A lettuce moisture detection method based on 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.

Recent Advances in THz Detection of Water

“…Li et al [ 111 ] established PLS and MLR prediction models based on THz time-domain spectral maxima, minima, and full spectra, as well as absorption coefficient spectra and refractive index, respectively, for the prediction and analysis of soybean leaf water contents, in which the PLS model based on THz time-domain spectral extremes predicted the best results. Zhang et al [ 112 ] used partial least squares regression (PLSR) to construct a model for quantitative analysis and the detection of the moisture content of lettuce based on THz spectra. Huang et al [ 113 ], who detected the moisture content in cattle feed based on THz and near-infrared (NIR) spectra, constructed prediction models based on head-to-tail spliced fused spectral data and spectral feature variables fused in the feature layer combined with PLSR, respectively, where the Rp, RMSEP, and RPD of the fused spectral data in the feature layer reached 0.9933, 0.0069, and 8.7386, with better results.…”

Determining Moisture Content of Basil Using Handheld Near-Infrared Spectroscopy

“…Advances in miniaturization, optics, and digitalization, alongside increased availability of computational power, are transforming NIR spectroscopy into a versatile, efficient, and widely available analytical method for real-time plant analysis and research. Extensive research has been conducted on the application of NIR spectroscopy in determining the moisture content of vegetables, specifically focusing on those characterized by high moisture content [1,3,[11][12][13]. The considerable water content and, consequently, the presence of hydrogen bonds in these vegetables contribute to an increased involvement of hydroxyl (OH) groups in their water structure.…”

“…Despite the promising potential of NIR spectroscopy in moisture content determination, a research gap exists in its application to basil leaves. Prior studies have predominantly focused on assessing moisture content and quality in leafy vegetables like lettuce or spinach [1,11,16,17], leaving basil, a significant and versatile herb within the indoor farming sector [18], largely unexplored in terms of such assessments. Furthermore, given basil's widespread culinary and medicinal use [19], the development of a dependable method for accurately predicting its moisture content holds considerable significance for the agricultural community.…”