Knowledge mapping of research on spectral technology in the fruit field using CiteSpace (1981-2021)

MA, Xueting; LIAO, Jiean; ZHAO, Jinfei; XI, Linqiao

doi:10.1590/fst.116622

Xueting MA

College of Mechanical and Electrical Engineering, Tarim University, Alar, China

Modern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous Region, Tarim University, Alar, China

https://orcid.org/0000-0002-7534-5519

Jiean LIAO ^**Corresponding author: 120100010@taru.edu.cn; gsxlq666@163.com

College of Mechanical and Electrical Engineering, Tarim University, Alar, China

Modern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous Region, Tarim University, Alar, China

Jinfei ZHAO

College of Mechanical and Electrical Engineering, Tarim University, Alar, China

Modern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous Region, Tarim University, Alar, China

Linqiao XI ^**Corresponding author: 120100010@taru.edu.cn; gsxlq666@163.com

College of Animal Science, Tarim University, Alar, China

*Corresponding author: 120100010@taru.edu.cn; gsxlq666@163.com

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of interest

The authors declare no conflict of interest.

Rank	Country	Centrality	Count	Proportion
1	China	0.06	1242	20.14%
2	USA	0.33	707	11.46%
3	India	0.09	306	4.96%
4	Spain	0.21	282	4.57%
5	Germany	0.24	271	4.39%
6	Italy	0.16	249	4.04%
7	Japan	0.06	210	3.40%
8	Brazil	0.02	196	3.18%
9	France	0.23	161	2.61%
10	England	0.14	150	2.43%

CNKI database				WoS database
Author	Institution	Count	Year	Author	Institution	Count	Year
Jialin Xi	Agricultural product quality and safety risk assessment experimental station	39	1981	KAWANO S	Kagoshima University	26	2000
Yande Liu	East China Jiaotong University	36	2003	MARTENS H	Kassel University	11	2000
Yibin Ying	Zhejiang University	29	2002	GREENSILL CV	Central Queensland University	10	2000
Meng Wang	Agricultural product quality and safety risk assessment experimental station	26	1981	GELADI P	Swedish University of Agricultural Sciences	9	2000
Linxia Wu	Agricultural product quality and safety risk assessment experimental station	26	1981	LAMMERTYN J	Katholieke Universiteit Leuven	9	2000

No.	Keyword	Frequency	Centrality	Year	No.	Keyword	Frequency	Centrality	Year
1	nondestructive testing	207	0.16	1981	11	Hyperspectral	18	0.01	2016
2	NIRS	97	0.13	2004	12	chemical composition	18	0.07	2003
3	hyperspectral imaging	36	0.04	2012	13	machine vision	16	0.02	2011
4	soluble solids	35	0.05	2007	14	agriculture products	15	0.01	1994
5	pesticide residues	33	0.19	2005	15	spectral analysis	15	0.04	2002
6	research progress	33	0.00	1981	16	PCA	13	0.03	2008
7	internal quality	31	0.02	2003	17	SSC	12	0.01	2007
8	VIS/NIR spectrum	27	0.04	2006	18	online detection	11	0.02	2010
9	Hyperspectral imaging technology	26	0.02	2013	19	quality detection	11	0.03	2002
10	near-infrared	26	0.13	2007	20	Atomic absorption spectrometry	10	0.09	1992

No.	Keyword	Frequency	Centrality	Year	No.	Keyword	Frequency	Centrality	Year
1	fruit	1204	0.43	1991	11	vegetable	177	0.07	1991
2	identification	352	0.08	1991	12	prediction	174	0.01	2006
3	quality	348	0.03	1992	13	polysaccharide	163	0.03	1993
4	antioxidant	281	0.06	1998	14	Extraction	160	0.03	1996
5	spectroscopy	270	0.18	1991	15	NIR	159	0.00	2006
6	antioxidant activity	229	0.02	2006	16	leave	156	0.06	1995
7	apple	213	0.14	1991	17	food	155	0.10	1991
8	spectra	190	0.13	1991	18	acid	154	0.04	1991
9	extract	187	0.03	2002	19	flavonoid	149	0.05	1997
10	plant	183	0.33	1991	20	constitute	148	0.02	1992

