Variation analysis in spectral indices of volatile chlorpyrifos and non-volatile imidacloprid in jujube ( Ziziphus jujuba Mill.) using near-infrared hyperspectral imaging (NIR-HSI) and gas chromatograph-mass spectrometry (GC–MS)

Su, Wen-Hao; Sun, Da‐Wen; He, Junfeng; Zhang, Ling-Biao

doi:10.1016/j.compag.2017.04.017

Cited by 52 publications

(26 citation statements)

References 101 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hyperspectral imaging obtains the images of continuous narrow wavebands and generates the spectrum for each pixel in the image [90]. The extensively used imaging spectrometer is described as line scanning device.…”

Section: Hyperspectral Imagingmentioning

confidence: 99%

Advanced Machine Learning in Point Spectroscopy, RGB- and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review

2020

Smart Cities

Self Cite

View full text Add to dashboard Cite

Crop productivity is readily reduced by competition from weeds. It is particularly important to control weeds early to prevent yield losses. Limited herbicide choices and increasing costs of weed management are threatening the profitability of crops. Smart agriculture can use intelligent technology to accurately measure the distribution of weeds in the field and perform weed control tasks in selected areas, which cannot only improve the effectiveness of pesticides, but also increase the economic benefits of agricultural products. The most important thing for an automatic system to remove weeds within crop rows is to utilize reliable sensing technology to achieve accurate differentiation of weeds and crops at specific locations in the field. In recent years, there have been many significant achievements involving the differentiation of crops and weeds. These studies are related to the development of rapid and non-destructive sensors, as well as the analysis methods for the data obtained. This paper presents a review of the use of three sensing methods including spectroscopy, color imaging, and hyperspectral imaging in the discrimination of crops and weeds. Several algorithms of machine learning have been employed for data analysis such as convolutional neural network (CNN), artificial neural network (ANN), and support vector machine (SVM). Successful applications include the weed detection in grain crops (such as maize, wheat, and soybean), vegetable crops (such as tomato, lettuce, and radish), and fiber crops (such as cotton) with unsupervised or supervised learning. This review gives a brief introduction into proposed sensing and machine learning methods, then provides an overview of instructive examples of these techniques for weed/crop discrimination. The discussion describes the recent progress made in the development of automated technology for accurate plant identification as well as the challenges and future prospects. It is believed that this review is of great significance to those who study automatic plant care in crops using intelligent technology.

show abstract

Section: Hyperspectral Imagingmentioning

confidence: 99%

Advanced Machine Learning in Point Spectroscopy, RGB- and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review

2020

Smart Cities

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, more advanced research has been performed in laboratories to objectively quantify and certify the quality attributes of food products based on various spectroscopic technologies (Damez and Clerjon ; Su and others ). Among them, Fourier transform infrared (FT‐IR) spectroscopy, Raman spectroscopy, and hyperspectral imaging (HSI) are considered to be very promising tools for quality analysis due to their noninvasive and rapid detection features, as well as their ability to explore food materials with minimal sample preparation (Lu and others ; Alexandrakis and others ; Lohumi and others ; Zhang and others ; Lee and Herrman ; Li and others ; Su and Sun ; Xie and others ; Baum and others ; Su and others ; Su and others ; Zhao and others ). Although several reviews have been published on these novel techniques, they have only focused on one or two spectroscopic methods that has been applied to just a handful of food products, such as chicken meat (Xiong and others ), fruits (Li and others ), red meats (Xiong and others ), muscle foods (Herrero ; Cheng and others ), and liquid foods (Wang and others ).…”

Section: Introductionmentioning

confidence: 99%

Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review

Sun

2017

Comp Rev Food Sci Food Safe

Self Cite

134

View full text Add to dashboard Cite

Fourier transform infrared (FT-IR) and Raman and hyperspectral imaging (HSI) techniques have emerged as reliable analytical methods for effectively characterizing and quantifying quality attributes of different categories of powdery food products (such as milk powder, tea powder, cocoa powder, coffee powder, soybean flour, wheat flour, and chili powder). In addition to the ability for gaining rapid information about food chemical components (such as moisture, protein, and starch), and classifying food quality into different grades, such techniques have also been implemented to determine trace impurities in pure foods and other properties of particulate foods and ingredients with avoidance of extensive sample preparation. Developments of corresponding quality evaluation systems based on FT-IR, Raman, and HSI data that measure food quality parameters and ensure product authentication, would bring about technical and economic benefits to the food industry by enhancing consumer confidence in the quality of its products. Accordingly, a comprehensive review of the mushrooming spectroscopy-based FT-IR, Raman, and HSI literature is carried out in this article. The spectral data collected, the chemometric methods used, and the main findings of recent research studies on quality assessments of powdered materials are discussed and summarized. Providing a review in such a flourishing research field is relevant as a signpost for future study. The conclusion details the promise of how such noninvasive and powerful analytical techniques can be used for rapid and accurate determinations of powder quality attributes in both academical and industrial settings.

show abstract

“…An NIR-hyperspectral imaging technique and GC-MS were used to detect two pesticides (chlorpyrifos and imidacloprid) in jujube fruits in the spectral 900-1700 nm. Based on the extracted spectral data, a simplified E S -AWLSGSD-RC-LWPLSR model was established with eight characteristic wavelengths, having correlation coefficients of cross-validation of 0.757 and 0.898 for chlorpyrifos and imidacloprid, respectively [108]. Moreover, an NIR-hyperspectral imaging system has also been used to map the distribution of pesticide residues in mulberry leaves [107].…”

Section: Near-infrared Spectroscopy Based Multi-residue Detectionsmentioning

confidence: 99%

Rapid Multi-Residue Detection Methods for Pesticides and Veterinary Drugs

et al. 2020

View full text Add to dashboard Cite

The excessive use or abuse of pesticides and veterinary drugs leads to residues in food, which can threaten human health. Therefore, there is an extremely urgent need for multi-analyte analysis techniques for the detection of pesticide and veterinary drug residues, which can be applied as screening techniques for food safety monitoring and detection. Recent developments related to rapid multi-residue detection methods for pesticide and veterinary drug residues are reviewed herein. Methods based on different recognition elements or the inherent characteristics of pesticides and veterinary drugs are described in detail. The preparation and application of three broadly specific recognition elements—antibodies, aptamers, and molecular imprinted polymers—are summarized. Furthermore, enzymatic inhibition-based sensors, near-infrared spectroscopy, and SERS spectroscopy based on the inherent characteristics are also discussed. The aim of this review is to provide a useful reference for the further development of rapid multi-analyte analysis of pesticide and veterinary drug residues.

show abstract

Variation analysis in spectral indices of volatile chlorpyrifos and non-volatile imidacloprid in jujube ( Ziziphus jujuba Mill.) using near-infrared hyperspectral imaging (NIR-HSI) and gas chromatograph-mass spectrometry (GC–MS)

Cited by 52 publications

References 101 publications

Advanced Machine Learning in Point Spectroscopy, RGB- and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review

Advanced Machine Learning in Point Spectroscopy, RGB- and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review

Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review

Rapid Multi-Residue Detection Methods for Pesticides and Veterinary Drugs

Contact Info

Product

Resources

About