Authors:
J.J. Lin School of Physics and Electronics, Nanning Normal University, Nanning 530001, China

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Q.H. Meng School of Physics and Electronics, Nanning Normal University, Nanning 530001, China
Key Laboratory of New Electric Functional Materials of Guangxi Colleges and Universities, Nanning Normal University, Nanning 530001, China

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Z.F. Wu School of Physics and Electronics, Nanning Normal University, Nanning 530001, China
Key Laboratory of New Electric Functional Materials of Guangxi Colleges and Universities, Nanning Normal University, Nanning 530001, China

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S.Y. Pei School of Physics and Electronics, Nanning Normal University, Nanning 530001, China
Key Laboratory of New Electric Functional Materials of Guangxi Colleges and Universities, Nanning Normal University, Nanning 530001, China

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P. Tian School of Physics and Electronics, Nanning Normal University, Nanning 530001, China

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X. Huang School of Physics and Electronics, Nanning Normal University, Nanning 530001, China

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Z.Q. Qiu School of Physics and Electronics, Nanning Normal University, Nanning 530001, China

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H.J. Chang School of Physics and Electronics, Nanning Normal University, Nanning 530001, China

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C.Y. Ni School of Physics and Electronics, Nanning Normal University, Nanning 530001, China

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Y.Q. Huang Key Laboratory of Environmental Evolution and Resource Utilization of the Beibu Gulf, Ministry of Education & Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation, Nanning Normal University, Nanning 530001, China

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Y. Li Guangxi Technical Instruction Office for Fruit, Nanning 530022, China

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Abstract

This paper explores the prediction of the soluble solid content (SSC) in the visible and near-infrared (400–1,000 nm) regions of Baise mango. Hyperspectral images of Baise mangoes with wavelengths of 400–1,000 nm were obtained using a hyperspectral imaging system. Multiple scatter correction (MSC) was chosen to remove the effect of noise on the accuracy of the partial least squares (PLS) regression model. On this basis, the characteristic wavelengths of mango SSC were selected using the competitive adaptive reweighted sampling (CARS), genetic algorithm (GA), uninformative variable elimination (UVE), and combined CARS + GA-SPA, CARS + UVE-SPA, and GA + UVE-SPA characteristic wavelength methods. The results show that the combined MSC-CARS + GA-SPA-PLS algorithm can reduce redundant information and improve the computational efficiency, so it is an effective method to predict the SSC of mangoes.

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  • Fan, S., Huang, W., Guo, Z., Zhang, B., and Zhao, C. (2015). Prediction of soluble solids content and firmness of pears using hyperspectral reflectance imaging. Food Analytical Methods, 8(8): 19361946.

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  • Mishra, P., Woltering, E., Brouwer, B., and van Hogeveen, E.E. (2021). Improving moisture and soluble solids content prediction in pear fruit using near-infrared spectroscopy with variable selection and model updating approach. Postharvest Biology and Technology, 171: 111348.

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  • Pu, H.B., Liu, D., Wang, L., and Sun, D.W. (2016). Soluble solids content and pH prediction and maturity discrimination of lychee fruits using visible and near infrared hyperspectral imaging. Food Analytical Methods, 9(1): 235244.

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  • Rungpichayapichet, P., Nagle, M., Yuwanbun, P., Khuwijitjaru, P., Mahayothee, B., and Müller, J. (2017). Prediction mapping of physicochemical properties in mango by hyperspectral imaging. Biosystems Engineering, 159: 109120.

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  • Sun, X.D., Subedi, P., and Walsh, K.B. (2020). Achieving robustness to temperature change of a NIRS-PLSR model for intact mango fruit dry matter content. Postharvest Biology and Technology, 162: 111117.

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  • Wang, L.L., Lin, Y.W., Wang, X.F., Xiao, N., Xu, Y.D., Li, H.D., and Xu, Q.S. (2018). A selective review and comparison for interval variable selection in spectroscopic modeling. Chemometrics and Intelligent Laboratory Systems, 172: 229240.

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  • Weingerl, V. and Unuk, T. (2015). Chemical and fruit skin colour markers for simple quality control of tomato fruits. Croatian Journal of Food Science and Technology, 7(2): 7685.

