[1]孙晓明,陈小龙,余向阳,等.基于近红外光谱分析技术的水蜜桃产地溯源[J].江苏农业学报,2020,(02):507-512.[doi:doi:10.3969/j.issn.1000-4440.2020.02.035]
 SUN Xiao-ming,CHEN Xiao-long,YU Xiang-yang,et al.Traceability of honey peach origin using near infrared spectroscopy analysis techniques[J].,2020,(02):507-512.[doi:doi:10.3969/j.issn.1000-4440.2020.02.035]
点击复制

基于近红外光谱分析技术的水蜜桃产地溯源()
分享到:

江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2020年02期
页码:
507-512
栏目:
加工贮藏·质量安全
出版日期:
2020-04-30

文章信息/Info

Title:
Traceability of honey peach origin using near infrared spectroscopy analysis techniques
作者:
孙晓明陈小龙余向阳卞立平孙爱东
(江苏省农业科学院农产品质量安全与营养研究所/省部共建国家重点实验室培育基地——江苏省食品质量安全重点实验室,江苏南京210014)
Author(s):
SUN Xiao-mingCHEN Xiao-longYU Xiang-yangBIAN Li-pingSUN Ai-dong
(Institute of Food Safety and Nutrition, Jiangsu Academy of Agricultural Sciences/Jiangsu Key Laboratory for Food Quality and Safety-State Key Laboratory Cultivation Base of Ministry of Science and Technology, Nanjing 210014, China)
关键词:
水蜜桃产地溯源近红外光谱主成分分析-线性判别分析判别偏最小二乘支持向量机
Keywords:
honey peachgeographical origin traceabilitynear infrared spectroscopyprincipal component analysis-linear discriminant analysisdiscriminant partial least squaressupport vector machine
分类号:
TS207.7
DOI:
doi:10.3969/j.issn.1000-4440.2020.02.035
文献标志码:
A
摘要:
利用近红外光谱分析技术对来自3个省份的水蜜桃进行研究,比较主成分分析-线性判别分析(PCA-LDA)、 判别偏最小二乘法(DPLS)、 支持向量机(SVM)等方法对光谱数据识别的有效性差异。结果表明, SVM的准确率和召回率均高达94.47%,明显优于 PCA-LDA和DPLS,更适用于水蜜桃产地溯源。
Abstract:
In this study, honey peaches from three provinces were analyzed by near infrared spectroscopy analysis technique, and the effectiveness of principal component analysis-linear discriminant analysis (PCA-LDA), discriminant partial least squares (DPLS) and support vector machine (SVM) for spectral data recognition was compared. The results showed that the precision and recall rate of SVM were 94.47%. The SVM method was obviously better than PCA-LDA and DPLS, and it was more suitable for traceability of honey peach origin.

参考文献/References:

[1]管骁,古方青,杨永健. 近红外光谱技术在食品产地溯源中的应用进展[J]. 生物加工过程, 2014, 12(2):77-82.
[2]钱丽丽,于果,迟晓星,等. 农产品产地溯源技术研究进展[J]. 食品工业, 2018, 39(1):246-249.
[3]COZZOLINO D. An overview of the use of infrared spectroscopy and chemometrics in authenticity and traceability of cereals[J]. Food Research International, 2014, 60(6):262-265.
[4]LOHUMI S, LEE S, LEE H, et al. A review of vibrational spectroscopic techniques for the detection of food authenticity and adulteration[J]. Trends in Food Science & Technology, 2015, 46(1):85-98.
[5]陈璐,谷晓红,张丙春,等. 食品产地溯源技术研究进展[J].安徽农业科学, 2015, 43(36):109-111.
[6]曾楚锋,张丽芬,徐娟娣,等.农产品产地溯源技术研究进展[J].食品工业科技, 2013,34(6):367-371.
[7]张勇,王督,李雪,等.基于近红外光谱技术的农产品产地溯源研究进展[J].食品安全质量检测学报,2018,9(23):6161-6166.
[8]HU X, LIU S, LI X, et al. Geographical origin traceability of cabernet sauvignon wines based on infrared fingerprint technology combined with chemometrics[J]. Scientific Reports, 2019, 9:8256-8263.
[9]EISENSTECKEN D, STRZ B, ROBATSCHER P, et al. The potential of near infrared spectroscopy (NIRS) to trace apple origin:Study on different cultivars and orchard elevations[J]. Postharvest Biology and Technology, 2019, 147:123-131.
[10]MANFREDI M, ROBOTTI E, QUASSO F, et al. Fast classification of hazelnut cultivars through portable infrared spectroscopy and chemometrics[J]. Spectrochim Acta A Mol Biomol Spectrosc, 2018, 189:427-435.
[11]MOSCETTI R, HAFF R P, STELLA E, et al. Feasibility of NIR spectroscopy to detect olive fruit infested by Bactrocera oleae[J]. Postharvest Biology and Technology, 2015, 99:58-62.
[12]仇逊超. 红松仁脂肪的近红外光谱定量检测[J]. 江苏农业学报, 2018, 34(3):217-223.
[13]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.
[14]罗微,杜焱喆,章海亮. PCA和SPA的近红外光谱识别白菜种子品种研究[J]. 光谱学与光谱分析, 2016, 36(11):3536-3541.
[15]顾玉琦,刘瑞婷,寿国忠,等. 应用近红外光谱技术快速鉴别铁皮石斛的产地[J]. 江苏农业科学, 2016, 44(5):365-368.
[16]向伶俐,李梦华,李景明,等. 近、中红外光谱法融合判定葡萄酒产地[J]. 光谱学与光谱分析, 2014, 34(10):2662-2666.
[17]李勇,严煌倩,龙玲,等. 化学计量学模式识别方法结合近红外光谱用于大米产地溯源分析[J]. 江苏农业科学, 2017, 45(21):193-195.
[18]姜亦南,蔺明煊,何帅,等. 基于红外光谱法结合SIMCA模式识别不同产地三七[J]. 中医药学报, 2019, 47 (1):54-57.
[19] 李剑,李臻峰,宋飞虎,等. 基于近红外光谱的水蜜桃采摘期的鉴别方法[J]. 传感器与微系统, 2017, 36(10):48-50.
[20]王铭海,郭文川,商亮,等. 基于近红外漫反射光谱的多品种桃可溶性固形物的无损检测[J]. 西北农林科技大学学报(自然科学版), 2014, 42(2):142-148.
[21]JOLLIFFE I T. Principal component analysis[M]. New York:Springer, 2002.
[22]邵圣枝,陈元林,张永志,等. 稻米中同位素与多元素特征及其产地溯源PCA-LDA判别[J]. 核农学报, 2015, 29(1):119-127.
[23]陈庆,黄蕾,李雪梅. 基于主成分判别分析的高光谱遥感影像分类方法[J]. 地理空间信息, 2016, 14(1):76-78.
[24]贾文珅. 基于多源信息融合的龙井茶产地鉴别研究[D]. 长春:吉林大学, 2014.
[25]NELLO C, JOHN S. 支持向量机导论[M]. 北京:电子工业出版社, 2004:82-98.
[26]褚璇,王伟,赵昕,等. 近红外光谱和特征光谱的山茶油掺假鉴别方法研究[J]. 光谱学与光谱分析, 2017, 37(1):75-79.
[27]周志华. 机器学习[M]. 北京:清华大学出版社, 2016:30.
[28]褚小立. 近红外光谱分析技术实用手册[M]. 北京:机械工业出版社, 2016:115-117.

相似文献/References:

[1]千春录,朱芹,高姗,等.外源褪黑素处理对采后水蜜桃冷藏品质及冷害发生的影响[J].江苏农业学报,2020,(03):702.[doi:doi:10.3969/j.issn.1000-4440.2020.03.024]
 QIAN Chun-lu,ZHU Qin,GAO Shan,et al.Effects of exogenous melatonin treatment on cold storage quality and chilling injury of postharvest peach fruit[J].,2020,(02):702.[doi:doi:10.3969/j.issn.1000-4440.2020.03.024]

备注/Memo

备注/Memo:
收稿日期:2019-07-09基金项目:国家重点研发计划项目(2017YFC1601000);江苏省农业科技自主创新基金项目[CX(18)3054];国家现代农业产业技术体系桃体系项目(CARS-30-5-03)作者简介:孙晓明(1986-),江苏海安人,硕士,助理研究员,主要从事农业信息化、农产品质量安全研究。(E-mail)sunxiaoming2226@163.com通讯作者:孙爱东,(E-mail)idong.sun@qq.com
更新日期/Last Update: 2020-05-18