首页 > 分享 > 水果成熟度近红外光谱及高光谱成像无损检测研究进展

水果成熟度近红外光谱及高光谱成像无损检测研究进展

花匠小妙招
2024-12-28 23:11

摘要: 成熟度作为一项水果品质重要评价指标，与水果的采收、储存、加工、运输、销售等环节息息相关，也是其产量和质量的关键影响因素之一。本文综述了国内外近十年来利用近红外光谱和高光谱成像技术检测水果成熟度的研究现状。从水果成熟度定性判别和成熟度参数定量预测两个方面入手，详细分析了光谱仪器工作波段、光谱采集方式、光谱采样区域、成熟度表征因子、单一成熟度参数、多元成熟度指数对最终检测模型精度和稳定性的影响，最后展望了近红外光谱和高光谱成像技术在水果成熟度检测方向的未来发展趋势，以期为相关领域研究工作提供科学依据和技术参考。

Abstract: Maturity, as an essential evaluation index of fruit quality, is closely related to the harvest, storage, processing, transportation, sales and other links of fruit, and is also one of the key factors affecting its output and quality. In this paper, the research status of fruit maturity detection using near-infrared spectroscopy and hyperspectral imaging technology in the recent ten years are reviewed. Starting from qualitative identification of fruit maturity and quantitative prediction of maturity parameters, the effects of spectral instrument working band, spectral acquisition mode, spectral sampling area, maturity characterization factor, single maturity parameter, and multiple maturity index on the accuracy and stability of the final detection model were analyzed in detail. Finally, the development trend of near-infrared spectroscopy and hyperspectral imaging technology in fruit maturity detection has been prospected in order to provide a scientific basis and technical reference for related research work.

表 1 不同水果的光谱采样区域及分类结果

Table 1 Spectral sampling regions and classification results of different fruits

检测对象光谱采样区域建模方法结果参考文献荔枝整个果面PLSDA准确率：光谱集Ⅰ为90.63%，光谱集Ⅱ为96.88%[16] 杏果实赤道处着色和未着色两面FDA识别准确率：“Bergarouge”品种为92%，“Harostar”品种为89%[24]柑橘柑橘表面划分6个区域，每个区域采集一次PSO分类准确率为70.5%[25]石榴果实赤道处等间距的四个点PCA分类准确率为93.25%[26]樱桃整个果面LDA分类准确率为96.4%[27]梨梨赤道处采集12次光谱数据PLSDA识别率为97.22%[28]哈密瓜果实赤道处每隔120°采集一个区域的光谱数据SVM分类准确率为94%[29]西瓜西瓜赤道处的三个点KNN识别率为91.67%[32]柿子柿子正反两面各四个方形区域LDA识别率为95.3%[33]

表 2 不同水果的成熟度参数定量预测方法及结果

Table 2 Quantitative prediction methods and results of maturity parameters of different fruits

