Study on Method for On-Line Identification of Wheat Mildew by Array Fiber Spectrometer
JIANG Xue-song1, ZHAO Tian-xia2, LIU Xiao2, ZHOU Yue-chun2, SHEN Fei2*, JU Xing-rong2, LIU Xing-quan3, ZHOU Hong-ping1*
1. College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
2. College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China
3. School of Agriculture and Food Science, Zhejiang Forestry University, Hangzhou 311300, China
Abstract：Wheat is one of the main strategic stored grain varieties in China. But wheat is susceptible to fungal infection, which affects its safety as food. Early detection of harmful fungal infection in wheat is the precondition to control its hazard. However, current methods for mold detection, such as plate counting and fluorescence staining, are usually laboratory-intensive and time-consuming, and can not fulfill the need for on-site testing. Concerning this issue, this work intends to apply array fiber spectrometer series and chemometrics to establish an on-line method for detection of wheat mildew, and to provide a reference for further development of on-line sensing instruments for grain quality and safety. Sterile wheat kernels were inoculated with 5 different spore suspensions of fungal strains respectively, which were F. moniliforme 83227, F. proliferatum 195647, F. nivale 3.503, A. parasiticus 3.3950 and A. ochraceus 3.3486. Wheat samples were then stored at 28 ℃ and 85% relative humidity after inoculation to accelerate mildew process. At storage stage of 0, 1, 3, 5 and 7 d, Vis/NIR spectra of samples were collected by an on-line sensing system which was mainly conposed of an array fiber spectrometer and a diffuse reflectance probe. Total colony count of samples was also determined according to plate count method. Spectra of samples were measured at moving speed of 0.15 m·s-1 with integral time of 20 ms. Each sample was collected three times and the acquisition band ranged from 600 to 1 600 nm. Then original spectra of samples were firstly pre-processed by smoothing, multivariate scatter correction and derivative transformation to eliminate spectral noise. Subsequently, principal component analysis (PCA) was used to discriminate wheat samples with different mildew degrees (storage stage). Finally, liner discriminant analysis (LDA) and partial least squares regression (PLSR) were employed to develop qualitative and quantitative analysis models for fungal infection in wheat. Wheat samples undergone three stages during the storage period according to colony counts, which were not mildew, mildew and serious mildew. Analysis of original and second derivative spectra indicated that fungal infection resulted in significant changes in wheat spectra. PCA results showed that there was a certain trend of separation between wheat with different mildew degrees. The overall recognition rate obtained by LDA for the classification of samples with different mildew degrees was more than 90.0%. Colony counts in wheat samples was predicted by PLSR and coefficient of determination for the prediction set (R2p), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) value obtained were 0.859 2, 0.401 Log CFU·g-1 and 2.65, respectively. The combination of array fiber spectrometer series and chemometrics is feasible for on-line detection of wheat mildew. In further studies, natural infected wheat samples and samples contaminated with more representative fungal strains should be incorporated to enhance the robustness and applicability of the calibration model.
蒋雪松，赵天霞，刘 潇，周曰春，沈 飞，鞠兴荣，刘兴泉，周宏平. 阵列式光纤光谱仪的小麦霉变在线检测[J]. 光谱学与光谱分析, 2018, 38(12): 3729-3735.
JIANG Xue-song, ZHAO Tian-xia, LIU Xiao, ZHOU Yue-chun, SHEN Fei, JU Xing-rong, LIU Xing-quan, ZHOU Hong-ping. Study on Method for On-Line Identification of Wheat Mildew by Array Fiber Spectrometer. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(12): 3729-3735.
[1] SHI Jian-rong, LIU Xin, QIU Jian-bo, et al(史建荣, 刘 馨, 仇剑波, 等). Scientia Agricultura Sinica(中国农业科学), 2014, 47(18): 3641.
[2] McMullen M, Bergstrom G, Wolf E D, et al. Plant Disease, 2012, 96(12): 1712.
[3] Porep J U, Kammerer D R, Carle R. Trends in Food Science & Technology, 2015, 46(2): 211.
[4] Jamrógiewicz M. Journal of Pharmaceutical and Biomedical Analysis, 2012, 66: 1.
[5] ZHU Xiao-li, LU Wan-zhen(褚小立, 陆婉珍). Electronic Instrumentation Customer(仪器仪表用户), 2013, 20(2): 11.
[6] Orina I, Manley M, Williams P J. Food Research International, 2017, 100: 74.
[7] GUAN Er-qi, CUI Gui-jin, BIAN Ke, et al(关二旗, 崔贵金, 卞 科, 等). Journal of the Chinese Cereals and Oils Association(中国粮油学报), 2016, 31(11): 125.
[8] Shahin M A, Symons S J, Hatcher D W. Food and Bioprocess Technology, 2014, 7(1): 224.
[9] Cui C, Fearn T. Journal of Near Infrared Spectroscopy, 2017, 25(1): 5.
[10] Shen F, Wu Q, Shao X, et al. Journal of Food Science and Technology, 2018, 55(3): 1175.
[11] ZHANG Ling-shuai, WANG Wei-dong, GU Yun-hong, et al(张灵帅，王卫东，谷运红，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2011，31(5)：1254.
[12] Kaya-Celiker H, Mallikarjunan P K, Kaaya A. Food Control, 2015, 52：103.
[13] ZHAO Hai-yan, GUO Bo-li, WEI Yi-min, et al(赵海燕, 郭波莉, 魏益民, 等). Scientia Agricultura Sinica(中国农业科学), 2011, 44(7): 1451.
