Cotton Science

Development of a rapid non-destructive method for the detection of cottonseed oil content by near-infrared spectroscopy

Tian Boyu, Huang Yiwen, Zhou Dayun, Huang Longyu, Wu Yuzhen, Fu Shouyang, Xu Qing, Guo Jinggong, Peng Jun, Kuang Meng

Cotton Science, 2023, 35(4): 325-333. DOI: 10.11963/cs20230005

Fig. 4 Distribution of oil content difference between actual value and predicted value in validation sample set

Other figure/table from this article

Fig. 1 Distribution of cottonseed oil content in calibration sample and verification sample A: Distribution of cottonseed oil content in calibration samples; B: Distribution of cottonseed oil content in verification samples.
Fig. 2 NIR spectra of cottonseed kernel, naked seed and gross seed A, C, E: Original NIR spectra of cotton seed kernel, delinted seed and linted seed; B, D, F: Processed spectra of cotton seed kernel, delinted seed and linted seed, and the data processing method were the SNV plus DET combined with mathematical parameters of 4, 6, 6, 1; 3, 9, 9, 1; 4, 7, 7, 1.
Table 1 Evaluation parameters of oil content models for different mathematical processing of three morphological types of cottonseed
Table 2 Verification results for oil content predictation model by validation samples
Fig. 3 Correlation of oil content value between NIRS model predition and chemical method detected in validation sample set A: Optimal calibration model of oil content in kernel; B: Optimal calibration model of oil content in delinted seed; C: Optimal calibration model of oil content in linted seed.