Detection of anthocyanin contents in ripening winegrape skins by near-infrared hyperspectral imaging technology

YANG Shuqin; PENG Kang; LIU Xu

doi:10.7671/j.issn.1001-411X.2018.05.016

Journal of South China Agricultural University > 2018 > 39(5): 110-117. > DOI: 10.7671/j.issn.1001-411X.2018.05.016

YANG Shuqin, PENG Kang, LIU Xu. Detection of anthocyanin contents in ripening winegrape skins by near-infrared hyperspectral imaging technology[J]. Journal of South China Agricultural University, 2018, 39(5): 110-117. DOI: 10.7671/j.issn.1001-411X.2018.05.016

Citation:

PDF (1012 KB)

Detection of anthocyanin contents in ripening winegrape skins by near-infrared hyperspectral imaging technology

YANG Shuqin^{1, 2,},
PENG Kang¹,
LIU Xu³

1.
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
2.
Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Yangling 712100, China
3.
College of Enology, Northwest A&F University, Yangling 712100, China

More Information

Received Date: January 04, 2018
Available Online: May 17, 2023

Graphical Abstract

Abstract

Abstract

Objective
To detect the anthocyanin contents of winegrape skins during ripening stages using hyperspectral imaging technology.

Method
The 900–1 700 nm near-infrared hyperspectral imaging technology and multiple regression methods were used to build prediction models for anthocyanin contents in skins of different winegrape varieties during ripening stage. Totally 75 groups of grape samples belonging to 6 varieties were collected at 4–5 phases of mature stage, and their hyperspectral images were scanned. The spectrum data were enhanced by different preprocessing methods. Dimensionality reduction was then performed by principal component analysis (PCA) and successive projections algorithm (SPA). The anthocyanin contents measured by chemical method were used as reference values, and the prediction models of anthocyanin contents were built using support vector regression (SVR) method.

Result
The SPA-SVR model had the best performance of prediction with the determination coefficient ( $R^2_{\rm p}$ ) being 0.869 1 and the root mean square error of prediction (RMSEp) being 0.135 9.

Conclusion
It is feasible to use the hyperspectral imaging technology to realize non-destructive and fast detection of the anthocyanin contents in winegrape skins of different varieties during ripening.
- hyperspectral imaging,
- winegrape,
- peel,
- anthocyanin,
- successive projections algorithm,
- support vector regression

FullText(HTML)

References (28)

