Rapid detection of protein content in rice based on Raman and near-infrared spectroscopy fusion strategy combined with characteristic wavelength selection

Wang, Zhiqiang; Zeng, Changhao; bao, changhao; Li, Zhijiang; Zhang, Dongjie; Zhu, Feng

doi:10.1016/j.infrared.2023.104563

Cited by 35 publications

(15 citation statements)

References 35 publications

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“…Conversely, selecting fewer feature data may result in the omission of valuable information. To address this challenge, Wang et al (2023) introduced an improved binary particle swarm optimization (IBPSO) algorithm. Unlike traditional binary particle swarm optimization, the IBPSO algorithm eliminates the need to modify each bit separately in the particle encoding, thereby enhancing its behavior.…”

Section: Agricultural Productsmentioning

confidence: 99%

“…In this context, SDFTs have gained increasing attention. SDFTs involve integrating spectral information or spectral and non‐spectral information from diverse sources, applying chemometrics and machine learning techniques to process the data, and establishing detection models at various fusion levels to provide a more comprehensive view of sample information (Wang et al., 2023). Generally, spectral data fusion is divided into three levels (Dai et al., 2022), that is, low‐level data fusion (LLDF), mid‐level data fusion (MLDF), and high‐level data fusion (HLDF).…”

Section: Principles Of Spectral Data Fusionmentioning

confidence: 99%

“…First, these strategies enhance the information derived from independent samples through various in situ detection methods, thereby mitigating the need for extensive sample data, especially those challenging to obtain. This enrichment ensures the model's high performance (Robert et al., 2021; Wang, Liu et al., 2021, 2023). Second, MLDF‐generated models not only uphold prediction accuracy but also significantly decrease prediction time, presenting a cost‐saving advantage in the realm of on‐site rapid detection applications for the food industry (Ren et al., 2022; Shen et al., 2020).…”

Section: Principles Of Spectral Data Fusionmentioning

confidence: 99%

See 2 more Smart Citations

Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives

Guo,

Wang,

Lin

et al. 2024

Comp Rev Food Sci Food Safe

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In recent years, the food industry has shown a growing interest in the development of rapid and nondestructive analytical methods. However, the utilization of a solitary nondestructive detection technique offers only a constrained extent of physical or chemical insights regarding the sample under examination. To overcome this limitation, the amalgamation of spectroscopy with data fusion strategies has emerged as a promising approach. This comprehensive review delves into the fundamental principles and merits of low‐level, mid‐level, and high‐level data fusion strategies within the domain of food analysis. Various data fusion techniques encompassing spectra‐to‐spectra, spectra‐to‐machine vision, spectra‐to‐electronic nose, and spectra‐to‐nuclear magnetic resonance are summarized. Moreover, this review also provides an overview of the latest applications of spectral data fusion techniques (SDFTs) for classification, adulteration, quality evaluation, and contaminant detection within the purview of food safety analysis. It also addresses current challenges and future prospects associated with SDFTs in real‐world applications. Despite the extant technical intricacy, the ongoing evolution of online data fusion platforms and the emergence of smartphone‐based multi‐sensor fusion detection technology augur well for the pragmatic realization of SDFTs, endowing them with formidable capabilities for both qualitative and quantitative analysis in the realm of food analysis.

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Section: Agricultural Productsmentioning

confidence: 99%

Section: Principles Of Spectral Data Fusionmentioning

confidence: 99%

Section: Principles Of Spectral Data Fusionmentioning

confidence: 99%

See 1 more Smart Citation

Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives

Guo,

Wang,

Lin

et al. 2024

Comp Rev Food Sci Food Safe

View full text Add to dashboard Cite

show abstract

“…It limited the model transfer between different instruments, and it was difficult to meet the rapid detection requirements of oil octane content. 12,14 In order to solve the problems of slow operation speed and cumbersome steps of these traditional model transfer algorithms. Ni Lijun et al proposed a method (screening wavelengths with consistent and stable signals [SWCSS]) for model sharing between different nearinfrared spectrometers by using the difference spectrum analysis of master and slave spectrometers to select wavelengths.…”

