A lettuce moisture detection method based on terahertz time-domain spectroscopy

• Autor(a) principal: Zhang,Xiaodong
• Data de Publicação: 2022
• Outros Autores: Duan,Zhaohui, Mao,Hanping, Gao,Hongyan, Zuo,Zhiyu
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Ciência Rural
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782022000600153
• topic: water stress; successive projection algorithm algorithm; partial least square regression; terahertz time-domain spectroscopy

ABSTRACT: For non-destructive detection of water stress in lettuce, terahertz time-domain spectroscopy (THz-TDS) was used to quantitatively analyze water content in lettuce. Four gradient lettuce water contents were used . Spectral data of lettuce were collected by a THz-TDS system, and denoised using the S-G derivative, Savitzky-Golay (S-G) smoothing and normalization filtering. The fitting effect of the pretreatment method was better than that of regression fitting, and the S-G derivative fitting effect was obtained. Then a calibration set and a verification set were divided by the Kennan-Stone algorithm, sample set partitioning based on joint X-Y distance (SPXY) algorithm, and the random sampling (RS) algorithm, and the parameters of RS were optimized by regression fitting. The stability competitive adaptive reweighted sampling, iteratively retained information variables and interval combination optimization were used to select characteristic wavelengths, and then continuous projection was used on basis of the three algorithms above. After the successive projection algorithm was re-screened, partial least squares regression was used into modeling. The regression coefficients Rc 2 and RMSEC reach 0.8962 and 412.5% respectively, and Rp 2 and RMSEP of the verification set are 0.8757 and 528.9% respectively.