References

[1]	PARPINELLO G P, VERSARI A, CHINNICI F, et al. Relationship among sensory descriptors, consumer preference and color parameters of Italian Novello red wines[J]. Food Res Int, 2009, 42(10): 1389-1395.
[2]	NÚÑEZ V, MONAGAS M, GOMEZ-CORDOVÉS M C, et al. Vitis vinifera, L. cv. Graciano grapes characterized by its anthocyanin profile[J]. Postharvest Biol Tec, 2004, 31(1): 69-79.
[3]	ALCALDE-EON C, ESCRIBANO-BAILÓN M T, SANTOS-BUELGA C, et al. Changes in the detailed pigment composition of red wine during maturity and ageing : A comprehensive study[J]. Anal Chim Acta, 2006, 563(1/2): 238-254.
[4]	段玮, 唐荣华, 田清青, 等. 欧洲越橘类保健食品中花色苷含量测定方法的研究[J]. 中国食品卫生杂志, 2011, 23(4): 306-310.
[5]	李颖畅, 孟宪军. 酶法提取蓝莓果中花色苷的研究[J]. 食品工业科技, 2008(4): 215-218.
[6]	王继萍, 柏广新, 李劲然, 等. 溶剂萃取法提取蓝莓中花色苷[J]. 分析化学, 2012, 40(12): 1952-1953.
[7]	NILE S H, KIM D H, KEUM Y S. Determination of anthocyanin content and antioxidant capacity of different grape varieties[J]. Ciênc Téc Vitiviníc, 2015, 30(2): 60-68.
[8]	刘旭, 吴迪, 梁曼, 等. 基于高光谱的酿酒葡萄果皮花色苷含量多元回归分析[J]. 农业机械学报, 2013, 44(12): 180-186.
[9]	LU R. Detection of bruises on apples using near-infrared hyperspectral imaging[J]. Trans Asae, 2003, 46(2): 523-530.
[10]	陈欣欣, 郭辰彤, 张初, 等. 高光谱成像技术的库尔勒梨早期损伤可视化检测研究[J]. 光谱学与光谱分析, 2017, 37(1): 150-155.
[11]	周竹, 李小昱, 陶海龙, 等. 基于高光谱成像技术的马铃薯外部缺陷检测[J]. 农业工程学报, 2012, 28(21): 221-228.
[12]	薛龙, 黎静, 刘木华. 基于高光谱图像技术的水果表面农药残留检测试验研究[J]. 光学学报, 2008, 28(12): 2277-2280.
[13]	张令标, 何建国, 刘贵珊, 等. 基于可见/近红外高光谱成像技术的番茄表面农药残留无损检测[J]. 食品与机械, 2014(1): 82-85.
[14]	赵曼彤, 李柏承, 周瑶, 等. 香梨表面低浓度农药残留高光谱检测研究[J]. 光学技术, 2016, 42(5): 408-412.
[15]	陶斐斐, 王伟, 李永玉, 等. 冷却猪肉表面菌落总数的快速无损检测方法研究[J]. 光谱学与光谱分析, 2010, 30(12): 3405-3409.
[16]	CHO B K, CHEN Y R, KIM M S. Multispectral detection of organic residues on poultry processing plant equipment based on hyperspectral reflectance imaging technique[J]. Comput Electron Agr, 2007, 57(2): 177-189.
[17]	思振华, 何建国, 刘贵珊, 等. 基于高光谱图像技术羊肉表面污染无损检测[J]. 食品与机械, 2013(5): 75-79.
[18]	单佳佳, 吴建虎, 陈菁菁, 等. 基于高光谱成像的苹果多品质参数同时检测[J]. 光谱学与光谱分析, 2010, 30(10): 2729-2733.
[19]	马本学, 肖文东, 祁想想, 等. 基于漫反射高光谱成像技术的哈密瓜糖度无损检测研究[J]. 光谱学与光谱分析, 2012, 32(11): 3093-3097.
[20]	张雷蕾, 李永玉, 彭彦昆, 等. 基于高光谱成像技术的猪肉新鲜度评价[J]. 农业工程学报, 2012, 28(7): 254-259.
[21]	QIAO J, WANG N, NGADI M O, et al. Prediction of drip-loss, pH, and color for pork using a hyperspectral imaging technique[J]. Meat Sci, 2007, 76(1): 1-8.
[22]	王婉娇, 王松磊, 贺晓光, 等. 冷鲜羊肉冷藏时间和水分含量的高光谱无损检测[J]. 食品科学, 2015, 36(16): 112-116.
[23]	GONZÁLEZ-CABALLERO V, SÁNCHEZ M T, FERNÁNDEZ-NOVALES J, et al. On-vine monitoring of grape ripening using near-infrared spectroscopy[J]. Food Anal Method, 2012, 5(6): 1377-1385.
[24]	刘旭, 吴迪, 梁曼, 等. 基于高光谱的酿酒葡萄果皮花色苷含量多元回归分析[J]. 农业机械学报, 2013, 44(12): 180-186.
[25]	芦永军, 曲艳玲, 宋敏. 近红外相关光谱的多元散射校正处理研究[J]. 光谱学与光谱分析, 2007, 27(5): 877-880.
[26]	高洪智, 卢启鹏, 丁海泉, 等. 基于连续投影算法的土壤总氮近红外特征波长的选取[J]. 光谱学与光谱分析, 2009, 29(11): 2951-2954.
[27]	刘思伽, 田有文, 张芳, 等. 采用二次连续投影法和BP人工神经网络的寒富苹果病害高光谱图像无损检测[J]. 食品科学, 2017, 38(8): 277-282.
[28]	张学工. 关于统计学习理论与支持向量机[J]. 自动化学报, 2000, 26(1): 32-42.