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  • Weng, S., Guo, B., Tang, P., Yin, X., Pan, F., Zhao, J., Huang, L., and Zhang, D. (2020). Rapid detection of adulteration of minced beef using Vis/NIR reflectance spectroscopy with multivariate methods. Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy, 230: 118005.

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  • Zhang, C., Liu, F., and He, Y. (2018). Identification of coffee bean varieties using hyperspectral imaging: influence of preprocessing methods and pixel-wise spectra analysis. Scientific Reports, 8(1): 2166.

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  • Zhang, H.L., Zhan, B.S., Pan, F., and Luo, W. (2020). Determination of soluble solids content in oranges using visible and near infrared full transmittance hyperspectral imaging with comparative analysis of models. Postharvest Biology and Technology, 163: 111148.

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Please, download the file from HERE.

Senior editors

Editor(s)-in-Chief: András Salgó

Co-ordinating Editor(s) Marianna Tóth-Markus

Co-editor(s): A. Halász

       Editorial Board

  • L. Abrankó (Szent István University, Gödöllő, Hungary)
  • D. Bánáti (University of Szeged, Szeged, Hungary)
  • J. Baranyi (Institute of Food Research, Norwich, UK)
  • I. Bata-Vidács (Agro-Environmental Research Institute, National Agricultural Research and Innovation Centre, Budapest, Hungary)
  • F. Békés (FBFD PTY LTD, Sydney, NSW Australia)
  • Gy. Biró (National Institute for Food and Nutrition Science, Budapest, Hungary)
  • A. Blázovics (Semmelweis University, Budapest, Hungary)
  • F. Capozzi (University of Bologna, Bologna, Italy)
  • M. Carcea (Research Centre for Food and Nutrition, Council for Agricultural Research and Economics Rome, Italy)
  • Zs. Cserhalmi (Food Science Research Institute, National Agricultural Research and Innovation Centre, Budapest, Hungary)
  • M. Dalla Rosa (University of Bologna, Bologna, Italy)
  • I. Dalmadi (Szent István University, Budapest, Hungary)
  • K. Demnerova (University of Chemistry and Technology, Prague, Czech Republic)
  • M. Dobozi King (Texas A&M University, Texas, USA)
  • Muying Du (Southwest University in Chongqing, Chongqing, China)
  • S. N. El (Ege University, Izmir, Turkey)
  • S. B. Engelsen (University of Copenhagen, Copenhagen, Denmark)
  • E. Gelencsér (Food Science Research Institute, National Agricultural Research and Innovation Centre, Budapest, Hungary)
  • V. M. Gómez-López (Universidad Católica San Antonio de Murcia, Murcia, Spain)
  • J. Hardi (University of Osijek, Osijek, Croatia)
  • H. He (Henan Institute of Science and Technology, Xinxiang, China)
  • K. Héberger (Research Centre for Natural Sciences, ELKH, Budapest, Hungary)
  • N. Ilić (University of Novi Sad, Novi Sad, Serbia)
  • D. Knorr (Technische Universität Berlin, Berlin, Germany)
  • H. Köksel (Hacettepe University, Ankara, Turkey)
  • K. Liburdi (Tuscia University, Viterbo, Italy)
  • M. Lindhauer (Max Rubner Institute, Detmold, Germany)
  • M.-T. Liong (Universiti Sains Malaysia, Penang, Malaysia)
  • M. Manley (Stellenbosch University, Stellenbosch, South Africa)
  • M. Mézes (Szent István University, Gödöllő, Hungary)
  • Á. Németh (Budapest University of Technology and Economics, Budapest, Hungary)
  • P. Ng (Michigan State University,  Michigan, USA)
  • Q. D. Nguyen (Szent István University, Budapest, Hungary)
  • L. Nyström (ETH Zürich, Switzerland)
  • L. Perez (University of Cordoba, Cordoba, Spain)
  • V. Piironen (University of Helsinki, Finland)
  • A. Pino (University of Catania, Catania, Italy)
  • M. Rychtera (University of Chemistry and Technology, Prague, Czech Republic)
  • K. Scherf (Technical University, Munich, Germany)
  • R. Schönlechner (University of Natural Resources and Life Sciences, Vienna, Austria)
  • A. Sharma (Department of Atomic Energy, Delhi, India)
  • A. Szarka (Budapest University of Technology and Economics, Budapest, Hungary)
  • M. Szeitzné Szabó (National Food Chain Safety Office, Budapest, Hungary)
  • S. Tömösközi (Budapest University of Technology and Economics, Budapest, Hungary)
  • L. Varga (University of West Hungary, Mosonmagyaróvár, Hungary)
  • R. Venskutonis (Kaunas University of Technology, Kaunas, Lithuania)
  • B. Wróblewska (Institute of Animal Reproduction and Food Research, Polish Academy of Sciences Olsztyn, Poland)

