基于NIRS与PLS-DA的红提产地鉴别研究

doi:10.19640/j.cnki.jtau.2020.01.005

Journal of Tianjin Agricultural University ›› 2020, Vol. 27 ›› Issue (1): 21-23.doi: 10.19640/j.cnki.jtau.2020.01.005

• Researches and Scientific Notes • Previous Articles Next Articles

Identification of red grape origin based on NIRS and PLS-DA

LÜ Chen-fei^1a, YANG Jing-hui^{1a,Corresponding Author}, LIU Yan-jun^1a, YANG Ren-jie^1b, ZHANG Chao², WU Nan^1b

1. Tianjin Agricultural University, a. College of Horticulture and Landscape, b. College of Engineering and Technology, Tianjin 300384, China;
2. Tianjin Tianshi College, Tianjin 301700, China

Received:2019-05-05 Published:2020-04-01

Abstract

Abstract: The red grape producing areas are widely distributed and the ecological types are complex. The fruit quality and market effects of red grapes in different producing areas are quite different. Based on near-infrared spectroscopy（NIRS）and partial least squares discrimination（PLS-DA）, the red grape fruits from different regions were identified and authenticated. American red grape, Peruvian red grape and domestic red grape were purchased from supermarkets, each containing 45 samples, totaling 135 samples. Near-infrared diffuse reflectance spectra of all samples were collected in the range of 4 000~12 000 cm^-1. Principal component（PCA）analysis of the spectral data showed that the PCA method could not fully realize the cluster identification of the three types of red grape producing areas. Finally, the PLS-DA model was established to discriminate the three red grape producing areas, and the recognition rate was 100%.

Key words: red grape, origin identification, near-infrared spectroscopy, partial least squares discriminant analysis

CLC Number:

S663.1

LÜ Chen-fei^1a, YANG Jing-hui^1a, LIU Yan-jun^1a, YANG Ren-jie^1b, ZHANG Chao², WU Nan^1b. Identification of red grape origin based on NIRS and PLS-DA[J]. Journal of Tianjin Agricultural University, 2020, 27(1): 21-23.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: http://xuebao.tjau.edu.cn/EN/10.19640/j.cnki.jtau.2020.01.005

http://xuebao.tjau.edu.cn/EN/Y2020/V27/I1/21

References

[1] 李海. 红提葡萄生产中存在的问题及对策[J]. 农业技术与装备,2017(9):46-47.
[2] 周国华,周成敏,吴健,等. 不同海拔区域对红提葡萄生长的影响[J]. 农业科技通讯,2017(3):91-93.
[3] Lee J D,Shannon J G,Choung M G.Selection for protein content in soybean from single F₂ seed by near infrared reflectance spectroscopy[J]. Euphytica,2010,172(1):117-123.
[4] Song H J,Kim Y D,Jeong M J,et al.Rapid selection of theanine-rich green tea(Camellia sinensis L.)trees and metabolites profiling by Fourier transform near-infrared(FT-IR)spectroscopy[J]. Plant Biotechnol Rep,2015,9(2):55-65.
[5] Joshi A,Raigond P,Singh B.Near-infrared spectroscopy:a non-destructive tool for processing quality evaluation of potatoes[J]. Ind J Plant Physiol,2017,22(2):258-262.
[6] Rodríguez-Zúñiga U F,Farinas C S,Carneiro R L,et al. Fast determination of the composition of pretreated sugarcane bagasse using near-Infrared spectroscopy[J]. Bio Energy Research,2014,7(4):1441-1453.
[7] Khan K M,Krishna H,Majumder S K,et al.Detection of urea adulteration in milk using near-infrared raman spectroscopy[J]. Food Anal Methods,2015,8(1):93-102.
[8] Pereira J M,Leme L M,Perdoncini M R F G,et al. Fast discrimination of milk contaminated with Salmonella sp. via near-infrared spectroscopy[J]. Food Anal Methods,2018,11(7):1878-1885.
[9] Jha S N,Garg R.Non-destructive prediction of quality of intact apple using near infrared spectroscopy[J]. Food Sci Technol,2010,47(2):207-213.
[10] 何勇,李晓丽,邵咏妮. 基于主成分分析和神经网络的近红外光谱苹果品种鉴别方法研究[J]. 光谱学与光谱分析,2006(5):850-853.
[11] 廖秋红,何绍兰,谢让金,等. 基于近红外光谱的纽荷尔脐橙产地识别研究[J]. 中国农业科学,2015,48(20):4111-4119.
[12] 胡玉珍,唐洁,熊苏慧,等. 不同产地山楂与野山楂果实UPLC指纹图谱的建立及模式识别[J]. 中成药,2018,40(4):982-987.
[13] 王睿,王强,王存,等. 基于金属元素组成的新疆石榴产地识别分析[J]. 食品科学,2015,36(6):202-205.
[14] 刘悦,李静怡,范刚,等. 不同产地中国沙棘的傅里叶变换红外光谱识别研究[J]. 光谱学与光谱分析,2016,36(4):948-954.
[15] 陈晓峰,张东峰,郝明明,等. 基于机器视觉的枸杞产地识别研究[J]. 农业科技与装备,2013(11):33-35.

Identification of red grape origin based on NIRS and PLS-DA

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 3

Recommended Articles

Metrics

Comments

[1]	XU Chen-shan, WU Nan, HAI Xia, WANG Jun, TIAN Jia, LIU Xiang-meng, BAO Zhen-bo, PENG Jin-xing. Feasibility of rapid detection of oxytetracycline in milk by mid-infrared spectroscopy [J]. Journal of Tianjin Agricultural University, 2018, 25(3): 70-73.
[2]	LIAO Cai-qi, SUN Chang-hong, YANG Xiao, JIN Hao, YANG Yan-rong, YANG Ren-jie, ZHANG Wei-yu. Discrimination of carmine pigment in tea water based on near infrared spectroscopy [J]. Journal of Tianjin Agricultural University, 2018, 25(1): 72-75.
[3]	JIAO Dong-sheng, SUN Xue-shan, ZHU Wen-bi, YANG Ren-jie, JIN Hao, ZHANG Wei-yu. Classification of Adulterated Honey Based on Two-dimensional Infrared Correlation Spectroscopy [J]. Journal of Tianjin Agricultural University, 2017, 24(2): 67-71.