Rapid and non-destructive identification of different brands and inferior oatsby near infrared spectroscopy combined with chemometrics methods

Authors

LI Shangke, College of Food Science and Technology, Hunan Provincial Key Laboratory of Food Science and Biotechnology, Hunan Agricultural University, Changsha, Hunan 410128, China
DU Guorong, College of Food Science and Technology, Hunan Provincial Key Laboratory of Food Science and Biotechnology, Hunan Agricultural University, Changsha, Hunan 410128, China; Beijing Work Station, Technology Center, Shanghai Tobacco Group Co., Ltd., Beijing 101
DING Shenghua, Hunan Agricultural Product Processing Institute, Hunan Academy of Agricultural Sciences, Changsha, Hunan 410125, China
SHAN Yang, Hunan Agricultural Product Processing Institute, Hunan Academy of Agricultural Sciences, Changsha, Hunan 410125, China
JIANG Liwen, College of Food Science and Technology, Hunan Provincial Key Laboratory of Food Science and Biotechnology, Hunan Agricultural University, Changsha, Hunan 410128, China
LIU Xia, College of Food Science and Technology, Hunan Provincial Key Laboratory of Food Science and Biotechnology, Hunan Agricultural University, Changsha, Hunan 410128, China
LI Pao, College of Food Science and Technology, Hunan Provincial Key Laboratory of Food Science and Biotechnology, Hunan Agricultural University, Changsha, Hunan 410128, China; Hunan Agricultural Product Processing Institute, Hunan Academy of Agricultural Science

Abstract

In order to identify the inferior oat samples, and the samples from different local and imported brands, a nondestructive identification method based on near infrared spectroscopy and chemometrics methods was proposed. Spectra of five different brands of oat samples and inferior samples were obtained. Continuous wavelet transform was used for the baseline elimination. Wavenumber selection based on the standard deviation and relative standard deviation was discussed for improving the accuracy of the method, and 15 informative wavenumbers were obtained. Principal component analysis method was used for classification. The results showed that the baseline elimination was achieved by continuous wavelet transform method. Acceptable classification can be achieved with the help of principal component analysis and informative wavenumber selection. It shows that the near infrared spectroscopy combined with chemometrics methods can be used to the rapid identification of the oat samples of different brands and inferior.

Publication Date

2-28-2019

First Page

72

Last Page

76

DOI

10.13652/j.issn.1003-5788.2019.02.014

Recommended Citation

Shangke, LI; Guorong, DU; Shenghua, DING; Yang, SHAN; Liwen, JIANG; Xia, LIU; and Pao, LI (2019) "Rapid and non-destructive identification of different brands and inferior oatsby near infrared spectroscopy combined with chemometrics methods," Food and Machinery: Vol. 35: Iss. 2, Article 14.
DOI: 10.13652/j.issn.1003-5788.2019.02.014
Available at: https://www.ifoodmm.cn/journal/vol35/iss2/14

