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Article(id=1320883876168323375, tenantId=1146029695717560320, journalId=1260987677001138203, issueId=1320883859747615068, articleNumber=null, orderNo=null, doi=10.13386/j.issn1002-0306.2025020053, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1739289600000, receivedDateStr=2025-02-12, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1792852856914, onlineDateStr=2026-10-24, pubDate=1773504000000, pubDateStr=2026-03-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792852856914, onlineIssueDateStr=2026-10-24, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792852856914, creator=13701087609, updateTime=1792852856914, updator=13701087609, issue=Issue{id=1320883859747615068, tenantId=1146029695717560320, journalId=1260987677001138203, year='2026', volume='47', issue='6', pageStart='1', pageEnd='505', issueExtLink='null', onlineDate='null', pubDate='1773504000000', pubDateStr='2026-03-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792852852999, creator='13701087609', updateTime=1792852852999, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=284, endPage=293, ext={EN=ArticleExt(id=1320883876394815792, articleId=1320883876168323375, tenantId=1146029695717560320, journalId=1260987677001138203, language=EN, title=Rapid and Nondestructive Prediction of Major Quality Indicators of Kiwifruits Using Hyperspectral Imaging, columnId=1261343843585241413, journalTitle=Science and Technology of Food Industry, columnName=Analysis and Determination, runingTitle=null, highlight=null, articleAbstract=

In this study, hyperspectral imaging technology was used to achieve rapid and nondestructive detection of key quality indicators of kiwifruits, including soluble solids content, pH, firmness, and color e value. A hyperspectral imaging system was employed to capture hyperspectral images of kiwifruits, and the average spectral reflectance values from regions of interest were extracted for each sample. Simultaneously, reference values for the quality indicators were measured according to Chinese national standard methods. The original spectra were preprocessed using four methods: Second derivative, first derivative, multi-scatter calibration, and standard normal variation (SNV). Based on the full spectra, partial least-squares regression and principal component regression models were developed to predict kiwifruit quality indicators, and the optimal spectral preprocessing method was selected. To enhance model efficiency and stability, characteristic wavelengths were selected from the full spectra using a successive projections algorithm and competitive adaptive reweighted sampling (CARS). Multiple linear regression (MLR) and error back-propagation neural network models were then constructed using these selected characteristic wavelengths. Comparison of established models using the characteristic wavelengths with those utilizing the full spectra revealed that the SNV-CARS-MLR model had the best ability to assess kiwifruit quality indicators. Specifically, the prediction correlation coefficients (Rp) for soluble solids content, pH, firmness, and color e value were 0.9552, 0.8870, 0.8799, and 0.9079, respectively. The respective root mean square errors of the predictions were 0.9088, 0.0715, 2.3746, and 0.0285, and the respective residual predictive deviation (RPD) values were 3.3800, 2.0349, 2.1350 and 2.2838. All RPD values exceeded two, indicating strong model performance. Finally, pseudo-colouring was employed to input pixel information from hyperspectral images into the SNV-CARS-MLR model to visualise the distribution and compare kiwifruits of different quality grades. In conclusion, hyperspectral imaging technology enables rapid, nondestructive detection of key quality indicators in kiwifruits.

, authors=Jing SHANG, Zhiyang LIU, Tao TAN, Jin LI, Qinglong MENG*, authorsList=Jing SHANG, Zhiyang LIU, Tao TAN, Jin LI, Qinglong MENG, authorCompany=null, correspAuthors=Qinglong MENG, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright © 2026 Science and Technology of Food Industry. All rights reserved., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1320883878122869053, articleId=1320883876168323375, tenantId=1146029695717560320, journalId=1260987677001138203, language=CN, title=基于高光谱成像的猕猴桃主要品质指标的快速无损检测, columnId=1261343846139572559, journalTitle=食品工业科技, columnName=分析检测, runingTitle=null, highlight=null, articleAbstract=

为实现猕猴桃主要品质指标（可溶性固形物含量、酸度、硬度和色差e值）的快速无损检测。利用高光谱成像系统采集猕猴桃的图像信息并提取感兴趣区域的原始光谱反射率，同时参照国标方法测定其主要品质指标参考值。分析四种光谱预处理方法（一阶导数、二阶导数、多元散射校正和标准正态变换（SNV））对原始光谱的预处理效果，并基于全光谱信息建立预测猕猴桃品质指标的偏最小二乘回归和主成分回归模型，以确定较优的光谱预处理方法；然后，分别采用连续投影算法和竞争性自适应重加权算法（CARS）对全光谱信息进行降维处理以提升模型的运行效率和稳定性，将筛选的最优特征波长作为输入变量建立预测猕猴桃品质指标的多元线性回归（MLR）和误差反向传播神经网络模型；分析对比基于特征波长与全波长的建模结果，得出猕猴桃品质指标（可溶性固形物含量、酸度、硬度和色差e值）的最优预测模型为SNV-CARS-MLR模型，其中预测集相关系数Rp分别为0.9552、0.8870、0.8799和0.9079，均方根误差分别为0.9088、0.0715、2.3764和0.0285，剩余预测偏差分别为3.3800、2.0349、2.1350和2.2838，剩余预测偏差均大于2，表明模型的检测性能很好。最后，采用伪彩色技术，将猕猴桃高光谱图像上的像素点信息输入最优的SNV-CARS-MLR模型中，实现了猕猴桃各品质指标含量分布可视化，可以直观地显示出不同品质猕猴桃的差异化。综上，利用高光谱成像技术可以实现猕猴桃主要品质指标的快速无损检测。

, authors=尚静, 刘志洋, 谭涛, 李缙, 孟庆龙*, authorsList=尚静, 刘志洋, 谭涛, 李缙, 孟庆龙, authorCompany=null, correspAuthors=孟庆龙, authorNote=