Section: Introductionmentioning

confidence: 99%

“…These collected spectral data contained a lot of redundant information, which will lead to a large amount of calculation in modeling, and greatly reduce the transfer efficiency of standard sample model transfer methods of S/B, DS, and PDS. It limited the model transfer between different instruments, and it was difficult to meet the rapid detection requirements of oil octane content 12,14 . In order to solve the problems of slow operation speed and cumbersome steps of these traditional model transfer algorithms.…”

Section: Introductionmentioning

confidence: 99%

Application of stable consistency wavelength in optimizing gasoline RON near‐infrared analysis model transfer

Wang

Yuan

et al. 2023

Journal of Chemometrics

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The purpose of model transfer is to solve the problem that multivariate calibration models cannot be shared among different near‐infrared spectrometers. Taking gasoline as the research object, the transfer analysis of its octane number model was carried out. The gasoline samples collected by two near‐infrared spectrometers of the same type were used as the research object. The screening wavelengths with consistent and stable signals (SWCSS) combined with competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and successive projections algorithm (SPA) were used to reduce the adverse effects of invalid wavelengths in the SWCSS method; therefore, the analysis ability of the master model to the slave samples was improved. Partial least squares regression (PLSR) models based on SWCSS‐UVE, SWCSS‐CARS, and SWCSS‐SPA algorithms were established, and comparison was made between their analytical capabilities for slave samples and those of SWCSS, direct standardization (DS) algorithm , piecewise direct standardization (PDS) algorithm, and slope/bias (S/B) algorithm. The results shown that the SWCSS‐UVE and SWCSS‐CARS methods can be used to establish models from the 231 and 6 wavelengths selected from the consistent wavelengths, respectively. The root mean square error of prediction (RMSEP) of the gasoline octane number (RON) content of the direct analysis of the spectrum measured by the slave machine was reduced from 5.7490 to 0.3226 and 0.3250, respectively, which was better than the single SWCSS and DS, PDS, and SWCSS‐SPA methods, and was close to the model transfer accuracy of the S/B algorithm. The transfer accuracy of SWCSS‐UVE and SWCSS‐CARS was not much different, but the wavelength variable involved in the model transfer of the latter was much smaller than that of the former, and the AIC value of SWCSS‐CARS was −59.59, which was much smaller than the akaike information criterion (AIC) value of SWCSS‐UVE 398.42.

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Feature Variable Selection for Near‐Infrared Spectroscopy Based on Simulated Annealing Bee Colony Algorithm

Shi,

Tong,

Liu

et al. 2024

Journal of Chemometrics

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Variable selection is an effective method to enhance the modeling performance of near‐infrared spectroscopy. Given the promising application prospects of intelligent optimization algorithms in spectral feature variable selection, this article combines the artificial bee colony algorithm with the simulated annealing algorithm to construct a simulated annealing bee colony algorithm (SABC). To explore the feasibility of SABC for spectral variable selection, SABC was applied to construct a partial least squares spectral quantitative detection model for corn stover cellulose and soil organic matter contents. The modeling performance was compared with that of the full spectrum, genetic algorithm, simulated annealing algorithm, and artificial bee colony algorithm; it was found that the model regression precision established by SABC was the best. For the cellulose and organic matter content detection models, the coefficients of determination of the validation set were 0.9433 and 0.9853, with the relative root mean squared error of 1.7901% and 0.8011%, and the residual prediction deviation of 4.1741 and 8.3931, respectively, which could meet the corresponding actual detection needs. SABC adopted the strategy of multiple runs to select the repeated wavelength variables, effectively reduced variable dimensions and model complexity, improved the prediction performance of the regression model, and provided a new approach for building a high‐performance near‐infrared spectroscopy (NIRS) quantitative calibration model.

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Rapid detection of protein content in rice based on Raman and near-infrared spectroscopy fusion strategy combined with characteristic wavelength selection

Cited by 35 publications

References 35 publications

Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives

Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives

Application of stable consistency wavelength in optimizing gasoline RON near‐infrared analysis model transfer

Feature Variable Selection for Near‐Infrared Spectroscopy Based on Simulated Annealing Bee Colony Algorithm

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