Non-destructive prediction of colour and water-related properties of frozen/thawed beef meat by Raman spectroscopy coupled multivariate calibration

Investigation of heat-induced pork batter quality detection and change mechanisms using Raman spectroscopy coupled with deep learning algorithms

Pork batter quality significantly affects its product. Herein, this study explored the use of Raman spectroscopy combined with deep learning algorithms for rapidly detecting pork batter quality and revealing the mechanisms of quality changes during heating. Results showed that heating increased β-sheet content (from 26.38 to 41.42%) and exposed hidden hydrophobic groups, which formed aggregates through chemical bonds. Dominant hydrophobic interactions further cross-linked these aggregates, establishing a more homogeneous and denser network at 80 °C. Subsequently, convolutional neural networks (CNN), long short-term memory neural networks (LSTM), and CNN-LSTM were comparatively used to predict gel strength and whiteness in batters based on the Raman spectrum. Thereinto, CNN-LSTM provided the optimal results for gel strength (Rp = 0.9515, RPD = 3.1513) and whiteness (Rp = 0.9383, RPD = 3.0152). Therefore, this study demonstrated the potential of Raman spectroscopy combined with deep learning algorithms as non-destructive tools for predicting pork batter quality and elucidating quality change mechanisms.

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

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.

Rapid Measurement of Total Saponins, Mannitol, and Naringenin in Dendrobium officinale by Near-Infrared Spectroscopy and Chemometrics

Dendrobium officinale has drawn increasing attention as a dual-use plant with herbal medicine and food applications. The efficient quality evaluation of D. officinale is essential to ensuring its nutritional and pharmaceutical value. Given that traditional analytical methods are generally time-consuming, expensive, and laborious, this study developed a rapid and efficient approach to assess the quality of D. officinale from different geographical origins by near-infrared (NIR) spectroscopy and chemometrics. Total saponins, mannitol, and naringenin were utilized as quality indicators. Two wavelength selection methods, namely, uninformative variable elimination and competitive adaptive reweighted sampling (CARS), were utilized to enhance the prediction accuracy of the quantification model. Moreover, multiple spectral pretreatment methods were applied for model optimization. Results indicated that the partial least squares (PLS) model constructed based on the wavelengths selected by CARS exhibited superior performance in predicting the contents of the quality indicators. The coefficient of determination (RP2) and root mean square error (RMSEP) in the independent test sets were 0.8949 and 0.1250 g kg-1 for total saponins, 0.9664 and 0.2192 g kg-1 for mannitol, and 0.8570 and 0.003159 g kg-1 for naringenin, respectively. This study revealed that NIR spectroscopy and the CARS-PLS model could be used as a rapid and accurate technique to evaluate the quality of D. officinale.

Portable Raman spectroscopy coupled with PLSR analysis for monitoring and predicting of the quality of fresh-cut Chinese yam at different storage temperatures

Portable Raman spectroscopy coupled with partial least squares regression (PLSR) model was performed for monitoring and predicting four quality indicators, moisture content, water activity, polysaccharide content and microbial content of the fresh-cut Chinese yam at different storage temperatures. The variations in the four key indicators were first depicted through a spider web diagram as the storage temperature changed. More importantly, the four key indicators can be accurately monitored and predicted through optimized PLSR models combining with Raman spectroscopy. Among all of the PLSR models for the four indicators, the regression model for moisture content was relatively the best. In addition, storage temperature played a significant role on the model performance of PLSR. The model performance for all indicators at room temperature and high temperature was better than the corresponding PLSR models at refrigeration and freezing conditions. Especially at 25 ℃, the R2 in the calibration set basically reached 0.9. These observations indicated that portable Raman spectroscopy, a simple and easy-to-use detection technique, can monitor and predict the multiple quality indicators of fresh-cut Chinese yam combined with effectively PLSR model, which would be conducive to their applications in food industry.

Prediction model of radiotherapy outcome for Ocular Adnexal Lymphoma using informative features selected by chemometric algorithms

BACKGROUND

Ocular Adnexal Lymphoma (OAL) is a non-Hodgkin's lymphoma that most often appears in the tissues near the eye, and radiotherapy is the currently preferred treatment. There has been a controversy regarding the prognostic factors for systemic failure of OAL radiotherapy, the thorough evaluation prior to receiving radiotherapy is highly recommended to better the patient's prognosis and minimize the likelihood of any adverse effects.

PURPOSE

To investigate the risk factors that contribute to incomplete remission in OAL radiotherapy and to establish a hybrid model for predicting the radiotherapy outcomes in OAL patients.

METHODS

A retrospective chart review was performed for 87 consecutive patients with OAL who received radiotherapy between Feb 2011 and August 2022 in our center. Seven image features, derived from MRI sequences, were integrated with 122 clinical features to form comprehensive patient feature sets. Chemometric algorithms were then employed to distill highly informative features from these sets. Based on these refined features, SVM and XGBoost classifiers were performed to classify the effect of radiotherapy.

RESULTS

The clinical records of from 87 OAL patients (median age: 60 months, IQR: 52-68 months; 62.1% male) treated with radiotherapy were reviewed. Analysis of Lasso (AUC = 0.75, 95% CI: 0.72-0.77) and Random Forest (AUC = 0.67, 95% CI: 0.62-0.70) algorithms revealed four potential features, resulting in an intersection AUC of 0.80 (95% CI: 0.75-0.82). Logistic Regression (AUC = 0.75, 95% CI: 0.72-0.77) identified two features. Furthermore, the integration of chemometric methods such as CARS (AUC = 0.66, 95% CI: 0.62-0.72), UVE (AUC = 0.71, 95% CI: 0.66-0.75), and GA (AUC = 0.65, 95% CI: 0.60-0.69) highlighted six features in total, with an intersection AUC of 0.82 (95% CI: 0.78-0.83). These features included enophthalmos, diplopia, tenderness, elevated ALT count, HBsAg positivity, and CD43 positivity in immunohistochemical tests.

CONCLUSION

The findings suggest the effectiveness of chemometric algorithms in pinpointing OAL risk factors, and the prediction model we proposed shows promise in helping clinicians identify OAL patients likely to achieve complete remission via radiotherapy. Notably, patients with a history of exophthalmos, diplopia, tenderness, elevated ALT levels, HBsAg positivity, and CD43 positivity are less likely to attain complete remission after radiotherapy. These insights offer more targeted management strategies for OAL patients. The developed model is accessible online at: https://lzz.testop.top/.