尚静（1988−），女，硕士，副教授，研究方向：农产品品质无损检测，E-mail：

, correspAuthorsNote=
孟庆龙（1989−），男，博士，教授，研究方向：农产品品质无损检测，E-mail：。
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尚静（1988−），女，硕士，副教授，研究方向：农产品品质无损检测，E-mail：

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尚静（1988−），女，硕士，副教授，研究方向：农产品品质无损检测，E-mail：

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Postharvest Biology and Technology, 2021, 172: 111385., articleTitle=null, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1320883878533910846, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320883876168323375, xref=null, ext=[AuthorCompanyExt(id=1320883878542299455, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320883876168323375, companyId=1320883878533910846, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Food Science and Engineering, Guiyang University, Guiyang 550005, China), AuthorCompanyExt(id=1320883878546493760, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320883876168323375, companyId=1320883878533910846, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=贵阳学院食品科学与工程学院，贵州贵阳 550005)])], figs=[ArticleFig(id=1320883882145206628, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320883876168323375, language=EN, label=Fig.1, caption=Hyperspectral image acquisition system, figureFileSmall=zO3QTCTrc/ANH4OEsZeY7w==, figureFileBig=w3DhReuuHFMJKNu0/YAABQ==, tableContent=null), ArticleFig(id=1320883882220704101, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320883876168323375, language=CN, label=图1, caption=高光谱图像采集系统

注：1.CCD相机；2.光谱成像仪；3.镜头；4.标准漫反射光源；5.移动平台；6.暗箱；7.高性能计算机。

, figureFileSmall=zO3QTCTrc/ANH4OEsZeY7w==, figureFileBig=w3DhReuuHFMJKNu0/YAABQ==, tableContent=null), ArticleFig(id=1320883882417836390, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320883876168323375, language=EN, label=Fig.2, caption=Original spectral and pretreatment spectral reflectance curves for kiwifruit samples, figureFileSmall=Ik0R8wq/G3/HCGcka5BLgQ==, figureFileBig=xphYIHaJCcoqnBLaMzyZqA==, tableContent=null), ArticleFig(id=1320883882577219943, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320883876168323375, language=CN, label=图2, caption=猕猴桃样本的原始光谱和预处理光谱反射率曲线, figureFileSmall=Ik0R8wq/G3/HCGcka5BLgQ==, figureFileBig=xphYIHaJCcoqnBLaMzyZqA==, tableContent=null), ArticleFig(id=1320883882677883240, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320883876168323375, language=EN, label=Fig.3, caption=Characteristic wavelengths selected by CARS, figureFileSmall=/2yQHwIKY4NJ7NPLCr+pDg==, figureFileBig=NnRn3MuMYu5V32Rvnjn1Gw==, tableContent=null), ArticleFig(id=1320883882753380713, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320883876168323375, language=CN, label=图3, caption=CARS选取特征波长过程, figureFileSmall=/2yQHwIKY4NJ7NPLCr+pDg==, figureFileBig=NnRn3MuMYu5V32Rvnjn1Gw==, tableContent=null), ArticleFig(id=1320883882812100970, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320883876168323375, language=EN, label=Fig.4, caption=Characteristic wavelengths selected by SPA, figureFileSmall=9Oyy1cwn1P16XTHthKlTQw==, figureFileBig=tFfhl+Ehhe9oB9bmlvwOdQ==, tableContent=null), ArticleFig(id=1320883882883404139, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320883876168323375, language=CN, label=图4, caption=SPA选取特征波长过程

注：主要数据以红星标出。

, figureFileSmall=9Oyy1cwn1P16XTHthKlTQw==, figureFileBig=tFfhl+Ehhe9oB9bmlvwOdQ==, tableContent=null), ArticleFig(id=1320883882954707308, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320883876168323375, language=EN, label=Fig.5, caption=Prediction results for quality indicators of kiwifruits, figureFileSmall=Pg3vx2n1zFuaZbgeZxvPOQ==, figureFileBig=Z1vs8dDIZGYJMlTFbq53tA==, tableContent=null), ArticleFig(id=1320883883034399085, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320883876168323375, language=CN, label=图5, caption=猕猴桃各品质指标预测结果, figureFileSmall=Pg3vx2n1zFuaZbgeZxvPOQ==, figureFileBig=Z1vs8dDIZGYJMlTFbq53tA==, tableContent=null), ArticleFig(id=1320883883114090864, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320883876168323375, language=EN, label=Fig.6, caption=Visualization map of each quality indicators of different kiwifruit samples, figureFileSmall=eQmpOYUnLd5aU0IBuu9BVg==, figureFileBig=9Nq0tgSP7Mb5z4oQeufqJA==, tableContent=null), ArticleFig(id=1320883883202171249, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320883876168323375, language=CN, label=图6, caption=不同猕猴桃样本各品质指标含量分布图, figureFileSmall=eQmpOYUnLd5aU0IBuu9BVg==, figureFileBig=9Nq0tgSP7Mb5z4oQeufqJA==, tableContent=null), ArticleFig(id=1320883884884087155, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320883876168323375, language=EN, label=Table 1, caption=

Statistical results of the quality indicators

, figureFileSmall=null, figureFileBig=null, tableContent=
品质参数校正集预测集
样本数范围平均值标准差样本数范围平均值标准差
SSC（%）1357.10~18.7014.613.00457.60~18.6014.863.07
酸度1202.94~3.633.260.37403.02~3.543.250.37
硬度（kg·cm−2）1050.26~20.1110.715.48350.82~18.9810.605.07
色差e值900.51~0.820.660.08300.55~0.790.690.07
), ArticleFig(id=1320883884967973236, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320883876168323375, language=CN, label=表1, caption=

