Portable<scp>Near‐Infrared</scp>Spectroscopy in Food Analysis

Miseo, Ellen V.; Meyer, Felicity J.; Ryan, James A.

doi:10.1002/9781119636489.ch37

Cited by 3 publications

(3 citation statements)

References 140 publications

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“…Advancements in chemometrics and instrumental measurement techniques have increasingly highlighted the application of near-infrared (NIR) spectroscopy analysis in scientific research and industrial applications because of its nondestructive, rapid, and precise nature. Its widespread application spans various sectors, including food science [1], environmental chemistry [2], daily chemical industry [3], traditional Chinese medicine [4], polymer materials [5], petroleum, and biochemistry [6].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of Near-Infrared Spectroscopy Using the BEST-1DConvNet Model

Li,

Deng

2024

Processes

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In the quest for enhanced precision in near-infrared spectroscopy (NIRS), in this study, the application of a novel BEST-1DConvNet model for quantitative analysis is investigated against conventional support vector machine (SVM) approaches with preprocessing such as multiplicative scatter correction (MSC) and standard normal variate (SNV). We assessed the performance of these methods on NIRS datasets of diesel, gasoline, and milk using a Fourier Transform Near-Infrared (FT-NIR) spectrometer having a wavelength range of 900–1700 nm for diesel and gasoline and 4000–10,000 nm for milk, ensuring comprehensive spectral capture. The BEST-1DConvNet’s effectiveness in chemometric predictions was quantitatively gauged by improvements in the coefficient of determination (R2) and reductions in the root mean square error (RMSE). The BEST-1DConvNet model achieved significant performance enhancements compared to the MSC + SNV + 1D + SVM model. Notably, the R2 value for diesel increased by approximately 48.85% despite a marginal RMSE decrease of 0.92%. R2 increased by 11.30% with a 3.32% RMSE reduction for gasoline, and it increased by 8.71%, accompanied by a 3.51% RMSE decrease for milk. In conclusion, the BEST-1DConvNet model demonstrates superior predictive accuracy and reliability in NIRS data analysis, marking a substantial leap forward in spectral analysis technology. This advancement could potentially streamline their integration into various industrial applications and highlight the role of convolutional neural networks in future chemometric methodologies.

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Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of Near-Infrared Spectroscopy Using the BEST-1DConvNet Model

Li,

Deng

2024

Processes

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“…By absorbing electromagnetic waves in the 780–2526 nm range, NIR provides information from molecular vibrations and combined overtones of chemical groups such as C-H, O-H, N-H and S-H, which can be analyzed by chemometrics to relate to target parameters for qualitative and quantitative evaluation [ 26 ], i.e., model establishment between NIR spectra and target parameters. NIR technology has been used for the quality evaluation in various foods in terms of physical, chemical, nutritional, textural and microbial properties, with good or satisfied and even excellent performance, indicating a great potential of NIR in food industrial application [ 27 , 28 ]. However, few studies on NIR technology for the quality evaluation of fruit peels have been reported, especially vitamin C in citrus peels.…”

Section: Introductionmentioning

confidence: 99%

Toward Achieving Rapid Estimation of Vitamin C in Citrus Peels by NIR Spectra Coupled with a Linear Algorithm

Zhang

Lin

et al. 2023

Molecules

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Citrus peels are rich in bioactive compounds such as vitamin C and extraction of vitamin C is a good strategy for citrus peel recycling. It is essential to evaluate the levels of vitamin C in citrus peels before reuse. In this study, a near-infrared (NIR)-based method was proposed to quantify the vitamin C content of citrus peels in a rapid way. The spectra of 249 citrus peels in the 912‒1667 nm range were acquired, preprocessed, and then related to measured vitamin C values using the linear partial least squares (PLS) algorithm, indicating that normalization correction (NC) was more suitable for spectral preprocessing and NC-PLS model built with full NC spectra (375 wavelengths) showed a better performance in predicting vitamin C. To accelerate the predictive process, wavelength selection was conducted, and 15 optimal wavelengths were finally selected from NC spectra using the stepwise regression (SR) method, to predict vitamin C using the multiple linear regression (MLR) algorithm. The results showed that SR-NC-MLR model had the best predictive ability with correlation coefficients (rP) of 0.949 and root mean square error (RMSEP) of 14.814 mg/100 mg in prediction set, comparable to the NC-PLS model in predicting vitamin C. External validation was implemented using 40 independent citrus peels samples to validate the suitability of the SR-NC-MLR model, obtaining a good correlation (R2 = 0.9558) between predicted and measured vitamin C contents. In conclusion, it was reasonable and feasible to achieve the rapid estimation of vitamin C in citrus peels using NIR spectra coupled with MLR algorithm.

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“…Infrared (IR) imaging focuses on wavelengths between 8 and 14 μm 1 indicating heat radiated from objects, therefore, it can highlight thermal radiation objects even under poor lighting or severe sheltering in many applications, in military, 2 safety inspection, 3 non-destructive testing, 4 unmanned driving, 5 and so on 6 – 8 However, IR imaging suffers from low resolution, contrast, and signal-to-noise ratio 9 . Compared to IR imaging, visible (VIS) imaging focusing on wavelengths between 380 and 760 nm can capture reflected light, guaranteeing abundant texture details of targets.…”

Section: Introductionmentioning

confidence: 99%

Physics driven deep Retinex fusion for adaptive infrared and visible image fusion

Gu,

Xiao,

Guan

et al. 2023

Opt. Eng.

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.Infrared (IR) imaging can highlight thermal radiation objects even under poor lighting or severe sheltering but suffers from low resolution, contrast, and signal-to-noise ratio. While visible (VIS) light imaging can guarantee abundant texture details of targets, it is invalid in low lighting or sheltering conditions. Therefore, IR and VIS image fusion has more extensive applications, but it is a still challenging work because conventional methods cannot balance dynamic range, edge enhancement, and lightness constancy during fusion. To overcome these drawbacks, we propose a self-supervised dataset-free method for adaptive IR and VIS image fusion named deep Retinex fusion (DRF). The key idea of DRF is first generating component priors that are disentangled from a physical model using generative networks; then combining these priors, which are captured by networks via adaptive fusion loss functions based on Retinex theory; and finally reconstructing the IR and VIS fusion results. Furthermore, to verify the effectiveness of our reported physics driven DRF, qualitative and quantitative experiments via comparing with other state-of-the-art methods are performed using public datasets and in practical applications. These results prove that DRF can provide distinctions between day and night scenes and preserve abundant texture details and high-contrast IR information. Additionally, DRF can adaptively balance IR and VIS information and has good noise immunity. Therefore, compared to large dataset trained methods, DRF, which works without any dataset, achieves the best fusion performance.

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PortableNear‐InfraredSpectroscopy in Food Analysis

Cited by 3 publications

References 140 publications

Quantitative Analysis of Near-Infrared Spectroscopy Using the BEST-1DConvNet Model

Quantitative Analysis of Near-Infrared Spectroscopy Using the BEST-1DConvNet Model

Toward Achieving Rapid Estimation of Vitamin C in Citrus Peels by NIR Spectra Coupled with a Linear Algorithm

Physics driven deep Retinex fusion for adaptive infrared and visible image fusion

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