A novel NIR spectral calibration method: Sparse coefficients wavelength selection and regression (SCWR)

Lei, Tong; Sun, Da‐Wen

doi:10.1016/j.aca.2020.03.007

Cited by 24 publications

(8 citation statements)

References 64 publications

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“…Then, the properly calibrated and validated regression model can be used significantly quicker and more efficiently compared with the conventional methods [27]. Therefore, chemometric methods are used to reduce the complexity of NIR spectral datasets and to build prediction models [17,20,37,41]. MPLS regression was used to estimate some chemical and quality constituents of potato tubers [14,17,25,40].…”

Section: Discussionmentioning

confidence: 99%

Rapid Estimation of Potato Quality Parameters by a Portable Near-Infrared Spectroscopy Device

Meno

Rodríguez-Flores

Seijo

2021

Sensors

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The aim of the present work was to determine the main quality parameters on tuber potato using a portable near-infrared spectroscopy device (MicroNIR). Potato tubers protected by the Protected Geographical Indication (PGI “Patata de Galicia”, Spain) were analyzed both using chemical methods of reference and also using the NIR methodology for the determination of important parameters for tuber commercialization, such as dry matter and reducing sugars. MicroNIR technology allows for the attainment/estimation of dry matter and reducing sugars in the warehouses by directly measuring the tubers without a chemical treatment and destruction of samples. The principal component analysis and modified partial least squares regression method were used to develop the NIR calibration model. The best determination coefficients obtained for dry matter and reducing sugars were of 0.72 and 0.55, respectively, and with acceptable standard errors of cross-validation. Near-infrared spectroscopy was established as an effective tool to obtain prediction equations of these potato quality parameters. At the same time, the efficiency of portable devices for taking instantaneous measurements of crucial quality parameters is useful for potato processors.

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Section: Discussionmentioning

confidence: 99%

Rapid Estimation of Potato Quality Parameters by a Portable Near-Infrared Spectroscopy Device

Meno

Rodríguez-Flores

Seijo

2021

Sensors

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“…With respect to HSI, sparsity can also be enforced through wavelength selection processes that identify a small number of information-rich wavelengths and discard all other wavelengths. Lei and Sun [40] developed a sparse coefficients wavelength selection and regression (SCWR) method for NIR spectral calibration to select the wavelengths that contributed most to the determination of the spectral response. They applied this method to a dataset if NIR spectra from potatoes with dehydration loss as the response variable.…”

Section: Sparse Representationmentioning

confidence: 99%

Advanced Optical Technologies in Food Quality and Waste Management

Chauvin¹,

Duran²,

Ng³

et al. 2021

Innovation in the Food Sector Through the Valorization of Food and Agro-Food by-Products

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Food waste is a global problem caused in large part by premature food spoilage. Seafood is especially prone to food waste because it spoils easily. Of the annual 4.7 billion pounds of seafood destined for U.S. markets between 2009 and 2013, 40 to 47 percent ended up as waste. This problem is due in large part to a lack of available technologies to enable rapid, accurate, and reliable valorization of food products from boat or farm to table. Fortunately, recent advancements in spectral sensing technologies and spectroscopic analyses show promise for addressing this problem. Not only could these advancements help to solve hunger issues in impoverished regions of the globe, but they could also benefit the average consumer by enabling intelligent pricing of food products based on projected shelf life. Additional technologies that enforce trust and compliance (e.g., blockchain) could further serve to prevent food fraud by maintaining records of spoilage conditions and other quality validation at all points along the food supply chain and provide improved transparency as regards contract performance and attribution of liability. In this chapter we discuss technologies that have enabled the development of hand-held spectroscopic devices for detecting food spoilage. We also discuss some of the analytical methods used to classify and quantify spoilage based on spectral measurements.

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“…Moreover, it has shown great potential in the prediction of qualitative and quantitative properties in a wide range , (e.g., agricultural products, plants, biomedicals, and pharmaceutical samples). Numerous methods, including principal component regression, multivariate linear regression, partial least squares (PLS), neural network, nonlinear PLS, and locally weighted regression, have been proposed to determine the presence of a linear or nonlinear relationship with NIR spectral data. − …”

Section: Introductionmentioning

confidence: 99%

Modified Hybrid Strategy Integrating Online Adjustable Oil Property Characterization and Data-Driven Robust Optimization under Uncertainty: Application in Gasoline Blending

et al. 2022

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With the increasing challenges in environmental protection, product quality, and market profit, the development of a feasible optimization approach coupling accurate oil property online prediction has become crucial to the intelligent production of refineries. However, traditional property modeling with near-infrared (NIR) spectroscopy cannot dynamically extract the synergistic effect between wavelengths and cause inaccurate property prediction. The success rate of the data-driven robust optimization (DDRO) of the refining process is influenced by process uncertainty. Thus, we proposed a modified hybrid strategy on online NIR prediction modeling with adjustable characteristic wavelength selection and DDRO under uncertainty for the refining process. An adjustable feature space variable extraction (AFSVE) method was first proposed to dynamically select the characteristic wavelengths for constructing accurate property prediction models. A data-driven robust optimization of gasoline blending model was rendered through dual transformation using the uncertainty set of oil property derived from principal component analysis combined with robust kernel density estimation. An industrial application showed the best prediction accuracy of property models with the AFSVE method, and a high blending success rate was obtained by the proposed modified hybrid strategy, which hedges against uncertainties, including measuring error and blending effect, and boosts environmentally friendly and intelligent process operation.

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A novel NIR spectral calibration method: Sparse coefficients wavelength selection and regression (SCWR)

Cited by 24 publications

References 64 publications

Rapid Estimation of Potato Quality Parameters by a Portable Near-Infrared Spectroscopy Device

Rapid Estimation of Potato Quality Parameters by a Portable Near-Infrared Spectroscopy Device

Advanced Optical Technologies in Food Quality and Waste Management

Modified Hybrid Strategy Integrating Online Adjustable Oil Property Characterization and Data-Driven Robust Optimization under Uncertainty: Application in Gasoline Blending

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