品质指标统计结果

, figureFileSmall=null, figureFileBig=null, tableContent=
品质参数校正集预测集
样本数范围平均值标准差样本数范围平均值标准差
SSC（%）1357.10~18.7014.613.00457.60~18.6014.863.07
酸度1202.94~3.633.260.37403.02~3.543.250.37
硬度（kg·cm−2）1050.26~20.1110.715.48350.82~18.9810.605.07
色差e值900.51~0.820.660.08300.55~0.790.690.07
), ArticleFig(id=1320883885039276405, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320883876168323375, language=EN, label=Table 2, caption=

Full wavelengths PLSR prediction models using different preprocessing methods

, figureFileSmall=null, figureFileBig=null, tableContent=
品质指标预处理方法校正集预测集
RcRMSECRpRMSEPRPD
SSC原始光谱0.96660.75940.93681.10552.8638
SNV0.96830.74770.94860.97273.1580
MSC0.96850.74330.94671.01163.0514
SD0.98110.58690.92241.12142.5403
FD0.95310.90820.94700.95753.1163
酸度原始光谱0.89280.07780.79070.10031.5849
SNV0.88250.08300.80770.08781.6573
MSC0.89470.07890.79700.08901.6354
SD0.94570.05590.79580.09761.5959
FD0.92440.06650.80330.10581.4399
硬度原始光谱0.91162.28310.71683.49081.3375
SNV0.94931.71550.80862.97481.7055
MSC0.94971.70840.78793.17531.5981
SD0.96551.46030.56634.67181.0507
FD0.92912.01110.79583.75121.3713
色差e值原始光谱0.94370.02590.81410.04021.6234
SNV0.95120.02390.82440.03701.7583
MSC0.96610.02000.81080.03841.6924
SD0.96820.01940.64540.05451.2724
FD0.91690.03160.81430.03551.7085
), ArticleFig(id=1320883885244797302, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320883876168323375, language=CN, label=表2, caption=

采用不同预处理方法的全波长PLSR预测模型

, figureFileSmall=null, figureFileBig=null, tableContent=
品质指标预处理方法校正集预测集
RcRMSECRpRMSEPRPD
SSC原始光谱0.96660.75940.93681.10552.8638
SNV0.96830.74770.94860.97273.1580
MSC0.96850.74330.94671.01163.0514
SD0.98110.58690.92241.12142.5403
FD0.95310.90820.94700.95753.1163
酸度原始光谱0.89280.07780.79070.10031.5849
SNV0.88250.08300.80770.08781.6573
MSC0.89470.07890.79700.08901.6354
SD0.94570.05590.79580.09761.5959
FD0.92440.06650.80330.10581.4399
硬度原始光谱0.91162.28310.71683.49081.3375
SNV0.94931.71550.80862.97481.7055
MSC0.94971.70840.78793.17531.5981
SD0.96551.46030.56634.67181.0507
FD0.92912.01110.79583.75121.3713
色差e值原始光谱0.94370.02590.81410.04021.6234
SNV0.95120.02390.82440.03701.7583
MSC0.96610.02000.81080.03841.6924
SD0.96820.01940.64540.05451.2724
FD0.91690.03160.81430.03551.7085
), ArticleFig(id=1320883885328683383, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320883876168323375, language=EN, label=Table 3, caption=

Full wavelengths PCR prediction models using different preprocessing methods

, figureFileSmall=null, figureFileBig=null, tableContent=
品质指标预处理方法校正集预测集
RcRMSECRpRMSEPRPD
SSC原始光谱0.89911.29650.86981.60901.9677
SNV0.93251.08040.92391.21032.5380
MSC0.97271.07740.92411.22062.5289
SD0.95280.92040.91511.14002.4988
FD0.94100.901580.94680.96323.0978
酸度原始光谱0.83830.09180.77880.10571.5035
SNV0.85410.09180.81430.08751.6624
MSC0.85330.09210.81460.08751.6623
SD0.96200.04690.70360.11921.3067
FD0.96440.04610.74640.12151.2538
硬度原始光谱0.96251.50780.71833.69011.2652
SNV0.93541.93020.81582.91471.7407
MSC0.94871.72470.79853.09291.6407
SD0.90812.30480.64933.99551.2286
FD0.92292.09310.80313.66321.4043
色差e值原始光谱0.91390.03180.81640.04051.6117
SNV0.91200.03170.83760.03611.8012
MSC0.93240.02800.82460.03781.7195
SD0.92610.02910.64480.05431.2773
FD0.92120.03080.80780.03711.6355
), ArticleFig(id=1320883885425152376, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320883876168323375, language=CN, label=表3, caption=

采用不同预处理方法的全波长PCR预测模型

, figureFileSmall=null, figureFileBig=null, tableContent=
品质指标预处理方法校正集预测集
RcRMSECRpRMSEPRPD
SSC原始光谱0.89911.29650.86981.60901.9677
SNV0.93251.08040.92391.21032.5380
MSC0.97271.07740.92411.22062.5289
SD0.95280.92040.91511.14002.4988
FD0.94100.901580.94680.96323.0978
酸度原始光谱0.83830.09180.77880.10571.5035
SNV0.85410.09180.81430.08751.6624
MSC0.85330.09210.81460.08751.6623
SD0.96200.04690.70360.11921.3067
FD0.96440.04610.74640.12151.2538
硬度原始光谱0.96251.50780.71833.69011.2652
SNV0.93541.93020.81582.91471.7407
MSC0.94871.72470.79853.09291.6407
SD0.90812.30480.64933.99551.2286
FD0.92292.09310.80313.66321.4043
色差e值原始光谱0.91390.03180.81640.04051.6117
SNV0.91200.03170.83760.03611.8012
MSC0.93240.02800.82460.03781.7195
SD0.92610.02910.64480.05431.2773
FD0.92120.03080.80780.03711.6355
), ArticleFig(id=1320883885500649849, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320883876168323375, language=EN, label=Table 4, caption=