References

[1] 郑殿升. 中国燕麦的多样性[J]. 植物遗传资源学报, 2010, 11(3): 249-252.
[2] 田斌强, 赵莉君, 谢笔钧. 燕麦淀粉研究进展[J]. 食品科学, 2014, 35(21): 287-291.
[3] MIRMOGHTADAIE L, KADIVAR M, SHAHEDI M. Effects of succinylation and deamidation on functional properties of oat protein isolate[J]. Food Chemistry, 2009, 114(1): 127-131.
[4] MOHAMED A, BIRESAW G. Oats protein isolate: thermal, rheological, surface and functional properties[J]. Food Research International, 2009, 42(1): 107-114.
[5] 顾军强, 钟葵, 王立, 等. 不同燕麦品种用于加工燕麦片的适宜性评价[J]. 中国粮油学报, 2016, 31(3): 18-24.
[6] 张维库, 续洁琨, 赵莹, 等. 液相色谱法测定燕麦麸皮芦丁的含量[J]. 亚太传统医药, 2010, 6(10): 40-41.
[7] 王超群, 张晖, 钱海峰, 等. 基于气相色谱定量检测面粉中燕麦粉的添加量[J]. 现代食品科技, 2016(11): 316-322.
[8] JAN U P, DIETMA R K, REINHOLD C. On-line application of near infrared (NIR) spectroscopy in food production[J]. Trends in Food Science and Technology, 2015, 46(2): 211-230.
[9] 李娟, 梁漱玉. 近红外快速无损检测食用油品质的研究进展[J]. 食品与机械, 2016, 32(11): 225-228.
[10] TAN Zong, LOU Ting-ting, HUANG Zhi-xuan, et al. Single-Drop raman imaging exposes the trace contaminants in milk[J]. Journal of Agricultural and Food Chemistry, 2017, 65(30): 6 274-6 281.
[11] 郭成, 马月, 梁梦醒, 等. 基于近红外光谱结合波长优选检测单颗葡萄的SSC含量[J]. 食品与机械, 2016, 32(9): 39-43.
[12] TURZA S, TOTH A, VARADI M. Multivariate classification of different soyabean varieties[J]. Journal of Near Infrared Spectroscopy, 1998, 6(1): 183-187.
[13] 王茜, 吴习宇, 庞兰, 等. 枇杷内部品质近红外光谱无损检测[J]. 食品与机械, 2016, 32(5): 67-70, 97.
[14] 陈建, 陈晓, 李伟, 等. 基于近红外光谱技术和人工神经网络的玉米品种鉴别方法研究[J]. 光谱学与光谱分析, 2008, 28(8): 1 806-1 808.
[15] ESTCBAN D, GONZALEZ J M, PIZARRO C. An evaluation of orthogonal signal correction methods for the characterisation of arabica and robusta coffee varieties by NIRS[J]. Analytica Chimica Acta, 2004, 514(1): 57-67.
[16] 莫欣欣, 孙通, 刘木华, 等. 基于近红外光谱的食用植物油中反式脂肪酸含量快速定量检测及模型优化研究[J]. 分析化学, 2017, 45(11): 1 694-1 702.
[17] DE M M, MANUELIAN C L, TON S, et al. Prediction of sodium content in commercial processed meat products using near infrared spectroscopy[J]. Meat Science, 2017, 125(2 017): 61-65.
[18] 管骁, 饶立, 刘静, 等. 结合数据融合技术与近红外光谱的休闲苹果脆片综合品质评价[J]. 食品与机械, 2016, 32(12): 45-49.
[19] 赵昕, 张任, 王伟, 李春阳. 基于近红外光谱快速定量检测面粉中曲酸的方法建立[J]. 食品科学, 2018, 39(8): 249-255.
[20] 赵秀芳, 李卫建, 黄伟, 等. 燕麦干草品质的近红外光谱定量分析[J]. 光谱学与光谱分析, 2008, 28(9): 2 094-2 097.
[21] 阴佳鸿, 毛培胜, 黄鸾, 等. 不同含水量劣变燕麦种子活力的近红外光谱分析[J]. 红外, 2010, 31(7): 39-44.
[22] ALBANELL E, MINARRO B, CARRASCO N. Detection of low-level gluten content in flour and batter by near infrared reflectance spectrosopy (NIRS)[J]. Journal of Cereal Science, 2012, 56(2): 490-495.
[23] LI Pao, DU Guo-rong, MA Yan-jun, et al. A novel multivariate calibration method based on variable adaptive boosting partial least squares algorithm[J]. Chemometrics and Intelligent Laboratory Systems, 2018, 176(176): 157-161.
[24] JORDAN D, MIKSAD R W, POWERS E J. Implementation of the continuous wavelet transform for digital time series analysis[J]. Review of Scientific Instruments, 1997, 68(3): 1 484-1 494.
[25] 陈达, 苏庆德, 邵学广, 等. 近红外光谱技术用于复杂植物样品中无机离子测定的新方法[J]. 光谱学与光谱分析, 2004, 24(12): 1 540-1 542.
[26] 梁逸曾, 吴海龙, 沈国励, 等. 分析化学计量学的若干新进展[J]. 中国科学: B辑 化学, 2006, 36(2): 93-100.
[27] CHEN Da, SHAO Xue-guang, HU Bin, et al. Simultaneous wavelength selection and outlier detection in multivariate regression of near-infrared spectra[J]. Analytical Sciences, 2005, 21(2): 161-166.
[28] LI Pao, DU Guo-rong, CAI Wen-sheng, et al. Rapid and nondestructive analysis of pharmaceutical products using near-infrared diffuse reflectance spectroscopy[J]. Journal of Pharmaceutical and Biomedical Analysis, 2012, 70(21): 288-294.
[29] 张初, 刘飞, 何勇, 等. 近红外光谱技术用于豆浆粉品牌与劣质豆浆粉的鉴别[J]. 光谱学与光谱分析, 2014, 34(7): 1 826-1 830.