Application of Near Infrared Reflectance (NIR) spectroscopy to predict the moisture, protein, and fat content of beef for gourmet hamburger preparation

Dias, Cristiana S. A. M. Maduro; Nunes, Helder; Melo, T.M.M.V.; Rosa, H.J.D.; Silva, CC; Borba, Alfredo Emílio Silveira de

doi:10.1016/j.livsci.2021.104772

Cited by 12 publications

(3 citation statements)

References 21 publications

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“…From the purple spectral difference curve, it can be seen that the reflectance changes differently at different wavelengths. A common feature is that the reflectance of peanuts changes most near 1910 nm and 1420 nm, which is related to the first-order frequency doubling stretching vibration of O-H [49,50]. In terms of spectral reflection characteristics, there are obvious spectral absorption valleys at 1209 nm, 1471 nm, 1727 nm, 1934 nm and 2484 nm.…”

Section: Moisture Content and Spectral Reflectance Characteristicsmentioning

confidence: 99%

Identification of Moldy Peanuts under Different Varieties and Moisture Content Using Hyperspectral Imaging and Data Augmentation Technologies

Liu

Jiang

et al. 2022

Foods

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Aflatoxins in moldy peanuts are seriously toxic to humans. These kernels need to be screened in the production process. Hyperspectral imaging techniques can be used to identify moldy peanuts. However, the changes in spectral information and texture information caused by the difference in moisture content in peanuts will affect the identification accuracy. To reduce and eliminate the influence of this factor, a data augmentation method based on interpolation was proposed to improve the generalization ability and robustness of the model. Firstly, the near-infrared hyperspectral images of 5 varieties, 4 classes, and 3 moisture content gradients with 39,119 kernels were collected. Then, the data augmentation method called the difference of spectral mean (DSM) was constructed. K-nearest neighbors (KNN), support vector machines (SVM), and MobileViT-xs models were used to verify the effectiveness of the data augmentation method on data with two gradients and three gradients. The experimental results show that the data augmentation can effectively reduce the influence of the difference in moisture content on the model identification accuracy. The DSM method has the highest accuracy improvement in 5 varieties of peanut datasets. In particular, the accuracy of KNN, SVM, and MobileViT-xs using the data of two gradients was improved by 3.55%, 4.42%, and 5.9%, respectively. Furthermore, this study provides a new method for improving the identification accuracy of moldy peanuts and also provides a reference basis for the screening of related foods such as corn, orange, and mango.

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Section: Moisture Content and Spectral Reflectance Characteristicsmentioning

confidence: 99%

Identification of Moldy Peanuts under Different Varieties and Moisture Content Using Hyperspectral Imaging and Data Augmentation Technologies

Liu

Jiang

et al. 2022

Foods

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“…In addition, by treating each wavelength variable in the spectral data as an individual, the feature variables with large absolute values of regression coefficients in the PLSR model can be selected by using competitive adaptive reweighted sampling (CARS) and extracted to optimize the model, reduce redundant wavelength variables, and improve the discriminative accuracy ( Li, Huang, Song, Zhang, & Min, 2019 ). Standard normal variables (SNVs), which are corrected PLSR and cross-validation, were utilized to treat the collected spectral data (400–2500 nm) of 66 meat samples, and the results showed that NIRS was helpful in the successful prediction of the fat and protein contents of beef ( Maduro Dias et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Identification of aged-rice adulteration based on near-infrared spectroscopy combined with partial least squares regression and characteristic wavelength variables

Song

et al. 2023

Food Chemistry: X

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“…Several studies have confirmed that NIR has an unlimited potential to assess meat quality, being suitable for all meat species in large-scale quality evaluation [4]. Among many others, NIR can rapidly analyze meat color, pH, and tenderness in intact fresh beef [12]; moisture, protein, and fat content of beef burger [13]; minced lamb tenderness and different types of sheep meat [14,15]; protein, moisture, connective tissue, and ash content in goat minced loin [16]; and springiness, moisture, ash, protein, lipids, pH, and color of different minced parts of fresh chicken [17,18]. In pork meat, NIRs was used to determine moisture content, protein, and intramuscular fat in different sets of intact and minced samples [1,[19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

SVM Regression to Assess Meat Characteristics of Bísaro Pig Loins Using NIRS Methodology

Vasconcelos

Dias

Leite

et al. 2023

Foods

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This study evaluates the ability of the near infrared reflectance spectroscopy (NIRS) to estimate the aW, protein, moisture, ash, fat, collagen, texture, pigments, and WHC in the Longissimus thoracis et lumborum (LTL) of Bísaro pig. Samples (n = 40) of the LTL muscle were minced and scanned in an FT-NIR MasterTM N500 (BÜCHI) over a NIR spectral range of 4000–10,000 cm−1 with a resolution of 4 cm−1. The PLS and SVM regression models were developed using the spectra’s math treatment, DV1, DV2, MSC, SNV, and SMT (n = 40). PLS models showed acceptable fits (estimation models with RMSE ≤ 0.5% and R2 ≥ 0.95) except for the RT variable (RMSE of 0.891% and R2 of 0.748). The SVM models presented better overall prediction results than those obtained by PLS, where only the variables pigments and WHC presented estimation models (respectively: RMSE of 0.069 and 0.472%; R2 of 0.993 and 0.996; slope of 0.985 ± 0.006 and 0.925 ± 0.006). The results showed NIRs capacity to predict the meat quality traits of Bísaro pig breed in order to guarantee its characterization.

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Application of Near Infrared Reflectance (NIR) spectroscopy to predict the moisture, protein, and fat content of beef for gourmet hamburger preparation

Cited by 12 publications

References 21 publications

Identification of Moldy Peanuts under Different Varieties and Moisture Content Using Hyperspectral Imaging and Data Augmentation Technologies

Identification of Moldy Peanuts under Different Varieties and Moisture Content Using Hyperspectral Imaging and Data Augmentation Technologies

Identification of aged-rice adulteration based on near-infrared spectroscopy combined with partial least squares regression and characteristic wavelength variables

SVM Regression to Assess Meat Characteristics of Bísaro Pig Loins Using NIRS Methodology

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