[1]	LI Zhibo, DONG Shiman, ZENG Changying, ZHAO Pingjuan, LI Shuxia, PENG Ming. Correlation between soluble sugar and tolerance to drought stress of cassava stem under low temperature storage[J]. Journal of South China Agricultural University, 2022, 43(4): 58-66. DOI: 10.7671/j.issn.1001-411X.202109030
[2]	WU Yuhuan, XIE Hui, XU Chunling, ZHANG Chao, JING Xingxing. The Tolerance Ability to Low Temperature of Radopholus similis Reduced Cultured Longterm on Carrot Callus and Recovered Rejuvenated on Anthurium andraeanum[J]. Journal of South China Agricultural University, 2013, 34(2): 162-166. DOI: 10.7671/j.issn.1001-411X.2013.02.008
[3]	QIU Zhen-guo, PENG Hai-feng, CHEN Xiong-hui, WAN Bang-hui. Effect of Genetic Backgrounds on Sterility Expression of Filial Generation Derived from a Thermo-Sensitive Genic Male Sterile Line of Non-Pollen Type XianS in Rice[J]. Journal of South China Agricultural University, 2012, 33(3): 282-287. DOI: 10.7671/j.issn.1001-411X.2012.03.003
[4]	LUO Huan liang 1,XU Wei li 2,LI Jian zhong 3,FENG Qing liang 4,CUI Tang bin 1,HUANG Zhuo lie 5. Study on the Acceptation of Acacia mangium to Low Temperature Stress[J]. Journal of South China Agricultural University, 2002, 23(2): 51-53. DOI: 10.7671/j.issn.1001-411X.2002.02.015
[5]	Lu Yanpeng,Wan Banghui,Chen Xionghui,Peng Haifeng,Liang Keqin. Preliminary Genetic Studies on Leaf Edge Color in Rice "Yids" with Short Day Low Temperature Induced Male Sterile[J]. Journal of South China Agricultural University, 1999, (3): 6-9.
[6]	Wan Banghui Chen Xionghui Lu Yanpeng Liang Keqing. STUDIES OF UTILITATION OF RICE GERMPLASM WITH SHORT PHOTOPERIOD LOW TEMPERATURE INDUCED MALE STERILE[J]. Journal of South China Agricultural University, 1998, (1): 35-39.
[7]	STUDY ON SENSITIVITY OF THE FERTILITY OF PHOTOPERIOD-TEMPERATURE SENSITIVE GENIC MALE STERILE LINES OF RICE TO PHOTO AND THERMO REPONSES[J]. Journal of South China Agricultural University, 1997, (4): 8-11.
[8]	Che Xionghui, Wan Banghui, Chen Weidong, Liang Keqin. TEMPERATURE-SENSITIVE STAGE OF PHOTOPEKIOD AND THERMOPERIOD SENSITIVE GENIC MALE-STERILE LINE IN RICE[J]. Journal of South China Agricultural University, 1996, (1): 96-101.
[9]	Wan Banghui, Deng Guofu. OBSERVATION ON INDIVIDUAL FERTILITY OF THE PHOTOPERIOD-TEMPERATURE SENSITIVE GENIC MALE STERILE LINES OF RICE[J]. Journal of South China Agricultural University, 1995, (1): 7-12.
[10]	Ouyang Yongwen Wu Weiguang. THE INFLUENCE OF HIGH TEMPERATURE ON THE GROWTH AND DEVELOPMENT OF MALE ACCESSORY GLANDS AND THE STERILITY IN SILKWORM (Bombyx man L. )[J]. Journal of South China Agricultural University, 1993, (2): 1-9.

Cited By

Get Citation

PDF

XML

Article views (1633) PDF downloads (2056)

Turn off MathJax

Article Contents

Abstract

References

Detection of anthocyanin contents in ripening winegrape skins by near-infrared hyperspectral imaging technology

Abstract

References

Related Articles

Catalog

Detection of anthocyanin contents in ripening winegrape skins by near-infrared hyperspectral imaging technology

Abstract

References

Related Articles

Catalog

Export File

Citation

Format

Content