Estimation of the prior storage period of lamb’s lettuce based on visible/near infrared reflectance spectroscopy

Jacobs, B.A.J.G.; Verlinden, Bert; Bobelyn, Els; Decombel, An; Bleyaert, Peter; Lommel, Joris Van; Vandevelde, Isabel; Saeys, Wouter; Nicolaı̈, Bart

doi:10.1016/j.postharvbio.2015.11.007

Cited by 10 publications

(3 citation statements)

References 40 publications

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“…The performance of partial least squares (PLS) models has been enhanced by numerous variable selection methods, such as genetic algorithms (GAs), uninformative variable elimination based on Monte Carlo (MC-UVE), random frog (RF), successive projections algorithms (SPAs), variable importance in projection (VIP), and competitive adaptive reweighted sampling (CARS). Variable selection by MC-UVE combined with GAs has been applied in predictions of soluble solid contents in watermelon [17] and the prior storage period of lamb's lettuce [18]. The RF proved to be a promising selector of cancer-related genes [19].…”

Section: Journal Of Food Qualitymentioning

confidence: 99%

Key Variables Screening of Near-Infrared Models for Simultaneous Determination of Quality Parameters in Traditional Chinese Food “Fuzhu”

Wang

Zhang

et al. 2018

Journal of Food Quality

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The traditional Chinese food Fuzhu is a dried soy protein-lipid film formed during the heating of soymilk. This study investigates whether a simple and accurate model can nondestructively determine the quality parameters of intact Fuzhu. The diffused reflectance spectra (1000–2499 nm) of intact Fuzhu were collected by a commercial near-infrared (NIR) spectrometer. Among various preprocessing methods, the derivative by wavelet transform method optimally enhanced the characteristic signals of Fuzhu spectra. Uninformative variable elimination based on Monte Carlo (MC-UVE), random frog (RF), and competitive adaptive reweighted sampling (CARS) were proposed to select key variables for partial least squares (PLS) calculation. The strong performance of the developed models is attributed to the high ratios of prediction to deviation values (3.32–3.51 for protein, 3.62–3.89 for lipid, and 4.27–4.55 for moisture). The prediction set was used to assess the performances of the best models of protein (CARS-PLS), lipid (RF-PLS), and moisture (CARS-PLS), which resulted in greater coefficients of determination of 0.958, 0.966, and 0.976, respectively, and lower root mean square errors of prediction of 0.656%, 0.442%, and 0.123%, respectively. Combined with chemometrics methods, the NIR technique is promising for simultaneous testing of quality parameters of intact Fuzhu.

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Section: Journal Of Food Qualitymentioning

confidence: 99%

Key Variables Screening of Near-Infrared Models for Simultaneous Determination of Quality Parameters in Traditional Chinese Food “Fuzhu”

Wang

Zhang

et al. 2018

Journal of Food Quality

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“…[24] According to the comparison of model performance under different spectral pre-treatments, the modeling ranges, and PCs, the quantitative model was obtained by determining the optimum modeling conditions. [30] The evaluation indicators of the model included determination coefficients of calibration (RC 2 ) and validation (RV 2 ) and root mean square error of calibration (RMSEC) and validation (RMSEV).…”

Section: Calibration and Validationmentioning

confidence: 99%

“…[22] EW selection can improve the performance of models because a large number of irrelevant wavelengths can impair model robustness, and the accuracy of the PLS-algorithm reduces with increasing numbers of wavelength variables. [23,24] Therefore, the objective of this study is to provide a reference for rapid detection of GBTS with pre-processing of the FT-NIR spectra and selection of the most useful wavelengths. …”

Section: Introductionmentioning

confidence: 99%

A novel method to determine total sugar of Goji berry using FT-NIR spectroscopy with effective wavelength selection

Gao

2017

International Journal of Food Properties

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This study investigated the feasibility of determining the content of Goji berry total sugar (GBTS) through Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometrics. A total of 114 Goji berry samples from different regions in China were scanned with a FT-NIR reflectance spectroscopic system. X-loading weights and correlation coefficients were utilised to identify the most influential wavelength after the selection of spectra processing methods. Partial least squares regression was applied to build models, and paired t test was used for external validation. The model with raw spectra after pre-treatment with Norris gap derivatives and effective wavelength of 8700-4000 cm −1 , which were determined by the correlation coefficients, exhibited the best predicative ability. In addition, the determination coefficients (R 2 ) of calibration validation set were both greater than 0.9 when principal component number was 6. The model presents good performance, with root mean square errors (RMSE) of 0.406 and 0.695 for the prediction of calibration validation set, relatively. The use of FT-NIR spectroscopy to implement quick determination of GBTS is thus feasible. ARTICLE HISTORY

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Online determination of quality parameters of dried soybean protein–lipid films (Fuzhu) by NIR spectroscopy combined with chemometrics

et al. 2018

View full text Add to dashboard Cite

Estimation of the prior storage period of lamb’s lettuce based on visible/near infrared reflectance spectroscopy

Cited by 10 publications

References 40 publications

Key Variables Screening of Near-Infrared Models for Simultaneous Determination of Quality Parameters in Traditional Chinese Food “Fuzhu”

Key Variables Screening of Near-Infrared Models for Simultaneous Determination of Quality Parameters in Traditional Chinese Food “Fuzhu”

A novel method to determine total sugar of Goji berry using FT-NIR spectroscopy with effective wavelength selection

Online determination of quality parameters of dried soybean protein–lipid films (Fuzhu) by NIR spectroscopy combined with chemometrics

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