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Pandiselvam R, Prithviraj V, Manikantan MR, Kothakota A, Rusu AV, Trif M, Mousavi Khaneghah A. Recent advancements in NIR spectroscopy for assessing the quality and safety of horticultural products: A comprehensive review. Front Nutr 2022; 9:973457. [PMID: 36313102 PMCID: PMC9597448 DOI: 10.3389/fnut.2022.973457] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 08/02/2022] [Indexed: 11/22/2022] Open
Abstract
The qualitative and quantitative evaluation of agricultural products has often been carried out using traditional, i.e., destructive, techniques. Due to their inherent disadvantages, non-destructive methods that use near-infrared spectroscopy (NIRS) coupled with chemometrics could be useful for evaluating various agricultural products. Advancements in computational power, machine learning, regression models, artificial neural networks (ANN), and other predictive tools have made their way into NIRS, improving its potential to be a feasible alternative to destructive measurements. Moreover, the incorporation of suitable preprocessing techniques and wavelength selection methods has arguably proven its practical feasibility. This review focuses on the various computation methods used for processing the spectral data collected and discusses the potential applications of NIRS for evaluating the quality and safety of agricultural products. The challenges associated with this technology are also discussed, as well as potential future perspectives. We conclude that NIRS is a potentially useful tool for the rapid assessment of the quality and safety of agricultural products.
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Affiliation(s)
- R. Pandiselvam
- Physiology, Biochemistry and Post-Harvest Technology Division, ICAR –Central Plantation Crops Research Institute, Kasaragod, Kerala, India,*Correspondence: R. Pandiselvam
| | - V. Prithviraj
- Department of Food Engineering, National Institute of Food Technology Entrepreneurship and Management, Sonipat, Haryana, India
| | - M. R. Manikantan
- Physiology, Biochemistry and Post-Harvest Technology Division, ICAR –Central Plantation Crops Research Institute, Kasaragod, Kerala, India,M. R. Manikantan
| | - Anjineyulu Kothakota
- Agro-Processing and Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (NIIST), Trivandrum, Kerala, India
| | - Alexandru Vasile Rusu
- Life Science Institute, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, Cluj-Napoca, Romania,Animal Science and Biotechnology Faculty, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, Cluj-Napoca, Romania
| | - Monica Trif
- Food Research Department, Centre for Innovative Process Engineering (CENTIV) GmbH, Stuhr, Germany,Monica Trif
| | - Amin Mousavi Khaneghah
- Department of Fruit and Vegetable Product Technology, Prof. Waclaw Dabrowski Institute of Agriculture and Food Biotechnology-State Research Institute, Warsaw, Poland
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Zhou J, Liu X, Sun R, Sun L. Rapid Nondestructive Detection of the Pulp Firmness and Peel Color of Figs by NIR Spectroscopy. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-022-02314-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Chen H, Liu X, Chen A, Cai K, Lin B. Parametric-scaling optimization of pretreatment methods for the determination of trace/quasi-trace elements based on near infrared spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 229:117959. [PMID: 31884401 DOI: 10.1016/j.saa.2019.117959] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 12/13/2019] [Accepted: 12/13/2019] [Indexed: 06/10/2023]
Abstract
This work proposes a parametric-scaling strategy to optimize the pretreatments of near infrared (NIR) spectroscopic data, so as to cope with the difficulty of NIR technology in detecting trace or quasi-trace elements. This novel strategy helps enhancing the signal to noise ratio and contributes to extracting features from the raw spectrum, so that the information corresponding to the trace elements could be detected much easier. However, due to the complexity of NIR data, it is difficult to comprehensively evaluate and compare the performance of different pretreatment methods, especially when multiple target components are determined simultaneously. For this reason, we create some comprehensive model indicators to define the goodness of pretreatments in simultaneous multiple detection of trace elements. In this paper two near infrared data sets have been investigated, one is used to determinate the key indices in the primary screening of thalassemia and the other one is used to detect the heavy metal pollutants in farmland soil. Results show that the proposed parametric-scaling optimization strategy can improve the effect of pretreatments in the determination of trace/quasi-trace elements, and the model performance with the optimized pretreated data is significantly superior to that with the raw data. The optimized Savitzky-Golay smoother (SGS) keeps its merits in the real data examples. Especially, the newly emerged methods optical path length estimation and correction (OPLEC) and Whittaker smoother (WTK), as well as their parametric-scaling modified methods, show their advantages in the comparison with other pretreatments. According to the results of our experiments, they have shown promising potential in the NIR rapid analysis of trace/quasi-trace elements in the field of biomedical science and agricultural science. This is expected to be tested for other analytes with larger variation.
