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Evaluation of growth characteristics of Aspergillus parasiticus inoculated in different culture media by shortwave infrared(SWIR) hyperspectral imaging
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作者 Xuan Chu Wei Wang +7 位作者 Xinzhi Ni Haitao Zheng Xin Zhao Hong Zhuang Kurt C.Lawrence Chunyang Li Yufeng Li Chengjun Lu 《Journal of Innovative Optical Health Sciences》 SCIE EI CAS 2018年第5期69-83,共15页
The growth characteristics of Aspergillus parasitic us incubated on two culture media were ex-amined using shortwave infrared(SWIR,1000-2500 nm)hyperspectral imaging(HSI)in this work.HSI images of the A.parasiticus co... The growth characteristics of Aspergillus parasitic us incubated on two culture media were ex-amined using shortwave infrared(SWIR,1000-2500 nm)hyperspectral imaging(HSI)in this work.HSI images of the A.parasiticus colonies growing on rose bengal medium(RBM)and maize agar medium(MAM)were recorded daily for 6 days.The growth phases of A.parasiticus were indicated through the pixel number and average spectra of colonies.On score plot of the first principal component(PC1)and PC2,four growth zones with varying mycelium densities were identified.Eight characteristic wavelengths(1095,1145,1195,1279,1442,1655,1834 and 1929 nm)were selected from PC1 loading,average spectra of each colony as well as each growth zone.F urthermore,support vector machine(S VM)classifier based on the eight wavelengths was built,and the classification accuracies for the four zones(from outer to inner zones)on the colonies on RBM were 99.77%,9935%,99.75%and 99.60%and 99.77%,9939%,99.31%and 98.22%for colonies on MAM.In addition,a new score plot of PC2 and PC3 was used to differ-entiate the colonies incubated on RBM and MAM for 6 days.Then characteristic wavelengths of 1067,1195,1279,1369,1459,1694,1834 and 1929 nm were selected from the loading of PC2 and PCg.Based on them,a new SVM model was developed to diferentiate colonies on RBM and MAM with accuracy of 100.00%and 9999%,respectively.In conclusion,SWIR hyperspectral image is a powerful tool for evaluation of growth characteristics of A.parasiticus incubated in diferent culture media. 展开更多
关键词 Aspergilus parasiticus growth characteristics characteristic wavelengths shortwave infrared(SWIR)hyperspectral imaging
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Shortwave infrared hyperspectral imaging for detection of pH value in Fuji apple 被引量:3
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作者 Guo Zhiming Huang Wenqian +2 位作者 Chen Liping Peng Yankun Wang Xiu 《International Journal of Agricultural and Biological Engineering》 SCIE EI CAS 2014年第2期130-137,共8页
pH value is regarded as one of the most important attributes that affect sensory characteristics and edible quality of apple.The objective of the research was to explore the feasibility of applying shortwave infrared ... pH value is regarded as one of the most important attributes that affect sensory characteristics and edible quality of apple.The objective of the research was to explore the feasibility of applying shortwave infrared hyperspectral imaging system to detect the pH value of apple.A shortwave infrared hyperspectral imaging system was developed over the wavelength region of 1000-2500 nm and used to acquire hyperspectral images of apple samples.After reflectance calibration,mean reflectance spectral was calculated by averaging the intensity of all pixels within the roundness region of interest(ROI).Synergy interval partial least squares(siPLS)algorithms as an effective multivariable method was conducted on the calibration of regression model to estimate the pH value in Fuji apple.The performance of the final model was back-evaluated according to root mean square error of calibration(RMSEC)and correlation coefficient(Rc)in calibration set,and tested in prediction set.The optimal prediction siPLS model was obtained with correlation coefficient(Rp)of 0.8474 and mean square error of prediction(RMSEP)of 0.0398.The results indicated that shortwave infrared hyperspectral imaging combined with siPLS chemometrics could be an accurate and fast method for nondestructive prediction of pH value in Fuji apple. 展开更多
关键词 shortwave infrared hyperspectral imaging synergy interval partial least squares pH value APPLE
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