Research content	Significance	Example
Pathogen detection	The disease is one of the serious threats to the quality of fruit, which directly affects the grading of fruit. It is a powerful measure to prevent disease and remove disease fruit in time.	Qin et al. (2021)Qin, K., Chen, G., Zhang, J. Y., & Fu, X. P. (2021). Optimization of fruit pose and modeling method for online spectral detection of apple moldy core. Guangpuxue Yu Guangpu Fenxi, 41(11), 3405-3410. used visible and near/infrared spectral analysis technology to detect and study apple mold heart disease. Especially, the discriminative effect of the four kinds of apples in the online conveying posture (vertical handle up, vertical handle down, horizontal handle facing the conveying direction, and horizontal handle vertical conveying direction) was optimized and analyzed. The results show that PLS-DA can be used as an effective method to identify mold heart disease in apples, and placing the handle vertically can be used as an effective posture for online detection.
Origin traceability	The perfect traceability system of agricultural products can greatly guarantee the quality of fruits and promote the sales of fruits.	Lu et al. (2021)Lu, S. Y., Zhang, L. L., Pan, J. R., Yang, D. H., Sui, Y. N., & Zhu, C. (2021). Study on the indetification of the geographical origin of cherries using Raman spectroscopy and LSTM. Guangpuxue Yu Guangpu Fenxi, 41(4), 1177-1181. proposed a technology that combines long-term and short-term memory (LSTM) network and Raman spectroscopy technology to realize rapid and non-destructive identification of cherries from different regions. Taking cherries from the United States, Shandong and Sichuan as research samples, the spectral data of cherries from different places of origin were obtained by Raman spectrometer, and the discrimination model was built based on LSTM network, which provided a new idea for the origin tracing of cherries.
Variety identification	There are many varieties of the same kind of fruit, with different colors and tastes. Species identification based on spectral technology can easily provide raw materials for food production	Rong et al. (2020)Rong, D., Wang, H., Ying, Y., Zhang, Z., & Zhang, Y. (2020). Peach variety detection using VIS-NIR spectroscopy and deep learning. Computers and Electronics in Agriculture, 175, 105553. http://dx.doi.org/10.1016/j.compag.2020.105553. http://dx.doi.org/10.1016/j.compag.2020.... constructed a one-dimensional convolutional neural network and established a visible/near-infrared spectrum database containing five kinds of peaches. This method can realize the identification of peach varieties, with an accuracy of 94.4% in the test data set.

Research content	Significance	Example
Sugar or acidity detection	Taste is one of the bases for people to choose fruit, thus sugar content and acidity are important factors that affect the taste. In recent years, it has been used as an important reference for fruit taste.	Wu et al. (2021)Wu, X., Li, G. L., & He, F. Y. (2021). Nondestructive analysis of internal quality in pears with a self-made near-infrared spectrum detector combined with multivariate data processing. Foods, 10(6), 1315. http://dx.doi.org/10.3390/foods10061315. PMid:34200438. http://dx.doi.org/10.3390/foods10061315... established a partial least squares regression (PLSR) model using a self-made NIRS detector and an improved variable selection algorithm to detect the SSC content of pears. It shows that the prediction model based on GA-BOSS selection variable and GA-VACAA selection variable is the best.
Brittle or hardness testing	Fruit texture mainly includes brittleness, hardness, etc., which reflect the physical properties and organizational structure of fruits and affect the taste of eating. Traditional fruit texture testing methods use destructive testing methods such as pressure hardness testers or texture analyzers, which are inefficient and damage the commodity value of apples after testing.	Sun et al. (2021)Sun, R., Zhou, J. Y., & Yu, D. (2021). Nondestructive prediction model of internal hardness attribute of fig fruit using NIR spectroscopy and RF. Multimedia Tools and Applications, 80(14), 21579-21594. http://dx.doi.org/10.1007/s11042-021-10777-4. http://dx.doi.org/10.1007/s11042-021-107... proposed a rapid nondestructive testing method for fig hardness based on VIS/NIR. The results show that using VIS/NIR diffuse reflectance spectroscopy combined with sample set partition based on joint X-Y distance (SPXY), RF and PLS algorithms can detect the hardness of figs without destroying them.
Shelf life testing	Shelf life is an important index to measure fruit life. Accurate judgment of the shelf life can better grasp the storage life and quality information of fruits, and provide an important reference for sales planning.	Li et al. (2019)Li, X., Liu, Y. D., Ouyang, A. G., Sun, X. D., Jiang, X. G., Hu, J., & Ouyang, Y. P. (2019). Study on non-destructive testing model of hyperspectral imaging for shelf life of crisp pear. Guangpuxue Yu Guangpu Fenxi, 39(8), 2578-2583. took pears as the research object and used hyperspectral imaging technology combined with partial least squares discriminant (PLS) to determine the shelf life of pears. Through PCA compression on the original images of samples with different shelf-life, the weight coefficient data of three different shelf-life are obtained. The discrimination model is established with the average gray value of the characteristic image as the independent variable and the shelf-life as the dependent variable. The results showed that the method was effective in the determination of pear shelf life.
External damage detection	The skin of fruits is bruised or broken due to bumping and extrusion during picking and handling, and some minor damages are difficult to identify with the naked eye, resulting in discoloration and decay. Early detection and elimination are the main measures at present.	Chen et al. (2018)Chen, S.-Y., Zhang, S.-H., Zhang, S., & Tan, Z.-J. (2018). Detection of early tiny bruises in apples using confocal Raman spectroscopy. Guangpuxue Yu Guangpu Fenxi, 38(2), 430-435. used Raman spectroscopy combined with chemometrics to quickly identify early minor damage to apples. The original Raman spectra were smoothed and denoised by Savitzky-Golay (SG) convolution, baseline corrected by adaptive iterative reweighted penalized least squares (airPLS) algorithm, and the classification discriminant model was established by nonlinear support vector machine (SVM) regression algorithm, with the 97.8% classification accuracy rate.

[1] *Corresponding author: 120100010@taru.edu.cn; gsxlq666@163.com

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Knowledge mapping of research on spectral technology in the fruit field using CiteSpace (1981-2021)

Abstract