Nondestructive determining the soluble solids content of citrus using near infrared transmittance technology combined with the variable selection algorithm
文献类型： 外文期刊
作者： Tian, Xi 1 ; Li, Jiangbo 2 ; Yi, Shilai 3 ; Jin, Guoqiang 4 ; Qiu, Xiaoying 5 ; Li, Yongjie 4 ;
作者机构： 1.China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China
2.Beijing Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China
3.Southwest Univ, Citrus Res Inst, Chongqing 400712, Peoples R China
4.Coinnovat Ctr Citrus Ind Technol Linhai, Linhai 31700, Peoples R China
5.Stn Popularizing Special Technol Linhai, Linhai 31700, Peoples R China
6.Coinnovat Ctr Citrus Ind Technol Linhai, 219 Dongfang Ave, Linhai 31700, Peoples R China
关键词： Full transmittance spectrum; Spectral preprocessing; Thick-skin fruits; Soluble solids content; Variable selection algorithm
期刊名称：ARTIFICIAL INTELLIGENCE IN AGRICULTURE
ISSN：
年卷期： 2020 年 4 卷
页码：
收录情况： SCI
摘要： Nondestructive determination the internal quality of thick-skin fruits has always been a challenge. In order to investigate the prediction ability of full transmittance mode on the soluble solid content (SSC) in thick-skin fruits, the full transmittance spectra of citrus were collected using a visible/near infrared (Vis/NIR) portable spectrograph (550-1100 nm). Three obvious absorption peaks were found at 710, 810 and 915 nm in the original spectra curve. Four spectral preprocessing methods including Smoothing, multiplicative scatter correction (MSC), standard normal variate (SNV) and first derivative were employed to improve the quality of the original spectra. Subsequently, the effective wavelengths of SSC were selected from the original and pretreated spectra with the algorithms of successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and genetic algorithm (GA). Finally, the prediction models of SSC were established based on the full wavelengths and effective wavelengths. Results showed that SPA performed the best performance on eliminating the useless information variable and optimizing the number of effective variables. The optimal prediction model was established based on 10 characteristic variables selected from the spectra pretreated by SNV with the algorithm of SPA, with the correlation coefficient, root mean square error, and residual predictive deviation for prediction set being 0.9165, 0.5684 & DEG;Brix and 2.5120, respectively. Overall, the full transmittance mode was feasible to predict the internal quality of thick-skin fruits, like citrus. Additionally, the combination of spectral preprocessing with a variable selection algorithm was effective for developing the reliable prediction model. The conclusions of this study also provide an alternative method for fast and real-time detection of the internal quality of thick-skin fruits using Vis/NIR spectroscopy.& COPY; 2020 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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