Prediction results of MLR and BP models based on the feature wavelengths

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品质指标预测模型特征波长提取方法特征波长数校正集预测集
RcRMSECRpRMSEPRPD
SSCMLRCARS350.96310.80500.95520.90883.3800
SPA390.96250.81110.95480.91963.3404
BPCARS350.95550.88640.94431.00013.0716
SPA390.94530.98800.93901.05062.9240
酸度MLRCARS160.88800.08770.88700.07152.0349
SPA290.86900.08730.86000.08161.7828
BPCARS160.88110.08440.83290.09011.6143
SPA290.79510.10730.77660.09341.5581
硬度MLRCARS260.89082.47920.87992.37642.1350
SPA40.41654.96120.75033.46201.4655
BPCARS260.87112.71740.86292.68791.8875
SPA40.52204.66280.70153.58791.4141
色差e值MLRCARS430.97290.01790.90790.02852.2838
SPA190.89770.03410.84200.03591.8130
BPCARS430.94150.02760.86850.03411.9098
SPA190.91620.03120.84300.03561.8269
), ArticleFig(id=1320883885563564410, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320883876168323375, language=CN, label=表4, caption=

基于特征波长构建的MLR和BP模型预测结果

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品质指标预测模型特征波长提取方法特征波长数校正集预测集
RcRMSECRpRMSEPRPD
SSCMLRCARS350.96310.80500.95520.90883.3800
SPA390.96250.81110.95480.91963.3404
BPCARS350.95550.88640.94431.00013.0716
SPA390.94530.98800.93901.05062.9240
酸度MLRCARS160.88800.08770.88700.07152.0349
SPA290.86900.08730.86000.08161.7828
BPCARS160.88110.08440.83290.09011.6143
SPA290.79510.10730.77660.09341.5581
硬度MLRCARS260.89082.47920.87992.37642.1350
SPA40.41654.96120.75033.46201.4655
BPCARS260.87112.71740.86292.68791.8875
SPA40.52204.66280.70153.58791.4141
色差e值MLRCARS430.97290.01790.90790.02852.2838
SPA190.89770.03410.84200.03591.8130
BPCARS430.94150.02760.86850.03411.9098
SPA190.91620.03120.84300.03561.8269
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基于高光谱成像的猕猴桃主要品质指标的快速无损检测
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尚静 , 刘志洋 , 谭涛 , 李缙 , 孟庆龙 *
食品工业科技 | 分析检测 2026,47(6): 284-293
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食品工业科技 |分析检测 2026 , 47 (6) : 284 -293
基于高光谱成像的猕猴桃主要品质指标的快速无损检测
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尚静 , 刘志洋, 谭涛, 李缙, 孟庆龙*
作者信息
  • 贵阳学院食品科学与工程学院，贵州贵阳 550005
通讯作者:
孟庆龙（1989−），男，博士，教授，研究方向：农产品品质无损检测，E-mail：。
作者简介:

尚静（1988−），女，硕士，副教授，研究方向：农产品品质无损检测，E-mail：

Rapid and Nondestructive Prediction of Major Quality Indicators of Kiwifruits Using Hyperspectral Imaging
Jing SHANG , Zhiyang LIU, Tao TAN, Jin LI, Qinglong MENG*
Affiliations
  • School of Food Science and Engineering, Guiyang University, Guiyang 550005, China
出版时间: 2026-03-15 doi: 10.13386/j.issn1002-0306.2025020053
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摘要
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为实现猕猴桃主要品质指标（可溶性固形物含量、酸度、硬度和色差e值）的快速无损检测。利用高光谱成像系统采集猕猴桃的图像信息并提取感兴趣区域的原始光谱反射率，同时参照国标方法测定其主要品质指标参考值。分析四种光谱预处理方法（一阶导数、二阶导数、多元散射校正和标准正态变换（SNV））对原始光谱的预处理效果，并基于全光谱信息建立预测猕猴桃品质指标的偏最小二乘回归和主成分回归模型，以确定较优的光谱预处理方法；然后，分别采用连续投影算法和竞争性自适应重加权算法（CARS）对全光谱信息进行降维处理以提升模型的运行效率和稳定性，将筛选的最优特征波长作为输入变量建立预测猕猴桃品质指标的多元线性回归（MLR）和误差反向传播神经网络模型；分析对比基于特征波长与全波长的建模结果，得出猕猴桃品质指标（可溶性固形物含量、酸度、硬度和色差e值）的最优预测模型为SNV-CARS-MLR模型，其中预测集相关系数Rp分别为0.9552、0.8870、0.8799和0.9079，均方根误差分别为0.9088、0.0715、2.3764和0.0285，剩余预测偏差分别为3.3800、2.0349、2.1350和2.2838，剩余预测偏差均大于2，表明模型的检测性能很好。最后，采用伪彩色技术，将猕猴桃高光谱图像上的像素点信息输入最优的SNV-CARS-MLR模型中，实现了猕猴桃各品质指标含量分布可视化，可以直观地显示出不同品质猕猴桃的差异化。综上，利用高光谱成像技术可以实现猕猴桃主要品质指标的快速无损检测。