检测对象成熟度参数建模方法结果参考文献石榴TSS、pH、硬度PLSRRMSEP分别为0.22，0.038，0.68[15] 甜瓜ERPLSRRMSEC=0.047；RMSEP=0.041[17] 梨SSC、硬度、糖含量PLSR${r_p}$分别是0.653，0.609，0.8971[18,35]榴莲DMCPLSR${r_{cv}}$ 为0.82，RMSECV为2.68[37]杏SSC、TAPLSR${r_p}$分别为0.92，0.88；RMSEP分别为0.98，3.62[38] 柑橘SSC/TAMPLSRRPD为1.21[39]火龙果第一主成分PLSRRPD为3.26[40]芒果${{rm{I}}_{rm{m}}}$PLSR${r_c}$为0.74，${r_p}$为0.68[41] 西瓜SSC、水分、番茄红素PLSR${r_p}$分别为0.862，0.939，0.751；RPD分别为1.83，2.79，1.13[22,42]苹果SSC、淀粉含量、硬度PLSR$R_p^2$分别为0.83，0.79，0.38[36,43]葡萄总酚、糖、TA、pHMPLSRSEP分别为1.97、1.61、3.89、0.18[44-45]哈密瓜果肉颜色a*、b*、C*、h*、MPLSR相关系数分别是0.96、0.85、0.82、0.96[46]桃子硬度、WSPPLSRRPD分别为1.67、1.31[47-48]蓝莓SSC、硬度、花青素、鲜重相关性分析${{rm{I}}_{{rm{AD}}} }$与蓝莓理化指标有较高相关性[49]牛油果DMCPLSRRMSEP为1.53[50]甘蔗蔗糖含量PLSR$R_c^2$为0.94，RMSEC为0.7[51] 注：SSC：可溶性固形物；TA：可滴定酸度；TSS：总可溶性固形物；WSP：水溶性果胶含量；PLSR：偏最小二乘回归；MPLSR：多元偏最小二乘回归；DMC：干物质含量；${{rm{I}}_{{rm{AD}}} }$：吸光度指数；${{rm{I}}_{rm{m}}}$：成熟度指数；ER：食用比；${r_c}$：校正集相关系数；${r_p}$：预测集相关系数；${r_{cv}}$：交叉验证相关系数；RMSEC：校正集均方根误差；RMSEP：预测集均方根误差；RMSECV：交叉验证均方根误差；$R_c^2$：校正集决定系数；$R_p^2$：预测集决定系数；RPD：性能偏差比；SEP：标准预测误差。 [1]

John P J. Handbook on post harvest management of fruits and vegetables[J]. Agrotécnica,2013:49−52.

[2]

Kader A A. Flavor quality of fruits and vegetables[J]. Journal of the Science of Food and Agriculture,2008,88(11):1863−1868. doi: 10.1002/jsfa.3293

[3]

Obasi M O. Evaluation of growth and development in mango fruits Cvs. Julie and peter to determine maturity[J]. Bio Research,2005,2(2):22−26.

[4] 兰海鹏, 张宏, 唐玉荣. 一种基于成熟规律的水果成熟度评价方法: 中国, 104597217[P]. 2015-05-06.

Lan Haipeng, Zhang Hong, Tang Yurong. A fruit maturity evaluation method based on maturity law: China, 104597217[P]. 2015-05-06.

[5]

Elmasry G, Nassar A, Wang N, et al. Spectral methods for measuring quality changes of fresh fruits and vegetables[J]. Stewart Postharvest Review,2008,4(4):1−13.

[6]

Jha S N, Matsuoka T. Non-destructive techniques for quality evaluation of intact fruits and vegetables[J]. Food Science & Technology International Tokyo,2000,6(4):248−251.

[7] 彭彦颖, 孙旭东, 刘燕德. 果蔬品质高光谱成像无损检测研究进展[J]. 激光与红外,2010(6):16−22. [Peng Yanying, Sun Xudong, Liu Yande. Research progress of hyperspectral imaging in nondestructive detection of fruits and vegetables quality[J]. Laser & Infrared,2010(6):16−22. [8]

Elmasry G M, Nakauchi S. Image analysis operations applied to hyperspectral images for non-invasive sensing of food quality-a comprehensive review[J]. Biosystems Engineering,2016,142:53−82. doi: 10.1016/j.biosystemseng.2015.11.009

[9]

Nicola B M, Defraeye T, De Ketelaere B, et al. Nondestructive measurement of fruit and vegetable quality[J]. Review of Food Science & Technology,2014,5(1):285−312.

[10]

Arendse E, Fawole O A, Magwaza L S, et al. Non-destructive prediction of internal and external quality attributes of fruit with thick rind: A review[J]. Journal of Food Engineering,2018,217:11−23. doi: 10.1016/j.jfoodeng.2017.08.009

[11] 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: 73−79.

Chu Xiaoli. Molecular spectroscopy analytical technology combined with chemometrics and its applications[M]. Beijing: Chemical Industry Press, 2011: 73−79.

