Non-destructive and rapid detection of the internal chemical composition of granules samples by spectral transfer

Liu, Yachao; Li, Yongyu; Peng, Yankun; Yan, Shuai; Zhao, Xinlong; Han, Dong

doi:10.1016/j.chemolab.2020.104174

Cited by 6 publications

(7 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…MT means that after mathematical processing, the model of host or source samples could be used in the target machine or target samples to achieve model sharing and effective utilization [36,37]. This study used the DS algorithm to conduct MT on stewed beef samples with two physical forms [24,37]. The DS algorithm can perform direct correction of spectral data.…”

Section: Model Transfermentioning

confidence: 99%

“…In Formulas ( 13) and ( 14), X p and X q represent the spectral variables of two different samples, y p and y q represent the physicochemical variables of two different samples, and Z represents the number of spectral wave points of the samples. Some studies have pointed out that the transmission results of different indicators may be related to the standard sets selected from various forms during transmission [24]. In this study, an attempt was made to select standard samples from the calibration set of broken and intact physical samples, select the different quantities of standard samples to form the standard sample set, and compare the MT effect of two standard sample sets.…”

Section: Selection Of the Transfer Samplesmentioning

confidence: 99%

“…Typical model transfer algorithms include Direct Standardization (DS) [20], Piecewise Direct Standardization (PDS), Double Window Piecewise Direct Standardization (DW-PDS) [21], and Orthogonal Signal Correction (OSC) [22]. These algorithms could also be applied to transfer models between different physical states of the same substance [23][24][25]. Pereira et al [23] applied the DWPDS method to model the delivery of drug powder mixtures to complete tablet samples.…”

Section: Introductionmentioning

confidence: 99%

“…They successfully applied the Partial Least Squares (PLS) model based on powder mixtures to complete tablet prediction of nevirapine. Liu et al [24] proposed a new method for predicting protein and amylose contents in rice. They used the spectrum transfer based on DS, transforming the spectrum of particles into the corresponding powders, and successfully achieved the prediction of grain rice with the powder model.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Rapid Detection of Total Viable Count in Intact Beef Dishes Based on NIR Hyperspectral Hybrid Model

Wei,

Zhang,

et al. 2023

Sensors

View full text Add to dashboard Cite

The total viable count (TVC) of bacteria is an important index to evaluate the freshness and safety of dishes. To improve the accuracy and robustness of spectroscopic detection of total viable bacteria count in a complex system, a new method based on a near-infrared (NIR) hyperspectral hybrid model and Support Vector Machine (SVM) algorithms was developed to directly determine the total viable count in intact beef dish samples in this study. Diffuse reflectance data of intact and crushed samples were tested by NIR hyperspectral and processed using Multiplicative Scattering Correction (MSC) and Competitive Adaptive Reweighted Sampling (CARS). Kennard–Stone (KS) and Samples Set Partitioning Based on Joint X-Y Distance (SPXY) algorithms were used to select the optimal number of standard samples transferred by the model combined with root mean square error. The crushed samples were transferred into the complete samples prediction model through the Direct Standardization (DS) algorithm. The spectral hybrid model of crushed samples and full samples was established. The results showed that the Determination Coefficient of Calibration (RP2) value of the total samples prediction set increased from 0.5088 to 0.8068, and the value of the Root Mean Square Error of Prediction (RMSEP) decreased from 0.2454 to 0.1691 log10 CFU/g. After establishing the hybrid model, the RMSEP value decreased by 9.23% more than before, and the values of Relative Percent Deviation (RPD) and Reaction Error Relation (RER) increased by 12.12% and 10.09, respectively. The results of this study showed that TVC instewed beef samples can be non-destructively determined based on the DS model transfer method combined with the hybrid model strategy. This study provided a reference for solving the problem of poor accuracy and reliability of prediction models in heterogeneous samples.

show abstract

Section: Model Transfermentioning

confidence: 99%

Section: Selection Of the Transfer Samplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Rapid Detection of Total Viable Count in Intact Beef Dishes Based on NIR Hyperspectral Hybrid Model

Wei,

Zhang,

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Common pre-processing methods for spectral data include scaling (centring, normalisation, etc. [24]), baseline correction (first-order derivative, continuous wavelet transform, etc. [25]), scattering correction (multiplicative scattering correction (MSC), standard normal transform, etc.…”

Section: Introductionmentioning

confidence: 99%

Salinity Monitoring at Saline Sites with Visible–Near-Infrared Spectral Data

Liu

Bao

et al. 2021

Minerals

View full text Add to dashboard Cite

To address the global phenomenon of the salinisation of large land areas, a quantitative inversion model of the salinity of saline soils and soil visible–near-infrared (NIR) spectral data was developed by considering saline soils in Zhenlai County, Jilin Province, China as the research object. The original spectral data were first subjected to Savitzky–Golay (SG) smoothing, multiplicative scattering correction (MSC) pre-processing, and a combined transformation technique. The pre-processed spectral data were then analysed to construct the difference index (DI), ratio index (RI), and normalised difference index (NDI), and the Spearman rank correlation coefficient (r) between these three spectral indices and the salt content in the samples was calculated, while a combined spectral index (r > 0.8) was eventually selected as a sensitive spectral index. Finally, a quantitative inversion model for the salinity of saline soils was developed, and the model’s accuracy was evaluated based on partial least squares regression (PLSR), the random forest (RF) algorithm, and the radial basis function (RBF) neural network algorithm. The results indicated that the inversion of soil salt content using the selected combination of spectral indices based on the RBF neural network algorithm was the most effective, with the prediction model yielding an R2 value of 0.950, a root mean square error (RMSE) of 1.014, and a relative percentage deviation (RPD) of 4.479, which suggested a good prediction effect.

show abstract

Predicting components of pulpwood feedstock for different physical forms and tree species using NIR spectroscopy and transfer learning

Zhang,

Zhong,

et al. 2023

Cellulose

View full text Add to dashboard Cite

Non-destructive and rapid detection of the internal chemical composition of granules samples by spectral transfer

Cited by 6 publications

References 32 publications

Rapid Detection of Total Viable Count in Intact Beef Dishes Based on NIR Hyperspectral Hybrid Model

Rapid Detection of Total Viable Count in Intact Beef Dishes Based on NIR Hyperspectral Hybrid Model

Salinity Monitoring at Saline Sites with Visible–Near-Infrared Spectral Data

Predicting components of pulpwood feedstock for different physical forms and tree species using NIR spectroscopy and transfer learning

Contact Info

Product

Resources

About