猕猴桃  /  品质指标  /  高光谱成像  /  化学计量学  /  无损检测

In this study, hyperspectral imaging technology was used to achieve rapid and nondestructive detection of key quality indicators of kiwifruits, including soluble solids content, pH, firmness, and color e value. A hyperspectral imaging system was employed to capture hyperspectral images of kiwifruits, and the average spectral reflectance values from regions of interest were extracted for each sample. Simultaneously, reference values for the quality indicators were measured according to Chinese national standard methods. The original spectra were preprocessed using four methods: Second derivative, first derivative, multi-scatter calibration, and standard normal variation (SNV). Based on the full spectra, partial least-squares regression and principal component regression models were developed to predict kiwifruit quality indicators, and the optimal spectral preprocessing method was selected. To enhance model efficiency and stability, characteristic wavelengths were selected from the full spectra using a successive projections algorithm and competitive adaptive reweighted sampling (CARS). Multiple linear regression (MLR) and error back-propagation neural network models were then constructed using these selected characteristic wavelengths. Comparison of established models using the characteristic wavelengths with those utilizing the full spectra revealed that the SNV-CARS-MLR model had the best ability to assess kiwifruit quality indicators. Specifically, the prediction correlation coefficients (Rp) for soluble solids content, pH, firmness, and color e value were 0.9552, 0.8870, 0.8799, and 0.9079, respectively. The respective root mean square errors of the predictions were 0.9088, 0.0715, 2.3746, and 0.0285, and the respective residual predictive deviation (RPD) values were 3.3800, 2.0349, 2.1350 and 2.2838. All RPD values exceeded two, indicating strong model performance. Finally, pseudo-colouring was employed to input pixel information from hyperspectral images into the SNV-CARS-MLR model to visualise the distribution and compare kiwifruits of different quality grades. In conclusion, hyperspectral imaging technology enables rapid, nondestructive detection of key quality indicators in kiwifruits.

