Determination of Soluble Solids Content in Cuiguan Pear by Vis/NIR Diffuse Transmission Spectroscopy and Variable Selection Methods

Xu, Wenli; Sun, Tong; Wu, Wen-Qiang; Tian, Huimin; Hu, Tao; Liu, Muhua

doi:10.1007/978-3-642-54930-4_27

Cited by 5 publications

(5 citation statements)

References 12 publications

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“…A comparison of both systems in in-line applications allowed very good results to be obtained for SSC predictions, with an R 2 between 0.82 and 0.99. Compared with the SSC analysis off-line and also in transmission mode (Xu et al, 2014), the in-line results are better than those performed off-line (r p =0.96). With respect to off-line analyses but in reflectance mode (Li et al, 2013 andNicolaï et al, 2008), in-line results were still better than those performed off-line (r p =0.91 and R 2 =0.60, respectively).…”

Section: Comparison Between Off-line and In-line Applicationsmentioning

confidence: 70%

“…In most off-line applications with fruits, the acquisition mode used is reflectance, except for the study conducted by Khatiwada et al (2016) and the two studies by McGlone et al (2002 and, which were carried out in transmittance mode with apples, as well as the studies by Abebe (2006) and Jie et al (2013) with watermelons or Xu et al (2014) with pears. In the case of in-line applications the situation is similar: the predominant acquisition modes are reflectance, used in all in-line applications with olives (Salguero-Chaparro et al, 2012, and the transmittance mode in the case of pears (Xu et al, 2012;Sun et al, 2016).…”

Section: Comparison Between Off-line and In-line Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Monitoring strategies for quality control of agricultural products using visible and near-infrared spectroscopy: A review

Cortés

Blasco

Aleixos

et al. 2019

Trends in Food Science & Technology

224

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Background The increasing demand for quality assurance in agro-food production requires sophisticated analytical methods for in-line quality control. One of these techniques is visible and near-infrared (VIS-NIR) spectroscopy, which has low running costs, does not need sample preparation, and is non-destructive, environmentally friendly, and fast. Despite these advantages, only a limited amount of research has been conducted on VIS-NIR in-line applications to measure, control, and predict quality in fruits and vegetables. Scope and Approach The applicability of VIS-NIR spectroscopy for the off-line and in-line monitoring of quality in postharvest products has been addressed in this review. The document focuses on the comparison between the two processes for the same agro-food product, highlighting the main advantages and disadvantages, problems, solutions, and differences. Key Findings and Conclusions VIS-NIR techniques, combined with chemometric methods, have shown great potential due to their fast detection speed, and the possibility of simultaneously predicting multiple quality parameters or distinguishing between products according to the objectives. Being able to automate processes is a great advantage compared to routine off-line analyses, mainly due to the savings achieved in time, material, and personnel. However, in numerous cases, in-line implementation has not been accomplished in the corresponding studies, hence the scarcity of real in-line applications. Recent demands, together with the advances being made in the technology and a reduction in the price of equipment, makes VIS-NIR technology an analytical alternative for continuous realtime food quality controls, which will become predominant in the next few years.

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Section: Comparison Between Off-line and In-line Applicationsmentioning

confidence: 70%

Section: Comparison Between Off-line and In-line Applicationsmentioning

confidence: 99%

Monitoring strategies for quality control of agricultural products using visible and near-infrared spectroscopy: A review

Cortés

Blasco

Aleixos

et al. 2019

Trends in Food Science & Technology

224

View full text Add to dashboard Cite

show abstract

“…And the model with a wavelength correlation coefficient greater than a certain threshold is selected. CC is a common approach for performing band selection and is more frequently used in building NIR prediction models [36][37][38].…”

Section: Introductionmentioning

confidence: 99%

A Novel Variable Selection Method Based on Binning-Normalized Mutual Information for Multivariate Calibration

et al. 2023

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Variable (wavelength) selection is essential in the multivariate analysis of near-infrared spectra to improve model performance and provide a more straightforward interpretation. This paper proposed a new variable selection method named binning-normalized mutual information (B-NMI) based on information entropy theory. “Data binning” was applied to reduce the effects of minor measurement errors and increase the features of near-infrared spectra. “Normalized mutual information” was employed to calculate the correlation between each wavelength and the reference values. The performance of B-NMI was evaluated by two experimental datasets (ideal ternary solvent mixture dataset, fluidized bed granulation dataset) and two public datasets (gasoline octane dataset, corn protein dataset). Compared with classic methods of backward and interval PLS (BIPLS), variable importance projection (VIP), correlation coefficient (CC), uninformative variables elimination (UVE), and competitive adaptive reweighted sampling (CARS), B-NMI not only selected the most featured wavelengths from the spectra of complex real-world samples but also improved the stability and robustness of variable selection results.

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“…Internal quality indicators, such as SSC, hardness, TA, dry matter content and internal disorders, have all been successfully predicted using NIR spectroscopy to assess the quality of fruit [16][17][18][19][20][21][22][23][24][25]. The technology has been applied to determine the cultivars or varieties of soybean seeds (Glycine max), rice (Oryza sativa), Dendrobium, peanuts (Arachis hypogeae), wheat (Triticum aestivum) and raisins among others [26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Nondestructive Testing of Pear Based on Fourier Near-Infrared Spectroscopy

Chen

et al. 2022

Foods

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Fourier transform near-infrared (FT-NIR) spectroscopy is a nondestructive, rapid, real-time analysis of technical detection methods with an important reference value for producers and consumers. In this study, the feasibility of using FT-NIR spectroscopy for the rapid quantitative analysis and qualitative analysis of ‘Zaosu’ and ‘Dangshansuli’ pears is explored. The quantitative model was established by partial least squares (PLS) regression combined with cross-validation based on the spectral data of 340 pear fresh fruits and synchronized with the reference values determined by conventional assays. Furthermore, NIR spectroscopy combined with cluster analysis was used to identify varieties of ‘Zaosu’ and ‘Dangshansuli’. As a result, the model developed using FT-NIR spectroscopy gave the best results for the prediction models of soluble solid content (SSC) and titratable acidity (TA) of ‘Dangshansuli’ (residual prediction deviation, RPD: 3.272 and 2.239), which were better than those developed for ‘Zaosu’ SSC and TA modeling (RPD: 1.407 and 1.471). The results also showed that the variety identification of ‘Zaosu’ and ‘Dangshansuli’ could be carried out based on FT-NIR spectroscopy, and the discrimination accuracy was 100%. Overall, FT-NIR spectroscopy is a good tool for rapid and nondestructive analysis of the internal quality and variety identification of fresh pears.

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Determination of Soluble Solids Content in Cuiguan Pear by Vis/NIR Diffuse Transmission Spectroscopy and Variable Selection Methods

Cited by 5 publications

References 12 publications

Monitoring strategies for quality control of agricultural products using visible and near-infrared spectroscopy: A review

Monitoring strategies for quality control of agricultural products using visible and near-infrared spectroscopy: A review

A Novel Variable Selection Method Based on Binning-Normalized Mutual Information for Multivariate Calibration

Nondestructive Testing of Pear Based on Fourier Near-Infrared Spectroscopy

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