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Sensing of Moisture Content of In-Shell Peanuts by NIR Reflectance Spectroscopy

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Journal of Sensor Technology 2012年第1期2卷 1-7页

作者： Jaya Sundaram chari v. kandala Konda Naganathan Gov.ndarajan Jeyam Subbiah Bio-Systems Engineering University of Nebraska Lincoln USA United States Department of Agriculture Agricultural Research Service National Peanut Research Laboratory Dawson USA

It was found earlier that moisture content (MC) of intact kernels of grain and nuts could be determined by Near Infra Red (NIR) reflectance spectrometry. Howev.r, if the MC v.lues can be determined while the nuts are in their shells, it would sav. lot of labor and money spent in shelling and cleaning the nuts. Grain and nuts absorb low lev.ls of NIR, and when NIR radiation is incident on them, a substantial portion of the radiation is reflected back. Thus, studying the NIR reflectance spectra emanating from in-shell peanuts, an attempt is made for the first time to determine the MC of in-shell peanuts. In-shell peanuts of two different market types, v.rginia and v.lencia, were conditioned to different moisture lev.ls between 6% and 26% (wet basis), and separated into calibration and v.lidation groups. NIR absorption spectral data from 1000 nm to 2500 nm in 1 nm interv.ls were collected from both groups. Measurements were obtained on 30 replicates within each moisture lev.l. Reference MC v.lues for each moisture lev.l in these groups were obtained using standard air-ov.n method. Partial Least Square (PLS) analysis was performed on the calibration data, and prediction models were dev.loped. The Standard Error of Calibration (SEC), and R2 of the calibration models were computed to select the best calibration model. The selected models were used to predict the moisture content of peanuts in the v.lidation sets. Predicted MC v.lues of the v.lidation samples were compared with their standard air-ov.n moisture v.lues. Goodness of fit was determined based on the lowest Standard Error of Prediction (SEP) and highest R2 v.lue obtained for the prediction models. The model, with reflectance plus normalization spectral data with an SEP of 0.74 for v.lencia and 1.57 for v.rginia type in-shell peanuts was selected as the best model. The corresponding R2 v.lues were 0.98 for both peanut types. This work establishes the possibility of sensing MC of intact in-shell peanuts by NIR ref

关键词： In-Shell Peanuts NIR Spectroscopy Pretreatments Partial Least Square Standard Error of Prediction Relativ. Percent Dev.ation

Application of NIR Reflectance Spectroscopy on Rapid Determination of Moisture Content of Wood Pellets

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American Journal of Analytical Chemistry 2015年第12期6卷 923-932页

作者： Jaya Sundaram Sudhagar Mani chari v. K. kandala Ronald A. Holser College of Engineering University of Georgia Athens USA National Peanut Research Laboratory Dawson USA Russell Research Center Athens USA

NIR spectroscopy was used to measure the moisture concentration of wood pellets. Pellets were conditioned to v.rious moisture lev.ls between 0.63% and 14.16% (wet basis) and the moisture concentration was v.rified using a standard ov.n method. Samples from v.rious moisture lev.ls were separated into two groups, as calibration and v.lidation sets. NIR absorption spectral data from 400 nm to 2500 nm with 0.5 nm interv.ls were collected using pellets within the calibration and v.lidation sample sets. Spectral wav.length ranges were taken as independent v.riables and the MC of the pellets as the dependent v.riable for the analysis. Measurements were obtained on 30 replicates within each moisture lev.l. Partial Least Square (PLS) analysis was performed on both raw and preprocessed spectral data of calibration set to determine the best calibration model based on Standard Error of Calibration (SEC) and coefficient of multiple determinations (R2). The PLS model that yielded the best fit was used to predict the moisture concentration of v.lidation group pellets. Relativ. Percent Dev.ation (RPD) and Standard Error of Prediction (SEP) were calculated to v.lidate goodness of fit of the prediction model. Baseline and Multiple Scatter Corrected (MSC) reflectance spectra with 1st deriv.tiv. model gav. the highest RPD v.lue of 4.46 and R2 of 0.95. Also it’s SEP (0.670) and RMSEP (0.782) were less than the other models those had RPD v.lue more than 3.0 with less number of factors. Therefore, this model was selected as the best model for moisture content prediction of wood pellets.

关键词： Wood Pellets NIR Reflectance Spectroscopy Moisture Content Partial Least Square Relativ. Percent Dev.ation

Capacitance Sensor for Nondestructiv. Determination of Total Oil Content in Peanut Kernels

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Journal of Sensor Technology 2013年第3期3卷 42-46页

作者： chari v. kandala Rao C. N. Rachaputi Daniel O’Connor Agricultural Research Serv.ce United States Department of Agriculture Dawson USA Center for Plant Science University of Queensland Kingaroy Australia

In this work, attempts were made to estimate the total oil content (TOC) in single peanut kernels, using the CI meter (chari’s Impedance meter, described below). Mature peanut kernels of selected v.rieties with a range of oil contents from 47% to 61% were placed one at a time, between the parallel-plate electrodes of the CI meter, and the impedance (Z) and phase angle (q) of the system were measured, and capacitance, C was computed at 1, 5 and 9 MHz. After the measurements, the TOC of each kernel was determined by Soxhlet method. Using the known TOC v.lues, and the corresponding C, Z and q v.lues, initially on a calibration group of kernels, calibration equations were dev.loped. Using the model coefficients from the calibration, the TOCs of kernel samples of 31 div.rse peanut genotypes grown in different env.ronments in Australia were determined. The method predicted the TOC v.lues of peanut kernels of 31 peanut genotypes, within 2% of the Soxhlet v.lues, with an R2 of 0.87 (P 0.001).

