Development and comparison of classification models on VIS-NIR hyperspectral imaging spectra for qualitative detection of the Staphylococcus aureus in fresh chicken breast.

Chicken is at risk of contaminated foodborne pathogens in the production process. Timely and nondestructive detection of foodborne pathogens in chicken is essential for food security. The study aims to explore the feasibility of developing efficient classification models for qualitative detection of Staphylococcus aureus in chicken breast using the hyperspectral imaging technique. Principal component analysis was used to process the full spectral information and three wavelength selection methods (competitive adaptive reweighted sampling, genetic algorithm, and successive projections algorithm) were applied to extract effective wavelengths. These methods were combined with the support vector machine algorithm to develop conventional classification models, respectively. In addition, a convolutional neural network model based on deep learning was designed and trained for comparison. The performance of the convolutional neural network model was significantly better than that of conventional classification models. The overall accuracy for chicken sample classifications was improved from 83.88% to 91.38%. The results demonstrated that deep learning can effectively extract spectral features and promote the application of hyperspectral imaging in foodborne pathogens detection of chicken products.

Microalgae Bioactive Carbohydrates as a Novel Sustainable and Eco-Friendly Source of Prebiotics: Emerging Health Functionality and Recent Technologies for Extraction and Detection.

There is a global interest in the novel consumption, nutritional trends, and the market of new prebiotic sources and their potential functional impacts. Commercially available nutritional supplements based on microalgae that are approved to be edible by FDA, like Arthrospira platensis (Cyanobacteria) and Chlorella vulgaris (Chlorophyta) become widely attractive. Microalgae are rich in carbohydrates, proteins, and polyunsaturated fatty acids that have high bioactivity. Recently, scientists are studying the microalgae polysaccharides (PS) or their derivatives (as dietary fibers) for their potential action as a novel prebiotic source for functional foods. Besides, the microalgae prebiotic polysaccharides are used for medication due to their antioxidant, anticancer, and antihypertensive bioactivities. This review provides an overview of microalgae prebiotics and other macromolecules' health benefits. The phytochemistry of various species as alternative future sources of novel polysaccharides were mentioned. The application as well as the production constraints and multidisciplinary approaches for evaluating microalgae phytochemistry were discussed. Additionally, the association between this potential of combining techniques like spectroscopic, chromatographic, and electrochemical analyses for microalgae sensation and analysis novelty compared to the chemical methods was emphasized.

Structure analysis and non-invasive detection of cadmium-phytochelatin2 complexes in plant by deep learning Raman spectrum.

Plants synthesize phytochelatins to chelate in vivo toxic heavy metal ions and produce nontoxic complexes for tolerating the stress. Detection of the complexes would simplify the identification of high phytoremediation cultivars, as well as assessment of plant food for safe consumption. Thus, a confocal Raman spectroscopy combined with density functional theory and deep learning was used for characterizing phytochelatin2 (PC2), and Cd-PC2 mixtures. Results showed the PC2 chelate Cd2+ in a 2:1 ratio to produce Cd(PC2)2; Cd-S bonds of the Cd(PC2)2 have signature Raman vibrations at 305 and 610 cm-1 which are the most distinctive spectral signatures for varieties of Cd-PCs complexes. The PC2 was used as a natural probe to stabilize the chemical status of Cd, and to enrich and magnify Raman signature of the trace Cd for deep learning models which enabled condition of the Cd(PC2)2 in pak choi leaf to be visualized, quantified, and classified by directly using raw spectra of the leaf. This study provides a general protocol by using Raman information for structure analysis and non-invasive detection of heavy metal-PCs complexes in plants and provides a novel idea for simplifying identification of high phytoremediation cultivars, as well as assessment of heavy metal related food safeties.

Hydro-Electro Hybrid Priming Promotes Carrot (Daucus carota L.) Seed Germination by Activating Lipid Utilization and Respiratory Metabolism.

