[1]彭雅玲,邱雪,张海红,等.近红外光谱技术检测灵武长枣果肉硬度和贮藏时间[J].江苏农业学报,2019,(01):182-188.[doi:doi:10.3969/j.issn.1000-4440.2019.01.026]
 PENG Ya-ling,QIU Xue,ZHANG Hai-hong,et al.Near-infrared spectroscopy for the determination of hardness and storage time of jujube fruit[J].,2019,(01):182-188.[doi:doi:10.3969/j.issn.1000-4440.2019.01.026]
点击复制

近红外光谱技术检测灵武长枣果肉硬度和贮藏时间()
分享到:

江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2019年01期
页码:
182-188
栏目:
加工贮藏·质量安全
出版日期:
2019-02-26

文章信息/Info

Title:
Near-infrared spectroscopy for the determination of hardness and storage time of jujube fruit
作者:
彭雅玲邱雪张海红吴宝婷朱韵昇
(宁夏大学农学院,宁夏银川750021)
Author(s):
PENG Ya-lingQIU XueZHANG Hai-hongWU Bao-ting ZHU Yun-sheng
(College of Agronomy, Ningxia University, Yinchuan 750021, China)
关键词:
近红外光谱果肉硬度贮藏时间
Keywords:
near-infrared spectroscopyflesh firmnessstorage time
分类号:
S665.1
DOI:
doi:10.3969/j.issn.1000-4440.2019.01.026
文献标志码:
A
摘要:
利用近红外光谱(400~1 000 nm)系统采集140个灵武长枣样本的光谱信息,采用不同方法预处理原始光谱数据,优选出最佳预处理方法。分别建立竞争性自适应加权算法(CARS)和连续投影算法(SPA)提取特征变量的果肉硬度偏最小二乘回归(PLSR)预测模型,并利用原始光谱建立灵武长枣贮藏时间的偏最小二乘判别(PLS-DA)模型。结果表明,去趋势法(Detrend)为最优预处理方法;建立的Detrend-CARS-PLSR模型效果较好,果肉平均硬度校正集和预测集模型相关系数均为0.868;果肉最大硬度校正集和预测集模型相关系数分别为0.914、0.849。建立的贮藏时间PLS-DA判别模型的校正集判别准确率为98%,预测集判别准确率为99%。说明,采用近红外光谱技术对灵武长枣贮藏过程中长枣果肉硬度和贮藏时间的快速预测具有可行性。
Abstract:
Spectral information of 140 Lingwu long jujube samples was collected by using near-infrared spectroscopy (400-1 000 nm). Different methods were applied to preprocess the original spectrum, and the optimal pretreatment method was selected. The competitive adaptive reweighed sampling (CARS) and successive projections algorithm (SPA) were used to select characteristic wavelengths, and the partial least squares regression (PLSR) model was established based on characteristic wavelengths for predicting flesh firmness of Lingwu long jujube. The partial least squares discriminate analysis (PLS-DA) models of long jujube storage time were established based on full spectrum. The results indicated that the Detrend method was the optimal pretreatment method, the Detrend-CARS-PLSR model was the best, and correlation coefficients of average flesh firmness for calibration set and prediction set were 0.868 and 0.868, and correlation coefficients of maximum flesh firmness for calibration set and prediction set were 0.914 and 0.849, respectively. The PLS-DA discriminant model of storage time was established and the discrimination accuracy of calibration set and prediction set were 98% and 99%. In conclusion, it is feasible to predict flesh firmness and storage time of Lingwu long jujube based on near-infrared spectroscopy technique.

参考文献/References:

