|本期目录/Table of Contents|

[1]谭涛,冯树南,温青纯,等.高光谱成像技术在水果品质检测中的应用研究进展[J].江苏农业科学,2024,52(6):11-18.
 Tan Tao,et al.Research progress on application of hyperspectral imaging technology in detection of fruit quality[J].Jiangsu Agricultural Sciences,2024,52(6):11-18.
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《江苏农业科学》[ISSN:1002-1302/CN:32-1214/S]

卷:
第52卷
期数:
2024年第6期
页码:
11-18
栏目:
专论与综述
出版日期:
2024-03-20

文章信息/Info

Title:
Research progress on application of hyperspectral imaging technology in detection of fruit quality
作者:
谭涛1冯树南1温青纯1黄人帅12孟庆龙12尚静12
1.贵阳学院食品与制药工程学院,贵州贵阳 550005; 2.贵州省农产品无损检测工程研究中心,贵州贵阳 550005
Author(s):
Tan Taoet al
关键词:
高光谱成像水果品质缺陷物理化学属性无损检测
Keywords:
-
分类号:
TS255.7;S127
DOI:
-
文献标志码:
A
摘要:
高光谱成像技术结合成像技术和光谱技术,可以从样本中获取其空间和光谱信息。因此,高光谱成像技术能够识别和检测水果的各种化学成分及其空间分布,在水果品质的检测中备受关注。本文首先综述了高光谱成像原理及系统装置,并展开讨论了高光谱图像的校正方法、多种光谱预处理、数据降维和样本集划分方法,从定量和定性角度对模型的构建方法和性能评估进行了分析。其次,总结了高光谱成像技术在水果内部品质(可溶性固形物含量、酸度、硬度、水分含量)和外部品质(损伤、缺陷和纹理)检测和分级中的最新研究进展。最后,对高光谱成像技术在水果品质检测与分级中的应用前景提出展望,以期为优化水果品质的检测方法提供理论依据。同时,也指出了当前可能存在的挑战和局限性。
Abstract:
-

参考文献/References:

