期刊
JOURNAL OF FOOD SCIENCE AND TECHNOLOGY-MYSORE
卷 57, 期 12, 页码 4541-4550出版社
SPRINGER INDIA
DOI: 10.1007/s13197-020-04493-4
关键词
Red bayberry; pH value; Soluble solids content; PLSR; LS-SVM
资金
- Zhejiang Provincial Top Key Discipline of Biology, Zhejiang Provincial Universities Key Discipline of Botany
- Natural Science Foundation of Zhejiang Province [LQ19C160003]
Color has strong relationship with food quality. In this paper, partial least square regression (PLSR) and least square-support vector machine (LS-SVM) models combined with six different color spaces (NRGB, CIELAB, CMY, HSI, I1I2I3, and YCbCr) were developed and compared to predict pH value and soluble solids content (SSC) in red bayberry. The results showed that PLSR and LS-SVM models coupled with color space could predict pH value in red bayberry (r = 0.93-0.96, RMSE = 0.09-0.12, MAE = 0.07-0.09, and MRE = 0.04-0.06). In addition, the minimum errors (RMSE = 0.09, MAE = 0.07, and MRE = 0.04) and maximum correlation coefficient value (r = 0.96) were found with the PLSR based on CMY, I1I2I3, and YCbCr color spaces. For predicting SSC, PLSR models based on CIELAB color space (r = 0.90, RMSE = 0.91, MAE = 0.69 and MRE = 0.12) and HSI color space (r = 0.89, RMSE = 0.95, MAE = 0.73 and MRE = 0.13) were recommended. The results indicated that color space combined with chemometric is suitable to non-destructively detect pH value and SSC of red bayberry.
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