Selection of Informative Spectral Wavelength for Evaluating and Visualising Enterobacteriaceae Contamination of Salmon Flesh

He, Haixiang; Sun, Da‐Wen

doi:10.1007/s12161-015-0122-x

Cited by 16 publications

(7 citation statements)

References 68 publications

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“…In general, a VIP score value below one identifies an unimportant variable which probably will be eliminated while reducing the data volume [17]. Moreover, even if a wavelength has a VIP score value above one, the contiguous wavelengths in the data set can lead to problems of multi-collinearity and information redundancy, since contiguous wavelengths connote similar spectral information [25]. The number of variables can be reduced by selecting major peaks with VIP scores above one and, eventually, developing the PLS-DA model using those selected wavebands.…”

Section: Methodsmentioning

confidence: 99%

Rapid Measurement of Soybean Seed Viability Using Kernel-Based Multispectral Image Analysis

Baek

Kusumaningrum

Kandpal

et al. 2019

Sensors

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Viability is an important quality factor influencing seed germination and crop yield. Current seed-viability testing methods rely on conventional manual inspections, which use destructive, labor-intensive and time-consuming measurements. The aim of this study is to distinguish between viable and nonviable soybean seeds, using a near-infrared (NIR) hyperspectral imaging (HSI) technique in a rapid and nondestructive manner. The data extracted from the NIR–HSI of viable and nonviable soybean seeds were analyzed using a partial least-squares discrimination analysis (PLS-DA) technique for classifying the viable and nonviable soybean seeds. Variable importance in projection (VIP) was used as a waveband selection method to develop a multispectral imaging model. Initially, the spectral profile of each pixel in the soybean seed images was subjected to PLS-DA analysis, which yielded a reasonable classification accuracy; however, the pixel-based classification method was not successful for high accuracy detection for nonviable seeds. Another viability detection method was then investigated: a kernel image threshold method with an optimum-detection-rate strategy. The kernel-based classification of seeds showed over 95% accuracy even when using only seven optimal wavebands selected through VIP. The results show that the proposed multispectral NIR imaging method is an effective and accurate nondestructive technique for the discrimination of soybean seed viability.

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Section: Methodsmentioning

confidence: 99%

Rapid Measurement of Soybean Seed Viability Using Kernel-Based Multispectral Image Analysis

Baek

Kusumaningrum

Kandpal

et al. 2019

Sensors

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“…Optimal wavebands are necessary to select for PLSR model optimization, as hundreds of spectra often have redundant information and multicollinearity among contiguous wavebands, which reduces efficiency for data processing [31] .…”

Section: Optimal Waveband Selection and Model Optimizationmentioning

confidence: 99%

LW-NIR hyperspectral imaging for rapid prediction of TVC in chicken flesh

Wang

et al. 2019

International Journal of Agricultural and Biological Engineering

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Total viable count (TVC) is often used as an important indicator for chicken freshness evaluation. In this study, 112 fresh chicken flesh samples were acquired after slaughtered and their hyperspectral images were collected in the LW-NIR (900-1700 nm) range. The full LW-NIR spectra (486 wavebands) within the images were extracted and applied to related to reference TVC values measured in different storage periods, using partial least squares regression (PLSR) algorithm, resulting in high correlation coefficients (R) and low root mean square errors (RMSE), for either raw spectra or pretreatment spectra. By using regression coefficients (RC) method, 20, 18, 17 and 20 optimal wavebands were respectively selected from raw spectra, baseline correction (BC) spectra, Savitzky-Golay convolution smoothing (SGCS) spectra and standard normal variate (SNV) spectra and applied for the optimization of original full waveband PLSR model. By comparison, RC-PLSR model based on the SGCS spectra showed a better performance in TVC prediction with R C of 0.98 and RMSEC of 0.35 log 10 CFU/g in calibration set, and R P of 0.98 and RMSEP of 0.44 log 10 CFU/g in prediction set. At last, by transferring the best RC-PLSR model, the dynamic TVC change during the storage was visualized by color maps to indicate the TVC spoilage degree. The overall study revealed that LW-NIR hyperspectral imaging combined with PLSR could be used to predict the freshness of chicken flesh.

