Food and Fermentation Industries >

2022 , Vol. 48 >Issue 16: 270 - 276

DOI: https://doi.org/10.13995/j.cnki.11-1802/ts.029223

Establishment of quantitative models for blueberry storage quality based on near infrared spectroscopy combined with extreme learning machine

  • ZHU Jinyan ,
  • ZHU Yujie ,
  • FENG Guohong ,
  • ZENG Mingfei ,
  • LIU Siqi
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  • College of Engineering and Technology, Northeast Forestry University, Harbin 150040, China

Received date: 2021-09-07

  Revised date: 2021-10-19

  Online published: 2022-09-16

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Abstract

Near infrared spectroscopy technology joint extreme learning machine (ELM) method was used to establish a quantitative detection model of blueberry storage quality, so as to achieve the rapid non-destructive detection of the soluble solids, vitamin C and anthocyanin of blueberry fruit and provide technical reference for online quality testing during the low temperature storage of fresh blueberries. The LabSpec 5000 spectrometer was used to collect near infrared spectra of 150 blueberry samples in 5 different storage times, the abnormal samples were screened by combined X/Y anomaly sample identification and rejection method, and the sample set was divided by the joint X-Y distance sample set division method. By comparing and analyzing the effect of pretreatment methods such as standard normal variate, multiplicative scatter correction, first-order derivative on model performance, the optimal pretreatment method of each of the three components of blueberries was determined. The characteristic band was selected by the synergy interval partial least squares algorithm (SiPLS), and used as input to establish the ELM quantitative analysis model. The results of the ELM model were compared with the partial least-squares regression (PLSR). The results showed that the correction set correlation coefficients of the soluble solids, vitamin C and anthocyanin content of blueberries were 0.920 5, 0.908 7 and 0.942 1 respectively. The verification set correlation coefficients were 0.882 6, 0.897 2, and 0.869 3 respectively. The root mean square error of correction (RMSEC) were 0.766 4, 0.695 4, 1.671 0, respectively. The root mean square error of prediction (RMSEP) were 0.539 7, 0.624 3, 2.041 4, respectively. In this study, 1/5-2/5 variables of the whole spectrum were used to achieve better performance than the model established by the original variables. Compared with the traditional PLSR model, the accuracy of the ELM model established in this paper was significantly improved, indicating that SiPLS-ELM combined with near infrared spectroscopy has great potential in the online nondestructive detection of blueberry components.

Key words： near infrared spectroscopy; extreme learning machine; blueberry; synergy interval partial least squares; non-destructive testing

Cite this article

ZHU Jinyan , ZHU Yujie , FENG Guohong , ZENG Mingfei , LIU Siqi . Establishment of quantitative models for blueberry storage quality based on near infrared spectroscopy combined with extreme learning machine[J]. Food and Fermentation Industries, 2022 , 48(16) : 270 -276 . DOI: 10.13995/j.cnki.11-1802/ts.029223

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