关键词： Impedance Analyzer Parallel-Plate Electrodes Capacitance Phase Angle Total Oil Content (TOC)

Deep Transfer Learning Techniques in Intrusion Detection System-Internet of v.hicles: A State-of-the-Art Rev.ew

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Computers, Materials & Continua 2024年第8期80卷 2785-2813页

作者： Wufei Wu Jav.d Hassannataj Joloudari Senthil Kumar Jagatheesaperumal kandala N.v.P.SRajesh Silv.a Gaftandzhiev. Sadiq Hussain Rahimullah Rabih Najibullah Haqjoo Mobeen Nazar Hamed v.hdat-Nejad Rositsa Donev. School of Information Engineering Nanchang UniversityNanchang330031China Department of Computer Engineering Technical and Vocational University(TVU)Tehran***Iran Department of Computer Engineering University of BirjandBirjand***Iran Department of Computer Engineering Babol BranchIslamic Azad UniversityBabol***Iran Department of Electronics&Communication Engineering Mepco Schlenk Engineering CollegeSivakasi626005India School of Electronics Engineering VIT-AP UniversityNear Vijayawada522237India Faculty of Mathematics and Informatics University of Plovdiv Paisii HilendarskiPlovdiv4000Bulgaria Examination Branch Dibrugarh UniversityDibrugarh786004India Department of Computer Engineering Faculty of EngineeringUniversity of BirjandBirjand***Iran Faculty of Electrical and Computer Engineering University of BirjandBirjand***Iran Malaysian Institute of Information Technology Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Physics and Technology University of Plovdiv Paisii HilendarskiPlovdiv4000Bulgaria

The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of v.hicles(Iov.*** functional adv.ntages of Iov.include online communication serv.ces,accident prev.ntion,cost reduction,and enhanced traffic *** these benefits,Iov.technology is susceptible to cyber-attacks,which can exploit v.lnerabilities in the v.hicle network,leading to perturbations,disturbances,non-recognition of traffic signs,accidents,and v.hicle *** paper rev.ews the state-of-the-art achiev.ments and dev.lopments in applying Deep Transfer Learning(DTL)models for Intrusion Detection Systems in the Internet of v.hicles(IDS-Iov.based on anomaly ***-Iov.lev.rages anomaly detection through machine learning and DTL techniques to mitigate the risks posed by *** systems can autonomously create specific models based on network data to differentiate between regular traffic and *** these techniques,transfer learning models are particularly promising due to their efficacy with tagged data,reduced training time,lower memory usage,and decreased computational *** ev.luate DTL models against criteria including the ability to transfer knowledge,detection rate,accurate analysis of complex data,and *** rev.ew highlights the significant progress made in the field,showcasing how DTL models enhance the performance and reliability of IDS-Iov.*** examining recent adv.ncements,we prov.de insights into how DTL can effectiv.ly address cyber-attack challenges in Iov.env.ronments,ensuring safer and more efficient transportation networks.

关键词： Cyber-attacks internet of things internet of v.hicles intrusion detection system

Peanuts and Their Nutritional Aspects—A Rev.ew

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Food and Nutrition Sciences 2012年第12期3卷 1644-1650页

作者： v. S. Settaluri C. v. K. kandala N. Puppala J. Sundaram National Peanut Research Laboratory United States Department of Agriculture Agricultural Research Service Dawson USA New Mexico State Univ.rsity Las Cruces USA United States Department of Agriculture Agricultural Research Service Athens USA.

Peanut is a legume crop that belongs to the family of Fabaceae, genus Arachis, and botanically named as Arachis hypogaea. Peanuts are consumed in many forms such as boiled peanuts, peanut oil, peanut butter, roasted peanuts, and added peanut meal in snack food, energy bars and candies. Peanuts are considered as a v.tal source of nutrients. Nutrition plays an important role in growth and energy gain of liv.ng organisms. Peanuts are rich in calories and contain many nutrients, minerals, antioxidants, and v.tamins that are essential for optimum health. All these biomolecules are essential for pumping v.tal nutrients into the human body for sustaining normal health. This paper presents an ov.rv.ew of the peanut composition in terms of the constituent biomolecules, and their biological functions. This paper also discusses about the relationship between consumption of peanuts and their effect on human metabolism and physiology. It highlights the usefulness of considering peanuts as an essential component in human diet considering its nutritional v.lues.

关键词： Peanuts Nutrition Biomolecules v.tamins Minerals Plant Protein