[Study of the influence of scan number on near-infrared diffuse spectra of tomato leaf and model precision].
Near-infrared spectroscopy technique is non-destructive, simple, fast, highly efficient, cheap to implement, and very recurrent with no sample preparation, and has been a rapid and non-destructive modem qualitative and quantitative technique that has been widely used in many fields. As a powerful analytical tool in product quality determination, this technology is based on the measurement of vibration frequencies of chemical bonds in functional group such as C-C, C-H, O-H, C=O and N-H upon absorption of radiation. However, NIR spectra are affected by the status of spectrometer and the set of parameters when scanning, such as accuracy of wavelength, resolution of apparatus, noise, scan time and uniformity of sample size. To provide foundation with optimum test condition when modeling, the influence of scan number on NIR diffuse spectra of tomato leaf and chlorophyll prediction model precision was studied. 102 tomato leaf samples were used in this experiment. Partial least-squares (PLS) was used to develop models and evaluate and compare these models. The results show that scan number does have effect on NIR spectra and prediction models. Variance value of root mean square (RMS) noise of NIR spectra diminished gradually with the increment of scan number. The spectral quality with high scan number was high, however, the system error of instrument increased too. The spectral quality with low scan number was low, while the spectra were smooth and system error of instrument decreased too. The determination coefficient of chlorophyll calibration and prediction model was highest with 128 scan number, however, the model was not robust. But with 32 scan number, although the coefficient was low, the calibration and prediction model was robust and only a short test time was needed. At the same time, the difference of models to predict chlorophyll contents with different scan numbers was not distinct (alpha = 0.05). Different influence factors should be considered when modeling.
Jiang
Huan-yu
HY
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China. hyjiang@zju.edu.cn
Peng
Yong-shi
YS
Xie
Li-juan
LJ
Ying
Yi-bin
YB
D000465:Algorithms; D002734:Chlorophyll / Q000032:analysis*; D018551:Solanum lycopersicum / Q000737:chemistry*; D008956:Models, Chemical; D010936:Plant Extracts / Q000032:analysis / Q000737:chemistry; D018515:Plant Leaves / Q000737:chemistry*; D019265:Spectroscopy, Near-Infrared / Q000379:methods
D010936:Plant Extracts; D002734:Chlorophyll