[1]Pathmanaban P,Gnanavel B K,Anandan S S. Recent application of imaging techniques for fruit quality assessment[J]. Trends in Food Science & Technology,2019,94:32-42.
[2]徐赛,陆华忠,丘广俊,等. 水果品质无损检测研究进展及应用现状[J]. 广东农业科学,2020,47(12):229-236.
[3]田有文,吴伟,卢时铅,等. 深度学习在水果品质检测与分级分类中的应用[J]. 食品科学,2021,42(19):260-270.
[4]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.
[5]武琳霞,李玲,习佳林,等. 桃品质的无损检测技术研究进展[J]. 食品科学,2022,43(15):367-377.
[6]周伟,徐颖若. 基于PLC和图像处理的水果分类智能控制系统[J]. 农机化研究,2021,43(5):235-239.
[7]郝瑞龙,鲁任翔,王哲,等. 基于近红外光谱的芒果采后品质与贮放潜力预判的无损检测模型[J]. 热带作物学报,2022,43(9):1918-1927.
[8]Li Y F,Feng X Y,Liu Y D,et al. Apple quality identification and classification by image processing based on convolutional neural networks[J]. Scientific Reports,2021,11:16618.
[9]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.
[10]Lu B,Dao P,Liu J G,et al. Recent advances of hyperspectral imaging technology and applications in agriculture[J]. Remote Sensing,2020,12(16):2659.
[11]Wang B,Sun J F,Xia L M,et al. The applications of hyperspectral imaging technology for agricultural products quality analysis:a review[J]. Food Reviews International,2023,39(2):1043-1062.
[12]黄文倩,陈立平,李江波,等. 基于高光谱成像的苹果轻微损伤检测有效波长选取[J]. 农业工程学报,2013,29(1):272-277.
[13]Sun J F,Shi X J,Zhang H,et al. Detection of moisture content in peanut kernels using hyperspectral imaging technology coupled with chemometrics[J]. Journal of Food Process Engineering,2019,42(7):e13263.
[14]鞠皓,姜洪喆,周宏平. 油料作物与产品品质近红外光谱及高光谱成像检测研究进展[J]. 中国粮油学报,2022,37(9):303-310.
[15]Wu D,Sun D W. Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment:a review—Part Ⅰ:fundamentals[J]. Innovative Food Science & Emerging Technologies,2013,19:1-14.
[16]Jiao Y P,Li Z C,Chen X S,et al. Preprocessing methods for near-infrared spectrum calibration[J]. Journal of Chemometrics,2020,34(11):e3306.
[17]贾梦梦,殷勇,于慧春,等. 高光谱成像融合特征波长筛选监测番茄贮藏中品质变化的方法[J]. 光谱学与光谱分析,2023,43(3):969-975.
[18]Zou X B,Zhao J W,Povey M J W,et al. Variables selection methods in near-infrared spectroscopy[J]. Analytica Chimica Acta,2010,667(1/2):14-32.
[19]卢伟,蔡苗苗,张强,等. 高光谱和集成学习的黑枸杞快速分级方法[J]. 光谱学与光谱分析,2021,41(7):2196-2204.
[20]Tian H,Zhang L N,Li M,et al. Weighted SPXY method for calibration set selection for composition analysis based on near-infrared spectroscopy[J]. Infrared Physics & Technology,2018,95:88-92.
[21]Morais C L M,Santos M C D,Lima K M G,et al. Improving data splitting for classification applications in spectrochemical analyses employing a random-mutation Kennard-Stone algorithm approach[J]. Bioinformatics,2019,35(24):5257-5263.
[22]Zhang L N,Li G,Sun M X,et al. Kennard-Stone combined with least square support vector machine method for noncontact discriminating human blood species[J]. Infrared Physics & Technology,2017,86:116-119.
[23]Li Z G,Lv H,Li T H,et al. Reagent-free simultaneous determination of glucose and cholesterol in whole blood by FTIR-ATR[J]. Spectrochimica Acta Part A:Molecular and Biomolecular Spectroscopy,2017,178:192-197.
[24]Quelal-Vásconez M A,Lerma-García M J,Pérez-Esteve é,et al. Fast detection of cocoa shell in cocoa powders by near infrared spectroscopy and multivariate analysis[J]. Food Control,2019,99:68-72.
[25]Elmasry G,Kamruzzaman M,Sun D W,et al. Principles and applications of hyperspectral imaging in quality evaluation of agro-food products:a review[J]. Critical Reviews in Food Science and Nutrition,2012,52(11):999-1023.
[26]Liu J Y,Zeng L H,Ren Z H. Recent application of spectroscopy for the detection of microalgae life information:a review[J]. Applied Spectroscopy Reviews,2020,55(1):26-59.
