Feasibility of Infrared and Raman Spectroscopies for Identification of Juvenile Black Seabream (Sparus macrocephalus) Intoxicated by Heavy Metals

Chen, Xiaojing; Wu, Di; Guan, Xiaochun; Liu, Bo; Gui, Liu; Yan, Maocang; Chen, Huiling

doi:10.1021/jf403276y

Cited by 25 publications

(11 citation statements)

References 26 publications

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“…Therefore, those important variables can be selected according to the V value. The expression of V is shown in following formula (1).…”

Section: Pls-vip and Pls-uve Modelsmentioning

confidence: 99%

“…Multivariate calibration models have been widely applied in analytical chemistry, especially in the field of spectroscopy analysis . However, when full wavelengths are considered as input variables, many uninformative and noise variables may interfere with the input, which results in a reduction in the accuracy and robustness of a prediction model .…”

Section: Introductionmentioning

confidence: 99%

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Tuning parameter identification for variable selection algorithm using the sum of ranking differences algorithm

Nie

Meng

Chen

et al. 2019

Journal of Chemometrics

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Variable selection algorithms are often adopted to select the optimal variable from a full set of variables and are efficient for reducing the variable dimension and improving the model accuracy. Nonetheless, the parameters of the variable selection method and regression model, such as the number of latent variables of the partial least squares (PLS) model and the threshold value of the variable importance index, need to be identified. The parameters directly determine the final performance of the model. Currently, these parameters are often determined subjectively. As a result, the model results may be accidental because of the subjective determination of the parameters. To objectively identify these parameters, the sum of ranking differences (SRD) coupled with partial least squares‐variable importance in projection (PLS‐VIP‐SRD) and partial least squares‐uninformative variable elimination (PLS‐UVE‐SRD) algorithms was applied to determine the latent variable of the PLS model and the threshold value of the variable importance index. Furthermore, public near‐infrared data of corn were used as the calculation data. The final results show that the PLS‐VIP‐SRD and PLS‐UVE‐SRD models can more effectively and objectively determine the optimal parameter combination than the PLS‐VIP and PLS‐UVE models. Moreover, the selected variables are easier to interpret, and the prediction accuracy is also improved to some extent.

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“…Therefore, those important variables can be selected according to the V value. The expression of V is shown in following formula (1).…”

Section: Pls-vip and Pls-uve Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tuning parameter identification for variable selection algorithm using the sum of ranking differences algorithm

Nie

Meng

Chen

et al. 2019

Journal of Chemometrics

Self Cite

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“…As the relationship between the spectra of the tested sample and its quality may have linear or non-linear characteristics or both of them, two typical calibration methods of partial least square regression (PLSR) and least-squares support vector machines (LS-SVM) were considered and compared, in which the former one is a classical linear regression method, while the latter is commonly used to establish non-linear calibration models. PLSR has been widely employed for spectral quantitative prediction (Chen et al 2013;Huang et al 2014;Wu et al 2014b). In the PLSR calibration, the response variable is taken into account in the process of extracting the latent variables, which is an important advantage of PLSR against principal component regression.…”

Section: Spectral Preprocessing and Model Calibrationmentioning

confidence: 99%

Feasibility Study on Quantitative Pixel-Level Visualization of Internal Quality at Different Cross Sections Inside Postharvest Loquat Fruit

Zhu

Nie

et al. 2016

Food Anal. Methods

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Visualizing the quality variation inside loquat fruit at pixel level is important to deeply understand its ripening process and further optimize its preharvest planting pattern and postharvest storage strategy. Total soluble solids (TSS) is a major quality attribute of loquat fruit and detecting changes in its content can be used to monitor the ripening and postharvest senescence of loquat fruit. The refractometer method cannot provide detailed TSS distribution within loquat flesh, because the analysis focuses on measuring the mean TSS of only a flesh cube, which is used to make juice for the refractometer measurement. In this study, hyperspectral imaging was used to measure and visualize internal TSS distribution within loquat fruit. Loquat fruits with different TSS contents were selected and cut at different cross sections for imaging. Different calibration and wavelength selection algorithms were applied and compared. The uninformative variable elimination by partial least square regression model with preprocessing from spectral set I (468-1026 nm) was identified as the best model for the TSS determination of loquat flesh, which had a high prediction ability with a correlation coefficient of 0.960 and residual predictive deviation of 3.513. On the basis of the best model, the quantitative TSS distribution at different cross sections inside loquat fruit was visualized at pixel level. The results showed that hyperspectral imaging is a feasible way of visualizing the spatial changes of TSS distribution inside loquat fruit at pixel level, which would be helpful to understand the detailed TSS change inside postharvest loquat fruit.

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“…[7][8][9] It has been demonstrated that these spectroscopic methods can be intensively used to make a reasonable comprehensive evaluation of fish, chicken, beef, and pork. [10][11][12] In particular, hyperspectral imaging technique, as an emerging and promising tool, has gained importance in the inspection of meat quality and safety. 13,14 Considerable research has been reported on this novel optical measurement to assess quality and safety of meat, which generally encompass nutritional components analysis (moisture, fat, protein), 15 edible quality evaluation (color, marbling, tenderness), 16 technological attributes determination (water-holding capacity, pH) 17 as well as microbiological spoilage assessment.…”

Section: Introductionmentioning

confidence: 99%

Noninvasive Qualitative and Quantitative Assessment of Spoilage Attributes of Chilled Pork Using Hyperspectral Scattering Technique

Zhang

Peng

2016

Appl Spectrosc

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The objective of this research was to develop a rapid noninvasive method for quantitative and qualitative determination of chilled pork spoilage. Microbiological, physicochemical, and organoleptic characteristics such as the total viable count (TVC), Pseudomonas spp., total volatile basic-nitrogen (TVB-N), pH value, and color parameter L* were determined to appraise pork quality. The hyperspectral scattering characteristics from 54 meat samples were fitted by four-parameter modified Gompertz function accurately. Support vector machines (SVM) was applied to establish quantitative prediction model between scattering fitting parameters and reference values. In addition, partial least squares discriminant analysis (PLS-DA) and Bayesian analysis were utilized as supervised and unsupervised techniques for the qualitative identification of meat spoilage. All stored chilled meat samples were classified into three grades: "fresh," "semi-fresh," and "spoiled." Bayesian classification model was superior to PLS-DA with overall classification accuracy of 92.86%. The results demonstrated that hyperspectral scattering technique combined with SVM and Bayesian possessed a powerful capability for meat spoilage assessment rapidly and noninvasively.

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Feasibility of Infrared and Raman Spectroscopies for Identification of Juvenile Black Seabream (Sparus macrocephalus) Intoxicated by Heavy Metals

Cited by 25 publications

References 26 publications

Tuning parameter identification for variable selection algorithm using the sum of ranking differences algorithm

Tuning parameter identification for variable selection algorithm using the sum of ranking differences algorithm

Feasibility Study on Quantitative Pixel-Level Visualization of Internal Quality at Different Cross Sections Inside Postharvest Loquat Fruit

Noninvasive Qualitative and Quantitative Assessment of Spoilage Attributes of Chilled Pork Using Hyperspectral Scattering Technique

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