H, WANG W B, et al. Identification of winter jujube origin in Zhanhua and Shanxi by near infrared spectroscopy[J]. Shandong Agricultural Sciences,2016,48(3):133−136.
    [28]
    雷渊雄, 夏阿林, 黄炜, 等. 基于近红外光谱结合化学计量学的转基因大豆产地判别[J]. 食品与发酵工业,2022,48(12):6. [LEI Y X, XIA A L, HUAG W, et al. Identification of transgenic soybean origin based on near-infrared spectroscopy combined with stoichiometry[J]. Food and Fermentation Industries,2022,48(12):6. doi: 10.13995/j.cnki.11-1802/ts.028608
    [29]
    TEYE E, AMUAH C L Y, MCGRATH T, et al. Innovative and rapid analysis for rice authenticity using hand-held NIR spectrometry and chemometrics[J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy,2019,217:147−154. doi: 10.1016/j.saa.2019.03.085
    [30]
    李佳洁, 吴建虎, 张海波. 利用可见/近红外光谱技术对小米产地进行溯源研究[J]. 食品安全质量检测学报,2017,8(8):3037−3043. [LI J J, WU J H, ZHANG H B. The visible/near infrared spectroscopy was used to trace the origin of millet[J]. Journal of Food Safety & Quality,2017,8(8):3037−3043. doi: 10.3969/j.issn.2095-0381.2017.08.033
    [31]
    赵海燕, 郭波莉, 魏益民, 等. 近红外光谱对小麦产地来源的判别分析[J]. 中国农业科学,2011,44(7):1451−1456. [ZHAO H Y, GUO B L, WEI Y M, et al. Discriminant analysis of wheat origin by near infrared spectroscopy[J]. Scientia Agricultura Sinica,2011,44(7):1451−1456. doi: 10.3864/j.issn.0578-1752.2011.07.018
    [32]
    DOS SANTOS C A T, PÁSCOA R N M J, SARRAGUÇA M C, et al. Merging vibrational spectroscopic data for wine classification according to the geographic origin[J]. Food Research International,2017,102:504−510. doi: 10.1016/j.foodres.2017.09.018
    [33]
    汤丽华, 刘敦华. 基于近红外光谱技术的枸杞产地溯源研究[J]. 食品科学,2011,32(22):175−178. [TANG L H, LIU D H. Research on the origin traceability of Lycium barbarum based on near-infrared spectroscopy[J]. Food Science,2011,32(22):175−178.
    [34]
    WU T H, TUNG I C, HSU H C, et al. Quantitative analysis and discrimination of partially fermented teas from different origins using visible/near-infrared spectroscopy coupled with chemometrics[J]. Sensors,2020,20(19):5451. doi: 10.3390/s20195451
    [35]
    王彬, 王巧华, 肖壮, 等. 基于可见-近红外光谱及随机森林的鸡蛋产地溯源[J]. 食品工业科技,2017,38(24):243−247. [WNAG B, WNAG Q H, XIAO Z, et al. Traceability of egg origin based on visible-near infrared spectroscopy and random forest[J]. Science and Technology of Food Industry,2017,38(24):243−247. doi: 10.13386/j.issn1002-0306.2017.24.047
    [36]
    孙淑敏, 郭波莉, 魏益民, 等. 近红外光谱指纹分析在羊肉产地溯源中的应用[J]. 光谱学与光谱分析,2011,31(4):937−941. [SUN S M, GUO B L, WEI Y M, et al. Application of near-infrared spectral fingerprint analysis in traceability of mutton origin[J]. Spectroscopy and Spectral Analysis,2011,31(4):937−941. doi: 10.3964/j.issn.1000-0593(2011)04-0937-05
    [37]
    GHIDINI S, VARRÀ M O, DALL'ASTA C, et al. Rapid authentication of European sea bass (Dicentrarchus labrax L.) according to production method, farming system, and geographical origin by near infrared spectroscopy coupled with chemometrics[J]. Food Chemistry,2019,280:321−327. doi: 10.1016/j.foodchem.2018.12.075
    [38]
    SUN Y, LI Y, PAN L, et al. Authentication of the geographic origin of Yangshan region peaches based on hyperspectral imaging[J]. Postharvest Biology and Technology,2021,171:111320. doi: 10.1016/j.postharvbio.2020.111320
    [39]
    TIAN Y, SUN J, ZHOU X, et al. Research on apple origin classification based on variable iterative space shrinkage approach with stepwise regression–support vector machine algorithm and visible-near infrared hyperspectral imaging[J]. Journal of Food Process Engineering,2020,43(8):e13432.
    [40]
    张立欣, 杨翠芳, 陈杰, 等. 基于变量优选和近红外光谱技术的红富士苹果产地溯源[J]. 食品与发酵工业,2022,48(20):36−43. [ZHANG L X, YANG C F, CHEN J, et al. Origin tracing of red fuji apple based on variable selection and near infrared spectroscopy[J]. Food and Fermentation Industries,2022,48(20):36−43. doi: 10.13995/j.cnki.11-1802/ts.029991
    [41]
    吉海彦, 任占奇, 饶震红. 基于高光谱成像技术的不同产地小米判别分析[J]. 光谱学与光谱分析,2019,39(7):2271−2277. [JI H Y, REN Z Q, RAO Z H. Discriminant analysis of millet from different regions based on hyperspectral imaging[J]. Spectroscopy and Spectral Analysis,2019,39(7):2271−2277.
    [42]
    王庆国, 黄敏, 朱启兵, 等. 基于高光谱图像的玉米种子产地与年份鉴别[J]. 食品与生物技术学报,2014,33(2):163−170. [WANG Q G, HUANG M, ZHU Q B, et al. Identification of maize seed origin and year based on hyperspectral image[J]. Journal of Food Science and Biotechnology,2014,33(2):163−170.
