Trace Identification and Visualization of Multiple Benzimidazole Pesticide Residues on Toona sinensis Leaves Using Terahertz Imaging Combined with Deep Learning.

Molecular spectroscopy has been widely used to identify pesticides. The main limitation of this approach is the difficulty of identifying pesticides with similar molecular structures. When these pesticide residues are in trace and mixed states in plants, it poses great challenges for practical identification. This study proposed a state-of-the-art method for the rapid identification of trace (10 mg·L-1) and multiple similar benzimidazole pesticide residues on the surface of Toona sinensis leaves, mainly including benzoyl (BNL), carbendazim (BCM), thiabendazole (TBZ), and their mixtures. The new method combines high-throughput terahertz (THz) imaging technology with a deep learning framework. To further improve the model reliability beyond the THz fingerprint peaks (BNL: 0.70, 1.07, 2.20 THz; BCM: 1.16, 1.35, 2.32 THz; TBZ: 0.92, 1.24, 1.66, 1.95, 2.58 THz), we extracted the absorption spectra in frequencies of 0.2-2.2 THz from images as the input to the deep convolution neural network (DCNN). Compared with fuzzy Sammon clustering and four back-propagation neural network (BPNN) models (TrainCGB, TrainCGF, TrainCGP, and TrainRP), DCNN achieved the highest prediction accuracies of 100%, 94.51%, 96.26%, 94.64%, 98.81%, 94.90%, 96.17%, and 96.99% for the control check group, BNL, BCM, TBZ, BNL + BCM, BNL + TBZ, BCM + TBZ, and BNL + BCM + TBZ, respectively. Taking advantage of THz imaging and DCNN, the image visualization of pesticide distribution and residue types on leaves was realized simultaneously. The results demonstrated that THz imaging and deep learning can be potentially adopted for rapid-sensing detection of trace multi-residues on leaf surfaces, which is of great significance for agriculture and food safety.

Protective Application of Morus and Its Extracts in Animal Production.

Different components of the mulberry tree (fruits, leaves, twigs, and roots) are rich in active compounds, and have been reported to possess potent beneficial properties, including antioxidative, anti-inflammatory, antimicrobial, anticancer, anti-allergenic, antihypertensive, and neuroprotective. The mulberry and its extracts can effectively improve the growth performance and fitness of animals. They not only possess the properties of being safe and purely natural, but also they are not prone to drug resistance. According to the literature, the supplemental level of the mulberry and its extracts in animal diets varies with different species, physiological status, age, and the purpose of the addition. It has been observed that the mulberry and its extracts enhanced the growth performance, the quality of animal products (meat, egg, and milk), the antioxidant and the anti-inflammatory responses of animals. Furthermore, the mulberry and its extracts have antibacterial properties and can effectively moderate the relative abundance of the microbial populations in the rumen and intestines, thus improving the immunity function of animals and reducing the enteric methane (CH4) production in ruminants. Furthermore, the mulberry and its extracts have the potential to depurate tissues of heavy metals. Collectively, this review summarizes the nutrients, active compounds, and biological functions of mulberry tree products, as well as the application in livestock production with an aim to provide a reference for the utilization of the mulberry and its extracts in animal production.

Identification and Antioxidant Capacity of Free and Bound Phenolics in Six Varieties of Mulberry Seeds Using UPLC-ESI-QTOF-MS/MS.

Mulberry seeds are a byproduct of juice processing and may be an important resource for its abundant compounds. In this study, we analyzed the qualitative composition of free and bound phenolics from six varieties of mulberry seeds using UPLC-ESI-QTOF-MS/MS. Free phenolics (FPs) and bound phenolics (BPs) were measured using the Folin-Ciocalteu method; antioxidant capacity was determined by measuring 2,2-diphenyl-1-picrylhydrazyl radical-scavenging activity, using the ferric reducing antioxidant power assay. A total of 28 free and 11 bound phenolics were extracted and identified, wherein five free phenolics were found in mulberry matrices for the first time. The six varieties of mulberry seeds exhibited higher content of FPs than BPs, and there was a correlation between the phenolic content and antioxidant capacity. Consequently, three varieties were selected for their high phenolic content and antioxidant capacity. This study might offer a theoretical basis for the utilization of mulberry seed.

Nondestructive measurement of pectin polysaccharides using hyperspectral imaging in mulberry fruit.

Pectin polysaccharide is an important phytochemical with potential biomedical applications. It is commonly measured by time-consuming destructive chemical methods. This work demonstrates the feasibility of using visible and near-infrared hyperspectral imaging (HSI) techniques to rapidly measure pectin polysaccharides in intact mulberry fruits. Based on spatial information provided by HSI images, the representative spectrum of each whole mulberry was accurately extracted without background. The effects of storage temperature on two varieties of mulberries for model establishment were studied. The performances of two spectral ranges obtained by Si and InGaAs CCD detectors for pectin prediction were compared. The best predictions were obtained from dilute alkali soluble pectin and total soluble pectin in Dashi mulberry fruit stored at room temperature, with residual predictive deviation values of 2.317 and 1.935, respectively. Our results show that HSI is a promising alternative to the chemical method to rapidly and nondestructively measure the pectin content.

Building a stable and accurate model for heavy metal detection in mulberry leaves based on a proposed analysis framework and laser-induced breakdown spectroscopy.

Laser-induced breakdown spectroscopy (LIBS) was used to rapidly detect heavy metals in mulberry leaves. For the purpose of increasing detection stability and accuracy, a novel analysis framework consisting of a Kohonen self-organizing map (SOM), a variable selection method using the successive projection algorithm (SPA) and uninformative variable elimination (UVE), and a consensus modeling strategy was proposed for processing LIBS data to determine copper (Cu) and chromium (Cr) content. Results showed that the best regression model for Cu and Cr content achieved the residual predictive deviation (RPD) values of 10.0494 and 8.3874, respectively, and root mean square error of prediction (RMSEP) values of 110.4550 and 41.4561, respectively. The proposed strategy provides a high-accuracy and rapid alternative to the traditional method for monitoring heavy metals in mulberry leaves, which could guarantee the quality of mulberry leaves and potentially be used in food-related industries.

Heavy metal detection in mulberry leaves: Laser-induced breakdown spectroscopy data.

Five copper or chromium stress levels were carried out on mulberry leaf, and 20 samples were collected for each metal stress level. A total of 100 samples (copper or chromium) were processed into uniform pressed pellet. The mulberry leaf pellet was placed on a sample platform of laser-induced breakdown spectroscopy (LIBS) system. A laser was used to ablate the sample pellet and generate the emission lines, the intensity and delay time of laser ablation were 80 mJ and 4 µs respectively. To reduce the acquisition errors, 16 different positions of each sample were ablated for 5 accumulation. Then, 80 spectra were collected per sample and the average of them was considered as the sample spectrum for subsequent analysis. Finally, a total of 200 spectra of copper and chromium in mulberry leaves with a wavelength range of 219-877 nm were obtained for calibration analysis [1].