[12] 陆婉珍. 现代近红外光谱分析技术(第2版)(精)[M]. 北京: 中国石化出版社, 2007: 52−57.

Lu Wanzhen. Modern near infrared spectroscopy analytical technology)(Second Edition)[M]. Beijing: China’s Petrochemical Press, 2007: 52−57.

[13]

Gómez-Sanchis J, Gómez-Chova L, Aleixos N, et al. Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins[J]. Journal of Food Engineering,2008,89:80−86. doi: 10.1016/j.jfoodeng.2008.04.009

[14] 杨序纲, 吴琪琳. 拉曼光谱的分析与应用[M]. 北京: 国防工业出版社, 2008: 29.

Yang Xugang, Wu Qilin. Raman spectro-scopy analysis and application[M]. Beijing: National Defense Industry Press, 2008: 29.

[15]

Khodabakhshian R, Emadi B, Khojastehpour M, et al. Determining quality and maturity of pomegranates using multispectral imaging[J]. Journal of the Saudi Society of Agricultural Sciences,2015.

[16]

Pu H, Liu D, Wang L, et al. Soluble solids content and pH prediction and maturity discrimination of lychee fruits using visible and near infrared hyperspectral imaging[J]. Food Analytical Methods,2016,9(1):235−244. doi: 10.1007/s12161-015-0186-7

[17]

Wang A, Fu X, Xie L, et al. Application of visible/near-infrared spectroscopy combined with machine vision technique to evaluate the ripeness of melons (Cucumis melo L.)[J]. Food Analytical Methods,2015,8(6):1403−1412. doi: 10.1007/s12161-014-0026-1

[18]

Itoh H. Estimation of pear ripeness by hyperspectral laser scatter imaging[J]. IFAC Proceedings Volumes,2013,46(4):160−165. doi: 10.3182/20130327-3-JP-3017.00037

[19]

Zhang C, Guo C, Liu F, et al. Hyperspectral imaging analysis for ripeness evaluation of strawberry with support vector machine[J]. Journal of Food Engineering,2016,179(Jun.):11−18.

[20]

Pu Y, Sun D, Buccheri M, et al. Ripeness classification of bananito fruit (Musa acuminata, AA): A comparison study of visible spectroscopy and hyperspectral imaging[J]. Food Analytical Methods,2019,12:1693−1704.

[21]

Rungpichayapichet P, Mahayothee B, Nagle M, et al. Robust NIRS models for non-destructive prediction of postharvest fruit ripeness and quality in mango[J]. Postharvest Biology & Technology,2016,111:31−40.

[22]

Qi S, Song S, Jiang S, et al. Establishment of a comprehensive indicator to nondestructively analyze watermelon quality at different ripening stages[J]. Journal of Innovative Optical Health Sciences,2014,7(4):1350034.

[23]

Xuan W, He J C, Ye D P, et al. Navel orange maturity classification by multispectral indexes based on hyperspectral diffuse transmittance imaging[J]. Journal of Food Quality,2017,2017:1−7.

[24]

Camps C, Christen D. On-tree follow-up of apricot fruit development using a hand-held NIR instrument[J]. Journal of Food Agriculture & Environment,2009,7(2):394−400.

[25]

Rosli A D, Adenan N S, Hashim H, et al. Application of particle swarm optimization algorithm for optimizing ANN Model in recognizing ripeness of citrus[J]. Iop Conference,2018,340(1).

[26]

Khodabakhshian R, Emadi B, Khojastehpour M, et al. Non-destructive evaluation of maturity and quality parameters of pomegranate fruit by visible/near infrared spectroscopy[J]. International Journal of Food Properties,2017,20(1-4):41−52.

[27]

Li X, Wei Y, Xu J, et al. SSC and pH for sweet assessment and maturity classification of harvested cherry fruit based on NIR hyperspectral imaging technology[J]. Postharvest Biology & Technology,2018,143:112−118.

[28]

Xu W, Lin M, Huang Y, et al. Maturity stage distinction of pear based on visible/near infrared spectroscopy technology[J]. Journal of Physics Conference,2017,887(1).

