基于自制便携式近红外光谱仪的枇杷果实可溶性固形物无损检测及年度重复验证

doi:10.3785/j.issn.1008-9209.2019.10.231

浙江大学学报（农业与生命科学版）

2020, Vol. 46

Issue (1): 119-125 DOI: 10.3785/j.issn.1008-9209.2019.10.231

研究论文

基于自制便携式近红外光谱仪的枇杷果实可溶性固形物无损检测及年度重复验证

魏雨晴¹(

),王毓宁²,李绍佳¹,孙崇德¹,吴迪¹(

)

^1.浙江大学农业与生物技术学院果树科学研究所，杭州 310058
^2.江苏太湖地区农业科学研究所，江苏苏州 215106

Non-destructive detecting and annual duplicate verification of loquat fruit’s total soluble solids based on self-developed portable near-infrared spectrometer

Yuqing WEI¹(

),Yuning WANG²,Shaojia LI¹,Chongde SUN¹,Di WU¹(

)

^1.Institute of Fruit Science, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China
^2.Jiangsu Taihu Area Institute of Agricultural Sciences, Suzhou 215106, Jiangsu, China

全文: PDF(1255 KB) HTML

摘要：

采用自主研发的便携式枇杷果实品质无损检测仪，分别采集2018和2019年的枇杷光谱数据，然后采用化学计量学分析方法构建枇杷果实中的重要品质指标——可溶性固形物（total soluble solids, TSS）的检测模型，并开展独立样本年度验证。结果表明：建立的竞争性自适应重加权算法-最小二乘支持向量机（competitive adaptive reweighted sampling-least squares support vector machines, CARS-LS-SVM）模型的预测集相关系数和预测集均方根误差分别为0.818和1.453，说明枇杷果实品质无损检测仪可以实现‘白玉’品种枇杷可溶性固形物的快速无损检测，而且经过分级模型筛选后，一类好果率和二类好果率分别从50.00%和73.33%提升到了86.67%和85.71%。总之，本研究建立了更加实用的枇杷果实分级模型，实现了江苏省苏州地区‘白玉’品种采后枇杷果实的快速无损检测，为进一步提升苏州地区枇杷果实销售的经济价值和市场规范提供了强有力的技术支持。

关键词： 枇杷; 可溶性固形物; 无损检测; 近红外光谱技术; 年度重复验证

Abstract:

A self-developed portable non-destructive quality detector was used to collect spectral data of ‘Baiyu’ loquat fruit harvested in 2018 and 2019, respectively, and then chemometric analysis methods were used to build detection model for total soluble solids (TSS) of loquat fruit. Finally, annual verification of independent samples was conducted. The results showed that correlation coefficient of prediction (R_p) and root mean square error of prediction (RMSEP) of the established competitive adaptive reweighted sampling-least squares support vector machines (CARS-LS-SVM) model were 0.818 and 1.453, respectively, which indicated that the self-developed non-destructive detector could achieve rapid non-destructive measurement on TSS of ‘Baiyu’ loquat. Moreover, the grading and screening model improved the first-class fruit rate and the second-class fruit rate from 50.00% to 86.67%, 73.33% to 85.71%, respectively. In conclusion, this study establishes a more practical loquat fruit grading model, which achieves rapid non-destructive measurement of postharvest ‘Baiyu’ loquat fruit in Suzhou area of Jiangsu Province and provides technological support for further improving economic value of loquat fruit and market regulation in Suzhou.

Key words: loquat total soluble solids non-destructive detecting near-infrared spectroscopy technology annual duplicate verification

收稿日期: 2019-10-23 出版日期: 2020-02-25

CLC:

O 657.33

基金资助: “十三五”国家重点研发计划(2017YFD0401302);江苏省农业科技自主创新资金项目[CX（17）3029];浙江省科协育才工程(2018YCGC006)

通讯作者: 吴迪 E-mail: 21816042@zju.edu.cn;di_wu@zju.edu.cn

作者简介: 魏雨晴（https://orcid.org/0000-0002-2175-3787），E-mail：21816042@zju.edu.cn

	服务
	把本文推荐给朋友
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	魏雨晴
	王毓宁
	李绍佳
	孙崇德
	吴迪

引用本文:

魏雨晴,王毓宁,李绍佳,孙崇德,吴迪. 基于自制便携式近红外光谱仪的枇杷果实可溶性固形物无损检测及年度重复验证[J]. 浙江大学学报（农业与生命科学版）, 2020, 46(1): 119-125.

Yuqing WEI,Yuning WANG,Shaojia LI,Chongde SUN,Di WU. Non-destructive detecting and annual duplicate verification of loquat fruit’s total soluble solids based on self-developed portable near-infrared spectrometer. Journal of Zhejiang University (Agriculture and Life Sciences), 2020, 46(1): 119-125.

