Robustness of calibration model for prediction of lignin content in different batches of snow pears based on NIR spectroscopy

Snow pear is very popular in southwest China thanks to its fruit texture and potential medicinal value. Lignin content (LC) plays a direct and negative role (higher concentration and larger size of stone cells lead to thicker pulp and deterioration of the taste) in determining the fruit texture of snow pears as well as consumer purchasing decisions of fresh pears. In this study, we assessed the robustness of a calibration model for predicting LC in different batches of snow pears using a portable near-infrared (NIR) spectrometer, with the range of 1033–2300 nm. The average NIR spectra at nine different measurement positions of snow pear samples purchased at four different periods (batch A, B, C and D) were collected. We developed a standard normal variate transformation (SNV)-genetic algorithm (GA) -the partial least square regression (PLSR) model (master model A) - to predict LC in batch A of snow pear samples based on 80 selected effective wavelengths, with a higher correlation coefficient of prediction set (Rp) of 0.854 and a lower root mean square error of prediction set (RMSEP) of 0.624, which we used as the prediction model to detect LC in three other batches of snow pear samples. The performance of detecting the LC of batch B, C, and D samples by the master model A directly was poor, with lower Rp and higher RMSEP. The independent semi-supervision free parameter model enhancement (SS-FPME) method and the sequential SS-FPME method were used and compared to update master model A to predict the LC of snow pears. For the batch B samples, the predictive ability of the updated model (Ind-model AB) was improved, with an Rp of 0.837 and an RMSEP of 0.614. For the batch C samples, the performance of the Seq-model ABC was improved greatly, with an Rp of 0.952 and an RMSEP of 0.383. For the batch D samples, the performance of the Seq-model ABCD was also improved, with an Rp of 0.831 and an RMSEP of 0.309. Therefore, the updated model based on supervision and learning of new batch samples by the sequential SS-FPME method could improve the robustness and migration ability of the model used to detect the LC of snow pears and provide technical support for the development and practical application of portable detection device

Effect of spectrum measurement position on detection of Klason lignin content of snow pears by a portable NIR spectrometer

Abstract Snow pears are an important and widespread agricultural product that can relieve respiratory symptoms, constipation, and alcoholism. Lignin content (LC) has a direct and negative role on the fruit texture and taste of snow pears. Here, we studied the effect on the near‐infrared (NIR) spectroscopy determination of the LC in snow pears due to the position at which spectral measurements were obtained. NIR diffuse reflection spectra were collected from nine measurement positions on each sample by a portable NIR spectrometer. Partial least squares regression (PLSR) was used to develop spectrum compensation models of the LC for three local spectrum models, an average spectrum model, and a global spectrum model. The results indicated that the prediction accuracy of the LC was affected by the spectral measurement position. Compared with the local spectrum models and the global spectrum model, the average spectrum model had good prediction results. Next, synergy interval partial least squares, bootstrapping soft shrinkage, competitive adaptive reweighted sampling, genetic algorithm, and an improved variable stability and frequency analysis algorithm (VSFAA) method were used to select the most effective variables to build the PLSR model. The average spectrum calibration model established using the 10 effective variables selected by VSFAA reduced the influence of the variation of the spectral measurement position for LC prediction and achieved more promising results, with the correlation coefficient of calibration and prediction of 0.842 and 0.824, respectively. The root mean square error of cross‐validation and prediction were 0.736 and 0.694, respectively. The overall results showed that the average spectrum model based on the nine spectral measurement positions reduced the sensitivity to the variation of spectral measurement position for predicting the LC and combined the VSFAA variable selection algorithm to improve the accuracy and provide a robust model for prediction of LC in snow pears. Compared with the local spectrum position models and the global spectrum position model, the average spectrum position model combining the nine measurement positions (three stem‐calyx longitudes intersected three latitudes (stem, equator, calyx)) produced good prediction results and reduced the sensitivity to the variation of the spectral measurement position. The effective wavelengths (SNV‐VSFAA‐PLSR)‐average spectrum position model achieved good results, reducing the influence of the variation of the spectral measurement position for LC prediction, and the effective wavelengths selected from the average spectrum position model were helpful for offsetting the influence of the variation of spectral measurement position on the PLSR models based on the spectrum from the equatorial positions alone

Design and Experiment of Lightweight Dual-Mode Automatic Variable-Rate Fertilization Device and Control System

China’s agricultural facilities are developing rapidly and are mainly operated through household contracting. Due to a lack of suitable variable-rate fertilization devices, manual and blind fertilization still widely exists, resulting in fertilizer waste and environmental pollution. Meanwhile, existing fertilization devices cannot simultaneously meet the needs of different fertilization methods for crop cultivation, increasing the cost of mechanized fertilization. This study developed a lightweight dual-mode automatic variable-rate fertilization device and control system for strip fertilization and spreading fertilization. The least squares method was used to analyze the amount of fertilizer discharged per second at different volumes and rotational speeds of the fertilization device. The quadratic polynomial model fits well, with determination coefficients greater than 0.99. The automatic variable strip fertilization and spreading fertilization control models were established. Experiments with strip fertilization and spreading fertilization were carried out. The results of strip fertilization experiments show that the maximum relative error (Re) for granular nitrogen fertilizer (NF) was 6.81%, compound fertilizer (CF) was 6.2%, organic compound fertilizer (OCF) was 6.83%, and the maximum coefficient of variation (Cv) of uniformity was 8.91%. The results of spreading fertilization experiments show that the maximum Re of granular NF was 7.31%, granular CF was 6.76%, granular OCF was 7.43%, the Cv of lateral uniformity was 9.88%, and the Cv of total uniformity was 14.17%. The developed fertilization device and control system can meet the needs of different fertilization amounts, types, and methods for facility crop cultivation at different stages. This study’s results can provide a theoretical basis and technical support for designing and optimizing multifunctional precision variable-rate fertilization devices and control systems

Disulfiram/Copper Induce Ferroptosis in Triple-Negative Breast Cancer Cell Line MDA-MB-231

Background: The complex formed by disulfiram (DSF) and copper (Cu) is safe and effective for the prevention and treatment of triple-negative breast cancer (TNBC). Although previous studies have shown that DSF/Cu induces ferroptosis, the mechanism remains unclear. Methods: The mitochondrial morphology of TNBC treated with DSF/Cu was observed by transmission microscopy, and intracellular levels of iron, lipid reactive oxygen species (ROS), malondialdehyde, and glutathione were evaluated to detect the presence of ferroptosis. Target genes for the DSF/Cu-activated ferroptosis signaling pathway were examined by transcriptome sequencing analysis. Expression of the target gene, HOMX1, was detected by qRT-PCR, immunofluorescence and western blot. Results: The mitochondria of TNBC cells were significantly atrophied following treatment with DSF/Cu for 24 h. Addition of DSF/Cu supplement resulted in significant up-regulation of intracellular iron, lipid ROS and malondialdehyde levels, and significant down-regulation of glutathione levels, all of which are important markers of ferroptosis. Transcriptome analysis confirmed that DSF/Cu activated the ferroptosis signaling pathway and up-regulated several ferroptosis target genes associated with redox regulation, especially heme oxygenase-1 (HMOX-1). Inhibition of ferroptosis by addition of the ROS scavenger N-acetyl-L-cysteine (NAC) significantly increased the viability of DSF/Cu-treated TNBC cells. Conclusions: These results show that DSF/Cu increases lipid peroxidation and causes a sharp increase in HMOX1 activity, thereby inducing TNBC cell death through ferroptosis. DSF/Cu is a promising therapeutic drug for TNBC and could lead to ferroptosis-mediated therapeutic strategies for human cancer