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“…The ability of HSI to generate a large amount of spectral data over a wide range of wavelengths causes sizable amounts of redundant information due to multicollinearity with high covariance which requires high storage capacity and lengthy processing time (Baek et al, 2019; He & Sun, 2015). Therefore, the optimal variable selection method based on the Variable Importance in Projection (VIP) scores needs to be carried out in order to extract the most influential wavelengths in the multivariate models for specific quality attributes.…”

Section: Introductionmentioning

confidence: 99%

Prediction of total carotenoids, color, and moisture content of carrot slices during hot air drying using non‐invasive hyperspectral imaging technique

Saleh

Kulig

Arefi

et al. 2022

Food Processing Preservation

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The objective of this paper was to evaluate the performance of Partial Least Square Regression (PLSR) model and to assess the statistical agreement between two different measurement techniques, that is, Vis–NIR hyperspectral imaging (HSI) and standard laboratory methods for quality evaluation of dried carrots throughout the hot‐air drying process. Carrots at commercial maturity of 3.5 months after planting were harvested in two seasons (2017 and 2018) and dried in a convective hot air dryer at 50°C, 60°C, and 70°C. Quality measurements were examined at intervals of 30 minutes. PLSR was performed as a regression model to predict quality attributes in carrots, while Passing–Bablok and Deming regressions alongside Blant–Altman analysis were applied as method comparisons. Excellent prediction performance for moisture content was observed with high R2T and R2v at 0.92 and 0.90 with values of RMSET and RMSEv at 8.15% and 8.16%. Satisfactory prediction accuracies were observed for total carotenoids (R2v = 0.64 and RMSEv = 32.62) μg/g, L* (R2v = 0.68 and RMSEv = 32.62), a* (R2v = 0.69 and RMSEv = 1.18), and b* (R2v = 0.60 and RMSEv = 1.45). Selected wavelengths for total carotenoids, moisture content, L*, a*, and b* based on the highest score of VIP loadings were 531, 973, 531, 531, and 680 nm, respectively. An adequate agreement of Blant–Altman analysis between the two methods within the upper and lower limits of 95% confidence interval (CI) were obtained for total carotenoids from 95.68 μg/g to 82.34 μg/g, moisture content (25.18% to 22.93%), L* (2.88 to −3.30), a* (4.15 to 3.43), and b* (4.53 to −3.11) with mean differences at 6.67, 1.12, −0.21, 0.36, and 0.71, respectively. Good correlation coefficients (r) were also observed at 0.89, 0.91, 0.78, and 0.83 for moisture content, L*, a*, and b* with a moderate correlation of total carotenoids at 0.69. The results indicate the potential feasibility of using non‐invasive measurement of quality attributes using hyperspectral imaging during the drying of carrots. Novelty impact statement non‐invasive measurement using hyperspectral imaging for quality determination in carrots during convective drying demonstrated promising results. Multivariate analysis of Partial Least Square Regression showed a good modeling performance for quality prediction in dried carrots. A good statistical agreements between non‐invasive quality measurements using hyperspectral imaging and standard laboratory analysis were achieved by comparative analysis using Blant–Altman plot, Deming, and Passing–Bablok regression.

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Selection of Informative Spectral Wavelength for Evaluating and Visualising Enterobacteriaceae Contamination of Salmon Flesh

Cited by 16 publications

References 68 publications

Rapid Measurement of Soybean Seed Viability Using Kernel-Based Multispectral Image Analysis

Rapid Measurement of Soybean Seed Viability Using Kernel-Based Multispectral Image Analysis

LW-NIR hyperspectral imaging for rapid prediction of TVC in chicken flesh

Prediction of total carotenoids, color, and moisture content of carrot slices during hot air drying using non‐invasive hyperspectral imaging technique

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