链接本文:

http://www.zjujournals.com/agr/CN/10.3785/j.issn.1008-9209.2019.10.231 或 http://www.zjujournals.com/agr/CN/Y2020/V46/I1/119

图1 枇杷果实品质无损检测仪结构示意（A）和实物图（B）

图2 枇杷果实品质无损检测仪光路图

图3 2018和2019年度枇杷果实近红外光谱平均响应信号

表1 枇杷果实品质分级模型的回归结果

表2 枇杷果实品质分级模型的预测集分类结果

表3 提高分选标准后的枇杷果实品质分级模型的预测集分类结果

1	康孟利,凌建刚,姚凌巧.枇杷果实保鲜技术的应用及研究.农产品加工,2014(6):36-37. KANG M L, LING J G, YAO L Q. Application and research of loquat fruit preservation technology. Farm Products Processing, 2014(6):36-37. (in Chinese)
2	应义斌,刘燕德.水果内部品质光特性无损检测研究及应用.浙江大学学报(农业与生命科学版),2003,29(2):125-129. YING Y B, LIU Y D. Study and application of optical properties for nondestructive interior quality inspection of fruit. Journal of Zhejiang University (Agriculture and Life Sciences), 2003,29(2):125-129. (in Chinese with English abstract)
3	杨忠,黄安民,江泽慧.近红外光谱结合回归分析预测法判别木材的生物腐朽.林业科学,2012,48(10):120-124. YANG Z, HUANG A M, JIANG Z H. Discrimination of wood biological decay by NIR coupled with regression analysis prediction method. Scientia Silvae Sinicae, 2012,48(10):120-124. (in Chinese with English abstract)
4	吴迪,吴洪喜,蔡景波,等.基于无信息变量消除法和连续投影算法的可见-近红外光谱技术白虾种分类方法研究.红外与毫米波学报,2009,28(6):423-427. DOI:10.3724/sp.j.1010.2009.00423 WU D, WU H X, CAI J B, et al. Classifying the species of exopalaemon by using visible and near infrared spectra with uninformative variable elimination and successive projections algorithm. Journal of Infrared and Millimeter Waves, 2009,28(6):423-427. (in Chinese with English abstract) doi: 10.3724/sp.j.1010.2009.00423
5	BALABIN R M, SAFIEVA R Z, LOMAKINA E I. Gasoline classification using near infrared (NIR) spectroscopy data: comparison of multivariate techniques. Analytica Chimica Acta, 2010,671(1/2):27-35. DOI:10.1016/j.aca.2010.05.013 doi: 10.1016/j.aca.2010.05.013
6	BOBELYN E, SERBAN A S, NICU M, et al. Postharvest quality of apple predicted by NIR-spectroscopy: study of the effect of biological variability on spectra and model performance. Postharvest Biology & Technology, 2010,55(3):133-143. DOI:10.1016/j.postharvbio.2009.09.006 doi: 10.1016/j.postharvbio.2009.09.006
7	王茜,吴习宇,庞兰,等.枇杷内部品质近红外光谱无损检测.食品与机械,2016,32(5):67-70. DOI:10.13652/j.issn.1003-5788.2016.05.015 WANG Q, WU X Y, PANG L, et al. Nondestructive detection of internal quality of loquat by near infrared spectroscopy. Food & Machinery, 2016,32(5):67-70. (in Chinese with English abstract) doi: 10.13652/j.issn.1003-5788.2016.05.015
8	GIOVANELLI G, SINELLI N, BEGHI R, et al. NIR spectroscopy for the optimization of postharvest apple management. Postharvest Biology and Technology, 2014,87:13-20. DOI:10.1016/j.postharvbio.2013.07.041 doi: 10.1016/j.postharvbio.2013.07.041
9	HUANG L, MENG L, ZHU N, et al. A primary study on forecasting the days before decay of peach fruit using near-infrared spectroscopy and electronic nose techniques. Postharvest Biology and Technology, 2017,133:104-112. DOI:10.1016/j.postharvbio.2017.07.014 doi: 10.1016/j.postharvbio.2017.07.014
10	孙通,应义斌,刘魁武,等.梨可溶性固形物含量的在线近红外光谱检测.光谱学与光谱分析,2008,28(11):2536-2539. DOI：10.3964/j.issn.1000-0593(2008)11-2536-04 SUN T, YING Y B, LIU K W, et al. Online detection of soluble solids content of pear by near infrared transmission spectrum. Spectroscopy and Spectral Analysis, 2008,28(11):2536-2539. (in Chinese with English abstract) doi: 10.3964/j.issn.1000-0593(2008)11-2536-04
11	王平,聂振朋,罗君琴,等.无损伤检测技术在柑橘果实中的应用.浙江柑橘,2013,30(4):7-10. WANG P, NIE Z P, LUO J Q, et al. Application of nondestructive detecting technology in citrus fruits. Zhejiang Citrus, 2013,30(4):7-10. (in Chinese)
12	ZHU N, HUANG W N, WU D, et al. Quantitative visualization of pectin distribution maps of peach fruits. Scientific Reports, 2017,7(1):9275. DOI:10.1038/s41598-017-09817-7 doi: 10.1038/s41598-017-09817-7
13	HUANG L X, ZHOU Y B, MENG L W, et al. Comparison of different CCD detectors and chemometrics for predicting total anthocyanin content and antioxidant activity of mulberry fruit using visible and near infrared hyperspectral imaging technique. Food Chemistry, 2017,224:1-10. DOI:10.1016/j.foodchem.2016.12.037 doi: 10.1016/j.foodchem.2016.12.037
14	HUANG L X, LIU H R, ZHANG B, et al. Application of electronic nose with multivariate analysis and sensor selection for botanical origin identification and quality determination of honey. Food & Bioprocess Technology, 2015,8(2):359-370. DOI:10.1007/s11947-014-1407-6 doi: 10.1007/s11947-014-1407-6
15	YU K Q, ZHAO Y R, LIU Z Y, et al. Application of visible and near-infrared hyperspectral imaging for detection of defective features in loquat. Food & Bioprocess Technology, 2014,7(11):3077-3087. DOI:10.1007/s11947-014-1357-z doi: 10.1007/s11947-014-1357-z
16	PAN L Q, LU R F, ZHU Q B, et al. Measurement of moisture, soluble solids, sucrose content and mechanical properties in sugar beet using portable visible and near-infrared spectroscopy. Postharvest Biology and Technology, 2015,102:42-50. DOI:10.1016/j.postharvbio.2015.02.005 doi: 10.1016/j.postharvbio.2015.02.005