[1]吴龙国,王松磊,康宁波,等.基于高光谱成像技术的灵武长枣缺陷识别[J].农业工程学报,2015,31(20):281-286.
[2]姚佳,胡小松,廖小军,等.高静压对果蔬制品质构影响的研究进展[J].农业机械学报,2013,44(9):118-124,117.
[3]马庆华,王贵禧,梁丽松,等.冬枣的穿刺质地及其影响因素[J].林业科学研究,2011,24(5):596-601.
[4]梁静,孙锐,孙蕾,等.不同品种果桑穿刺试验质构特性分析[J].山东林业科技,2017,47(5):26-30.
[5]杜雪燕,王迅,柴沙驼,等.基于近红外光谱的天然牧草CNCPS组分分析与预测[J].江苏农业学报,2015,31(5):1115-1123.
[6]HUANG J, PENG S. Comparison and standardization among Chlorophyll meters in their readings on rice leaves[J].Plant Production Science,2004,7(1):97-100.
[7]石鲁珍,陈杰,张树艳,等.基于蒙特卡洛法红枣光谱水分模型研究[J].江苏农业科学,2018,46(14):205-208.
[8]陈辰,鲁晓翔,张鹏,等.基于可见-近红外漫反射光谱技术的葡萄贮藏期间可溶性固形物定量预测[J].食品科学,2015,36(20):109-114.
[9]CARAMES E T S, ALAMAR P D, POPPI R J,et al. Quality control of cashew apple and guava nectar by near infrared spectroscopy[J].Journal of Food Composition & Analysis,2017, 56:41-46.
[10]PAZ P, SANCHEZ M T, PEREZMARIN D, et al. Evaluating NIR instruments for quantitative and qualitative assessment of intact apple quality[J].Journal of the Science of Food & Agriculture, 2009,89(5):781-790.
[11]闫润,王新忠,邱白晶,等.基于特征光谱的草莓品种快速鉴别[J].农业机械学报,2013,44(9):182-186.
[12]刘燕德,吴明明,孙旭东,等.黄桃表面缺陷和可溶性固形物光谱同时在线检测[J].农业工程学报,2016,32(6):289-295.
[13]NICOLAI B M, THERON K I, LAMMERTYN J. Kernel PLS regression on wavelet transformed NIR spectra for prediction of sugar content of apple[J].Chemometrics & Intelligent Laboratory Systems,2007,85(2):243-252.
[14]MA T, LI X, INAGAKI T, et al. Noncontact evaluation of soluble solids content in apples by Near-infrared hyperspectral imaging[J].Journal of Food Engineering,2017,224:53-61.
[15]ELMASRY G, WANG N, ELSAYED A, et al. Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry[J].Journal of Food Engineering,2007, 81(1):98-107.
[16]马庆华,王贵禧,梁丽松.质构仪穿刺试验检测冬枣质地品质方法的建立[J].中国农业科学,2011,44(6):1210-1217.
[17]陈亚斌.基于高光谱和荧光高光谱技术的灵武长枣内部成分无损检测研究[D].银川:宁夏大学,2017.
[18]SU W H, BAKALIS S, SUN D W. Fourier transform mid-infrared-attenuated total reflectance (FTMIR-ATR) microspectroscopy for determining textural property of microwave baked tuber[J]. Journal of Food Engineering,2018,218:1-13.
[19]张初.基于光谱与光谱成像技术的油菜病害检测机理与方法研究[D].杭州:浙江大学,2016.
[20]左婷.基于高光谱图像技术的夏橙质构特性检测方法研究[D].武汉:华中农业大学,2015.
[21]欧阳爱国,谢小强,刘燕德,等.苹果可溶性固形物近红外在线光谱变量优选[J].农业机械学报,2014,45(4):220-225.
[22]WANG Q, XUE W Q, MA H X, et al.Quantitative analysis of seed purity for maize usingnear infrared spectroscopy[J].Transactions of the Chinese Society of Agricultural Engineering,2012:259-264.
[23]黄敏,朱晓,朱启兵,等.基于高光谱图像的玉米种子特征提取与识别[J].光子学报,2012,41(7):868-873.
[24]彭彦昆,赵芳,李龙,等.利用近红外光谱与PCA-SVM识别热损伤番茄种子[J].农业工程学报,2018,34(5):159-165.
[25]黄志明,林素英,傅明连,等.枇杷果实发育过程中果肉质地与胞壁酶活性的变化[J]. 热带作物学报,2012,33(1):24-29.
[26]商亮,谷静思,郭文川.基于介电特性及ANN的油桃糖度无损检测方法[J].农业工程学报,2013,29(17):257-264.

