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Frontiers of Agricultural Science and Engineering

ISSN 2095-7505

ISSN 2095-977X(Online)

CN 10-1204/S

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Front. Agr. Sci. Eng.    2019, Vol. 6 Issue (2) : 105-115    https://doi.org/10.15302/J-FASE-2019255
REVIEW
Factors influencing near infrared spectroscopy analysis of agro-products: a review
Xiao XU, Lijuan XIE, Yibin YING()
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
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Abstract

The near infrared (NIR) spectroscopy technique has wide applications in agriculture with the advantages of being nondestructive, sensitive, safe and rapid. However, there are still more than 40 error sources influencing the robustness and accuracy of its calibration and operation. Environmental, sample and instrument factors that influence the analysis are discussed in this review, including temperature, humidity and other factors that introduce uncertainty. Error sources from livestock products, fruit and vegetables, which are the most common objects in the field of NIR analysis, are also emphasized in the second part. In addition, studies utilizing different instruments, spectral pretreatments, variable selection methods, wavelength ranges, detection modes and calibration methods are tabulated to illustrate the complications they introduce and how they influence NIR analysis. It is suggested that large scale of data with abundant varieties can be used to build a more robust calibration model, in order to improve the robustness and accuracy of the NIR analytical model, and overcome problems caused by confining analysis to too many uniform samples.
Keywords agro-product      error source      influence factor      near infrared spectroscopy     
Corresponding Author(s): Yibin YING   
Just Accepted Date: 07 March 2019   Online First Date: 01 April 2019    Issue Date: 22 May 2019
 Cite this article:   
Xiao XU,Lijuan XIE,Yibin YING. Factors influencing near infrared spectroscopy analysis of agro-products: a review[J]. Front. Agr. Sci. Eng. , 2019, 6(2): 105-115.
 URL:  
https://academic.hep.com.cn/fase/EN/10.15302/J-FASE-2019255
https://academic.hep.com.cn/fase/EN/Y2019/V6/I2/105
Fig.1  Schematic diagram of experimental procedures using NIR spectroscopy for sugar content analysis
Fig.2  The average NIR spectra of watermelon juice at different temperatures. The arrow indicates direction of temperature increase. Reprinted from Yao et al.[37], with permission from Elsevier.
Fig.3  Schematic of the measurement positions of NIR reflectance spectra within an individual apple fruit. Reprinted from Fan et al.[55], with permission from Elsevier.
Object Attribute Detection mode Spectral range Pretreatment Modeling method Instrument Optimal model performance
Pork[2] Color
(L*, a*, b*),
pH value, TVB-N		 Diffuse reflectance 400–1000 nm S-G, SNV PLS Portable device RP: 0.92, 0.91, 0.92, 0.95, 0.96 (L*, a*, b*, pH value, TVB-N)
Pork[3] TVB-N, WBSF Reflectance 10000–4000 cm−1 SNV SI-PLS Antaris II FT-NIR spectrophotometer RC: 0.8398, RP: 0.8084 (TVB-N)
RC: 0.7533, RP: 0.7041 (WBSF)
Pork[4] Water content, cooking loss, tenderness Reflectance 350–1100 nm; 1000–2500 nm S-G, SNV PLS Online detection system RP: 0.9123, 0.9200, 0.9019 (respectively)
Pork[5] IMF Reflectance 1100–1830 nm * Modified PLS NIRS 6500 R2CV: 0.84–0.99
SECV: 0.14%–0.53%
Beef[5]
Beef[56] Fat, moisture, protein, myoglobin, stress 20%, stress 80%, WBSF, tenderness, juiciness, overall appraisal Reflectance 408–2492 nm; 1108–2492 nm; 1500–2460 nm MSC, SNV, SNVD, none PLS, modified PLS, PCR NIRS 6500 Optimal R2P: 0.98 (tenderness)
Broiler breast[57] Fatty acid Reflectance 400–2498 nm SNVD, WMSC Modified PLS NIRS 6500 R2C: 0.86–0.98
R2P: 0.83–0.97
Broiler breast[58] Fatty acid Reflectance 1100–1830 nm 2nd derivative Modified PLS LabSpec®2500 R2CV: less than 0.60
Tab.1  Analysis of livestock product quality by NIR spectroscopy
Object Attribute Detection mode Spectral range Pretreatment Modeling method Instrument Optimal model performance
Apple[7] SSC Diffuse reflectance 500–1100 nm; 1000–4000 nm * ICA-SVM Ocean Optics model USB2000 fiber spectrometer; antarisTMII method development sampling system RP: 0.9455
RMSEP: 0.3691%
Orange[8] SSC Interactance
reflectance transmittance		 460–1000 nm * PLS VIS-SWNIR CCD spectrometer RCV: 0.778–0.866
RMSECV: 0.329–0.518
Jujube[9] Inner insect-infestation Interactance, reflectance, transmittance 310–1100 nm; 1000–2150 nm * DA Handy Lambda II & Solid lambda NIR2.2t2 100% (interactance)
90% (reflectance)
97% (transmittance)
Peach[10] SSC, pH Diffuse reflectance 325–1075 nm S-G, MSC PLS, LS-SVM Fieldspec Pro FR, Analytical Spectral Devices, Inc. RP: 0.9537, RCV: 0.9485 (SSC)
RP: 0.9638, RCV: 0.9657 (pH)
Apple[11] ITB Diffuse transmittance 650–950 nm * PLS Prototype based on time-delayed integration spectroscopy; R2P: 0.7–0.9
RMSECV: 4%–7%
Prototype based on large aperture spectrometer R2P: ~0.9
RMSEP: ~4.1%
Pear[12] DM, SSC Reflectance 680–1000 nm; 1100–2350 nm S-G, SNV PLS Agriquant FT-NIR spectrometer R2CV: 0.78–0.84
Inner insect-infestation Reflectance 650–1100 nm; 600–1700 nm * PLS Two scanning spectrometers 82%, 76.9%
Blueberry[13] Inner insect-infestation Reflectance 650–1100 nm; 600–1700 nm Two scanning spectrometers
Tomato[14] Total soluble solids, lycopene and β-carotene Diffuse reflectance * MSC, 2nd derivative PLS Buchi NIRlab N-200 spectrometer RP: 0.9998, 0.9996, 0.9981
RMSEP: 0.4157, 21.5779, 0.7296
Mango[15] TSS, acidity, firmness, storage period Reflectance 1200–2400 nm 1st derivative, 2nd derivative MLR, PCA, PLS Quantum 120 R2P: 0.9276, 0.6085, 0.8226, 0.9380 (respectively)
Tab.2  Analysis of the quality of fruit and vegetables by NIR spectroscopy
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