kiwifruits  /  quality indicators  /  hyperspectral imaging  /  chemometrics  /  nondestructive detection
尚静, 刘志洋, 谭涛, 李缙, 孟庆龙. 基于高光谱成像的猕猴桃主要品质指标的快速无损检测. 食品工业科技, 2026 , 47 (6) : 284 -293 . DOI: 10.13386/j.issn1002-0306.2025020053
Jing SHANG, Zhiyang LIU, Tao TAN, Jin LI, Qinglong MENG. Rapid and Nondestructive Prediction of Major Quality Indicators of Kiwifruits Using Hyperspectral Imaging[J]. Science and Technology of Food Industry, 2026 , 47 (6) : 284 -293 . DOI: 10.13386/j.issn1002-0306.2025020053
正文
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猕猴桃，又称奇异果，因猕猴桃果肉含有多种微量元素和有机成分，且营养价值高，有着“水果之王”、“VC之王”的美称[1−2]。贵长猕猴桃的果形呈长圆柱形，果肉呈翠绿色，肉质多浆、酸甜适中，同时富含VC的独特品质，属“美味”猕猴桃类型，受到广大消费者的青睐[3]。水果的品质指标是由内部品质（可溶性固形物含量（soluble solids content，SSC）、酸度、硬度、VC等）和外部品质（大小、形状、表面缺陷、表皮颜色等）构成[4−6]。水果的SSC、酸度和硬度等反映果实的口感和风味，表皮颜色间接反映水果成熟度，这些品质是影响消费者购买需求的重要属性[7−8]。因此，SSC、酸度、硬度和表皮颜色是判别水果品质最重要的指标，也是决定水果采摘时间及储藏时间的重要因素[9−11]。
SSC、酸度、硬度和颜色的传统测量方法不仅破坏样本，而且存在检测效率低、耗时等缺陷[12]。高光谱成像技术具有快速、无损的优点，已被广泛地用于水果品质的快速无损检测[13−18]。近年来国内外研究学者已开展了关于苹果[19]、蓝莓[20]、柑橘[21]、樱桃[22]、香蕉[23]、猕猴桃[1,24]以及李子[25]等诸多水果品质的快速无损检测研究。此外，高光谱成像技术相对于近红外光谱技术的优势在于，它能够获得整个检测对象的光谱信息，有助于构建更稳定的预测模型，而且利用高光谱成像技术获取的图像信息可以实现水果品质指标的分布可视化，从而使检测水果品质指标更加直观。Meng等[1]利用高光谱成像技术结合化学计量学方法实现了不同采摘时间猕猴桃SSC、硬度、L*、a*和b*值的快速无损检测，并对各理化指标进行了可视化分析。董金磊等[24]运用高光谱成像技术实现了贮藏期猕猴桃糖度含量的预测。然而，关于贮藏期猕猴桃品质的多指标无损检测及其可视化分析研究鲜有报道。
本文以贵长猕猴桃为研究对象，运用高光谱成像技术结合化学计量学方法对猕猴桃SSC、酸度、硬度和色差e值进行无损检测，探究不同预处理方法对构建的偏最小二乘回归（partial least square regression，PLSR）和主成分回归（principal components regression，PCR）全波长模型性能的影响，利用较优的预处理方法结合竞争性自适应重加权算法（competitive adaptive reweighted sampling，CARS）和连续投影算法（successive projections algorithm，SPA）构建多元线性回归（multiple linear regression，MLR）和反向传播（back propagation，BP）神经网络特征波长模型，对比全波长和特征波长模型的性能，确立预测贵长猕猴桃主要品质指标的最佳模型。最后，利用伪彩色技术实现了猕猴桃各品质指标的可视化。
1　材料与方法
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1.1　材料与仪器
猕猴桃　品种‘贵长’，于2021年9月采摘自贵州省修文县龙关口猕猴桃基地。从不同的果树上挑选没有损伤和疤痕的猕猴桃样本共600个（采摘时猕猴桃的SSC在7.0%左右）。将采摘后的样本送至实验室（23±2 ℃）贮藏，实验前使用柔软的纸轻轻地去除样本表面的灰尘，并将其依次编号。
pHS-100型酸度计　成都世纪方舟科技有限公司；ATAGO PAL-α数字折射仪　日本Atago公司；TA.XT.plus质构仪　英国SMS公司；Ci7800台式分光光度仪　爱色丽（上海）色彩仪器商贸有限公司；高光谱图像采集系统（GaiaField-F-V10）　江苏双利合谱科技有限公司，系统原理如图1所示，其中CCD相机曝光时间为12.6 ms，样本距离镜头约40 cm，成像光谱仪的分辨率为3.5 nm。
1.2　实验方法
为了增大猕猴桃各品质指标的检测范围，分7批次开展实验，分别为：第1 d 87个，第5 d 85个，第8 d 42个，第10 d 56个，第12 d 75个，第14 d 138个，第16 d 117个。分别采集高光谱图像并测定其各品质指标参考值。
1.2.1　高光谱图像采集与校正
将高光谱成像仪预热30 min后，依次按照编好的序号将猕猴桃样本放在传输平台上采集样本的高光谱图像。由于高光谱图像采集系统中存在暗电流以及背景光源强度分布不均衡等因素，采集到的图像会带有一定的噪声信号，因此需要对原始高光谱图像进行黑白校正。黑白校正公式[26]如下：
$ \mathrm{R}=\frac{(\mathrm{I}-\mathrm{B})}{(\mathrm{W}-\mathrm{B})} $
式中：I为样本的原始高光谱图像；W为白板图像；B为暗图像；R为校正后的高光谱图像。
1.2.2　品质指标的测定
1.2.2.1　SSC含量的测定
利用折射仪按照NT/T 2637-2014规定的方法对猕猴桃的SSC进行测定。用一次性滴管将榨汁后的猕猴桃汁液滴到折射仪镜面上，重复测量三次，取三次结果的平均值作为最终SSC的参考值。
1.2.2.2　酸度的测定
猕猴桃酸度的测定参照国标GB/T 10468-1989 《水果和蔬菜产品pH值的测定方法》，首先在实验条件（23±2）℃下，分别用pH基准缓冲液（pH为4.00和6.86）标定pHS-100型酸度计，然后用蒸馏水将电极洗干净，并用滤纸将电极上的液体吸干净，最后测定猕猴桃果汁的pH，重复测定三次，取三次结果的平均值作为最终酸度的参考值。
1.2.2.3　硬度的测定
采用质构仪测定猕猴桃硬度，质构仪的探头直径为2 mm，探头刺入猕猴桃样本的距离为：10 mm，仪器测试时的速度：1 mm/s，测试前后的速度均为：2 mm/s。测量前需对测试部位去皮，测量部位为猕猴桃样本赤道位置。
1.2.2.4　色差e值的测定
采用台式分光光度仪测量猕猴桃样本的色差值（L*，a*，b*），测量部位为猕猴桃样本赤道位置。将猕猴桃样本置于观察口，然后用色样架固定样本进行测定，在Color IQC软件中保存数据。颜色参数用色差e值表示，其中色差e值计算公式[27]如下：
$ e=\frac{{a}^{\text{*}}}{{L}^{\text{*}}}+\frac{{a}^{\text{*}}}{{b}^{\text{*}}} $
1.2.3　建模方法
1.2.3.1　光谱数据预处理
