Nondestructive detection of apple defect combining optical fiber spectra with pattern recognition
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摘要: 为了实现基于光纤光谱技术结合模式识别无损检测苹果表面疤痕, 利用光纤光谱采集系统采集了完好无损和表面有疤痕苹果的光谱数据, 采用标准正态变换(SNV)和1阶导数对原始光谱数据进行预处理; 利用主成分分析方法对预处理后的光谱数据进行降维, 以提取能反映苹果表面疤痕的特征光谱; 利用k最近邻(KNN)模式识别方法和偏最小二乘判别分析方法, 建立了苹果表面疤痕的识别模型。结果表明, 采用主成分分析法选择了累计贡献率超过99%的前8个主成分作为样本集特征光谱数据, 很好地实现了光谱数据的降维; 利用1阶导数+KNN识别模型对校正集以及SNV+KNN识别模型对预测集中正常果和疤痕果的正确率识别均高达96.0%。验证了基于光纤光谱技术结合模式识别方法无损检测苹果表面疤痕的可行性。Abstract: In order to prove that the non-destructive detection of apple surface defect combining optical fiber spectroscopy with pattern recognition was effective, an optical fiber spectrum acquisition system was used to collect spectral data of apples with and without surface defect. Standard normal variation (SNV) and first derivative were used to preprocess the original spectral data. Principal component analysis (PCA) was used to reduce the dimension of the pre-processed spectral data to extract the characteristic spectra of apples with surface defect. By using k nearest neighbor (KNN) pattern recognition method and partial least squares discriminant analysis method, recognition model of apple defect was established. The results show that, the first eight principal components with cumulative contribution over 99% are selected as the characteristic spectral data of the sample set by using principal component analysis and the dimensionality reduction of spectral data is well realized. By using first order derivative+KNN recognition model for correction set and SNV+KNN recognition model for prediction concentration, the recognition rate of normal apples and defected apples is 96.0%. The feasibility of non-destructive detection of apple surface defect based on optical fiber spectroscopy and pattern recognition is verified.
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Keywords:
- fiber optics /
- nondestructive detection /
- pattern detection /
- apple defect
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Figure 4. Reflectance spectra
a—after SNV spectra preprocessing b—after derivative spectra preprocessing
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Figure 5. Scores scatter plot of P1 and P2
a—after SNV spectra preprocessing b—after derivative spectra preprocessing
Table 1 Contribution rate of principal component score
principal component P1 P2 P3 P4 P5 P6 P7 P8 contribution rate /% 55.63 31.59 5.86 3.54 2.27 0.55 0.45 0.32 cumulative contribution rate /% 55.63 87.22 93.08 96.62 97.89 98.44 98.89 99.21 
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Table 2 Discriminant results of KNN and PLS-DA models
discriminant models calibration set (150) prediction set (50) number rate/% number rate/% derivative+KNN 144 96.0 46 92.0 SNV+KNN 141 94.0 48 96.0 derivative+PLS-DA 142 94.7 46 92.0 SNV+PLS-DA 140 93.3 47 94.0
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[1] GUO W Ch, WANG M H, GU J S, et al. Identification of bruised kiwifruits during storage by near infrared spectroscopy and extreme learning machine[J]. Optics and Precision Engineering, 2013, 21(10): 2720-2727 (in Chinese). DOI: 10.3788/OPE.20132110.2720
[2] ZHENG C Y, GUO Zh H, JIN L. Measurement of total viable count on chilled mutton surface based on hyperspectral imaging technique[J]. Laser Technology, 2015, 39(2): 284-288 (in Chinese). http://d.old.wanfangdata.com.cn/Periodical/spgykj201420016
[3] ZOU X B, SHEN T T, SHI J Y, et al. Discrimination on maturity of plums based on hyperspectral imaging information[J]. Modern Food Science and Technology, 2016, 32(4): 235-240 (in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gzspgykj201712034
[4] ZHAO F, DONG J L, GUO W Ch. Effect of spectral extraction regions in hyperspectral images on the precision in determining the sugar content of kiwifruits [J]. Modern Food Science and Technology, 2016, 32(4): 223-228 (in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gzspgykj201604035
[5] GUO W Ch, ZHAO F, DONG J L. Nondestructive measurement of soluble solids content of kiwifruits using near-infrared hyperspectral imaging[J]. Food Analytical Methods, 2016, 9(1): 38-47. DOI: 10.1007/s12161-015-0165-z
[6] LI L L, WANG B, ZHANG X H, et al. Discrimination on maturity of plums based on hyperspectral imaging information[J]. Modern Food Science and Technology, 2017, 33(12): 228-232 (in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gzspgykj201712034
[7] ERKINBAEV C, HENDERSON K, PALIWAL J. Discrimination of gluten-free oats from contaminants using near infrared hyperspectral imaging technique[J]. Food Control, 2017, 80: 97-203. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=aea7759d3acbe4d1537f2ad9283dc7eb
[8] MO C, KIM G, KIM M S, et al. Fluorescence hyperspectral imaging technique for foreign substance detection on fresh-cut lettuce[J]. Journal of the Science of Food & Agriculture, 2017, 97(12): 3985-3993. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=6f229a28c10efc5f884e167d4a789311
[9] SONG X Sh, CHEN G Q, ZHU Zh W, et al. Determination of vo-lume fraction of acid in Chinese aged liquor by 3-D fluorescence spectrometry[J]. Laser Technology, 2018, 42(4):531-535 (in Chin-ese).
