Journal of Textile Research ›› 2022, Vol. 43 ›› Issue (11): 29-34.doi: 10.13475/j.fzxb.20210800106

• Fiber Materials • Previous Articles     Next Articles

Rapid quantitative detection of silk grafting ratio based on near infrared spectroscopy

WANG Rui1,2, SI Yinsong3, LU Haohao3, GAO Shuang3, FU Yaqin3()   

  1. 1. College of Textile Science and Engineering (International Institute of Silk), Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
    2. Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou, Zhejiang 310018, China
    3. School of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
  • Received:2021-08-02 Revised:2022-08-25 Online:2022-11-15 Published:2022-12-26
  • Contact: FU Yaqin E-mail:fyq01@zstu.edu.cn

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Abstract:

The grafting ratio of silk after chemical graft weight gaining treatment is difficult to measure directly, and the existing thermogravimetric analysis method is time-consuming and not suitable for rapid mass detection. In order to solve these problems, a rapid detection method by using near infrared spectroscopy (NIRS) was proposed. Based on NIRS combined with stoichiometry software, the partial least squares was selected as a correction method to establish prediction model of grafting ratio of methylacrylamide grafted silk. The model was optimized from three aspects of spectral pretreatment, modeling bands, and the optimal numbers of principal factor. The internal prediction accuracy of the established model is 91.03%. 19 samples not involved in the modeling were used for the robustness verification,and paired t-test of predicted and reference values showed that at a given significant level α=0.05, there was no significant difference between the results obtained from model prediction and weighing method. Results show that the NIRS technique can provide a rapid and effective method for the quantitative detection of silk grafting ratio.

Key words: near infrared spectroscopy, silk, grafting ratio, methacrylamide, quantitative analysis, partial least squares

CLC Number: 

  • TS102.1

Fig.1

Distribution of grafted silk samples"

Fig.2

Near infrared spectra of untreated silk and grafted silk"

Fig.3

Near infrared spectra of silk samples"

Fig.4

Relationship between PRESS value and principle factor numbers of different methods"

Tab.1

Model parameters of different pretreatment methods"

预处理方法 主因子数 RC SECV RP SEP
S-G平滑+S-G求导 8 0.994 1.462 0.992 1.855
S-G平滑+S-G求导+
均值中心化		 9 0.996 1.334 0.986 2.427
S-G平滑+差分求导 8 0.995 1.464 0.992 1.834
S-G平滑+差分求导+
均值中心化		 7 0.995 1.470 0.991 1.946

Fig.5

Spectra of samples obtained by combined pretreatment method of S-G smooth and difference derivative"

Fig.6

Correlation coefficient of sample absorbance and grafting ratio"

Tab.2

Prediction results of quantitative analysis model with different thresholds"

阈值 RC SECV RP SEP 预测准确率/%
0(全波段) 0.995 1.464 0.992 1.834 91.03
0.4 0.995 1.358 0.988 2.174 87.73
0.6 0.995 1.480 0.985 2.681 84.52
0.8 0.993 1.657 0.991 3.219 80.44

Fig.7

External validation accuracy of quantitative analysis model"

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