Non-destructive prediction and visualization of chemical composition in lamb meat using NIR hyperspectral imaging and multivariate regression

Kamruzzaman, Mohammed; ElMasry, Gamal; Sun, Da‐Wen; Allen, Paul

doi:10.1016/j.ifset.2012.06.003

Cited by 247 publications

(103 citation statements)

References 63 publications

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“…The PLS regression model was developed between the standardized spectral data and the reference MC, pH, and SSC of tomatoes and the resulting regression coefficient is called the beta coefficient. Because each pixel in a hyperspectral image has a spectrum, using HSI, the concentration of composition can be calculated for each pixel to visualize the distribution of the components in the sample [33]. In this study, the hyperspectral image was first unfolded into a 2-D matrix and then multiplied by the beta coefficient obtained from the calibration model.…”

Section: Chemical Images Of MC Ph and Ssc In Intact Tomatoesmentioning

confidence: 99%

Nondestructive Estimation of Moisture Content, pH and Soluble Solid Contents in Intact Tomatoes Using Hyperspectral Imaging

et al. 2017

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Abstract:The objective of this study was to develop a nondestructive method to evaluate chemical components such as moisture content (MC), pH, and soluble solid content (SSC) in intact tomatoes by using hyperspectral imaging in the range of 1000-1550 nm. The mean spectra of the 95 matured tomato samples were extracted from the hyperspectral images, and multivariate calibration models were built by using partial least squares (PLS) regression with different preprocessing spectra. The results showed that the regression model developed by PLS regression based on Savitzky-Golay (S-G) first-derivative preprocessed spectra resulted in better performance for MC, pH, and the smoothing preprocessed spectra-based model resulted in better performance for SSC in intact tomatoes compared to models developed by other preprocessing methods, with correlation coefficients (r pred ) of 0.81, 0.69, and 0.74 with root mean square error of prediction (RMSEP) of 0.63%, 0.06, and 0.33% Brix respectively. The full wavelengths were used to create chemical images by applying regression coefficients resulting from the best PLS regression model. These results obtained from this study clearly revealed that hyperspectral imaging, together with suitable analysis model, is a promising technology for the nondestructive prediction of chemical components in intact tomatoes.

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Section: Chemical Images Of MC Ph and Ssc In Intact Tomatoesmentioning

confidence: 99%

Nondestructive Estimation of Moisture Content, pH and Soluble Solid Contents in Intact Tomatoes Using Hyperspectral Imaging

et al. 2017

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“…A PLSR model was developed to select a few feature wavelengths from the highdimension hyperspectral data. The authors reported coefficients of determination of 0.92, 0.88, and 0.94 for protein, fat, and moisture content, respectively, which are better than those for beef and lamb (Kamruzzaman et al, 2012a).…”

Section: Porkmentioning

confidence: 96%

“…Potential application of near-infrared (NIR) hyperspectral imaging has been reported for determination of the chemical composition (water, fat, and protein content) of lamb meat (Kamruzzaman et al, 2012a;Pu et al, 2014). Kamruzzaman et al (2013) reported a PLSR method to detect water, fat, and protein content in lamb with coefficients of determination of 0.88, 0.88, and 0.63, respectively.…”

Section: Lambmentioning

confidence: 99%

Optical Methods and Techniques for Meat Quality Inspection

Peng¹,

Dhakal²

2015

Trans.ASABE

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“…Model building using full spectrum is not ideal because of the data capacity and long modeling time. [33,34] Selecting the wavelength ranges for modeling can simplify the model and improve its performance. Modeling exhibited good performance when the correlation coefficients were used instead of the X-loading weights.…”

Section: Ew Selectionmentioning

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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Non-destructive prediction and visualization of chemical composition in lamb meat using NIR hyperspectral imaging and multivariate regression

Cited by 247 publications

References 63 publications

Nondestructive Estimation of Moisture Content, pH and Soluble Solid Contents in Intact Tomatoes Using Hyperspectral Imaging

Nondestructive Estimation of Moisture Content, pH and Soluble Solid Contents in Intact Tomatoes Using Hyperspectral Imaging

Optical Methods and Techniques for Meat Quality Inspection

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

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