基于颜色空间的杨梅可溶性固形物含量和pH值无损测定
Nondestructive determination of soluble solids content and pH in red bayberry () based on color space.
作者信息
Feng Jie, Jiang Lingling, Zhang Jialei, Zheng Hong, Sun Yanfang, Chen Shaoning, Yu Meilan, Hu Wei, Shi Defa, Sun Xiaohong, Lu Hongfei
机构信息
College of Life Sciences and Medicine, Zhejiang Sci-Tech University, Hangzhou, 310018 China.
Zhejiang Province Key Laboratory of Plant Secondary Metabolism and Regulation, Hangzhou, 310018 China.
出版信息
J Food Sci Technol. 2020 Dec;57(12):4541-4550. doi: 10.1007/s13197-020-04493-4. Epub 2020 May 26.
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.
颜色与食品质量密切相关。本文建立并比较了偏最小二乘回归（PLSR）和最小二乘支持向量机（LS-SVM）模型与六种不同颜色空间（NRGB、CIELAB、CMY、HSI、I1I2I3和YCbCr）相结合，以预测杨梅的pH值和可溶性固形物含量（SSC）。结果表明，结合颜色空间的PLSR和LS-SVM模型可以预测杨梅的pH值（r = 0.93 - 0.96，RMSE = 0.09 - 0.12，MAE = 0.07 - 0.09，MRE = 0.04 - 0.06）。此外，基于CMY、I1I2I3和YCbCr颜色空间的PLSR模型具有最小误差（RMSE = 0.09，MAE = 0.07，MRE = 0.04）和最大相关系数值（r = 0.96）。对于预测SSC，推荐基于CIELAB颜色空间（r = 0.90，RMSE = 0.91，MAE = 0.69，MRE = 0.12）和HSI颜色空间（r = 0.89，RMSE = 0.95，MAE = 0.73，MRE = 0.13）的PLSR模型。结果表明，颜色空间与化学计量学相结合适用于无损检测杨梅的pH值和SSC。
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