[10] YIN X H, JIANG Y, LV B Ch, et al. Quantitative analysis of 2-mercaptobenzothiazole based on terahertz time-domain spectroscopy[J]. Laser Technology, 2019, 43(1):1-9 (in Chinese). http://d.old.wanfangdata.com.cn/Periodical/jgjs201901017
[11] PU H B, LIU D, WANG L, et al. Soluble solids content and pH prediction and maturity discrimination of lychee fruits using visible and near infrared hyperspectral imaging[J]. Food Analytical Methods, 2016, 9(1): 235-244. DOI: 10.1007/s12161-015-0186-7
[12] SIEDLISKA A, BARANOWSKI P, ZUBIK M, et al. Detection of pits in fresh and frozen cherries using a hyperspectral system in transmittance mode[J]. Journal of Food Engineering, 2017, 215: 61-71. DOI: 10.1016/j.jfoodeng.2017.07.028
[13] LIU Y D, WU M M, LI Y F, et al. Comparision of reflection and diffuse transmission for detecting solid soluble contents and ratio of sugar and acid in apples by on-line vis/NIR spectroscopy[J]. Spectroscopy and Spectral Analysis, 2017, 37(8): 2424-2429 (in Ch-inese). http://www.en.cnki.com.cn/Article_en/CJFDTotal-GUAN201708020.htm
[14] LIU Y D, XU H, SUN X D, et al. Non-destructive measurement of tomato maturity by near-infrared diffuse transmission spectroscopy[J]. Laser Technology, 2019, 43(1): 25-29 (in Chinese). http://d.old.wanfangdata.com.cn/Periodical/jgjs201901006
[15] ZHAO J, PENG Y K, ZHAO S W, et al. Detection of defects in apples based on hyperspectral imaging technology[J]. Journal of Food Safety and Quality, 2012, 3(6): 681-684 (in Chinese). http://en.cnki.com.cn/Article_en/CJFDTOTAL-SPAJ201206028.htm
[16] TIAN Y W, CHENG Y, WANG X Q, et al. Recognition method of insect damage and stem/calyx on apple based on hyperspectral imaging[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(4): 325-331 (in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=nygcxb2015040046
[17] ZHANG B F, YUAN L B, ZHANG B X. Study on prediction total acid and soluble sugar of tomato juice by near infrared optical fiber spectrometer technique[J]. Spectroscopy and Spectral Analysis, 2014, 34(2): 350-353 (in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gpxygpfx201402015
[18] NI L J, ZHANG L G. Basic chemometrics and its application[M]. Shanghai: East China University of Science and Technology Press, 2011: 64-69(in Chinese).
[19] LI Y, GUO J X, MUHETAER M, et al. Effect of spectral data pretreatment on visible/near infrared spectroscopy model of soluble so-lids content of apples[J]. Northern Horticulture, 2016, 20: 1-4 (in Chinese). http://www.en.cnki.com.cn/Article_en/CJFDTotal-BFYY201620001.htm
[20] HUANG W Q, CHEN L P, LI J B, et al. Effective wavelengths determination for detection of slight bruises on apples based on hyperspectral imaging[J]. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(1): 272-277 (in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=nygcxb201301036
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