[1]	刘妍,周新奇,俞晓峰,李永强,韩双来. 无损检测技术在果蔬品质检测中的应用研究进展[J]. 浙江大学学报（农业与生命科学版）, 2020, 46(1): 27-37.
[2]	王冉冉,刘鑫,尹孟,翟德昂,刘双喜,王金星. 面向苹果硬度检测仪的声振信号激励与采集系统设计[J]. 浙江大学学报（农业与生命科学版）, 2020, 46(1): 111-118.
[3]	吕晓菡,蒋锦琳,杨静,陈建瑛,岑海燕,傅鸿妃,周毅飞. 基于特征波长建模的近红外光谱技术检测辣椒素含量[J]. 浙江大学学报（农业与生命科学版）, 2019, 45(6): 760-766.
[4]	张萌,李光辉. 基于RELIEF算法和极限学习机的苹果轻微损伤高光谱检测方法[J]. 浙江大学学报（农业与生命科学版）, 2019, 45(1): 126-134.
[5]	陈方永，王引，倪海枝，张启，颜帮国. 浙江2个地产白沙枇杷种质资源亲缘关系鉴定[J]. 浙江大学学报(农业与生命科学版), 2016, 42(6): 739-746.
[6]	杨春平, 李钊君, 宁红, 陈华保, 龚国淑, 杨继芝, 张敏. HPLC检测枇杷中氯吡脲含量的前处理方法[J]. 浙江大学学报(农业与生命科学版), 2016, 42(3): 350-357.
[7]	曹乐平, 温芝元. 基于统计复杂性测度、多重分形谱等方法的柑橘品质分级[J]. 浙江大学学报(农业与生命科学版), 2015, 41(03): 309-319.
[8]	苏文浩，刘贵珊，何建国，王松磊，贺晓光，王伟，吴龙国. 高光谱图像技术结合图像处理方法检测马铃薯外部缺陷*[J]. 浙江大学学报(农业与生命科学版), 2014, 40(2): 188-196.
[9]	姚建松, 杨海清, 何勇. 基于可见-近红外光谱技术的油菜叶片叶绿素含量无损检测研究[J]. 浙江大学学报(农业与生命科学版), 2009, 35(4): 433-438.
[10]	孙虎, 张丽丽, 张敬泽, 胡东维. 枇杷花疫病病原菌鉴定(英文)[J]. 浙江大学学报(农业与生命科学版), 2009, 35(3): 237-242.
[11]	张京平, 刘孔绚, 王会. 富士苹果剖面CT值与pH值的关系研究[J]. 浙江大学学报(农业与生命科学版), 2009, 35(1): 89-92.
[12]	朱哲燕　鲍一丹. 大豆含水率电特性的试验研究[J]. 浙江大学学报(农业与生命科学版), 2005, 31(2): 220-224.
[13]	ANTIHUS Hernández Gómez　 ANNIA García Pereira　何勇. 水果蔬菜品质的无损检测方法的研究与应用[J]. 浙江大学学报(农业与生命科学版), 2004, 30(4): 369-374.
[14]	应义斌　刘燕德. 水果内部品质光特性无损检测研究及应用[J]. 浙江大学学报(农业与生命科学版), 2003, 29(2): 125-129.
[15]	胥芳　张立彬　计时鸣. 介电式水果品质分级机的原理及实现[J]. 浙江大学学报(农业与生命科学版), 2002, 28(3): 325-330.

Viewed

Full text

Abstract

Cited

Shared

Discussed