[29] 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. [Sun Jingtao, Ma Benxue, Dong Juan, et al. Study on maturity discrimination of hami melon with hyperspectral imaging technology combined with characteristic wavelengths selection methods and SVM[J]. Spectroscopy and Spectral Analysis,2017,37(7):2184−2191. [30] 李丽丽, 王斌, 张学豪, 等. 基于高光谱成像技术的李果实成熟度判别[J]. 现代食品科技,2019,35(6):258−263. [Li Lili, Wang Bin, Zhang Xuehao, et al. Discrimination of plum fruit maturity based on hyperspectral imaging technology[J]. Modern Food Science and Technology,2019,35(6):258−263. [31]

Lu H, Wang F, Liu X, et al. Rapid assessment of tomato ripeness using visible/near-infrared spectroscopy and machine vision[J]. Food Analytical Methods,2017,10(6):1721−1726. doi: 10.1007/s12161-016-0734-9

[32] 邹小波, 张俊俊, 黄晓玮, 等. 基于音频和近红外光谱融合技术的西瓜成熟度判别[J]. 农业工程学报,2019,35(9):301−307. [Zou Xiaobo, Zhang Junjun, Huang Xiaowei, et al. Distinguishing watermelon maturity based on acoustic characteristics and near infrared spectroscopy fusion technology[J]. Transactions of the Chinese Society of Agricultural Engineering,2019,35(9):301−307. doi: 10.11975/j.issn.1002-6819.2019.09.036 [33]

Wei X, Liu F, Qiu Z, et al. Ripeness classification of astringent persimmon using hyperspectral imaging technique[J]. Food & Bioprocess Technology,2014,7(5):1371−1380.

[34]

Celik M, Ozdemir A E, Candir E E, et al. Changes in quality parameters during fruit development and their relationship with optimum harvest maturity for 'Big Top' and 'Perfect Delight' nectarine cultivars[J]. Ⅳ International Postharvest Symposium,2010:715−722.

[35]

Zhang, Dongyan, Xu, et al. Fast prediction of sugar content in Dangshan pear (Pyrus spp.) using hyperspectral imagery data[J]. Food Analytical Methods,2018,11(8):2336−2345. doi: 10.1007/s12161-018-1212-3

[36]

Bertone E, Venturello A, Leardi R, et al. Prediction of the optimum harvest time of ‘Scarlet’ apples using DR-UV-Vis and NIR spectroscopy[J]. Postharvest Biology & Technology,2012,69(none):15−23.

[37]

Phuangsombut, Kaewkarn, Arthit, et al. Empirical reduction of rind effect on rind and flesh absorbance for evaluation of durian maturity using near infrared spectroscopy[J]. Postharvest Biology & Technology,2018:142.

[38]

Bureau S, Ruiz D, Reich M, et al. Rapid and non-destructive analysis of apricot fruit quality using FT-near-infrared spectroscopy[J]. Food Chemistry,2009,113(4):1323−1328. doi: 10.1016/j.foodchem.2008.08.066

[39]

Sánchez M T, Haba M J, Serrano I, et al. Application of NIRS for nondestructive measurement of quality parameters in intact oranges during on-tree ripening and at harvest[J]. Food Analytical Methods,2013,6(3):826−837. doi: 10.1007/s12161-012-9490-7

[40]

Wanitchang J, Terdwongworakul A, Wanitchang P, et al. Maturity sorting index of dragon fruit: Hylocereus polyrhizus[J]. Journal of Food Engineering,2010,100(3):409−416. doi: 10.1016/j.jfoodeng.2010.04.025

[41]

Jha S N, Narsaiah K, Jaiswal P, et al. Nondestructive prediction of maturity of mango using near infrared spectroscopy[J]. Journal of Food Engineering,2014,124(mar.):152−157.