由于光谱曲线中存在漂移和散射，需要对采集到的原始光谱数据作预处理。本研究中采用标准正态变换（standard normal variation，SNV）、多元散射校正（multi-scatter calibration，MSC）、二阶导数（second derivative，SD）和一阶导数（first derivative，FD）四种预处理方法对原始光谱进行预处理[28−30]。
1.2.3.2　特征波长的提取
原始光谱数据量大，存在冗余信息，为降低数据维度，提高计算机运行效率，本研究使用CARS和SPA筛选特征波长。其中，CARS以“适者生存”法则为指导，从校正集中选取样本作PLSR处理，将PLS回归系数中的绝对值大的系数对应的特征波长作为新的校正集，经过多次循环，得到的最优波长组合的交叉验证均方根误差最小，从而达到消除无关信息并降低变量间共线性的目的[31]。利用CARS提取特征波长时，设置采样次数为50次，运用五折交叉验证方法计算偏最小二乘模型的交叉验证均方根误差（RMSECV），通常依据RMSECV的最小值来确定最优的特征波长子集。
利用SPA选择特征波长时，首先将波长投影到其他波长上，计算出每个波长点对应的投影值，以投影值为基准，筛选并保留最大投影值所在的波长，通过不断计算筛选出最优的波长组合，得到光谱信息中含有最少冗余信息的变量组，使得变量之间的共线性最小[32]。通常根据模型中相对较小的均方根误差（RMSE）值来确定较优的特征波长数。
1.2.3.3　模型的建立与评价
本文基于原始光谱和预处理后的光谱分别建立预测猕猴桃SSC、酸度、硬度和色差e值的PLSR和PCR模型，并基于CARS和SPA提取的特征波长构建预测猕猴桃品质指标的MLR和BP神经网络简化模型，模型算法原理详见参考文献[1]。
通过校正集相关系数（correlation coefficient of calibration，Rc）及其均方根误差（root mean square error of calibration，RMSEC）、预测集相关系数（correlation coefficient of prediction，Rp）及其均方根误差（root mean square error of prediction，RMSEP）、剩余预测偏差（residual predictive deviation，RPD）来评价预测模型性能。当Rc和Rp越接近1，RMSEC和RMSEP越接近0时，说明模型的性能越好，且当1.5≤RPD<2，模型可进行粗略预测，2≤RPD<2.5，模型可进行很好的预测，RPD≥2.5，表明模型的预测性能非常好[33]。
1.3　数据处理
利用Spectral SENS软件获取所有样本的高光谱图像并进行黑白校正，利用ENVI 5.4软件提取所有样本的原始光谱数据，后续的光谱预处理、提取特征波长以及构建预测模型均采用MATLAB R2016b软件进行处理与分析。
2　结果与分析
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2.1　猕猴桃品质指标统计结果
猕猴桃样本的划分使用光谱-理化值共生距离（sample set partitioning based on joint x-y distance，SPXY）算法。SPXY算法将品质指标参考值和光谱数据同时考虑在内，有效地覆盖多维向量空间，使得划分的样本集具有代表性[34]。样本划分结果如表1所示。
表1中将猕猴桃样本按照3:1的比例划分。其中180个猕猴桃样本用于测定SSC，校正集135个，预测集45个；160个样本用于测定酸度，校正集120个，预测集40个；140个样本用于测定硬度，校正集105个，预测集35个；120个样本用于测定色差e值，校正集90个，预测集30个。从表1可以明显看出校正集SSC的最大值为18.70%、最小值为7.10%，酸度的最大值为3.63、最小值为2.94，硬度的最大值为20.11 kg/cm2、最小值为0.26 kg/cm2，色差e值的最大值为0.82、最小值为0.51，即校正集中各品质指标参考值的范围均比预测集中参考值的范围宽，这样划分的样本集有助于后期构建相对较好的无损检测模型。
2.2　光谱特征分析及预处理
为减少采集过程中随机噪声和背景干扰的影响，提高光谱分辨的灵敏度，保证模型的准确性，需要对原始光谱作预处理。本文采用SNV、MSC、SD、FD四种方法对原始光谱信息进行预处理。图2给出了原始光谱和经四种预处理方法处理后的光谱曲线。
由图2可知，经过预处理的光谱更加平滑。从图2（b）和图2（c）中可以看出，经过SNV和MSC预处理后，光谱的散射在一定程度上得到消除，背景干扰得到有效消除，基线漂移现象也得到改善。图2（d）和图2（e）为经过SD和FD预处理后的光谱图，光谱中的背景和基线漂移干扰得到有效消除，强化了光谱带特征，光谱峰的信息更加突出，出现了几处明显的吸收峰。在450 nm左右出现的光谱变化，可能与类胡萝卜素成分有关，680~700 nm区间的吸收特征，则与叶绿素a与叶绿素b的吸收相关[35]，800~900 nm区域则表现出水分与糖类物质的混合吸收峰特征，尤其在近红外970 nm附近，观察到显著的吸收波动，该波段为水分的特征吸收峰[36]。
2.3　基于全波长的预测模型建立
运用不同的光谱预处理方法对原始光谱进行预处理，基于预处理后的全光谱数据和各品质指标参考值建立预测猕猴桃主要品质指标的PLSR模型和PCR模型，以确定较优的光谱预处理方法和较优的全波长模型，建模结果如表2和表3所示。
对于预测SSC指标，相比较原始光谱构建的PLSR全波长模型，经SD预处理后建立的SD-PLSR模型的Rp和PRD减小，而RMSEP增大，模型的性能并未得到提高，而经过SNV、MSC和FD预处理后模型的性能均有所提高，其中SNV-PLSR模型性能更优，具有最高的RPD值（3.1580）。在PCR模型中，经过FD预处理后的模型出现了过拟合现象（Rp大于Rc），对于SNV、MSC和SD预处理方法，发现经过SNV预处理后建立的SNV-PCR模型具有相对较高的RPD值（2.5380），模型的性能相对较优。进一步对比SNV-PLSR和SNV-PCR预测模型，预测SSC的最优的全波长模型为SNV-PLSR。
对于预测酸度指标，相比较原始光谱构建的PLSR模型，经过SNV、MSC和SD预处理后建立的PLSR模型的Rp和RPD增大，RMSEP减小，模型的性能均有所提高，其中SNV-PLSR模型性能更优，具有相对更大的RPD值（1.6573）；在PCR模型中，经过SD和FD预处理后建立的PCR模型的RPD值减小，模型预测性能下降；而经过SNV和MSC预处理后建立的PCR模型的RPD增大，模型预测性能提升，相对于MSC-PCR模型，SNV-PCR模型的RPD相对较高，因此SNV-PCR模型预测性能更佳，其RPD为1.6624。进一步对比SNV-PLSR和SNV-PCR预测模型，预测酸度最优的全波长模型为SNV-PCR。
对于预测硬度指标，相对于原始光谱构建的PLSR模型，经过SD预处理后建立的PLSR模型的RPD减小，模型的预测性能并未提高，而经过SNV、MSC和FD预处理后模型的预测性能均有所提高，其中经过SNV预处理后的SNV-PLSR模型的预测性能最佳，其RPD为1.7055；在PCR模型中，经过SD预处理后PCR模型的预测性能下降，对于其他3种预处理方法，发现经SNV预处理后建立的PCR模型中具有最大的PRD值（1.7407）。进一步对比SNV-PLSR和SNV-PCR预测模型，预测硬度的最优的全波长模型为SNV-PCR。