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Affiliation(s)
- Huazhou Chen
- College of Science, Guilin University of Technology, Guilin 541004, China; Center for Data analysis and Algorithm Technology, Guilin University of Technology, Guilin 541004, China.
| | - Xiaoke Liu
- Department of Statistical Science, University College London, WC1E 6BT, United Kingdom
| | - An Chen
- College of Science, Guilin University of Technology, Guilin 541004, China; Center for Data analysis and Algorithm Technology, Guilin University of Technology, Guilin 541004, China
| | - Ken Cai
- College of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
| | - Bin Lin
- College of Science, Guilin University of Technology, Guilin 541004, China; Center for Data analysis and Algorithm Technology, Guilin University of Technology, Guilin 541004, China
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Chen H, Xu L, Jia Z, Cai K, Shi K, Gu J. Determination of Parameter Uncertainty for Quantitative Analysis of Shaddock Peel Pectin using Linear and Nonlinear Near-infrared Spectroscopic Models. ANAL LETT 2018. [DOI: 10.1080/00032719.2017.1384479] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Huazhou Chen
- College of Science, Guilin University of Technology, Guilin, Guangxi, China
| | - Lili Xu
- School of Ocean, Qinzhou University, Qinzhou, China
| | - Zhen Jia
- College of Science, Guilin University of Technology, Guilin, Guangxi, China
| | - Ken Cai
- College of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Kai Shi
- College of Science, Guilin University of Technology, Guilin, Guangxi, China
| | - Jie Gu
- College of Science, Guilin University of Technology, Guilin, Guangxi, China
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Ouyang Q, Liu Y, Chen Q, Zhang Z, Zhao J, Guo Z, Gu H. Intelligent evaluation of color sensory quality of black tea by visible-near infrared spectroscopy technology: A comparison of spectra and color data information. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2017; 180:91-96. [PMID: 28279828 DOI: 10.1016/j.saa.2017.03.009] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Revised: 03/02/2017] [Accepted: 03/02/2017] [Indexed: 05/12/2023]
Abstract
Instrumental test of black tea samples instead of human panel test is attracting massive attention recently. This study focused on an investigation of the feasibility for estimation of the color sensory quality of black tea samples using the VIS-NIR spectroscopy technique, comparing the performances of models based on the spectra and color information. In model calibration, the variables were first selected by genetic algorithm (GA); then the nonlinear back propagation-artificial neural network (BPANN) models were established based on the optimal variables. In comparison with the other models, GA-BPANN models from spectra data information showed the best performance, with the correlation coefficient of 0.8935, and the root mean square error of 0.392 in the prediction set. In addition, models based on the spectra information provided better performance than that based on the color parameters. Therefore, the VIS-NIR spectroscopy technique is a promising tool for rapid and accurate evaluation of the sensory quality of black tea samples.
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Affiliation(s)
- Qin Ouyang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China; State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, PR China
| | - Yan Liu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China; State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, PR China.
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, PR China.
| | - Jiewen Zhao
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Zhiming Guo
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Hang Gu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
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Application of Curve Fitting and Wavelength Selection Methods for Determination of Chlorogenic Acid Concentration in Coffee Aqueous Solution by Vis/NIR Spectroscopy. FOOD ANAL METHOD 2016. [DOI: 10.1007/s12161-016-0650-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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