相似文献/References:

[1]张平平,张瑜,唐果,等.近红外光谱技术检测小麦谷蛋白大聚体含量[J].江苏农业学报,2017,(06):1207.[doi:doi:10.3969/j.issn.1000-4440.2017.06.002]
 ZHANG Ping-ping,ZHANG Yu,TANG Guo,et al.Measurement of SDS-unextractable polymeric protein content in wheat flour based on near-infrared spectroscopy (NIRS) technique[J].,2017,(01):1207.[doi:doi:10.3969/j.issn.1000-4440.2017.06.002]
[2]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,(03):692.[doi:doi:10.3969/j.issn.1000-4440.2018.03.031]
 QIU Xun-chao.Quantitative detection of fat in peeled Korean pine seeds using near infrared spectroscopy[J].,2018,(01):692.[doi:doi:10.3969/j.issn.1000-4440.2018.03.031]
[3]张津源,张德贤,张苗.基于连续投影算法的小麦蛋白质含量近红外光谱预测分析[J].江苏农业学报,2019,(04):960.[doi:doi:10.3969/j.issn.1000-4440.2019.04.030]
 ZHANG Jin yuan,ZHANG De xian,ZHANG Miao.Prediction and analysis of wheat protein content by nearinfrared spectroscopy based on successive projections algorithm[J].,2019,(01):960.[doi:doi:10.3969/j.issn.1000-4440.2019.04.030]
[4]曲歌,陈争光,张庆华.基于无信息变量消除法的水稻种子发芽率测定[J].江苏农业学报,2019,(05):1015.[doi:doi:10.3969/j.issn.1000-4440.2019.05.002]
 QU Ge,CHEN Zheng-guang,ZHANG Qing-hua.Study on germination rate of rice seed based on uninformation variable elimination method[J].,2019,(01):1015.[doi:doi:10.3969/j.issn.1000-4440.2019.05.002]
[5]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.[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,(01):507.[doi:doi:10.3969/j.issn.1000-4440.2020.02.035]
[6]方瑶,谢天铧,郭渭,等.基于近红外光谱的金鲳鱼新鲜度快速检测技术[J].江苏农业学报,2021,(01):213.[doi:doi:10.3969/j.issn.1000-4440.2021.01.028]
 FANG Yao,XIE Tian-hua,GUO Wei,et al.Rapid detection technology of pomfret freshness based on near infrared spectroscopy[J].,2021,(01):213.[doi:doi:10.3969/j.issn.1000-4440.2021.01.028]
[7]谢文涌,柴琴琴,林旎,等.基于Stacking集成学习的马兜铃酸及其类似物鉴别[J].江苏农业学报,2021,(02):503.[doi:doi:10.3969/j.issn.1000-4440.2021.02.028]
 XIE Wen-yong,CHAI Qin-qin,LIN Ni,et al.Discrimination of aristolochic acid and its analogues based on stacking ensemble learning[J].,2021,(01):503.[doi:doi:10.3969/j.issn.1000-4440.2021.02.028]
[8]沈广辉,曹瑶瑶,刘馨,等.近红外高光谱成像结合特征波长筛选识别小麦赤霉病瘪粒[J].江苏农业学报,2021,(02):509.[doi:doi:10.3969/j.issn.1000-4440.2021.02.029]
 SHEN Guang-hui,CAO Yao-yao,LIU Xin,et al.Identification of Fusarium damaged kernels using near infrared hyperspectral imaging and characteristic bands selection[J].,2021,(01):509.[doi:doi:10.3969/j.issn.1000-4440.2021.02.029]
[9]仇逊超,张春越,张怡卓,等.流形学习在红松籽仁蛋白质含量近红外检测中的应用[J].江苏农业学报,2023,(01):246.[doi:doi:10.3969/j.issn.1000-4440.2023.01.028]
 QIU Xun-chao,ZHANG Chun-yue,ZHANG Yi-zhuo,et al.Application of manifold learning in quantitative detection of protein in Korean pine seed kernels using near-infrared quantitative detection[J].,2023,(01):246.[doi:doi:10.3969/j.issn.1000-4440.2023.01.028]

备注/Memo

备注/Memo:
收稿日期:2018-05-02 基金项目:国家自然科学基金地区科学基金项目(31860422);宁夏高校科学研究项目(NGY2016019) 作者简介:彭雅玲(1993-),女,甘肃白银人,硕士研究生,研究方向为农产品无损检测。(E-mail)m18341654254_2@163.com 通讯作者:张海红,(E-mail)nxdwjyxx@126.com
更新日期/Last Update: 2019-02-27