对于预测色差e值指标，相对于原始光谱构建的PLSR模型，经过SD预处理后建立的PLSR模型的RPD减小，模型的预测性能并未提高，而经过SNV、MSC和FD预处理后模型的预测性能均有所提高，其中经过SNV预处理后的SNV-PLSR模型的预测精度最好，其RPD为1.7583；在PCR模型中，经过SD预处理后PCR模型的RPD值减小，预测性能下降，对于基于SNV、MSC和FD预处理后建立的PCR模型，发现经过SNV预处理后构建的SNV-PCR模型具有更大RPD值（1.8012），模型的性能更好。进一步对比SNV-PLSR和SNV-PCR预测模型，预测色差e值的最优的全波长模型为SNV-PCR。
由以上分析可知，对于预测SSC，PLSR模型比PCR模型的预测效果更优，对于预测酸度、硬度和色差e值，PCR模型比PLSR模型的预测效果表现更好；通过比较基于原始光谱和预处理后光谱数据建立PLSR和PCR预测模型，得出经过SNV预处理后的光谱数据建立的全波长模型的性能更佳。因此，本文对SNV预处理后的光谱数据进一步提取特征波长。
2.4　光谱数据的降维
由于高光谱图像维数大，使得所含的信息量大，且存在多重共线性问题。因此，筛选出数据中的特征波长，建立特征波长与各品质指标含量的关系尤为重要。本文运用CARS和SPA对经过SNV预处理后的原始光谱数据进行降维。
2.4.1　CARS提取特征波长
图3为采用CARS筛选表征四种品质指标特征波长过程。由图3可知，随着采样次数的增加，RMSECV的趋势是先减小后增大。对于预测SSC（图3a），第21次采样时RMSECV值最小，此时提取的最优变量包含了35个特征波长（仅占全光谱的13.7%）；同理，对于预测酸度、硬度和色差e值，采样次数分别为29、24和19时，RMSECV值最小，分别选取了16、26和43个特征波长，分别占全波长的6.25%、10.2%和16.8%。
2.4.2　SPA提取特征波长
图4给出了RMSE值随模型中变量个数的变化趋势。从图4（a）、图4（b）和图4（d）中可以得出，对于预测SSC、酸度和色差e值时，RMSE值随着模型中变量个数的增多呈现出减小的趋势，但是当模型中的有效波长数增到一定数值时，RMSE值减小的趋势相对较小，即当变量个数分别大于39、29、19以后，对应的RMSE值减小的趋势相对较小。因此，本研究中分别提取了39、29、19个波长作为预测SSC、酸度和色差e值的特征波长，分别占全光谱的15.23%、11.33%、7.42%。从图4（c）中可以得出，对预测硬度，SPA仅筛选出4个特征波长，建模结果相对较差。
2.5　基于特征波长的品质指标预测
将CARS和SPA筛选的特征波长作为自变量，对应的各品质指标参考值作为因变量，分别输入到MLR和BP神经网络模型中，模型对猕猴桃各品质指标的预测结果见表4。
从表4可以看出，对于预测SSC，基于CARS筛选的特征波长建立的模型的预测性能优于基于SPA筛选的特征波长建立的模型；相比于BP神经网络模型，CARS-MLR模型的预测性能要好于CARS-BP和SPA-BP模型；进一步对比表2中预测SSC的全波长SNV-PLSR模型，CARS-MLR模型性能更优，其中CARS-MLR模型的Rp、RMSEP及RPD分别为0.9552、0.9088和3.3800，且RPD大于2.5，表明模型预测效果非常好。由以上分析，预测SSC的最佳模型为SNV-CARS-MLR。
同理，对于预测酸度、硬度和色差e值，最优的预测模型均为SNV-CARS-MLR，其Rp分别为：0.8870、0.8799、0.9079，RMSEP分别为：0.0715、2.3764、0.0285，RPD为：2.0349、2.1350、2.2838，且RPD均大于2，表明SNV-CARS-MLR模型对酸度、硬度和色差e值均具有很好的预测性能。为验证猕猴桃各品质指标预测模型的精度及稳定性，图5给出了SNV-CARS-MLR模型对SSC、酸度、硬度和色差e值的预测结果。
从图5可以看出，除少数样本偏离回归线以外，对SSC、酸度、硬度和色差e值的预测值与实测值的线性关系相对较好，说明SNV-CARS-MLR模型能够很好地预测猕猴桃的SSC、酸度、硬度和色差e值。
2.6　猕猴桃主要品质指标的可视化
本研究进一步将猕猴桃高光谱图像上每个像素点的光谱信息输入SNV-CARS-MLR模型中来预测猕猴桃果实每个像素点的各品质指标含量，并利用伪彩色技术实现了不同品质猕猴桃主要品质指标含量的可视化。不同品质猕猴桃各品质指标含量分布如图6所示。
从图6（a）可以看出，不同品质的猕猴桃外观颜色差异很小。图6（b）~图6（e）中颜色不同表示猕猴桃各品质指标含量的变化，从可视化图像中可以看出，不同品质猕猴桃样本的SSC含量和硬度值差异较明显，然而不同品质猕猴桃样本的酸度含量和色差e值差异相对不明显。
3　结论
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本文以贵长猕猴桃为研究对象，采用高光谱成像技术和化学计量学方法，结合不同的光谱预处理和特征变量提取方法，探究了多种预测猕猴桃主要品质指标（SSC、酸度、硬度和色差e值）的检测模型。通过对比基于四种预处理方法（SNV、MSC、SD、FD）构建的PLSR和PCR模型的预测效果，发现基于SNV预处理后所建立的模型预测性能相对较好，并得到了猕猴桃主要品质指标的最优全波长模型。采用CARS和SPA两种方法对SNV预处理后的数据进行降维，明显提升了预测模型的检测性能，通过对比基于特征波长构建的MLR和BP神经网络模型的预测效果，得出基于CARS筛选的特征波长所建立的MLR模型预测性能相对更优。进一步对比分析全波长模型和特征波长模型的预测性能，得出预测猕猴桃的SSC、酸度、硬度和色差e值的最优预测模型均为SNV-CARS-MLR，其中Rp分别为0.9552、0.8870、0.8799和0.9079，RMSEP分别为0.9088、0.0715、2.3764和0.0285，RPD分别为3.3800、2.0349、2.1350和2.2838，且RPD均大于2，表明模型的预测性能很好，各品质指标的预测性都能满足预测精度要求。从猕猴桃各品质指标含量分布可视化图中可以直观地显示出不同品质猕猴桃的差异化。研究表明高光谱成像技术结合化学计量学方法可以实现对猕猴桃主要品质指标的快速无损检测，为猕猴桃品质在线检测装备的开发奠定了一定的理论基础。

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2026年第47卷第6期
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doi: 10.13386/j.issn1002-0306.2025020053
  • 接收时间：2025-02-12
  • 首发时间：2026-10-24
  • 出版时间：2026-03-15
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  • 收稿日期：2025-02-12
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    贵阳学院食品科学与工程学院，贵州贵阳 550005

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孟庆龙（1989−），男，博士，教授，研究方向：农产品品质无损检测，E-mail：。
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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