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Visualizing distribution of pesticide residues in mulberry leaves using NIR hyperspectral imaging

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s11947-012-0933-3openalex-snapshot

Detecting Fertility and Early Embryo Development of Chicken Eggs Using Near-Infrared Hyperspectral Imaging

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Stepwise regression data envelopment analysis for variable reduction

inferredcites →https://doi.org/10.1016/j.carres.2015.03.002openalex-snapshot

Determination of the degree of acetylation and the distribution of acetyl groups in chitosan by HPLC analysis of nitrous acid degraded and PMP labeled products

inferredcites →https://doi.org/10.1016/j.compag.2013.09.011openalex-snapshot

Application of hyperspectral imaging technology to discriminate different geographical origins of Jatropha curcas L. seeds

inferredcites →https://doi.org/10.1016/j.compag.2014.05.001openalex-snapshot

A comparison of partial least squares (PLS) and sparse PLS regressions for predicting yield of Swiss chard grown under different irrigation water sources using hyperspectral data

inferredcites →https://doi.org/10.1016/j.foodchem.2013.11.107openalex-snapshot

Feasibility of using hyperspectral imaging to predict moisture content of porcine meat during salting process

inferredcites →https://doi.org/10.1016/j.foodchem.2014.12.045openalex-snapshot

Investigation on biochemical compositional changes during the microbial fermentation process of Fu brick tea by LC–MS based metabolomics

inferredcites →https://doi.org/10.1016/j.foodchem.2015.02.113openalex-snapshot

Practical aspects in gas chromatography–mass spectrometry for the analysis of pesticide residues in exotic fruits

inferredcites →https://doi.org/10.1016/j.foodchem.2015.04.120openalex-snapshot

Vis–NIR hyperspectral imaging in visualizing moisture distribution of mango slices during microwave-vacuum drying

inferredcites →https://doi.org/10.1016/j.foodchem.2015.05.001openalex-snapshot

Detection and quantification of adulteration of sesame oils with vegetable oils using gas chromatography and multivariate data analysis

inferredcites →https://doi.org/10.1016/j.foodchem.2015.06.106openalex-snapshot

Detection of cold injury in peaches by hyperspectral reflectance imaging and artificial neural network

inferredcites →https://doi.org/10.1016/j.jfoodeng.2013.11.006openalex-snapshot

A method for nondestructive prediction of pork meat quality and safety attributes by hyperspectral imaging technique

inferredcites →https://doi.org/10.1016/j.jfoodeng.2015.06.027openalex-snapshot

A multimodal machine vision system for quality inspection of onions

inferredcites →https://doi.org/10.1016/j.jfoodeng.2015.07.008openalex-snapshot

Application of independent components analysis with the JADE algorithm and NIR hyperspectral imaging for revealing food adulteration

inferredcites →https://doi.org/10.1016/j.lwt.2015.04.039openalex-snapshot

Data fusion and hyperspectral imaging in tandem with least squares-support vector machine for prediction of sensory quality index scores of fish fillet

inferredcites →https://doi.org/10.1515/ijfe-2014-0117openalex-snapshot

Rapid Discrimination of Apple Varieties via Near-Infrared Reflectance Spectroscopy and Fast Allied Fuzzy C-Means Clustering

inferredcites →https://doi.org/10.1515/ijfe-2014-0238openalex-snapshot

Determination of Volatile Compounds of Chinese Traditional Aromatic Sunflower Seeds ( Helianthus annulus L .)

inferredcites →https://doi.org/10.1515/intag-2015-0012openalex-snapshot

Prediction of moisture content uniformity using hyperspectral imaging technology during the drying of maize kernel

inferredcites →https://doi.org/10.1515/teme-2014-0005openalex-snapshot

Hyperspectral image unmixing involving spatial information by extending the alternating least-squares algorithm

inferredcites →https://doi.org/10.19026/ajfst.7.1629openalex-snapshot

Discrimination of Rice Varieties using LS-SVM Classification Algorithms and Hyperspectral Data

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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inferred← citeshttps://doi.org/10.2139/ssrn.4706089openalex-extracted

Determination of Pesticide Content in Sorghum Grains Using Hyperspectral Imaging Technology Combined with Stacked Machine Learning Models

inferred← citeshttps://doi.org/10.1007/978-981-95-4913-9_3openalex-snapshot

Nanomaterials in Pesticide Detection and Degradation

inferred← citeshttps://doi.org/10.1016/j.aiia.2019.02.001openalex-snapshot

Fusion of machine vision technology and AlexNet-CNNs deep learning network for the detection of postharvest apple pesticide residues

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FDF-MT: Multimodal decision-level fusion prediction of abamectin residues in Lycium barbarum

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Advancements in techniques used for identification of pesticide residue on crops

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Research and analysis of cadmium residue in tomato leaves based on WT-LSSVR and Vis-NIR hyperspectral imaging

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Progressive analytical techniques utilized for the detection of contaminants attributed to food safety and security

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TiO 2 compact layer induced charge transfer enhancement in a three-dimensional TiO 2 –Ag array SERS substrate for quantitative and multiplex analysis

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Recent advances in muscle food safety evaluation: Hyperspectral imaging analyses and applications

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Comparative Analysis of Non-Destructive Prediction Model of Soluble Solids Content for Malus micromalus Makino Based on Near-Infrared Spectroscopy

inferred← citeshttps://doi.org/10.1111/jfpe.13603openalex-snapshot

Identification of Lycium barbarum varieties based on hyperspectral imaging technique and competitive adaptive reweighted sampling ‐ whale optimization algorithm ‐ support vector machine

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Classification of Pesticide Residues in Sorghum Based on Hyperspectral and Gradient Boosting Decision Trees

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inferred← citeshttps://doi.org/10.1155/2020/1402965openalex-snapshot

The Kinetic Model of the Peel Brittleness of Stored Cucumis Melons Based on Visible/Near-Infrared Spectroscopy

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From Precision Mechanization to Smart Automation in Onion and Welsh Onion Production

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inferred← citeshttps://doi.org/10.3390/chemistry7040115openalex-snapshot

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inferred← citeshttps://doi.org/10.3390/chemosensors13110393openalex-snapshot

A Comprehensive Review of Non-Destructive Monitoring of Food Freshness and Safety Using NIR Spectroscopy and Biosensors: Challenges and Opportunities

inferred← citeshttps://doi.org/10.3390/foods11111609openalex-snapshot

Detection of Pesticide Residue Level in Grape Using Hyperspectral Imaging with Machine Learning

inferred← citeshttps://doi.org/10.3390/foods14142488openalex-snapshot

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Advances in Hyperspectral Imaging for Nondestructive Food Quality and Safety Detection

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Advances in Machine Learning and Deep Learning Algorithm-Assisted Hyperspectral Imaging for Food Quality and Safety Assessment

inferred← citeshttps://doi.org/10.3390/foods15193396openalex-snapshot

Hyperspectral Imaging for Agricultural and Food Sensing: Applications, Data Analytics, and Deployment Challenges

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Rapid Multi-Residue Detection Methods for Pesticides and Veterinary Drugs

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Multimodal Fusion-Driven Pesticide Residue Detection: Principles, Applications, and Emerging Trends

inferred← citeshttps://doi.org/10.3390/nano15211634openalex-snapshot

Nanomaterial-Enabled Spectroscopic Sensing: Building a New Paradigm for Precision Detection of Pesticide Residues

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NIR Hyperspectral Imaging Technology Combined with Multivariate Methods to Study the Residues of Different Concentrations of Omethoate on Wheat Grain Surface

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