Multi-task convolutional neural network for simultaneous monitoring of lipid and protein oxidative damage in frozen-thawed pork using hyperspectral imaging

Traditionally, tenderness has been assessed through shear force testing, which is inherently destructive, the accuracy is easily affected, and it results in considerable sample wastage. Although this technology has some drawbacks, it is still the most effective detection method currently available. In light of these drawbacks, non-destructive testing techniques have emerged as a preferred alternative, promising greater accuracy, efficiency, and convenience without compromising the integrity of the samples. This paper delves into applying five advanced non-destructive testing technologies in the realm of meat tenderness assessment. These include near-infrared spectroscopy, hyperspectral imaging, Raman spectroscopy, airflow optical fusion detection, and nuclear magnetic resonance detection. Each technology is scrutinized for its respective strengths and limitations, providing a comprehensive overview of their current utility and potential for future development. Moreover, the integration of these techniques with the latest advancements in artificial intelligence (AI) technology is explored. The fusion of AI with non-destructive testing offers a promising avenue for the development of more sophisticated, rapid, and intelligent systems for meat tenderness evaluation. This integration is anticipated to significantly enhance the efficiency and accuracy of the quality assessment in the meat industry, ensuring a higher standard of safety and nutritional value for consumers. The paper concludes with a set of technical recommendations to guide the future direction of non-destructive, AI-enhanced meat tenderness detection.

Section: Hyperspectral Imaging (His) Technologymentioning

confidence: 99%

Rapid Non-Destructive Detection Technology in the Field of Meat Tenderness: A Review

Li,

Wang,

Yang

et al. 2024

“…Furthermore, the two bands appearing near 500 nm and 650 nm are related to the oxidation of Hb or Mb [45,46]. During F-S and F-T processing, temperature fluctuations or discontinuities can induce an increase in lipid peroxides, thiobarbituric acid reactive substances (TBARS) and carbonyl content [47,48]. Additionally, Hb and Mb are gradually oxidized to methemoglobin (metHb) and metmyoglobin (metMb), which may lead to surface discoloration [49].…”

Section: Feature Wavelength Selectionmentioning

confidence: 99%

Discrimination among Fresh, Frozen–Stored and Frozen–Thawed Beef Cuts by Hyperspectral Imaging

Yu,

Chen,

Zhang

et al. 2024

The detection of the storage state of frozen meat, especially meat frozen–thawed several times, has always been important for food safety inspections. Hyperspectral imaging (HSI) is widely applied to detect the freshness and quality of meat or meat products. This study investigated the feasibility of the low-cost HSI system, combined with the chemometrics method, to classify beef cuts among fresh (F), frozen–stored (F–S), frozen–thawed three times (F–T–3) and frozen–thawed five times (F–T–5). A compact, low-cost HSI system was designed and calibrated for beef sample measurement. The classification model was developed for meat analysis with a method to distinguish fat and muscle, a CARS algorithm to extract the optimal wavelength subset and three classifiers to identify each beef cut among different freezing processes. The results demonstrated that classification models based on feature variables extracted from differentiated tissue spectra achieved better performances, with ACCs of 92.75% for PLS-DA, 97.83% for SVM and 95.03% for BP-ANN. A visualization map was proposed to provide detailed information about the changes in freshness of beef cuts after freeze–thawing. Furthermore, this study demonstrated the potential of implementing a reasonably priced HSI system in the food industry.

“…PLSR is a traditional multivariate linear statistical analysis method that maps multidimensional data onto orthogonal latent variable (LV) factors. The minimum predicted residual error sum of squares was used to determine the optimal number of LVs [33].…”

Section: Model Establishment and Evaluationmentioning

confidence: 99%

Application of Hyperspectral Imaging as a Nondestructive Technology for Identifying Tomato Maturity and Quantitatively Predicting Lycopene Content

Dai

Sun

Ren

et al. 2023

Self Cite

Maturity is a crucial indicator in assessing the quality of tomatoes, and it is closely related to lycopene content. Using hyperspectral imaging, this study aimed to monitor tomato maturity and predict its lycopene content at different maturity stages. Standard normal variable (SNV) transformation was applied to preprocess the hyperspectral data. Then, using competitive adaptive reweighted sampling (CARS), the characteristic wavelengths were selected to simplify the calibration models. Based on the full and characteristic wavelengths, a support vector classifier (SVC) model was developed to determine tomato maturity qualitatively. The results demonstrated that the classification accuracy using the characteristic wavelength led to the obtention of better results with an accuracy of 95.83%. In addition, the support vector regression (SVR) and partial least squares regression (PLSR) models were utilized to predict lycopene content. With a coefficient of determination for prediction (R2P) of 0.9652 and a root mean square error for prediction (RMSEP) of 0.0166 mg/kg, the SVR model exhibited the best quantitative prediction capacity based on the characteristic wavelengths. Following this, a visual distribution map was created to evaluate the lycopene content in tomato fruit intuitively. The results demonstrated the viability of hyperspectral imaging for detecting tomato maturity and quantitatively predicting the lycopene content during storage.