RESUMO: Para a detecção não destrutiva de estresse hídrico da alface, espectroscopia no domínio do tempo em terahertz (THz-TDS) foi usada para analisar quantitativamente o conteúdo de água na alface. Quatro gradientes de conteúdo de água de alface foram usados. Os dados espectrais da alface foram coletados por um sistema THz-TDS e denoised usando o derivado S-G, Savitzky-Golay (S-G) suavização e filtragem de normalização. O efeito de ajuste do método de pré-tratamento foi melhor do que o do ajuste de regressão, e o efeito de ajuste da derivada S-G foi obtido. Em seguida, um conjunto de calibração e um conjunto de verificação foram divididos pelo algoritmo Kennan-Stone, particionamento do conjunto de amostra com base no algoritmo de distância X-Y conjunta (SPXY) e o algoritmo de amostragem aleatória (RS), e os parâmetros de RS foram otimizados por ajuste de regressão. A amostragem adaptativa de estabilidade competitiva reponderada, variáveis de informação retidas iterativamente e otimização de combinação de intervalo foram usadas para selecionar comprimentos de onda característicos e, em seguida, a projeção contínua foi usada com base nos três algoritmos acima. Depois que o algoritmo de projeção sucessivo foi reprojetado, a regressão de mínimos quadrados parcial foi usada na modelagem. Os coeficientes de regressão R2 e erro quadrático médio (RMSEP) atingem 0,8962 e 412,50%, respectivamente, e R2 e RMSEP do conjunto de verificação são 0,8757 e 528,93%, respectivamente.

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.

Abstract Kernel direct harvesting is the mainstream technology of maize harvesting in the world today, it has significant impact on the maize kernel subsequent food processing. Direct harvest technology in china is not well developed due to the influence of growth environment, agronomy, etc., which leads kernel damage. The kernel damage is necessary studied in maize direct harvesting technology. Therefore, “Zheng Dan 958” was selected, the entrance clearance, export clearance, and cylinder speed as variables to carry out the kernel damage experiment. Processed the image of threshed maize kernel, extracted the crack and boundary characteristics of kernel damage, and established the BP neural network model to study the direct harvesting damage and optimize parameters. The results indicated that kernel damage increased with decreasing threshing clearance and increasing threshing intensity. In a certain threshing clearance, cylinder speed was the key factor affecting kernel damage. The R of model was above 0.95, the accuracy of damage quantitative identification was above 85%. When inlet clearance was 35 mm, outlet clearance was 15 mm, and cylinder speed was 300 rpm, kernel damage was small. Our findings will provide reference for kernel direct harvesting technology and improve harvest quality to meet food processing industry demands.

ABSTRACT Phosphorus deficiency can lead to serious reductions in crop yield and quality. Rapid and accurate determination of phosphorus stress status in vegetables is essential for ensuring the appropriate application of water and fertiliser, improving the yield and quality of vegetables, avoiding the waste of resources and serious non-point pollution source caused by the abuse of chemical fertilisers, and promoting green production and sustainability of facility agriculture. Chemical analysis and spectral or visual imaging methods for detection of phosphorus nutritional status are not conducive in facility production owing to their low accuracy and damage to plants. Owing to its penetration and fingerprint characteristics, terahertz time-domain spectroscopy (THz-TDS) can distinguish differences in internal components caused by excess phosphorus nutrients and changes in nucleic acids, nuclear proteins, phospholipids, and other macromolecules; thus, it could potentially be used to evaluate the nutritional status of crops. In this study, an innovative THz-TDS-based method was used to detect the nutritional status of phosphorus in lettuce. Lettuce was grown with different phosphorus levels using soilless cultivation and different nutrient solutions. Based on the standard formula of Yamasaki nutrient solution, the phosphorus content in the nutrient solutions was reduced or increased by 20%, 60%, 100%, and 150%, and lettuce samples exposed to each of these phosphorus concentrations were collected. Terahertz spectra are highly sensitive to water; thus, the lettuce samples were freeze-dried to minimise the effect of water and maintain their original quality and bioactivity. The spectra of lettuce were recorded using a TS7400 THz-TDS system; noise and interference were eliminated via normalisation based on Savitzky-Golay smoothing. The correction and validation sets were divided using sample set partitioning based on the joint x-y distance (SPXY). The stability competitive adaptive reweighted algorithm, iterative retention information variable algorithm, and interval combination optimisation algorithm were used to select the terahertz characteristic wavelength, and the successive projection algorithm was then used for secondary optimisation. Finally, a THz-TDS model of lettuce phosphorus was established using the partial least squares method with five principal component variables. The coefficient of determination of the model reached 0.7005, and the root-mean-square error of the predictions was 0.003273, indicating that this method has a high prediction accuracy.