[42] 王世芳, 韩平, 崔广禄, 等. SPXY算法的西瓜可溶性固形物近红外光谱检测[J]. 光谱学与光谱分析,2019,39(3):738−742. [Wang Shifang, Han Ping, Cui Guanglu, et al. The NIR detection research of soluble solid content in watermelon based on SPXY algorithm[J]. Spectroscopy and Spectral Analysis,2019,39(3):738−742. [43]

Beers V, Robbe, Aernouts, et al. Apple ripeness detection using Hyperspectral Laser Scatter Imaging[J]. Proceedings of Spie,2013,8881(5):88810K.

[44]

Ribera-Fonseca A, Noferini M, Jorquera-Fontena E, et al. Assessment of technological maturity parameters and anthocyanins in berries of cv. Sangiovese (Vitis vinifera L.) by a portable vis/NIR device[J]. Scientia Horticulturae,2016,209:229−235. doi: 10.1016/j.scienta.2016.06.004

[45]

Nogales-Bueno J, Hernández-Hierro J M, Rodríguez-Pulido F J, et al. Determination of technological maturity of grapes and total phenolic compounds of grape skins in red and white cultivars during ripening by near infrared hyperspectral image: A preliminary approach[J]. Food Chemistry,2014,152(jun. 1):586−591.

[46]

Sánchez M T, Torres I, María-José D L H, et al. First steps to predicting pulp colour in whole melons using near-infrared reflectance spectroscopy[J]. Biosystems Engineering,2014,123(Complete):12−18.

[47]

Yasuhiro, Uwadaira, Yasuyo, et al. An examination of the principle of non-destructive flesh firmness measurement of peach fruit by using VIS-NIR spectroscopy[J]. Heliyon,2018,4(2):e00531.

[48]

Zhang B, Peng B, Zhang C, et al. Determination of fruit maturity and its prediction model based on the pericarp index of absorbance difference (IAD) for peaches[J]. Plos One,2017,12(5):e0177511.

[49]

Ribera-Fonseca A, Noferini M, Rombolá A D, et al. Non-destructive assessment of highbush blueberry fruit maturity parameters and anthocyanins by using a visible/near infrared (vis/NIR) spectroscopy device: A preliminary approach[J]. Journal of Soil Science & Plant Nutrition,2016(ahead).

[50]

Wedding B, White R, Grauf S, et al. Non-destructive prediction of ‘Hass’ avocado dry matter via FT-NIR spectroscopy[J]. Journal of the Science of Food & Agriculture,2011,91(2):233−238.

[51]

Taira E, Ueno M, Saengprachatanarug K, et al. Direct sugar content analysis for whole stalk sugarcane using a portable near infrared instrument[J]. Journal of Near Infrared Spectroscopy,2013,21(4):281. doi: 10.1255/jnirs.1064

[52]

Bensaeed O M, Shariff A M, Mahmud A B, et al. Oil palm fruit grading using a hyperspectral device and machine learning algorithm[J]. 2014, 20(1).

[53]

Dharma Silalahi D, Rea O C E, Lansigan F P, et al. Using genetic algorithm neural network on near infrared spectral data for ripeness grading of oil palm (Elaeis guineensis Jacq.) fresh fruit[J]. Information Processing in Agriculture,2016,3(4):252−261. doi: 10.1016/j.inpa.2016.10.001

[54]

Cayuela J A, Camino M D C P. Prediction of quality of intact olives by near infrared spectroscopy[J]. European Journal of Lipid Science & Technology,2010,112(11):1209−1217.

[55]

Zou S, Tseng Y, Zare A, et al. Peanut maturity classification using hyperspectral imagery[J]. Biosystems Engineering,2019,188:165−177.

[56]

Lorente D, Aleixos N, Gómez-Sanchis J, et al. Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment[J]. Neuroimage,2012,5(4):1121−1142.

[57]

Lammertyn J, Peirs A, Baerdemaeker J D, et al. Light penetration properties of NIR radiation in fruit with respect to non-destructive quality assessment[J]. Postharvest Biology & Technology,2000,18(2):121−132.