[27]Li J,Huang B H,Wu C P,et al. Nondestructive detection of kiwifruit textural characteristic based on near infrared hyperspectral imaging technology[J]. International Journal of Food Properties,2022,25(1):1697-1713.
[28]王广来,王恩凤,王聪聪,等. 基于高光谱图像技术与迁移学习的水晶梨早期损伤检测[J]. 光谱学与光谱分析,2022,42(11):3626-3630.
[29]Munera S,Gómez-Sanchís J,Aleixos N,et al. Discrimination of common defects in loquat fruit cv.‘Algerie’ using hyperspectral imaging and machine learning techniques[J]. Postharvest Biology and Technology,2021,171:111356.
[30]刘燕德,王舜. 基于图像和光谱融合的脐橙货架期高光谱成像无损检测研究[J]. 光谱学与光谱分析,2022,42(6):1792-1797.
[31]Zhang H L,Zhang S,Dong W T,et al. Detection of common defects on mandarins by using visible and near infrared hyperspectral imaging[J]. Infrared Physics & Technology,2020,108:103341.
[32]Kang Z L,Geng J P,Fan R S,et al. Nondestructive testing model of mango dry matter based on fluorescence hyperspectral imaging technology[J]. Agriculture,2022,12(9):1337.
[33]杨宝华,高志伟,齐麟,等. 高光谱影像的鲜桃可溶性固形物含量预测模型[J]. 光谱学与光谱分析,2021,41(11):3559-3564.
[34]尚增强,杨东福,马质璞. 香蕉成熟期品质可视化与高光谱成像研究[J]. 保鲜与加工,2021,21(9):98-104.
[35]Gao S,Xu J H. Hyperspectral image information fusion-based detection of soluble solids content in red globe grapes[J]. Computers and Electronics in Agriculture,2022,196:106822.
[36]Lan W J,Jaillais B,Renard C M G C,et al. A method using near infrared hyperspectral imaging to highlight the internal quality of apple fruit slices[J]. Postharvest Biology and Technology,2021,175:111497.
[37]Seki H,Ma T,Murakami H,et al. Visualization of sugar content distribution of white strawberry by near-infrared hyperspectral imaging[J]. Foods,2023,12(5):931.
[38]丁燕,孙元明,李冬升,等. 基于CiteSpace对水果无损检测研究进展和趋势的可视化分析[J]. 食品工业科技,2023,44(16):444-453.
[39]沈宇,房胜,郑纪业,等. 基于高光谱成像技术的富士苹果轻微机械损伤检测研究[J]. 山东农业科学,2020,52(2):144-150.
[40]孟庆龙,张艳,尚静. 基于高光谱成像的猕猴桃表面疤痕无损识别[J]. 浙江农业学报,2019,31(8):1372-1378.
[41]张立秀,张淑娟,孙海霞,等. 高光谱技术结合网格搜索优化支持向量机的桃缺陷检测[J]. 食品与发酵工业,2023,49(16):269-275.
[42]欧阳爱国,刘昊辰,成龙,等. 高光谱图像特征结合光谱特征用于毛桃碰伤时间分类[J]. 光谱学与光谱分析,2021,41(8):2598-2603.
[43]Wang B,Yang H,Zhang S J,et al. Detection of defective features in Cerasus humilis fruit based on hyperspectral imaging technology[J]. Applied Sciences,2023,13(5):3279.
[44]陈玥瑶,夏静静,韦芸,等. 近红外光谱法无损检测平谷产大桃品质方法研究[J]. 分析化学,2023,51(3):454-462.
[45]高升,徐建华. 高光谱成像的红提总酸与硬度的预测及其分布可视化[J]. 食品科学,2023,44(2):327-336.
[46]李雄,刘燕德,欧阳爱国,等. 酥梨货架期的高光谱成像无损检测模型研究[J]. 光谱学与光谱分析,2019,39(8):2578-2583.
[47]葛春靖,张淑娟,孙海霞. 基于GA-BP神经网络玉露香梨可溶性固形物高光谱技术检测[J]. 现代食品科技,2021,37(5):296-302,278.
[48]Huang F H,Liu Y H,Sun X Y,et al. Quality inspection of nectarine based on hyperspectral imaging technology[J]. Systems Science & Control Engineering,2021,9(1):350-357.
[49]Xu M,Sun J,Cheng J H,et al. Non-destructive prediction of total soluble solids and titratable acidity in Kyoho grape using hyperspectral imaging and deep learning algorithm[J]. International Journal of Food Science & Technology,2023,58(1):9-21.
[50]孙静涛,马本学,董娟,等. 高光谱技术结合特征波长筛选和支持向量机的哈密瓜成熟度判别研究[J]. 光谱学与光谱分析,2017,37(7):2184-2191.
[51]Tian P,Meng Q H,Wu Z F,et al. Detection of mango soluble solid content using hyperspectral imaging technology[J]. Infrared Physics & Technology,2023,129:104576.

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备注/Memo

备注/Memo:
收稿日期:2023-06-07
基金项目:中央引导地方科技发展资金(编号:黔科中引地[2022]4050);贵州省科技计划(编号:黔科合基础[2020]1Y270);贵州省普通高等学校青年人才成长项目(编号:黔教合KY字[2020]081);贵阳市科技计划(编号:筑科合同[2021]43-15号);贵阳学院硕士研究生科研基金(编号:GYU-YJS[2022]-53);大学生创新创业训练计划(编号:S202210976046)。
作者简介:谭涛(1998—),男,贵州铜仁人,硕士研究生,主要从事农产品品质无损检测研究。E-mail:gyu_tt@163.com。
通信作者:尚静,硕士,副教授,主要从事基于高光谱成像技术的农产品品质无损检测研究。E-mail:shji0124@163.com。
更新日期/Last Update: 2024-03-20