 

Acta Alimentaria
E-mail: Acta.Alimentaria@uni-mate.hu

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2022  
Web of Science  
Total Cites
WoS
892
Journal Impact Factor 1.1
Rank by Impact Factor

Food Science and Technology (Q4)
Nutrition and Dietetics (Q4)

Impact Factor
without
Journal Self Cites
1.1
5 Year
Impact Factor
1
Journal Citation Indicator 0.22
Rank by Journal Citation Indicator

Food Science and Technology (Q4)
Nutrition and Dietetics (Q4)

Scimago  
Scimago
H-index
32
Scimago
Journal Rank
0.231
Scimago Quartile Score

Food Science (Q3)

Scopus  
Scopus
Cite Score
1.7
Scopus
CIte Score Rank
Food Science 225/359 (37th PCTL)
Scopus
SNIP
0.408

2021  
Web of Science  
Total Cites
WoS
856
Journal Impact Factor 1,000
Rank by Impact Factor Food Science & Technology 130/143
Nutrition & Dietetics 81/90
Impact Factor
without
Journal Self Cites
0,941
5 Year
Impact Factor
1,039
Journal Citation Indicator 0,19
Rank by Journal Citation Indicator Food Science & Technology 143/164
Nutrition & Dietetics 92/109
Scimago  
Scimago
H-index
30
Scimago
Journal Rank
0,235
Scimago Quartile Score

Food Science (Q3)

Scopus  
Scopus
Cite Score
1,4
Scopus
CIte Score Rank
Food Sciences 222/338 (Q3)
Scopus
SNIP
0,387

 

2020
 
Total Cites
768
WoS
Journal
Impact Factor
0,650
Rank by
Nutrition & Dietetics 79/89 (Q4)
Impact Factor
Food Science & Technology 130/144 (Q4)
Impact Factor
0,575
without
Journal Self Cites
5 Year
0,899
Impact Factor
Journal
0,17
Citation Indicator
 
Rank by Journal
Nutrition & Dietetics 88/103 (Q4)
Citation Indicator
Food Science & Technology 142/160 (Q4)
Citable
59
Items
Total
58
Articles
Total
1
Reviews
Scimago
28
H-index
Scimago
0,237
Journal Rank
Scimago
Food Science Q3
Quartile Score
 
Scopus
248/238=1,0
Scite Score
 
Scopus
Food Science 216/310 (Q3)
Scite Score Rank
 
Scopus
0,349
SNIP
 
Days from
100
submission
 
to acceptance
 
Days from
143
acceptance
 
to publication
 
Acceptance
16%
Rate
2019  
Total Cites
WoS
522
Impact Factor 0,458
Impact Factor
without
Journal Self Cites
0,433
5 Year
Impact Factor
0,503
Immediacy
Index
0,100
Citable
Items
60
Total
Articles
59
Total
Reviews
1
Cited
Half-Life
7,8
Citing
Half-Life
9,8
Eigenfactor
Score
0,00034
Article Influence
Score
0,077
% Articles
in
Citable Items
98,33
Normalized
Eigenfactor
0,04267
Average
IF
Percentile
7,429
Scimago
H-index
27
Scimago
Journal Rank
0,212
Scopus
Scite Score
220/247=0,9
Scopus
Scite Score Rank
Food Science 215/299 (Q3)
Scopus
SNIP
0,275
Acceptance
Rate
15%

 

Acta Alimentaria
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Acta Alimentaria
Language English
Size B5
Year of
Foundation
1972
Volumes
per Year
1
Issues
per Year
4
Founder Magyar Tudományos Akadémia    
Founder's
Address
H-1051 Budapest, Hungary, Széchenyi István tér 9.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 0139-3006 (Print)
ISSN 1588-2535 (Online)

 

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