Carrot (Daucus carota L.) is widely cultivated as one of the most important root crops, and developing an effective presowing treatment method can promote the development of modern mechanized precision sowing. In the present study, a novel seed priming technology, named hydro-electro hybrid priming (HEHP), was used to promote the germination of carrot seeds. Seed germination experiments showed that HEHP was able to increase the germination index (GI) and vigor index (VI) by 3.1-fold and 6.8-fold, respectively, and the effect was significantly superior to that of hydro-priming (HYD) and electrostatic field treatment (EF). The consumption and utilization rate of seed storage reserves were also greatly improved. Meanwhile, both glyoxysomes and mitochondria were found to appear ahead of time in the endosperm cells of HEHP through observations of the subcellular structure of the endosperm. Activities of isocitrate lyase (ICL), NAD-dependent malate dehydrogenase (MDH), pyruvate kinase (PK), and alcohol dehydrogenase (ADH) were significantly increased by HEHP. From transcriptome results, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to the glyoxylate cycle, glycolysis, gluconeogenesis, and the citrate cycle were significantly enriched and real-time quantitative PCR (qRT-PCR) analysis confirmed the expression pattern of 15 critical differentially expressed genes (DEGs) in these pathways. All DEGs encoding MDH, phosphoenolpyruvate carboxykinase (PEPCK), and PK were upregulated in HEHP; thus, it is reasonable to infer that the transformation of malate, oxalacetate, phosphoenolpyruvate, and pyruvate in the cytoplasm may be pivotal for the energy supply during early germination. The results suggest that the optimal effect of HEHP is achieved by initiating stored lipid utilization and respiratory metabolism pathways related to germination.

Analyzing cadmium-phytochelatin2 complexes in plant using terahertz and circular dichroism information.

Phytochelatins are plants' small metal-binding peptides which chelate internal heavy metals to form nontoxic complexes. Detecting the complexes in plants would simplify identification of cultivars with both high tolerance and enrichment capabilities for heavy metals which represent phytoextraction performance. Thus, a terahertz spectroscopy combined with density functional theory, chemometrics and circular dichroism was used for characterization of phytochelatin2 (PC2), Cd-PC2 mixture standards, and pak choi (Brassica chinensis) leaves as a plant model. Results showed PC2 chelates Cd2+ in a 2:1 ratio to form Cd(PC2)2 complex; Cd connected to thoils of PC2 and changed ß-turn and random coil of PC2 peptide chain to ß-Sheet which presented as terahertz vibrations of PC2 around 1.03 and 1.71 THz being suppressed; the best models for detecting the complex in pak choi were obtained by partial least squares regression modeling combined with successive projections algorithm selection; the models used PC2 as a natural probe for visualizing and quantifying chelated Cd in pak choi leaf and achieved a limit of detection up to 1.151 ppm. This study suggested that terahertz information of the heavy metal-PCs complexes is qualified for representing a simpler alternative to classical index for evaluating phytoextraction performance of plant; it provided a general protocol for structure analysis and detection of heavy metal-PCs complexes in plant by terahertz absorbance.

Spring onion seed demand forecasting using a hybrid Holt-Winters and support vector machine model.

Demand for spring onion seeds is variable and maintaining its supply is crucial to the success of seed companies. Spring onion seed demand forecasting, which can help reduce the high operational costs increased by long-period propagation and complex logistics, has not previously been investigated yet. This paper provides a novel perspective on spring onion seed demand forecasting and proposes a hybrid Holt-Winters and support vector machine (SVM) forecasting model. The model uses dynamic factors, including historical seed sales, seed inventory, spring onion crop market price and weather data, as inputs to forecast spring onion seed demand. Forecasting error, i.e. the difference between actual and forecasted demand, is assessed. Two advanced machine learning models are trained on the same dataset as benchmark models. Numerical experiments using actual commercial sales data for three spring onion seed varieties show the proposed hybrid model outperformed the statistical-based models for all three forecasting errors. Seed inventory, spring onion crop market price and historical seed sales are the most important dynamic factors, among which seed inventory has short-term influence while other two have mid-term influence on seed demand forecasting. The absolute minimum temperature is the only factor having long-term influence. This study provides a promising spring onion seed demand forecasting model that helps understand the relationships between seed demand and other dynamic factors and the model could potentially be applied to demand forecasting of other crop seeds to reduce total operational costs.