    [43]
    KIM M J, LIM J, KWON S W, et al. Geographical origin discrimination of white rice based on image pixel size using hyperspectral fluorescence imaging analysis[J]. Applied Sciences,2020,10(17):5794. doi: 10.3390/app10175794
    [44]
    吴静珠, 李晓琪, 林珑, 等. 基于AlexNet卷积神经网络的大米产地高光谱快速判别[J]. 中国食品学报,2022,22(1):282−288. [WU J Z, LI X Q, L L, et al. Hyperspectral identification of rice origin based on AlexNet Convolutional neural network[J]. Journal of Chinese Institute of Food Science and Technology,2022,22(1):282−288.
    [45]
    MU Q, KANG Z, GUO Y, et al. Hyperspectral image classification of wolfberry with different geographical origins based on three-dimensional convolutional neural network[J]. International Journal of Food Properties,2021,24(1):1705−1721. doi: 10.1080/10942912.2021.1987457
    [46]
    沈国芳, 黄俊航, 许麦成, 等. 基于近红外高光谱成像鉴别不同产地的红参[J]. 世界中医药,2021,16(23):3419−3423. [SHEN G F, HAUNG J H, XU M C, et al. Identification of red ginseng from different habitats based on near-infrared hyperspectral imaging[J]. World Chinese Medicine,2021,16(23):3419−3423. doi: 10.3969/j.issn.1673-7202.2021.23.003
    [47]
    LIU Y, HUANG J, LI M, et al. Rapid identification of the green tea geographical origin and processing month based on near-infrared hyperspectral imaging combined with chemometrics[J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy,2022,267:120537. doi: 10.1016/j.saa.2021.120537
    [48]
    王靖, 丁佳兴, 郭中华, 等. 基于近红外高光谱成像技术的宁夏羊肉产地鉴别[J]. 食品工业科技,2018,39(2):250−254, 260. [WANG J, DING J X, GUO Z H, et al. Identification of mutton origin in ningxia based on near-infrared hyperspectral imaging[J]. Science and Technology of Food Industry,2018,39(2):250−254, 260.
    [49]
    郑守国, 翁士状, 刘瑜凡, 等. 融合高光谱成像多类特征的名优牛肉种类鉴别[J]. 激光杂志,2021,42(8):57−61. [ZHENG S G, WNEG S Z, LIU Y F, et al. Identification of famous beef species based on multi-class hyperspectral imaging features[J]. Laser Journal,2021,42(8):57−61.
    [50]
    卢诗扬, 张雷蕾, 潘家荣, 等. 拉曼光谱结合LSTM长短期记忆网络的樱桃产地鉴别研究[J]. 光谱学与光谱分析,2021,41(4):1177−1181. [LU S Y, ZHANG L L, PAN J R, et al. Raman spectroscopy combined with lstm long short-term memory network for cherry origin identification[J]. Spectroscopy and Spectral Analysis,2021,41(4):1177−1181.
    [51]
    TRAKSELE L, SNITKA V. Surface-enhanced Raman spectroscopy for the characterization of Vaccinium myrtillus L. bilberries of the Baltic–Nordic regions[J]. European Food Research and Technology,2022,248(2):427−435. doi: 10.1007/s00217-021-03887-8
    [52]
    DIB S R, SILVA T V, NETO J A G, et al. Raman spectroscopy for discriminating transgenic corns[J]. Vibrational Spectroscopy,2021,112:103183. doi: 10.1016/j.vibspec.2020.103183
    [53]
    WANG Y, TAN F. Extraction and classification of origin characteristic peaks from rice Raman spectra by principal component analysis[J]. Vibrational Spectroscopy,2021,114:103249. doi: 10.1016/j.vibspec.2021.103249
    [54]
    孙娟, 张晖, 王立, 等. 基于拉曼光谱的大米快速分类判别方法[J]. 食品与机械,2016,32(1):41−45. [SUN J, ZAHNG H, WANG L, et al. Rapid classification and discrimination method of rice based on Raman spectroscopy[J]. Food & Machinery,2016,32(1):41−45.
    [55]
    HE S, LIU X, ZHANG W, et al. Discrimination of the Coptis chinensis geographic origins with surface enhanced Raman scattering spectroscopy[J]. Chemometrics and Intelligent Laboratory Systems,2015,146:472−477. doi: 10.1016/j.chemolab.2015.07.002
    [56]
    MAGDAS D A, GUYON F, BERGHIAN-GROSAN C, et al. Challenges and a step forward in honey classification based on Raman spectroscopy[J]. Food Control,2021,123:107769. doi: 10.1016/j.foodcont.2020.107769
    [57]
    杜馨, 孙晓荣, 刘翠玲, 等. 拉曼光谱结合偏最小二乘法对食用油品质快速检测研究[J]. 中国酿造,2019,38(12):171−174. [DU X, SUN X R, LIU C L, et al. Study on rapid detection of edible oil quality by Raman spectroscopy combined with partial least squares method[J]. China Brewing,2019,38(12):171−174. doi: 10.11882/j.issn.0254-5071.2019.12.034
    [58]
    BOYACI İ H, UYSAL R S, TEMIZ T, et al. A rapid method for determination of the origin of meat and meat products based on the extracted fat spectra by using of Raman spectroscopy and chemometric method[J]. European Food Research and Technology,2014,238(5):845−852. doi: 10.1007/s00217-014-2168-1
    [59]
    YAZGAN N N, GENIS H E, BULAT T, et al. Discrimination of milk species using Raman spectroscopy coupled with partial least squares discriminant analysis in raw and pasteurized milk[J]. Journal of the Science of Food and Agriculture,2020,100(13):4756−4765. doi: 10.1002/jsfa.10534

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